Technological Lock-in and the Democratisation of Environmental Data: A Comparative Scientometric Analysis of Gas and Liquid Sensing Trajectories

Technological Lock-in and the Democratisation of Environmental Data: A Comparative Scientometric Analysis of Gas and Liquid Sensing Trajectories | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Technological Lock-in and the Democratisation of Environmental Data: A Comparative Scientometric Analysis of Gas and Liquid Sensing Trajectories Antonio Ruiz-Gonzalez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9001946/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Digital innovation has democratised data processing. However, the physical acquisition of environmental data remains stratified, excluding non-experts and developing regions from water quality stewardship. The Internet of Things (IoT) and Artificial Intelligence (AI) have shifted the economic value of sensing from precision to data density. While gas sensing has successfully transitioned into a ubiquitous, software-defined commodity, liquid-phase sensing, especially Ion-Selective Electrodes (ISEs), remain locked in a high-cost, low-volume niche. This study presents a comparative longitudinal analysis of the economic, technological, and bibliometric trajectories of both fields to elucidate the drivers of this divergence. Using term co-occurrence networks from 1.3 million gas sensing publications and 109,000 ISE publications (1980–2024), we reveal a strong semantic migration in the gas sector from device physics to application-centric domains, contrasting with the ISE field's persistent fixation on material formulation. A "commoditisation feedback loop" was identified in the gas market, triggered by the expiration of key patents and the collapse of unit costs below $ 2.00, which unlocked a long tail of open-source innovation and data generation. In contrast, the ISE market remains stifled by the need of a Reference Electrode, and the high capital cost of legacy glass architectures, limiting annual research output to a fraction of its gas counterpart. We argue that the barriers to liquid sensing ubiquity are not intrinsic performance flaws, such as drift or selectivity, but rather the prohibitive marginal cost of experimentation. We conclude that to replicate the gas sensor revolution, the liquid sensing community must pivot from the pursuit of "perfect" chemical specificity to "sufficient" digital utility, leveraging emerging scalable manufacturing techniques and AI-driven sensor arrays to democratise hydrological data acquisition. Other Economics General Biochemistry Gas Sensors Ion-Selective Electrodes Bibliometrics Commoditisation Internet of Things Artificial Intelligence Figures Figure 1 Figure 2 1. Introduction The era of Artificial Intelligence (AI) and the Internet of Things (IoT) has shifted the economic value of sensors and data. Device values are increasingly derived from the volume and spatial density of inputs that feed predictive algorithms instead of the precision of datapoints itself [ 1 ]. However, while software and processing power have been democratised through open-source licensing (i.e., TensorFlow, PyTorch), the physical layer of data acquisition, remains largely locked behind prohibitive industrial capital expenditures [ 2 ]. The disparity in sensor costs creates epistemic inequalities. In the Global South, for example, where water security is a critical challenge [ 3 ], the prohibitive cost of Ion-Selective Electrodes (ISEs) forces reliance on centralised laboratory testing [ 4 ]. This centralisation creates data gaps that conceal environmental crises. In contrast, the commoditisation of gas sensors has enabled widespread, decentralised air quality monitoring in these same regions [ 5 ]. These pressures are particularly prominent in the case of chemical sensing, given the variances in data quality due to specificity, and selectivity constraints compared to physical sensors [ 6 ], as well as the costs of materials involved in the fabrication of devices. Inside the chemical sensing space, this technological disparity is evident when contrasting the evolution of gas detection against aqueous analysis. While the gas sensor industry has successfully stratified, offering a vast spectrum of devices ranging from highly specific analytical instruments to broadly selective, ubiquitous commodity sensors, the landscape for ISEs remains limited. Besides the universal adoption of pH probes [ 7 ], and electrical conductivity as a non-specific measuremet [ 8 , 9 ], the variety of accessible sensors for liquid-phase monitoring has failed to emerge. Looking at the trajectory the gas sensor market, modern chemoresistive gas sensors are typically built upon a sintered metal oxide semiconductor (MOS) scaffold, commonly Tin Dioxide (SnO 2 ) with different doping materials, and operating at specific temperatures to tune selectivity [ 10 ]. This architecture traces its origins to the 1960s, when Naoyoshi Taguchi patented the first practical device intended for domestic gas leak detection [ 11 ]. Taguchi’s innovation was to prioritise durability and manufacturing simplicity over selectivity. These sensors could withstand years of operation in harsh environments. Despite the low selectivity, often cross-reacting with alcohol, smoke, and humidity, the low cost and extreme durability of the Taguchi-type sensor allowed it to dominate the market. Today, these sensors are ubiquitous, relying on software compensation and sensor arrays (electronic noses) to correct for their lack of specificity [ 12 ]. Following the expiration of Taguchi’s key patents in the late 1980s, the technology underwent rapid commoditisation by allowing global manufacturers to replicate the ceramic sintering process [ 13 ]. Whereas early proprietary sensors were specialised components costing upwards of $ 50 (adjusted for inflation), the influx of "MQ-series" clones from mass-market manufacturers has driven current retail prices to under $ 2.00 per unit. This 25-fold reduction in cost enabled an expansion of applications, from automotive exhaust monitoring to domestic environmental monitoring, increasing the global gas sensor market to an estimated value of $ 3.14 billion in 2024 [ 14 ]. In contrast, the market for ISEs remains a fraction of this size, estimated at approximately $ 423 million [ 15 ]. The modern pH market emerged in 1934 from a request by the California Fruit Growers Exchange to measure the acidity of lemon juice, a process previously reliant on subjective litmus tests. In response, Arnold Beckman invented the "acidimeter," coupling a fragile glass electrode with a high-gain vacuum tube amplifier to read the faint electrical potential generated by hydrogen ions [ 16 ]. This device formed the foundation of National Technical Laboratories (now Beckman Coulter). Unlike the gas sensor industry, which rapidly pivoted to screen-printable ceramics to lower costs, the pH industry remained tethered to the "combination electrode" architecture. This design requires a skilled manufacturing process: blowing a microscopic bulb of lithium-doped silica glass, filling it with a buffered solution, and sealing it inside a secondary glass tube containing a reference electrode and a liquid junction. The expansion into other electrolytes (i.e., Sodium, Potassium, Nitrate) in the 1960s followed a similar, capital-intensive path. These sensors rely on the discovery that certain molecules, such as the antibiotic valinomycin, could selectively bind potassium ions [ 17 ]. To commercialise these devices, manufacturers must dissolve these "ionophores" into a liquid plasticiser and embed them within a Poly(vinyl chloride) (PVC) membrane [ 18 ]. Sensing materials in the case of ion-selective electrodes are significantly more expensive. For example, the active ingredient in a standard potassium sensor, the antibiotic valinomycin, is a complex biological molecule produced via fermentation. Its market price fluctuates around $ 200 per gram. In comparison, Tin Oxide (SnO 2 ), the active material for gas sensors, is a bulk industrial ceramic available for less than $ 0.05 per gram. This 4,000-fold disparity in raw material costs hinders the commoditisation of specific ion sensors. Consequently, current applications of ISEs are focused on professional sectors where measurement precision justifies the high unit cost. Dominant application remains clinical diagnostics, particularly in blood gas analysers used in critical care. Here, the ability of ISEs to accurately quantify electrolytes in whole blood is vital for monitoring patients with metabolic imbalances, renal failure, or cardiac arrhythmias [ 19 , 20 ]. In the environmental sector, these sensors are mandated by regulatory bodies for water quality compliance, serving as the standard method for monitoring pH and nitrate run-off in wastewater treatment plants and river systems [ 21 ]. Finally, the technology has found a niche in industrial bioprocessing, where sterilisable glass electrodes monitor fermentation tanks in the pharmaceutical industry [ 22 ]. The differences between ISEs and gas sensing technologies present a unique case study in the economics of sensing. This manuscript describes the economic, technological and social divers that allow sensing technologies to scale by systematically analysing enablers that propelled the gas sensor industry to ubiquity and identify specific barriers (i.e. material, manufacturing, and integration) that have limited ion-selective electrodes to niche markets. Publication trends and open-source community engagement over the last two decades are examined and correlated with major technological milestones, such as the democratisation of microcontrollers (i.e. Arduino, Raspberry Pi) and the explosion of edge-AI. To the best of our knowledge, this is the first time such a comparative analysis has been performed, leading to a set of technical recommendations to enable the scaling of the ISE sensor community. 2. Methodology 2.1. Bibliometric Data Collection and Network Analysis To map the longitudinal evolution of chemical sensing research, a quantitative bibliometric analysis was conducted using data extracted from the Dimensions database. This platform was selected for its comprehensive coverage of interdisciplinary citations and grey literature. For the gas sensing landscape, adoption trends of specific hardware architectures were quantified by querying specific commercial identifiers (i.e., "MQ-2", "TGS 813") in conjunction with the Boolean operators AND "gas" AND "sensor". To capture the broader thematic evolution of the field, term co-occurrence networks were constructed using VOSviewer (version 1.6.19). A standardised sample of 2,500 most-cited articles was extracted for each of the four defined historical eras to ensure comparable network density. For the liquid-sensing domain, a parallel search strategy was employed. General trends were identified using the query string ("ion-selective electrode" OR "ion sensor"), while specific application volume was tracked using ("pH" AND "sensor"). The resulting datasets were processed to extract title and abstract terms, with a minimum occurrence threshold applied to filter noise and visualise semantic clusters. 2.2. Patent Landscape Analysis To triangulate the academic trends with proprietary commercial activity, a patent landscape analysis was performed using the Google Patents database. Search queries were constructed to isolate the two dominant sensing modalities: gas sensors were queried using the string ("Metal Oxide" OR "MOS") AND "sensor" AND "gas", while liquid-phase sensors were targeted with ((("ion" OR "electrolyte") AND "sensor") OR "ion-selective electrode"). The datasets were analysed for total volume and priority date distribution to assess the "IP age" of the respective fields. 2.3. Economic Reconstruction and Market Valuation For the contemporary period (2010–2024), total market value and volume estimates were aggregated from a comparative meta-analysis of twelve major industry intelligence reports (including Grand View Research, Yole Group, and Transparency Market Research). Market value was strictly defined as revenue generated from sensor component distribution, encompassing three specific product classes: (1) Industrial Safety Devices (e.g., portable 4-gas monitors), (2) Residential Safety Alarms (i.e., CO detectors), and (3) Prototyping & Research Instrumentation (i.e., breakout boards). 2.4. Horizon Scanning and Commercial Landscape To assess the current state of sensor availability and technological tiering, a horizon scan was conducted across two distinct categories of supply chains. Global Component Distributors: Inventories of DigiKey, Mouser, and LCSC were surveyed to identify industrial-grade components, and pricing structures. Open-Source Hardware Marketplaces: Platforms catering to the maker and prototyping communities (Adafruit, SparkFun, and DFRobot) were analysed to identify "breakout board" availability, which serves as a proxy for accessibility to non-specialists. Devices identified in this scan were categorised into tiers based on integration level (analog component vs. digital system-on-chip), interface type (I2C/SPI vs. voltage), and target market. 2.5. Open-Source Community Engagement To quantify the expansion of sensing technology into non-expert domains, community engagement was measured on the largest open-source hardware repositories, Hackster.io and the DFRobot Communities. Quantitative searches were performed using broad keywords ("gas sensor", "pH sensor", "ion sensor") and specific model numbers. Engagement was assessed through project counts, view metrics, and user endorsements, serving as indicators of the "marginal cost of experimentation" and the extent of democratic adoption for each technology class. 3. Discussion 3.1. Horizon scan of MOS sensors To contextualise the current gas-sensing landscape and to contrast it with the comparatively limited commercial maturity of liquid-phase sensing technologies, a horizon scan was conducted across major electronic component distributors (DigiKey, Mouser, and LCSC) and open-source hardware marketplaces (Adafruit, SparkFun, and DFRobot). This survey revealed a highly stratified market in which innovation was driven by three distinct tiers of suppliers: digital innovations, industrial incumbents, and commodity aggregators. A total of thirteen distinct device architectures were identified in the horizon scan, distributed across three distinct market areas: Digital MEMS and Smart Systems, Industrial and Environmental Safety Incumbents, and Commodity and Prototyping Modules. In the first area, industry standards seem to be shifting from analogue voltage outputs toward fully integrated digital interfaces (I²C/SPI). In contrast to legacy sensors, which require external operational amplifiers and analogue-to-digital conversion, Micro-Electro-Mechanical Systems (MEMS) devices are designed to incorporate signal conditioning and processing directly on the silicon die. Five innovations were identified within this tier, primarily driven by the emergence of "software-defined sensing." A second tier was formed by industrial incumbents characterised by extensive legacy catalogues of analogue electrochemical and catalytic bead sensors optimised for specific toxic or combustible gases (e.g., CO, H₂S). While the breadth of application-specific analogue sensors was maintained, miniaturised and partially integrated designs were increasingly introduced to compete with MEMS-based offerings. This tier exhibited the greatest diversity of target analytes, with catalogues comprising hundreds of distinct part numbers tailored to regulatory and industrial monitoring requirements. Four technological shifts were identified in this sector, most notably the transition from liquid-electrolyte cells to Solid-Polymer Electrochemical sensors. Unlike traditional liquid cells which are prone to leakage and drying, these solid-state variants (e.g., EC Sense) allow for the printing of toxic gas sensors into flexible, ultra-thin form factors. Furthermore, the sector is adopting Molecular Property Spectrometry (MPS) to replace catalytic bead sensors (pellistors). MPS sensors analyse the thermodynamic properties of a gas mixture to classify specific hydrocarbons, such as distinguishing Methane from Propane, without the risk of sensor poisoning from silicones or sulphur that frequently compromises legacy industrial safety devices. The third tier was composed of commodity aggregators, largely dominated by Shenzhen-based manufacturers such as Winsen Electronics, along with numerous white-label producers. This segment was found to underpin the ubiquity of gas sensing in educational, prototyping, and hobbyist contexts. This group sustained a long-tail market characterised by high redundancy and marginal differentiation; for example, methane sensors across component aggregators yield over 40 distinct breakout board variants relying on similar sensing elements. The four representative architectures identified in this area are defined by the commoditisation of legacy sintered ceramic tube designs, known as the "MQ-Series." A recent trend in this sector is the introduction of "Lite" MEMS, such as the Winsen GM-Series, which replicate the small form factor of Tier 1 digital sensors but strip away the complex on-chip logic and calibration, offering a low-cost, low-power alternative that relies on the user's external microcontroller for signal processing. 3.2. ISEs sensor Horizon Scan To assess the current state of the liquid-phase sensing ecosystem and contrast it with the dynamic innovation observed in the gas sensor market, a parallel horizon scan was conducted across the same electronic component distributors (DigiKey, Mouser, LCSC) and open-source hardware marketplaces. This survey revealed a more consolidated market structure and technologically conservative. A total of seven distinct device architectures were identified, distributed across three comparable market areas: Laboratory and Process Analytics, Emerging Solid-State and Screen-Printed Technologies, and Commodity Analogue Probes. The first area represents the established standard for high-precision measurement. Unlike the gas sensor market, where innovation is driving miniaturisation and digital integration, this tier remains anchored to the macro-scale combination electrode architecture. Manufacturers in this category focus on reinforcing the glass bulb with epoxy bodies or implementing pre-pressurised reference junctions to prevent clogging in wastewater applications. While "smart" digital sensors exist in this space, the digital logic is typically housed in a detachable transmitter head rather than integrated into the sensing element itself. Consequently, the fundamental transducer remains an analogue, high-impedance device requiring frequent manual calibration with liquid buffer solutions, a maintenance burden that has been largely eliminated in modern gas sensors via algorithmic baseline correction. The second area represents the technological horizon for liquid sensing, attempting to mirror the MEMS revolution seen in gas detection. However, unlike the mass-market success of MEMS gas sensors, this tier remains largely confined to niche research and development applications. Two key innovations were identified: Ion-Sensitive Field-Effect Transistors (ISFETs) and Screen-Printed Electrodes (SPEs). Providers such as have successfully commercialised solid-state sensors that replace the fragile glass bulb with a semiconductor chip or a printed carbon track. This allows for flat, microlitre-volume samples, a significant leap forward for biomedical and soil analysis. Yet, despite being available for decades, ISFETs have failed to achieve ubiquitous adoption due to persistent issues with drift and light sensitivity. Furthermore, unlike the gas sector’s "system-on-chip" solutions which output ready-to-use digital data, these solid-state sensors typically remain analogue components, shifting the burden of signal amplification and temperature compensation to the end-user. The third tier mirrors the commodity aggregator segment of the gas market but lacks the "smart" middle ground found in gas sensing. Dominated by manufacturers such as Winsen Electronics and DFRobot, this area is characterised by the mass production of legacy liquid-filled probes. The horizon scan identified three primary commodity architectures in this space: the standard glass pH probe, the antimony pH probe for harsh environments, and the polymer-membrane ISE for specific ions like Nitrate or Calcium. 3.3. Gas sensor mechanism of action MOS chemiresistors represent the dominant architecture for gas sensing. Unlike the complex assembly required for optical or electrochemical devices, MOS sensors function as solid-state transducers based on surface reactivity. The fundamental mechanism relies on the adsorption of oxygen species (O − , O 2− ) onto the surface of a heated n-type semiconductor, typically Tin Dioxide (SnO 2 ) or Zinc Oxide (ZnO), which traps free electrons from the conduction band and establishes a depletion layer at the grain boundaries [ 23 ], and must be maintained at elevated temperatures generally between 200°C and 450°C to function effectively [ 24 ]. This electron entrapment creates a potential barrier resulting in high baseline electrical resistance (Fig. 1 .a.). The thermal requirement is a consequence of the thermodynamics of the sensing mechanism. Stoichiometric metal oxides behave as electrical insulators at room temperature, requiring thermal energy to excite charge carriers into the conduction band and to overcome the activation energy barriers for surface chemisorption [ 25 ]. In an environment of clean air, oxygen species adsorb onto this heated surface, trapping free electrons and establishing a depletion layer that results in a high baseline resistance. When a reducing gas encounters the sensor, it reacts with the surface oxygen, releasing trapped electrons and causing a measurable drop in resistance [ 23 , 25 ]. Since different reducing gases possess distinct activation energies for oxidation, the sensor’s specificity and power requirements of devices are highly influenced by its thermal setpoint (Table 1 ) [ 24 ]. Table 1 Technical specifications and operating parameters of the selected gas sensor models. Sensor Model Current / Power Op. Voltage (V) Range (ppm) Op. Humidity Temp Range (°C) Slope (α) Reference / Source TGS 813 167mA 24V (C) / 5V (H) 500–10k < 95% -10 to 40 ~ 0.60 [ 26 ] Pellistor 175mA 2.0V 0–100% LEL 0–100% -20 to 70 Linear [ 27 ] City Cell 0mA (Self) N/A 0–500 15–90% -20 to 50 Linear [ 28 ] MQ-2 160mA 5.0V 300–10k < 95% -20 to 50 ~ 0.60 [ 29 ] MQ-3 160mA 5.0V 25–500 < 95% -10 to 50 ~ 0.55 [ 30 ] MQ-4 160mA 5.0V 200–10k < 95% -10 to 50 ~ 0.60 [ 31 ] MQ-5 160mA 5.0V 200–10k < 95% -10 to 50 ~ 0.60 [ 32 ] MQ-6 160mA 5.0V 200–10k < 95% -10 to 50 ~ 0.60 [ 33 ] MQ-7 150mA 5V / 1.4V 20–2k < 95% -20 to 50 ~ 0.45 [ 34 ] MQ-8 160mA 5.0V 100–10k < 95% -10 to 50 ~ 0.60 [ 35 ] MQ-9 150mA 5V / 1.5V 10–1k / 100–10k < 95% -20 to 50 ~ 0.50 [ 36 ] MQ-131 160mA 5.0V 10ppb – 2ppm < 95% -10 to 45 ~ 0.65 [ 37 ] MQ-135 160mA 5.0V 10–1,000 < 95% -10 to 45 ~ 0.60 [ 38 ] MQ-136 160mA 5.0V 1–200 < 95% -10 to 45 ~ 0.55 [ 39 ] MQ-137 160mA 5.0V 5–500 < 95% -10 to 45 ~ 0.60 [ 40 ] MQ-138 160mA 5.0V 5–100 < 95% -10 to 45 ~ 0.60 [ 41 ] BME680 0.1–18mA 1.7–3.6V 0–500 IAQ 0–100% -40 to 85 Algorithmic [ 42 ] BME688 0.1–18mA 1.7–3.6V 0–500 IAQ 0–100% -40 to 85 Algorithmic [ 43 ] BME690 < 9mA 1.7–3.6V 0–500 IAQ 0–100% -40 to 85 Algorithmic [ 44 ] To refine the sensing mechanism, manufacturers frequently dope the oxide scaffold with noble metal catalysts such as Platinum (Pt) or Palladium (Pd), which lower the activation energy required for these surface reactions and improve selectivity towards specific analytes [ 45 ]. Despite the negligible cost of these raw semiconductor materials, the commercial scalability of gas sensors is currently bottlenecked by significant post-fabrication economic hurdles, specifically calibration and packaging. Selectivity and unit prices of common gas sensors are shown on Table 2 . Table 2 Summary of sensor performance metrics, including target gas specificity, selectivity ratings, known interferences, and unit price (€). Sensor Model Target Price (€) Selectivity Rating Main Interference Gases Reference / Source TGS 813 Combustible €15–20 Promiscuous Alcohol, Hydrogen, CO [ 26 ] Pellistor %LEL €30–45 Non-Selective Any combustible gas [ 27 ] City Cell CO €30–50 Selective Hydrogen (unless filtered), Ethylene [ 28 ] MQ-2 Smoke/LPG €3–6 Very Promiscuous Alcohol, Propane, Hydrogen, Methane [ 29 ] MQ-3 Alcohol €4–7 Promiscuous Benzene, Methane, LPG, CO [ 30 ] MQ-4 Methane €4–7 Semi-Selective Alcohol, LPG, Cooking Fumes [ 31 ] MQ-5 LPG/Nat Gas €4–7 Semi-Selective Alcohol (Low), Methane, Smoke [ 32 ] MQ-6 LPG/Butane €4–7 Semi-Selective Alcohol, Smoke, Isobutane [ 33 ] MQ-7 CO €6–10 Semi-Selective Hydrogen (Major), LPG, Alcohol [ 34 ] MQ-8 Hydrogen €5–8 Promiscuous Alcohol (See note below), LPG, CO [ 35 ] MQ-9 CO & Gas €6–10 Promiscuous LPG triggers CO side; CO triggers Gas side [ 36 ] MQ-131 Ozone €20–30 Semi-Selective Cl₂, NO₂ (Oxidising gases) [ 37 ] MQ-135 Air Quality €4–7 Very Promiscuous CO₂, Alcohol, Smoke, CO, Ammonia [ 38 ] MQ-136 H₂S €15–25 Semi-Selective SO₂, Alcohol (Ethanol) [ 39 ] MQ-137 Ammonia €15–25 Semi-Selective CO, Alcohol, Organic Amines [ 40 ] MQ-138 VOCs €15–25 Promiscuous Alcohol, Acetone, Formaldehyde [ 41 ] BME680 VOCs (IAQ) €15–22 Broad Spectrum Humidity, CO, Hydrogen, Ethanol [ 42 ] BME688 VOCs + AI €20–30 Broad (AI Filtered) (Same as above, AI filters some) [ 43 ] BME690 VOCs €14–18 Broad Spectrum Humidity, CO, Hydrogen [ 44 ] Unlike digital integrated circuits which can be electrically validated in milliseconds, gas sensors operate in the chemical domain and are subject to unavoidable stochastic variations in film thickness and grain structure during the sintering process [ 46 ]. This requires a "calibration tax," where every individual sensor unit must be exposed to known concentrations of target gas to establish a reliable baseline, requiring expensive gas mixing infrastructure that limits production throughput [ 46 , 47 ]. Furthermore, gas sensors present an engineering challenge. The sensing element must be hermetically sealed to protect delicate wire bonds and heater electronics from corrosion. However, it needs to be exposed to the external environment to facilitate gas diffusion. This requirement forces the adoption of specialised packages often capped with permeable membranes or steel mesh [ 48 ]. 3.4. ISEs mechanism of action ISEs represent the gold standard for liquid-phase sensing, functioning as potentiometric transducers. ISEs operate near zero current (high impedance), and rely on the generation of a phase boundary potential at the interface between a sample solution and a selective membrane [ 18 ]. This membrane, often composed of doped glass, crystalline lattice, or plasticised PVC containing specific ionophores, selectively binds or exchanges the target ion, establishing a charge separation that adheres to the Nernstian principle. The thermodynamic basis for this measurement is described by the Nernst equation. In an ideal scenario, the potential difference developed across the membrane is logarithmically proportional to the activity of the specific ion in the solution. For every decade change in concentration of a monovalent ion (i.e. K + , F − ), the sensor theoretically yields a potential shift of approximately 59.16 mV at 25 o C. Unlike MOS sensors which require thermal excitation to create conductivity, ISEs are generally passive devices, though their response slope is strictly temperature-dependent. To achieve specificity, manufacturers engineer the membrane composition to favor the thermodynamic partitioning of a single ion type. For instance, Valinomycin is embedded in PVC membranes to create a cavity specifically sized to trap Potassium ions K + , excluding smaller Na + (Table 3 ). Table 3 Technical specifications and electrochemical characteristics of representative Ion-Selective Electrodes. Sensor Type / Model Membrane Type pH Range Linear Range (M) Response Time (t90​) Temp Range (°C) Slope (mV/dec) Ref. Standard pH Doped Glass 0–14 10 − 1 – 10 − 14 < 30 s -5 to 100 + 59 [ 49 ] Fluoride (F − ) Solid State (LaF 3 ) 5–8 10 0 – 10 − 6 < 60 s 0 to 80 -59 [ 50 ] Potassium (K + ) PVC (Valinomycin) 2–12 10 0 – 10 − 5 < 60 s 0 to 50 + 59 [ 51 ] Calcium (Ca 2+ ) PVC (Ionophore) 4–10 10 0 – 10 − 5 < 30 s 0 to 40 + 30 [ 52 ] Nitrate (NO 3 − ) PVC (Ion Exchange) 2–11 10 − 1 – 10 − 5 < 60 s 0 to 40 -59 [ 53 ] Ammonium (NH 4 ) PVC (Nonactin) 4–10 10 − 1 – 10 − 5 < 60 s 0 to 50 + 59 [ 54 ] Sodium (Na+) Glass / PVC 9–12 10 − 1 – 10 − 6 < 60 s 0 to 50 + 59 [ 55 ] Chloride (Cl − ) Solid State (AgCl) 2–12 10 0 – 10 − 4 < 60 s 0 to 50 -59 [ 56 ] Lead (Pb 2 ) Solid State (PbS) 3–7 10 − 1 – 10 − 6 < 60 s 0 to 80 + 30 [ 57 ] Perfect selectivity is thermodynamically impossible. All ISEs suffer from interferences by ions of similar charge or radius, quantified by the Nicolsky-Eisenman coefficient [ 58 ]. Furthermore, unlike the raw semiconductor materials of gas sensors, the fabrication of robust ISE membranes adds significant cost and reduces operational lifespan. Selectivity coefficients and estimated unit prices are shown in Table 4 . Table 4 Summary of sensor performance metrics, including target ion specificity, selectivity coefficients, and economic analysis. Sensor Type Target Price (€) Selectivity Profile Main Interfering Ions Ref. Standard pH H + €20–50 Highly Selective Na + (Alkaline Error > pH 12) [ 59 ] Fluoride F − €150–250 Selective OH − (at high pH) [ 50 ] Potassium K + €100–180 Semi-Selective Cs + , NH 4 + , Na + [ 51 ] Calcium Ca 2+ €100–180 Semi-Selective Mg 2+ [ 60 ] Nitrate NO 3 − €120–200 Promiscuous ClO 4 − , I − [ 53 ] Ammonium NH 4 + €120–200 Semi-Selective K + (Major interference) [ 54 ] Sodium Na + €100–180 Semi-Selective Ag + , H + (pH sensitive) [ 55 ] Chloride Cl − €100–180 Promiscuous S 2− , CN − [ 56 ] Lead Pb 2+ €180–300 Semi-Selective Hg 2+ , Cu 2+ [ 57 ] 3.5. Historical Market Evolution of sensors 3.5.1 History of gas sensors The economic trajectory of the MOS sensor market offers a clear roadmap of the commoditization of devices. Naoyoshi Taguchi’s discovered in 1962 that the electrical resistance of heated Tin Dioxide (SnO 2 ) decreased in the presence of reducing gases. Motivated by a series of fatal domestic gas explosions in Japan, Taguchi patented the technology and founded Figaro Engineering Inc. in 1969. The early TGS (Taguchi Gas Sensor) series, specifically the TGS 109 and later the TGS 813, established the global standard for leak detection. During this "Proprietary Era" (1970–1990), the technology was protected by a number of international patents, allowing Figaro to maintain a monopoly pricing structure where a single sensor unit retailed for approximately $ 50– $ 80 (inflation-adjusted). Value during this period was locked in the proprietary chemical composition of the ceramic paste and the manual fabrication processes required to ensure consistency, restricting the technology to industrial safety equipment and high-end consumer alarms. Taguchi’s core patents expired in the late 1980s, allowing competitors to reverse-engineer the ceramic sintering process, leading to the emergence of manufacturers, who introduced the "MQ" series (i.e., MQ-2, MQ-4) [ 13 ]. These devices were functional clones of the TGS architecture. The resulting flood of supply drove unit prices down to the $ 10– $ 25 range. While these sensors lacked the rigorous calibration of their Japanese counterparts, their price-performance ratio was sufficient to expand the market beyond industry and into general residential air quality monitoring. The final and most disruptive shift (2010–Present), was driven by the economies of scale demanded by IoT. The explosion of open-source hardware (i.e., Arduino, Raspberry Pi) created a massive demand for low-cost breakout boards, pushing manufacturers to optimise the production of sensor modules. Today, the raw sensor component is effectively demonetised, selling for less than $ 2.00. The value has shifted entirely from the hardware to the software algorithms required to interpret its noisy output [ 61 ]. A comparison of sensor prices is shown in Table 5 . This collapse in unit pricing has fuelled an explosion in total market valuation. By transitioning from a low-volume industrial instrument to a ubiquitous component in smart home devices and automotive systems, the global gas sensor market has reached a valuation of approximately $ 2.7 billion in 2024, with projected growth to $ 4.5 billion by 2030 [ 62 ]. This stands in contrast to the ISE market, which remains estimated at $ 578 million [ 63 ]. Table 5 Historical price evolution, market valuation, and technological drivers of MOS gas sensors. Era / Range Dominant Model Unit Price (Adjusted) Global Market Value Economic / Technological Driver Ref. 1970–1990 Figaro TGS 813 $50.00 – $80.00 - IP Monopoly : Protected by Taguchi patents; manual fabrication; low global competition. [ 64 ] 1990–2010 Early MQ Series $10.00 – $25.00 ~ $ 800 Million Patent Expiration : Generic manufacturers reverse-engineer sintering process; market stratification begins. [ 65 , 66 ] 2010 – Present MQ-x Modules $1.50 – $5.00 ~ $ 2.5 Billion Commoditisation : Rise of Shenzhen supply chain; lower quality materials used to meet demand for non-critical uses. [ 67 , 68 ] 2015 – Present MEMS (BME680) $5.00 – $15.00 (Included above) Miniaturisation : Shift from ceramic tubes to MEMS wafers; price reflects integration of digital logic (ASIC). [ 69 ] 3.5.2 History of ISEs In contrast to the MOS trajectory, the commercialisation of ISEs followed a clinical-first model. In the mid-1960s, Frant and Ross’s invented of the fluoride electrode (using a single-crystal Lanthanum Fluoride membrane) [ 70 ] and Simon’s discovery that antibiotics like Valinomycin could serve as highly selective ionophores for K + [ 71 ]. This era, saw a rapid expansion of detectable analytes (Ca²⁺, Na⁺, Cl⁻). However, the ISE remained a liquid membrane-based device, requiring an internal filling solution and a glass or polymer membrane to function. This physical constraint tethered the technology to high-value laboratory and clinical settings, specifically the blood gas analysers that became standard in hospitals by the 1980s. As such, the liquid sensor industry suffers from path dependence, evolving to prioritise accuracy over cost. On the contrary, the gas industry started in domestic leak detection prioritising low cost over accuracy. Consequently, the ISE market never experienced the demonetisation seen in the gas sensor industry. While MOS sensors were driven by the volume of the smoke alarm and IoT markets, ISEs were driven by the precision of the medical market. The complexity of maintaining a stable reference potential in a miniaturised format acted as a technological barrier to commoditisatio. Because these sensors require constant hydration and frequent two-point calibration with liquid buffers, they could not be easily integrated into the "dry" world of consumer electronics or smartphones. This effectively capped the ISE market at its current ~ $ 578 million valuation [ 63 ]. 3.5.3 Strip sensors A third, distinct market trajectory is found in disposable colorimetric test strips. Unlike the digital architecture of MOS sensors or the fragile liquid-contact format of clinical ISEs, this sector is built on simple, single-use chemical pads. These "dipsticks" dominate the manual water quality market—from residential aquariums to field environmental monitoring—because of their ability to multiplex. A single paper strip can simultaneously assay Nitrate (NO 3 − ), Nitrite ( $ NO 2 − ), pH, and hardness (Ca 2+ /Mg 2+ ) within 60 seconds. With a unit price often falling between $ 0.20 – $ 0.50 per strip, they represent the most accessible form of chemical sensing globally. However, the "dipstick" model is incompatible with the IoT economy due to the "human-in-the-loop" requirement. While convenient for spot-checks, these strips provide only a semi-quantitative"snapshot of water quality, relying on subjective visual comparison against a colour chart. Recent studies have highlighted that this manual interpretation frequently leads to data bias, with user error rates in citizen science projects often exceeding 30% when compared to laboratory spectroscopy [ 72 ]. Furthermore, replicating the temporal resolution of a digital sensor (which logs data every minute) using strips is economically impossible; obtaining a comparable 24-hour data stream would require 1,440 manual tests, costing over $ 400 daily. Consequently, the global market for water quality test strips remains valued at approximately $ 233 million (2024) [ 73 ]. 3.5. Diversification and thematic evolution of the gas sensing field The diversification of the gas sensing research landscape was examined through a longitudinal analysis of term co-occurrence networks constructed for four distinct eras, defined by inflection points in the first derivative of annual publication counts. To ensure distinct thematic comparison across these historical phases, a standardised sample of 2,500 articles was extracted for analysis from each era. These periods corresponded to phases of accelerated thematic expansion and were used to capture shifts in research focus as the field evolved (Fig. 1 ). Across the four eras, a clear progression was observed from materials-centric investigations toward application-driven, data-centric sensing systems. In the earliest period (1981–1984), the co-occurrence network (Fig. 1 a) exhibited a sparse and tightly clustered structure, indicative of a field in its formative stage where implementation was largely restricted to the automotive and heavy industrial sectors. Dominant terms were associated with fundamental materials science and device physics, including tin dioxide, semiconductor, adsorption, oxygen, temperature, and conductivity. These terms formed a highly interconnected core, reflecting a research emphasis on elucidating the basic mechanisms of gas sensing in metal-oxide semiconductors for the binary detection of hazardous leaks, such as LPG or methane, rather than precise quantification. By the mid-1990s (1995–1999), the term networks expanded in both size and complexity (Fig. 1 b), marking the technology's transition from leak detection to environmental monitoring. Research focus during this period shifted toward engineering selectivity and fabrication strategies, as evidenced by the prominence of terms such as thin film, oxide, doping, catalyst, and selectivity. The appearance of fabrication-related keywords, including sol–gel and sputtering, reflected growing interest in controllable deposition techniques and microstructural tuning. Concurrently, early signal-processing concepts such as pattern recognition and analysis began to co-occur with materials terms, indicating the initial adoption of algorithmic methods to mitigate cross-sensitivity. Although materials science remained dominant, early references to environmental monitoring and volatile organic compounds suggested the beginnings of application-driven research in fixed-station air quality networks and process control. A pronounced diversification was observed during the 2005–2009 period, where the co-occurrence maps (Fig. 1 c) revealed multiple well-defined clusters corresponding to the technology's entry into the healthcare and diagnostic sectors. Nanostructuring emerged as a central theme, with nanowire, nanoparticle, and surface modification forming a dense cluster linked to enhanced sensitivity and lower detection limits. In parallel, application-focused clusters associated with air quality monitoring, breath analysis, medical diagnostics, and environmental sensing became prominent, indicating a shift toward real-world use cases. Systems-level concepts such as instrument, calibration, detection limit, and sensor array also gained centrality, reflecting increased attention to deployable sensing platforms rather than isolated sensing elements. This period marked a transition from predominantly laboratory-scale studies to integrated sensor systems tailored for specific applications, moving beyond industrial safety to non-invasive medical screening. In the most recent era (2019–2024), the co-occurrence networks (Fig. 1 d) displayed the highest degree of thematic fragmentation and structural complexity, consistent with a mature field now embedded within IoT and wearable ecosystems. Central nodes were increasingly associated with data-centric and computational concepts, including model, algorithm, dataset, machine learning, and classification. These terms formed strong links with system-level descriptors such as monitoring, value, and network, indicating a shift toward software-defined sensing architectures in which sensor hardware served primarily as a data source for downstream analytics. Application clusters related to healthcare, patient monitoring, wearables, and IoT deployments were strongly represented, highlighting the integration of gas sensors into broader cyber-physical systems. While materials-related terms persisted, they occupied more peripheral positions in the network, suggesting that sensing hardware had become standardised and commoditised, with innovation increasingly occurring at the level of data interpretation and system integration. Taken together, the term co-occurrence analysis revealed a clear longitudinal trajectory in which the gas sensing field evolved from a narrowly focused materials science discipline into a diversified, application-driven, and data-oriented research ecosystem. This thematic expansion closely paralleled the economic commoditisation of gas sensors described in the preceding section, supporting the interpretation that reductions in hardware cost and increased accessibility were key enablers of intellectual diversification and cross-disciplinary adoption. The delineation of the four eras was further supported by the presence of four pronounced peaks in annual publication rates (Fig. 1 (e–g)), which were interpreted as successive waves of accelerated research activity occurring over distinct historical intervals. The first peak, observed in the early 1980s (approximately 1981–1984), coincided with the consolidation of metal-oxide semiconductor gas sensing as a viable analytical technology. This period followed the commercialisation of Taguchi-type sensors in the 1970s and reflected intensified academic interest in elucidating the fundamental surface chemistry and charge-transport mechanisms underlying chemoresistive sensing. The associated growth in publications was therefore attributed primarily to foundational materials science and device physics rather than to external system-level enablers. A second peak was identified in the mid-to-late 1990s (approximately 1995–1999), temporally aligned with two converging developments: the widespread commoditisation of MOS gas sensors following patent expirations and the emergence of early electronic nose concepts. The term “electronic nose” was formalised in the late 1980s and early 1990s, with seminal work demonstrating that arrays of broadly selective sensors, combined with multivariate pattern recognition, could achieve functional selectivity. During this period, advances in personal computing, data acquisition hardware, and statistical classification methods lowered the barrier to implementing sensor arrays, leading to increased research output focused on selectivity enhancement through system-level approaches rather than material specificity alone. The third publication surge occurred during the mid-2000s (approximately 2005–2009) and coincided with the rapid expansion of open-source electronics and low-cost embedded computing. The introduction of the Arduino platform in 2005, followed by the early development of the Raspberry Pi project (publicly announced in 2006 and released in 2012), substantially democratised access to microcontrollers, analogue-to-digital conversion, and sensor interfacing. This period also overlapped with declining unit costs of gas sensors driven by globalised manufacturing. Collectively, these factors enabled gas sensing to move beyond specialised laboratories into educational, hobbyist, and interdisciplinary research contexts. The observed increase in publications during this era was therefore interpreted as a consequence of reduced economic and technical barriers to experimentation, facilitating application-driven research in environmental monitoring, portable instrumentation, and early IoT systems. The most recent and largest peak was observed in the period from approximately 2019 to 2024 and was temporally associated with the widespread adoption of machine learning and edge-AI techniques in sensor research. This era followed major advances in deep learning frameworks during the 2010s and the subsequent emergence of resource-efficient inference methods, often referred to as TinyML, which enabled machine-learning models to be deployed directly on microcontrollers. In parallel, commercially available “smart” gas sensors with integrated processing and digital interfaces became widely accessible. These developments transformed gas sensors into software-defined sensing platforms, where application-specific value was increasingly extracted through data-driven models rather than improvements in intrinsic selectivity. The magnitude and breadth of the publication peak in this period were therefore attributed to the convergence of mature sensing hardware, open-source software ecosystems, and embedded AI capabilities. When deconstructing these trends at the component level, distinct adoption dynamics emerge between proprietary industrial standards and commodity hardware (Fig. 1 (f–g)). While the industrial TGS-813 anchored the field with a prominent peak in 1981, the cyclical waves of interest manifested differently in the commodity sector. Notably, the MQ series exhibited intermediate peaks in 1986, 1996, and 2004, often preceding the secondary resurgences of the industrial standard (observed in 1997 and 2010). During the current artificial intelligence era, the growth trajectory of the proprietary TGS sensor appears less consistent compared to the robust, exponential rise of the MQ series. This steady acceleration in commodity sensor research is likely attributable to their wider availability, extensive public documentation, and negligible unit cost. Our network analysis reveals two distinct epistemic communities: the Gas community, which is open and application-focused, and the Liquid community, which is closed and material-focused. 3.6. Bibliometric Analysis of Ion-Selective Electrodes To quantify the divergence in research trajectories between gas and liquid-phase sensing, a parallel longitudinal analysis was performed on the ISE and pH sensors literature. While the gas sensor domain witnessed an explosive expansion, accumulating a total of 1,313,700 publications since 1974 with 115,469 outputs in 2024 alone, the liquid-phase sensing field has remained comparatively restricted. In the same period, research into ISEs produced a total of 109,016 manuscripts, with only 6,070 published in 2024, a nearly twenty-fold disparity in annual output. This magnitude gap suggests that, while gas sensing has successfully transitioned into a ubiquitous, data-driven commodity discipline, ISE research remains a specialised niche. To understand the qualitative evolution of this field, term co-occurrence networks were constructed for four distinct eras using a standardised sample of 2,500 articles per period, mirroring the methodology applied to the gas sensor landscape (Fig. 2 a-d)). In contrast to the rapid, chaotic expansion of the gas sensor networks, the ISE landscape exhibits a more linear and chemically focused evolutionary path. In the earliest period (1983–1988), the network (Fig. 2 a) is dominated by foundational terms such as calcium, potassium, selectivity coefficient, and neutral carrier. This cluster reflects an era focused on the discovery and synthesis of primary ionophores (e.g., valinomycin) and the establishment of the theoretical frameworks governing potentiometric response. Unlike the early gas sensor networks which focused on industrial leak detection, the primary drivers here were clinical analysis and serum electrolyte monitoring. By the second era (1993–1998), the network (Fig. 2 b) reveals a consolidation around membrane engineering. Dominant nodes such as PVC, membrane, plasticiser, and lipophilicity indicate a shift from ionophore discovery to the optimisation of the polymeric matrix. This period corresponds to the maturation of the classical liquid-contact ISE, where research prioritised the suppression of transmembrane fluxes and the improvement of sensor lifetime. A significant shift occurred in the third era (2003–2008) (Fig. 2 c), where the network structure pivots toward performance limits. The emergence of terms such as detection limit, trace, and environmental coincides with the "trace-level revolution" in the ISE field, pioneered by groups demonstrating that optimising the inner filling solution could push detection limits from micromolar to picomolar levels. However, the relatively sparse connectivity of this network compared to the equivalent gas sensor era (which was exploding with "electronic nose" concepts) suggests a field deepening its fundamental understanding rather than broadening its application base. In the most recent era (2019–2024), the ISE network (Fig. 2 d) finally exhibits signs of the "wearable shift" observed earlier in gas sensing, though at a smaller scale. New clusters emerge around solid-contact, wearable, sweat, and real-time, reflecting the modern effort to eliminate the liquid reference electrode and integrate ISEs into flexible electronics. However, distinct from the gas sensor network where machine learning and AI became central nodes, the modern ISE network remains anchored in materials science (i.e., conducting polymer, carbon nanotube), highlighting that the primary bottleneck in liquid sensing remains the physical transducer rather than data interpretation. The historical publication rates further evidence this divergence (Fig. 2 (e–f)). While the curve for pH sensors (Fig. 2 f) displays a smooth exponential growth similar to commodity gas sensors, the trajectory for general ISEs (Fig. 2 e) is far more complex. A notable plateau and slight decline in publication activity is observable between approximately 2000 and 2010. This "period of stagnation" contrasts with the explosive growth of MOS sensors during the same decade (driven by the Arduino/IoT boom). It implies that while gas sensors were benefiting from the democratisation of microelectronics, ISE research was stalled by the inherent limitations of the classical liquid-filled architecture. The subsequent resurgence in ISE publications post-2015 aligns with the breakthrough of robust solid-contact transducers, yet the growth remains linear rather than exponential, constrained by the high cost of materials and the lack of a "digital-native" sensor architecture equivalent to the MEMS gas sensor. While the gas sensing lexicon transitioned from device physics (i.e., sintering, grain boundary) to broad application domains (i.e., breath analysis, IoT), indicating a commoditisation of the hardware, the ISE literature displays a distinct semantic inertia. Rather than migrating toward application-centric terminology, the network vocabulary has cycled through iterative generations of materials science, shifting from the neutral carriers of the 1980s to the conducting polymers and carbon nanotubes of the modern era. This persistent dominance of fabrication and material descriptors implies that ISE technology has not yet achieved the maturity of its gas-sensing counterparts. Instead of expanding into deployment and data interpretation, the field remains locked in a cycle of transducer optimisation, still seeking the ideal material composition to overcome inherent stability limitations. 3.7. Sensors and the Open Source community The expansion of gas sensing technology was paired with the growth of open-source hardware and maker communities, which acted as amplifiers for adoption, experimentation, and downstream commercial demand. An analysis of major community-driven platforms revealed a substantial volume of publicly documented gas sensor projects, underscoring the role of participatory innovation in shaping the modern gas sensing ecosystem. On Hackster.io, a total of 905 projects related to gas sensing were identified at the time of analysis. These projects spanned a wide range of applications, including smoke and fire detection, indoor air quality monitoring, industrial safety alarms, environmental sensing stations, and early electronic nose implementations. Engagement metrics indicated sustained and broad interest. For example, one of the most referenced tutorials, “Smoke Detection using MQ-2 Gas Sensor”, accumulated over 606,000 views and more than 330 user endorsements. A complementary pattern was observed within manufacturer-hosted community ecosystems. On the DFRobot community platform, 101 distinct gas sensor–related projects were identified. These projects were typically structured as step-by-step build logs, incorporating detailed hardware schematics, firmware examples, and deployment guidance. Compared to general-purpose maker platforms, manufacturer communities exhibited a stronger alignment with specific product families and breakout boards, suggesting a tighter coupling between community experimentation and commercial offerings. The clustering of projects around particular sensor modules indicated that community engagement was not evenly distributed across the sensing landscape but instead concentrated on devices that were inexpensive, readily available, and well-supported by example code and documentation. Quantitative searches conducted on Hackster.io revealed a near-total absence of community-driven development for specific electrolytes. While the broader term "Ion sensor" returned 158 results, a qualitative review indicated these were predominantly news aggregations regarding academic breakthroughs rather than the reproducible hardware manuals found in the gas sector. Even the ubiquitous pH probe, the standard for liquid sensing, appeared in only 82 projects, less than 10% of the volume observed for gas sensors. The few identified projects were predominantly instructional demonstrations of basic pH probing involving off-the-shelf laboratory probes, with minimal evidence of the derivative innovation or "long-tail" replication observed for MQ-series or MEMS gas sensors. 3.8. Patent Landscape and Commercial Proprietary Trends To triangulate the academic trends with proprietary commercial activity, a patent landscape analysis was performed using the Google Patents database. Search queries were constructed to isolate the two dominant sensing modalities: gas sensors were queried using the string ("Metal Oxide" OR "MOS") AND "sensor" AND "gas", while liquid-phase sensors were targeted with ((("ion" OR "electrolyte") AND "sensor") OR "ion-selective electrode"). Datasets were analysed for total volume, priority date distribution, and assignee composition to assess the "IP age" and commercial focus of the respective fields. The search yielded 12,393 results for gas sensors, with the earliest relevant priority date identified in 2002, and 14,856 results for ion-selective sensors, dating from 2012. A qualitative audit of the metadata reveals a profound structural divergence that mirrors the "horizontal vs. vertical" split observed in the bibliometric analysis. The gas sensor patent landscape is characterised by high-level integration into consumer electronics. Dominant assignees include major consumer technology conglomerates and specialised semiconductor manufacturers. Moreover, patent titles frequently reference "electronic devices," "display devices," and "mobile communications," confirming that gas sensing IP has moved beyond the transducer level to focus on system-level integration into mass-market hardware. In contrast, the ion-selective sensor dataset displays significant semantic contamination from adjacent, high-capital industries. A substantial portion of the search results are not sensing patents but energy storage innovations (i.e., secondary batteries), where the terms "ion" and "electrolyte" are ubiquitous. When these artefacts are filtered out, the remaining genuine sensing IP is heavily concentrated in the "vertical" domain of clinical diagnostics. Key assignees in this space are predominantly large medical device and pharmaceutical entities, with patents related to "continuous analyte sensors" and "in vivo monitoring." Unlike the gas sector, where IP is distributed across a broad "long tail" of consumer applications, liquid sensing IP is locked within deep corporate silos focused on high-value, regulated medical devices. Furthermore, comparing these patent volumes against the bibliometric output exposes a massive disparity in the "Openness Ratio" of the two fields. The gas sensor field, with its ~ 1.3 million academic publications and ~ 12,000 patents, exhibits a ratio of roughly 100 papers for every patent. Conversely, the ISE field, with ~ 100,000 papers and a chemically-inflated patent count, operates under a much tighter ratio. This suggests a less open ecosystem. 3.9. Discussion The comparative analysis of the gas and liquid-phase sensing landscapes reveals a divergence in technological maturity, driven by the economics of data acquisition rather than the utility of the chemical information. The horizon scan exposed a gas sensor market that has successfully stratified into three distinct tiers, digital MEMS innovators, industrial incumbents, and commodity aggregators. This creates a supply chain of accessibility that caters to users across all levels of technical expertise. Devices such as the Bosch BME688 or Renesas ZMOD4410 utilise generic metal-oxide micro-hotplates, relying on machine learning algorithms to interpret dynamic conductivity slopes and classify complex odours. In contrast, ISEs market remains technologically conservative, anchored to the macro-scale combination electrode architecture. While gas sensors have evolved into "Digital Noses" with integrated I²C logic, liquid sensors remain analogue, high-impedance components, shifting the burden of signal amplification and calibration entirely to the end-user. This technological bifurcation is mirrored in the bibliometric data, which exposes a twenty-fold disparity in annual research output (115,469 publications for gas sensors versus 6,070 for ISEs in 2024). The semantic evolution of the two fields offers a causal explanation for this gap. Longitudinal co-occurrence networks reveal that the gas sensing lexicon has successfully migrated from "device physics" terminology (i.e., sintering, grain boundary) in the 1980s to "application" terminology (i.e., breath analysis, IoT, wearable) in the modern era. Conversely, the ISE literature displays a distinct semantic inertia. Rather than expanding into application domains, vocabulary has merely cycled through iterative generations of materials science, from the neutral carriers of the 1980s to the conducting polymers and carbon nanotubes of today. This persistent fixation on fabrication implies that ISE technology has not yet breached the maturity threshold required to enable widespread application-layer research. The mechanism governing this divergence is the "commoditisation feedback loop," a cycle where the collapse of unit costs triggers an increase in application diversity, which in turn drives total market value. Historical data suggests that the gas sensor industry catalysed this loop when the expiration of key patents allowed low cost sensors to flood the market, pushing unit prices below the critical threshold of $ 5.00. This affordability unlocked a "long tail" of horizontal innovation, allowing computer scientists, civil engineers, and hobbyists to integrate gas sensing into non-critical domains. This diversification leads to an expansion of total market valuation. The ubiquity of cheap sensors generates massive volumes of training data, which fuels the development of robust AI, increasing the utility of the hardware and driving a secondary cycle of demand. The ISE market, trapped by the high manufacturing costs, does not seem to have breached this accessibility threshold. Consequently, it remains stuck in a vertical innovation cycle. The concept of using sensor arrays to fingerprint complex liquids emerged synchronously with the Electronic Nose in the mid-1990s [ 74 , 75 ]. However, while the electronic nose turned the liability of poor selectivity into a feature through pattern recognition [ 75 ], the electronic tongue failed to disrupt the market. This failure was driven by physical constraints that software could not resolve, including the need for Reference Electrodes. Unlike a MOS array, which is a self-contained solid-state system, an electronic tongue relies on a single, fragile liquid-junction reference electrode. If this component drifts or clogs, the entire multivariate pattern collapses. Furthermore, while gas sensor heaters continuously burn off contaminants, liquid sensors operate at room temperature in complex matrices where biofouling causes irreversible drift. These physical realities have prevented the electronic tongue from becoming the commoditised engine of growth for the ISE field, leaving liquid sensing trapped in a cycle of material optimisation while gas sensing has ascended into a software-defined, data-driven ecosystem. An examination of the technical specifications of sensors revealed a differences between the perceived and real barriers to commercialisation. The liquid-sensing community frequently cites "insufficient selectivity," "signal drift," and "conditioning requirements" as primary obstacles preventing the widespread adoption of ISEs [ 18 ]. However, a direct comparison with MOS gas sensors suggests these are not true commercial limitations. In terms of selectivity, MOS sensors are notoriously promiscuous, reacting to broad families of reducing gases rather than specific molecules [ 23 ]. Similarly, while ISEs require a hydration period (conditioning) to equilibrate the phase boundary potential, MOS sensors mandate a significant "burn-in" period to stabilise the depletion layer. Even signal drift, often framed as a disqualifying limitation for solid-state ISEs, is a chronic issue in MOS technology, known as the "sleep effect," which manufacturers mitigate through algorithmic baseline correction [ 76 ]. However, three physical limitations of the ISE architecture present genuine barriers that software cannot resolve: material costs, durability and the need of a reference electrode. While a sintered MOS heater can operate continuously for over a decade [ 77 ], the lifespan of a typical liquid-contact ISE is often capped at six months [ 78 ]. This limited longevity is driven by the inevitable desiccation of the internal filling solution and the leaching of plasticisers from the polymeric membrane. Furthermore, every potentiometric measurement relies on a reference electrode to complete the circuit. This component is prone to clogging and potential drift, preventing the true miniaturisation achieved by the self-contained MOS pixel. Emerging All-Solid-State ISEs (ASS-ISEs) promise to circumvent these physical challenges by replacing the internal solution with a conductive solid transducer (i.e., carbon nanotubes or conducting polymers) [ 79 ]. This architecture mimics the robust, layer-by-layer fabrication of MEMS devices, theoretically allowing for storage in dry conditions and integration into wearable electronics. However, these devices currently trade durability for stability, given the formation of a microscopic water layer at the solid-contact interface often introduces random potential drifts that compromise the Nernstian response [ 80 ]. The path to ubiquity, therefore, lies in abandoning the pursuit of a highly selective sensor in favour of a more durable, but promiscuous sensor. A commoditised ISE market could leverage AI to compensate for imperfect selectivity. If manufacturing innovations, such as screen-printing [ 81 ] or aerosol deposition [ 82 ] can lower the cost of ionophore-based sensors to a fraction of their current price, the strict requirement for highly specific, expensive ionophores (i.e., Valinomycin) could be relaxed. Moving further, the industry could pivot entirely from fragile polymeric membranes to robust inorganic materials, such as Molybdenum oxide (MoO₃) [ 83 ] or Ruthenium oxide (RuO₂) [ 84 ], with proved ion sensing capabilities. Unlike organic ionophores which are prone to leaching and diffusion over time, these metal oxides offer superior durability and can be deposited as solid-state layers. While they typically exhibit lower intrinsic selectivity, often acting as broad-spectrum pH or redox probes, their mechanical resilience makes them ideal for the "sensor fusion" approach. A dense array of cheap, less selective sensors, maintaining a Nernstian sensitivity of 59 mV/decade, could allow machine learning models to deconvolute interferences in real-time. This shift would unlock massive application domains currently priced out of the market, from precision agriculture and nutrient monitoring to real-time sweat analysis, effectively replicating the data-driven explosion observed in the gas sensor industry. 5. Conclusions The comparative sociotechnical analysis reveals that the trajectory of sensor commercialisation is driven not by the intrinsic quality of analytical data, but by the political economy of data acquisition. The divergence between the ubiquitous, multi-billion-dollar gas sensor industry and the stagnant, niche market for ISEs serves as a testament to the power of the commoditisation feedback loop and technological lock-in. While the gas sensor sector successfully transitioned from a hardware-centric discipline focused on device physics to a software-defined ecosystem driven by application diversity, the ISE field remains hindered by a cycle of material optimisation. This stagnation is quantitatively evidenced by the twenty-fold disparity in annual research output and the semantic inertia of the ISE lexicon, which has failed to migrate from fabrication terminology to deployment use-cases over the last four decades. We argue that the fundamental barrier to the ubiquity of liquid sensing is not the lack of selectivity or the presence of signal drift, but the prohibitive "marginal cost of experimentation" imposed by the legacy combination electrode architecture. The history of the MOS gas sensor demonstrates that the collapse of unit costs below a critical accessibility threshold (enabled by the expiration of patents and the rise of mass-manufacturing among others) is the primary catalyst for horizontal innovation. By democratising access to hardware, the gas sector unlocked a "long tail" of non-expert developers who integrated sensors into broader cyber-physical systems, generating the massive datasets required to train robust machine learning models. In contrast, the high capital cost of ISEs have kept the technology locked within vertical, professional silos, creating an epistemic inequality where only well-funded institutions can generate hydrological data. To allow for a more widespread use of ISEs, the liquid sensing community must look beyond the laboratory. Emerging manufacturing paradigms, such as screen-printing and the use of durable metal oxides (e.g., Ruthenium or Molybdenum oxide), offer a viable pathway to overcome the limitations of reference electrodes. As such, the field can shift the burden of analytical resolution from the hardware to the algorithm by enabling lower intrinsic selectivity in exchange for mechanical robustness and negligible unit costs, Ultimately, this study reframes the challenge of liquid sensing not as a chemical problem, but as an economic problem awaiting a better distribution model. This paradigm shift is a prerequisite for the democratisation of environmental stewardship. 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Flammable gas sensor (Model: MQ-4) manual (Version 1.5) [Product manual]. Retrieved January 2 , (2026) from https://www.winsen-sensor.com/d/files/MQ-4.pdf Zhengzhou Winsen Electronics Technology Co., Ltd. (2018). Flammable gas sensor (Model: MQ-5) manual (Version 1.5) [Product manual]. Retrieved January 2 , (2026) from https://www.winsen-sensor.com/d/files/MQ-5.pdf Zhengzhou Winsen Electronics Technology Co., Ltd. (2015). Flammable gas sensor (Model: MQ-6) manual (Version 1.4) [Product manual]. Retrieved January 2 , (2026) from https://www.winsen-sensor.com/d/files/semiconductor/mq-6.pdf Zhengzhou Winsen Electronics Technology Co., Ltd. (2014). Toxic gas sensor (Model: MQ-7) manual (Version 1.3) [Product manual]. Retrieved January 2 , (2026) from https://cdn.sparkfun.com/datasheets/Sensors/Biometric/MQ-7%20Ver1.3%20-%20Manual.pdf Zhengzhou Winsen Electronics Technology Co., Ltd. (2014). Flammable gas sensor (Model: MQ-8) manual (Version 1.3) [Product manual]. Retrieved January 2 , (2026) from https://cdn.sparkfun.com/datasheets/Sensors/Biometric/MQ-8%20Ver1.3%20-%20Manual.pdf Zhengzhou Winsen Electronics Technology Co., Ltd. (2021). Toxic gas sensor (Model: MQ-9B) manual (Version 1.6) [Product manual]. Retrieved January 2 , (2026) from https://www.winsen-sensor.com/d/files/manual/mq-9b.pdf Zhengzhou Winsen Electronics Technology Co., Ltd. (2014). Ozone gas sensor (Model: MQ131 low concentration) manual (Version 1.3) [Product manual]. Retrieved January 2 , (2026) from https://cdn.sparkfun.com/assets/9/9/6/e/4/mq131-datasheet-low.pdf Zhengzhou Winsen Electronics Technology Co., Ltd. (2015). Air quality gas sensor (Model: MQ135) manual (Version 1.4) [Product manual]. Retrieved January 2 , (2026) from https://www.winsen-sensor.com/d/files/PDF/Semiconductor%20Gas%20Sensor/MQ135%20(Ver1.4)%20-%20Manual.pdf Zhengzhou Winsen Electronics Technology Co., Ltd. (2021). Hydrogen sulfide gas sensor (Model: MQ136) manual (Version 1.6) [Product manual]. Retrieved January 2 , (2026) from https://www.winsen-sensor.com/d/files/manual/mq136.pdf Zhengzhou Winsen Electronics Technology Co., Ltd. (2015). Ammonia gas sensor (Model: MQ137) manual (Version 1.4) [Product manual]. Retrieved January 2 , (2026) from https://www.winsen-sensor.com/d/files/semiconductor/mq137.pdf Zhengzhou Winsen Electronics Technology Co., Ltd. (2015). Gas sensor for VOC gas (Model: MQ138) manual (Version 1.4) [Product manual]. Retrieved January 2 , (2026) from https://www.winsen-sensor.com/d/files/PDF/Semiconductor%20Gas%20Sensor/MQ138%20(Ver1.4)%20-%20Manual.pdf Bosch SGH (2024) BME680 datasheet: Low power gas, pressure, temperature & humidity sensor (Version 1.9) [Data sheet]. Retrieved January 2, 2026, from https://www.bosch-sensortec.com/media/boschsensortec/downloads/datasheets/bst-bme680-ds001.pdf Bosch SGH (2024) BME688 datasheet: Digital low power gas, pressure, temperature & humidity sensor with AI (Version 1.3) [Data sheet]. Retrieved January 2, 2026, from https://www.bosch-sensortec.com/media/boschsensortec/downloads/datasheets/bst-bme688-ds000.pdf Bosch SGH (2025) BME690 datasheet: Digital low power temperature, humidity, pressure and gas sensor with AI (Version 1.4) [Data sheet]. Retrieved January 2, 2026, from https://www.bosch-sensortec.com/media/boschsensortec/downloads/datasheets/bst-bme690-ds001-00.pdf Yamazoe N (1991) New approaches for improving semiconductor gas sensors. Sens Actuators B 5(1):7–19 Lewis A, Peltier W, von Schneidemesser E (2018) Low-cost sensors for the measurement of atmospheric composition: overview of topic and future applications. Barandun G et al (2022) Challenges and Opportunities for Printed Electrical Gas Sensors. ACS Sens 7(10):2804–2822 Wu Y, Lei M, Xia X (2024) Res Progress MEMS Gas Sensors: Compr Rev Sens Mater 24(24):8125 Buck R et al (2002) Measurement of pH. Definition, standards, and procedures (IUPAC Recommendations 2002). Pure and Applied Chemistry - PURE APPL CHEM, 74: pp. 2169–2200 Thermo Scientific U (2021) Thermo Fisher Scientific, Waltham, MA Thermo Scientific, User Guide: Orion Potassium Ion Selective Electrode, Thermo Fisher Scientific, Waltham, MA , (2019) Company H, Calcium Ion Selective Electrode (ISE), Model ISE25Ca User Manual, Hach, Loveland, CO, 2022. Xylem YSI, Nitrate Ion Selective Electrode for Pro Series: Specification Sheet, YSI Inc., Yellow Springs, OH, 2020. Vernier Software & Technology, Ammonium Ion-Selective Electrode User Manual, Beaverton, OR, 2023. Thermo Scientific, User Guide: Orion Sodium Ion Selective Electrode, Thermo Fisher Scientific, Waltham, MA , (2020) Cole-Parmer, Chloride Ion Selective Electrode User Manual, Cole-Parmer, Vernon Hills, IL, 2021. (Typical solid-state AgCl pellet sensor specs). Thermo Scientific, User Guide: Orion Lead Ion Selective Electrode, Thermo Fisher Scientific, Waltham, MA, 2018. (The primary commercial PbS solid-state sensor still in wide use). Y. Umezawa et al., Potentiometric selectivity coefficients of ion-selective electrodes (IUPAC Technical Report), Pure and Applied Chemistry, vol. 72, no. 10, p. 1851– (2082) 2000 Mettler Toledo, pH Theory Guide: A Guide to pH Measurement and Sensor Troubleshooting, Mettler-Toledo AG, 2018. Hach Company, IntelliCAL™ ISeca Calcium Ion Selective Electrode User Manual, Hach, 2022. SNS Insider. (2024). IoT Sensors Market Size, Share & Growth Forecast to 2032. TechSci Research. (2025). Gas Sensors Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, 2020–2030. Market Report Analytics. (2025). Ion-Selective Electrode (ISE) Sensor Insights: Market Size Analysis to 2033. Figaro Engineering Inc. (2010). 40-Year History of Gas Sensors. Allied Market Research. (2012). Global Gas Sensors Market - Trends & Forecasts. Fine GF et al (2010) Metal Oxide Semi-Conductor Gas Sens Environ Monit 10(6):5469–5502 McKinsey Global Institute. (2015). The Internet of Things: Mapping the Value Beyond the Hype. Global Market Insights. (2025). Gas Sensor Market Size, Share & Forecast, 2024–2032. Yole Group / System Plus Consulting. (2017). Bosch BME680 Environmental Sensor: Technology & Cost Analysis. Frant MS, Ross JW Jr. (1966) Electrode for sensing fluoride ion activity in solution. Science 154(3756):1553–1555 Štefanac Z, Simon W (1967) Ion specific electrochemical behavior of macrotetrolides in membranes. Microchem J 12(1):125–132 von Benzon E et al (2025) Reliability of low-cost colorimetric phosphate and nitrate tests used by citizen scientists to assess river water quality. Volume 13–2025 Intel Market Research. (2025). Water Quality Chemical Test Strips Market Outlook 2026–2032. Toko K (1996) Taste sensor with global selectivity. Mater Sci Engineering: C 4(2):69–82 Gardner JW, Bartlett PN (1994) A brief history of electronic noses. Sens Actuators B 18(1):210–211 Gutierrez-Osuna R (2002) Pattern analysis for machine olfaction: a review. IEEE Sens J 2(3):189–202 Figaro Engineering Inc., Technical Information for TGS800 Series, Osaka, Japan, 2020. Oesch U, Simon W (1980) Lifetime of neutral carrier based ion-selective liquid-membrane electrodes. Anal Chem 52(4):692–700 Zachara JE et al (2004) Miniaturised all-solid-state potentiometric ion sensors based on PVC-membranes containing conducting polymers. Sens Actuators B 101(1):207–212 Fibbioli M et al (2000) Potential Drifts of Solid-Contacted Ion-Selective Electrodes Due to Zero-Current Ion Fluxes Through the Sensor Membrane. 12(16):1286–1292 Li L et al (2024) Printing technologies for the fabrication of ion-selective electrodes. Sens Bio-Sensing Res 44:100650 Ruiz-Gonzalez A, Choy K-L (2021) Integration of an Aerosol-Assisted Deposition Technique for the Deposition of Functional Biomaterials Applied to the Fabrication of Miniaturised Ion Sensors. 11(4):938 Shuk P, Guth U, Greenblatt M (2002) Ion-selective sensors based on molybdenum bronzes. J Solid State Electrochem 6:374–383 Tanumihardja E, Olthuis W, Van den Berg A (2018) Ruthenium Oxide Nanorods as Potentiometric pH Sensor for Organs-On-Chip Purposes. 18(9): p. 2901 Additional Declarations The authors declare potential competing interests as follows: Plantion Ltd. is a platform I established to support and formalise my independent research activities. It does not engage in financial operations or commercial research, and this work was conducted entirely separate from any company involvement. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9001946","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":598811798,"identity":"53f43322-8e1a-4f0b-be93-3c1c0873b907","order_by":0,"name":"Antonio Ruiz-Gonzalez","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-3921-2361","institution":"Plantion Ltd","correspondingAuthor":true,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Ruiz-Gonzalez","suffix":""}],"badges":[],"createdAt":"2026-03-01 14:04:46","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9001946/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9001946/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103797695,"identity":"e5b2b4df-e76c-4c59-91ed-03946dc1a241","added_by":"auto","created_at":"2026-03-03 04:48:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":842921,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal bibliometric analysis of the gas sensing research landscape (1981–2024). \u003cstrong\u003ea–d)\u003c/strong\u003eTerm co-occurrence networks constructed for four distinct eras, where node size is proportional to term frequency and edge thickness represents co-occurrence strength. Clusters (indicated by colour) reveal the thematic evolution of the field: \u003cstrong\u003ea)\u003c/strong\u003e 1981–1984: A formative period dominated by fundamental materials science (e.g., tin dioxide, conductivity) and industrial safety; \u003cstrong\u003eb)\u003c/strong\u003e1995–1999: Expansion into fabrication techniques and selectivity engineering (i.e., thin film, sol-gel); \u003cstrong\u003ec)\u003c/strong\u003e 2005–2009: A phase of diversification driven by nanotechnology and emerging medical applications (i.e., nanowire, breath analysis); \u003cstrong\u003ed)\u003c/strong\u003e 2019–2024: The current era of system integration and data analytics, characterised by \"soft\" computing terms (i.e., machine learning, IoT, wearable). \u003cstrong\u003ee)\u003c/strong\u003e Comparative bar chart displaying the total number of unique terms (blue) and total scientific outputs (red) per period, illustrating the exponential growth of the discipline. \u003cstrong\u003ef–g) \u003c/strong\u003eAnnual publication frequency for specific sensor architectures: \u003cstrong\u003ef)\u003c/strong\u003e The TGS 813, representing the proprietary industrial standard, and \u003cstrong\u003eg)\u003c/strong\u003e the MQ-2, illustrating the recent explosion in research utilisation of low-cost commodity hardware.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9001946/v1/241fdfc651a0b3186be6246f.png"},{"id":103797696,"identity":"3e7ab039-7cf1-4362-a6c5-5c6e91e72533","added_by":"auto","created_at":"2026-03-03 04:48:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":637233,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal bibliometric analysis of the Ion-Selective Electrode (ISE) research landscape (1983–2024). \u003cstrong\u003ea–d) \u003c/strong\u003eTerm co-occurrence networks constructed for four distinct eras using a standardised sample of 2,500 articles per period: \u003cstrong\u003ea)\u003c/strong\u003e1983–1988: Foundational era focused on ionophore discovery (i.e., calcium, neutral carrier); \u003cstrong\u003eb)\u003c/strong\u003e 1993–1998: Maturation of membrane engineering and PVC matrices; \u003cstrong\u003ec)\u003c/strong\u003e 2003–2008: Focus on trace-level detection limits and environmental monitoring; \u003cstrong\u003ed)\u003c/strong\u003e 2019–2024: Emergence of solid-contact and wearable applications. \u003cstrong\u003ee–f)\u003c/strong\u003e Annual publication rates for \u003cstrong\u003ee)\u003c/strong\u003egeneral Ion-Selective Electrodes, showing a period of stagnation (2000–2010) followed by a linear resurgence, contrasted with \u003cstrong\u003ef)\u003c/strong\u003e pH sensors, which exhibit a consistent exponential growth trajectory driven by universal utility.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9001946/v1/60166e4ad41e2f7c2d028e39.png"},{"id":104400772,"identity":"aee7f355-2b7f-408c-bad3-f96d47b0cb89","added_by":"auto","created_at":"2026-03-11 12:10:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3058837,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9001946/v1/237e8159-3963-4e08-b437-cfc5857e4be2.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: Plantion Ltd. is a platform I established to support and formalise my independent research activities. It does not engage in financial operations or commercial research, and this work was conducted entirely separate from any company involvement.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTechnological Lock-in and the Democratisation of Environmental Data: A Comparative Scientometric Analysis of Gas and Liquid Sensing Trajectories\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe era of Artificial Intelligence (AI) and the Internet of Things (IoT) has shifted the economic value of sensors and data. Device values are increasingly derived from the volume and spatial density of inputs that feed predictive algorithms instead of the precision of datapoints itself [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, while software and processing power have been democratised through open-source licensing (i.e., TensorFlow, PyTorch), the physical layer of data acquisition, remains largely locked behind prohibitive industrial capital expenditures [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The disparity in sensor costs creates epistemic inequalities. In the Global South, for example, where water security is a critical challenge [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], the prohibitive cost of Ion-Selective Electrodes (ISEs) forces reliance on centralised laboratory testing [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This centralisation creates data gaps that conceal environmental crises. In contrast, the commoditisation of gas sensors has enabled widespread, decentralised air quality monitoring in these same regions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese pressures are particularly prominent in the case of chemical sensing, given the variances in data quality due to specificity, and selectivity constraints compared to physical sensors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], as well as the costs of materials involved in the fabrication of devices. Inside the chemical sensing space, this technological disparity is evident when contrasting the evolution of gas detection against aqueous analysis. While the gas sensor industry has successfully stratified, offering a vast spectrum of devices ranging from highly specific analytical instruments to broadly selective, ubiquitous commodity sensors, the landscape for ISEs remains limited. Besides the universal adoption of pH probes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and electrical conductivity as a non-specific measuremet [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the variety of accessible sensors for liquid-phase monitoring has failed to emerge.\u003c/p\u003e \u003cp\u003eLooking at the trajectory the gas sensor market, modern chemoresistive gas sensors are typically built upon a sintered metal oxide semiconductor (MOS) scaffold, commonly Tin Dioxide (SnO\u003csub\u003e2\u003c/sub\u003e) with different doping materials, and operating at specific temperatures to tune selectivity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This architecture traces its origins to the 1960s, when Naoyoshi Taguchi patented the first practical device intended for domestic gas leak detection [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Taguchi\u0026rsquo;s innovation was to prioritise durability and manufacturing simplicity over selectivity. These sensors could withstand years of operation in harsh environments. Despite the low selectivity, often cross-reacting with alcohol, smoke, and humidity, the low cost and extreme durability of the Taguchi-type sensor allowed it to dominate the market. Today, these sensors are ubiquitous, relying on software compensation and sensor arrays (electronic noses) to correct for their lack of specificity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFollowing the expiration of Taguchi\u0026rsquo;s key patents in the late 1980s, the technology underwent rapid commoditisation by allowing global manufacturers to replicate the ceramic sintering process [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Whereas early proprietary sensors were specialised components costing upwards of \u003cspan\u003e$\u003c/span\u003e50 (adjusted for inflation), the influx of \"MQ-series\" clones from mass-market manufacturers has driven current retail prices to under \u003cspan\u003e$\u003c/span\u003e2.00 per unit. This 25-fold reduction in cost enabled an expansion of applications, from automotive exhaust monitoring to domestic environmental monitoring, increasing the global gas sensor market to an estimated value of \u003cspan\u003e$\u003c/span\u003e3.14\u0026nbsp;billion in 2024 [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, the market for ISEs remains a fraction of this size, estimated at approximately \u003cspan\u003e$\u003c/span\u003e423\u0026nbsp;million [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The modern pH market emerged in 1934 from a request by the California Fruit Growers Exchange to measure the acidity of lemon juice, a process previously reliant on subjective litmus tests. In response, Arnold Beckman invented the \"acidimeter,\" coupling a fragile glass electrode with a high-gain vacuum tube amplifier to read the faint electrical potential generated by hydrogen ions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This device formed the foundation of National Technical Laboratories (now Beckman Coulter). Unlike the gas sensor industry, which rapidly pivoted to screen-printable ceramics to lower costs, the pH industry remained tethered to the \"combination electrode\" architecture. This design requires a skilled manufacturing process: blowing a microscopic bulb of lithium-doped silica glass, filling it with a buffered solution, and sealing it inside a secondary glass tube containing a reference electrode and a liquid junction. The expansion into other electrolytes (i.e., Sodium, Potassium, Nitrate) in the 1960s followed a similar, capital-intensive path. These sensors rely on the discovery that certain molecules, such as the antibiotic valinomycin, could selectively bind potassium ions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To commercialise these devices, manufacturers must dissolve these \"ionophores\" into a liquid plasticiser and embed them within a Poly(vinyl chloride) (PVC) membrane [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Sensing materials in the case of ion-selective electrodes are significantly more expensive. For example, the active ingredient in a standard potassium sensor, the antibiotic valinomycin, is a complex biological molecule produced via fermentation. Its market price fluctuates around \u003cspan\u003e$\u003c/span\u003e200 per gram. In comparison, Tin Oxide (SnO\u003csub\u003e2\u003c/sub\u003e), the active material for gas sensors, is a bulk industrial ceramic available for less than \u003cspan\u003e$\u003c/span\u003e0.05 per gram. This 4,000-fold disparity in raw material costs hinders the commoditisation of specific ion sensors.\u003c/p\u003e \u003cp\u003eConsequently, current applications of ISEs are focused on professional sectors where measurement precision justifies the high unit cost. Dominant application remains clinical diagnostics, particularly in blood gas analysers used in critical care. Here, the ability of ISEs to accurately quantify electrolytes in whole blood is vital for monitoring patients with metabolic imbalances, renal failure, or cardiac arrhythmias [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the environmental sector, these sensors are mandated by regulatory bodies for water quality compliance, serving as the standard method for monitoring pH and nitrate run-off in wastewater treatment plants and river systems [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Finally, the technology has found a niche in industrial bioprocessing, where sterilisable glass electrodes monitor fermentation tanks in the pharmaceutical industry [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe differences between ISEs and gas sensing technologies present a unique case study in the economics of sensing. This manuscript describes the economic, technological and social divers that allow sensing technologies to scale by systematically analysing enablers that propelled the gas sensor industry to ubiquity and identify specific barriers (i.e. material, manufacturing, and integration) that have limited ion-selective electrodes to niche markets. Publication trends and open-source community engagement over the last two decades are examined and correlated with major technological milestones, such as the democratisation of microcontrollers (i.e. Arduino, Raspberry Pi) and the explosion of edge-AI. To the best of our knowledge, this is the first time such a comparative analysis has been performed, leading to a set of technical recommendations to enable the scaling of the ISE sensor community.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Bibliometric Data Collection and Network Analysis\u003c/h2\u003e \u003cp\u003eTo map the longitudinal evolution of chemical sensing research, a quantitative bibliometric analysis was conducted using data extracted from the Dimensions database. This platform was selected for its comprehensive coverage of interdisciplinary citations and grey literature.\u003c/p\u003e \u003cp\u003eFor the gas sensing landscape, adoption trends of specific hardware architectures were quantified by querying specific commercial identifiers (i.e., \"MQ-2\", \"TGS 813\") in conjunction with the Boolean operators AND \"gas\" AND \"sensor\". To capture the broader thematic evolution of the field, term co-occurrence networks were constructed using VOSviewer (version 1.6.19). A standardised sample of 2,500 most-cited articles was extracted for each of the four defined historical eras to ensure comparable network density.\u003c/p\u003e \u003cp\u003eFor the liquid-sensing domain, a parallel search strategy was employed. General trends were identified using the query string (\"ion-selective electrode\" OR \"ion sensor\"), while specific application volume was tracked using (\"pH\" AND \"sensor\"). The resulting datasets were processed to extract title and abstract terms, with a minimum occurrence threshold applied to filter noise and visualise semantic clusters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Patent Landscape Analysis\u003c/h2\u003e \u003cp\u003eTo triangulate the academic trends with proprietary commercial activity, a patent landscape analysis was performed using the Google Patents database. Search queries were constructed to isolate the two dominant sensing modalities: gas sensors were queried using the string (\"Metal Oxide\" OR \"MOS\") AND \"sensor\" AND \"gas\", while liquid-phase sensors were targeted with (((\"ion\" OR \"electrolyte\") AND \"sensor\") OR \"ion-selective electrode\"). The datasets were analysed for total volume and priority date distribution to assess the \"IP age\" of the respective fields.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Economic Reconstruction and Market Valuation\u003c/h2\u003e \u003cp\u003eFor the contemporary period (2010\u0026ndash;2024), total market value and volume estimates were aggregated from a comparative meta-analysis of twelve major industry intelligence reports (including Grand View Research, Yole Group, and Transparency Market Research). Market value was strictly defined as revenue generated from sensor component distribution, encompassing three specific product classes: (1) Industrial Safety Devices (e.g., portable 4-gas monitors), (2) Residential Safety Alarms (i.e., CO detectors), and (3) Prototyping \u0026amp; Research Instrumentation (i.e., breakout boards).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Horizon Scanning and Commercial Landscape\u003c/h2\u003e \u003cp\u003eTo assess the current state of sensor availability and technological tiering, a horizon scan was conducted across two distinct categories of supply chains.\u003c/p\u003e \u003cp\u003eGlobal Component Distributors: Inventories of DigiKey, Mouser, and LCSC were surveyed to identify industrial-grade components, and pricing structures.\u003c/p\u003e \u003cp\u003eOpen-Source Hardware Marketplaces: Platforms catering to the maker and prototyping communities (Adafruit, SparkFun, and DFRobot) were analysed to identify \"breakout board\" availability, which serves as a proxy for accessibility to non-specialists.\u003c/p\u003e \u003cp\u003eDevices identified in this scan were categorised into tiers based on integration level (analog component vs. digital system-on-chip), interface type (I2C/SPI vs. voltage), and target market.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Open-Source Community Engagement\u003c/h2\u003e \u003cp\u003eTo quantify the expansion of sensing technology into non-expert domains, community engagement was measured on the largest open-source hardware repositories, Hackster.io and the DFRobot Communities. Quantitative searches were performed using broad keywords (\"gas sensor\", \"pH sensor\", \"ion sensor\") and specific model numbers. Engagement was assessed through project counts, view metrics, and user endorsements, serving as indicators of the \"marginal cost of experimentation\" and the extent of democratic adoption for each technology class.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Horizon scan of MOS sensors\u003c/h2\u003e \u003cp\u003eTo contextualise the current gas-sensing landscape and to contrast it with the comparatively limited commercial maturity of liquid-phase sensing technologies, a horizon scan was conducted across major electronic component distributors (DigiKey, Mouser, and LCSC) and open-source hardware marketplaces (Adafruit, SparkFun, and DFRobot). This survey revealed a highly stratified market in which innovation was driven by three distinct tiers of suppliers: digital innovations, industrial incumbents, and commodity aggregators. A total of thirteen distinct device architectures were identified in the horizon scan, distributed across three distinct market areas: Digital MEMS and Smart Systems, Industrial and Environmental Safety Incumbents, and Commodity and Prototyping Modules.\u003c/p\u003e \u003cp\u003eIn the first area, industry standards seem to be shifting from analogue voltage outputs toward fully integrated digital interfaces (I\u0026sup2;C/SPI). In contrast to legacy sensors, which require external operational amplifiers and analogue-to-digital conversion, Micro-Electro-Mechanical Systems (MEMS) devices are designed to incorporate signal conditioning and processing directly on the silicon die. Five innovations were identified within this tier, primarily driven by the emergence of \"software-defined sensing.\"\u003c/p\u003e \u003cp\u003eA second tier was formed by industrial incumbents characterised by extensive legacy catalogues of analogue electrochemical and catalytic bead sensors optimised for specific toxic or combustible gases (e.g., CO, H₂S). While the breadth of application-specific analogue sensors was maintained, miniaturised and partially integrated designs were increasingly introduced to compete with MEMS-based offerings. This tier exhibited the greatest diversity of target analytes, with catalogues comprising hundreds of distinct part numbers tailored to regulatory and industrial monitoring requirements. Four technological shifts were identified in this sector, most notably the transition from liquid-electrolyte cells to Solid-Polymer Electrochemical sensors. Unlike traditional liquid cells which are prone to leakage and drying, these solid-state variants (e.g., EC Sense) allow for the printing of toxic gas sensors into flexible, ultra-thin form factors. Furthermore, the sector is adopting Molecular Property Spectrometry (MPS) to replace catalytic bead sensors (pellistors). MPS sensors analyse the thermodynamic properties of a gas mixture to classify specific hydrocarbons, such as distinguishing Methane from Propane, without the risk of sensor poisoning from silicones or sulphur that frequently compromises legacy industrial safety devices.\u003c/p\u003e \u003cp\u003eThe third tier was composed of commodity aggregators, largely dominated by Shenzhen-based manufacturers such as Winsen Electronics, along with numerous white-label producers. This segment was found to underpin the ubiquity of gas sensing in educational, prototyping, and hobbyist contexts. This group sustained a long-tail market characterised by high redundancy and marginal differentiation; for example, methane sensors across component aggregators yield over 40 distinct breakout board variants relying on similar sensing elements. The four representative architectures identified in this area are defined by the commoditisation of legacy sintered ceramic tube designs, known as the \"MQ-Series.\" A recent trend in this sector is the introduction of \"Lite\" MEMS, such as the Winsen GM-Series, which replicate the small form factor of Tier 1 digital sensors but strip away the complex on-chip logic and calibration, offering a low-cost, low-power alternative that relies on the user's external microcontroller for signal processing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. ISEs sensor Horizon Scan\u003c/h2\u003e \u003cp\u003eTo assess the current state of the liquid-phase sensing ecosystem and contrast it with the dynamic innovation observed in the gas sensor market, a parallel horizon scan was conducted across the same electronic component distributors (DigiKey, Mouser, LCSC) and open-source hardware marketplaces. This survey revealed a more consolidated market structure and technologically conservative. A total of seven distinct device architectures were identified, distributed across three comparable market areas: Laboratory and Process Analytics, Emerging Solid-State and Screen-Printed Technologies, and Commodity Analogue Probes.\u003c/p\u003e \u003cp\u003eThe first area represents the established standard for high-precision measurement. Unlike the gas sensor market, where innovation is driving miniaturisation and digital integration, this tier remains anchored to the macro-scale combination electrode architecture. Manufacturers in this category focus on reinforcing the glass bulb with epoxy bodies or implementing pre-pressurised reference junctions to prevent clogging in wastewater applications. While \"smart\" digital sensors exist in this space, the digital logic is typically housed in a detachable transmitter head rather than integrated into the sensing element itself. Consequently, the fundamental transducer remains an analogue, high-impedance device requiring frequent manual calibration with liquid buffer solutions, a maintenance burden that has been largely eliminated in modern gas sensors via algorithmic baseline correction.\u003c/p\u003e \u003cp\u003eThe second area represents the technological horizon for liquid sensing, attempting to mirror the MEMS revolution seen in gas detection. However, unlike the mass-market success of MEMS gas sensors, this tier remains largely confined to niche research and development applications. Two key innovations were identified: Ion-Sensitive Field-Effect Transistors (ISFETs) and Screen-Printed Electrodes (SPEs). Providers such as have successfully commercialised solid-state sensors that replace the fragile glass bulb with a semiconductor chip or a printed carbon track. This allows for flat, microlitre-volume samples, a significant leap forward for biomedical and soil analysis. Yet, despite being available for decades, ISFETs have failed to achieve ubiquitous adoption due to persistent issues with drift and light sensitivity. Furthermore, unlike the gas sector\u0026rsquo;s \"system-on-chip\" solutions which output ready-to-use digital data, these solid-state sensors typically remain analogue components, shifting the burden of signal amplification and temperature compensation to the end-user.\u003c/p\u003e \u003cp\u003eThe third tier mirrors the commodity aggregator segment of the gas market but lacks the \"smart\" middle ground found in gas sensing. Dominated by manufacturers such as Winsen Electronics and DFRobot, this area is characterised by the mass production of legacy liquid-filled probes. The horizon scan identified three primary commodity architectures in this space: the standard glass pH probe, the antimony pH probe for harsh environments, and the polymer-membrane ISE for specific ions like Nitrate or Calcium.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Gas sensor mechanism of action\u003c/h2\u003e \u003cp\u003eMOS chemiresistors represent the dominant architecture for gas sensing. Unlike the complex assembly required for optical or electrochemical devices, MOS sensors function as solid-state transducers based on surface reactivity. The fundamental mechanism relies on the adsorption of oxygen species (O\u003csup\u003e\u0026minus;\u003c/sup\u003e, O\u003csup\u003e2\u0026minus;\u003c/sup\u003e) onto the surface of a heated n-type semiconductor, typically Tin Dioxide (SnO\u003csub\u003e2\u003c/sub\u003e) or Zinc Oxide (ZnO), which traps free electrons from the conduction band and establishes a depletion layer at the grain boundaries [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and must be maintained at elevated temperatures generally between 200\u0026deg;C and 450\u0026deg;C to function effectively [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This electron entrapment creates a potential barrier resulting in high baseline electrical resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.a.).\u003c/p\u003e \u003cp\u003eThe thermal requirement is a consequence of the thermodynamics of the sensing mechanism. Stoichiometric metal oxides behave as electrical insulators at room temperature, requiring thermal energy to excite charge carriers into the conduction band and to overcome the activation energy barriers for surface chemisorption [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In an environment of clean air, oxygen species adsorb onto this heated surface, trapping free electrons and establishing a depletion layer that results in a high baseline resistance. When a reducing gas encounters the sensor, it reacts with the surface oxygen, releasing trapped electrons and causing a measurable drop in resistance [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Since different reducing gases possess distinct activation energies for oxidation, the sensor\u0026rsquo;s specificity and power requirements of devices are highly influenced by its thermal setpoint (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTechnical specifications and operating parameters of the selected gas sensor models.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensor Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent / Power\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOp. Voltage (V)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRange (ppm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOp. Humidity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTemp Range (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSlope (α)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eReference / Source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTGS 813\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24V (C) / 5V (H)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500\u0026ndash;10k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10 to 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePellistor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;100% LEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-20 to 70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCity Cell\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0mA (Self)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u0026ndash;90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-20 to 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300\u0026ndash;10k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-20 to 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10 to 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200\u0026ndash;10k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10 to 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200\u0026ndash;10k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10 to 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200\u0026ndash;10k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10 to 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5V / 1.4V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20\u0026ndash;2k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-20 to 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u0026ndash;10k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10 to 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5V / 1.5V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u0026ndash;1k / 100\u0026ndash;10k\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-20 to 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-131\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10ppb \u0026ndash; 2ppm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10 to 45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-135\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u0026ndash;1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10 to 45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-136\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10 to 45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-137\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026ndash;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10 to 45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-138\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.0V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10 to 45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e~\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBME680\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u0026ndash;18mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u0026ndash;3.6V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;500 IAQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-40 to 85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAlgorithmic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBME688\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u0026ndash;18mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u0026ndash;3.6V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;500 IAQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-40 to 85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAlgorithmic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBME690\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;9mA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u0026ndash;3.6V\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;500 IAQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u0026ndash;100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-40 to 85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAlgorithmic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo refine the sensing mechanism, manufacturers frequently dope the oxide scaffold with noble metal catalysts such as Platinum (Pt) or Palladium (Pd), which lower the activation energy required for these surface reactions and improve selectivity towards specific analytes [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Despite the negligible cost of these raw semiconductor materials, the commercial scalability of gas sensors is currently bottlenecked by significant post-fabrication economic hurdles, specifically calibration and packaging. Selectivity and unit prices of common gas sensors are shown on Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of sensor performance metrics, including target gas specificity, selectivity ratings, known interferences, and unit price (\u0026euro;).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensor Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrice (\u0026euro;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSelectivity Rating\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMain Interference Gases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference / Source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTGS 813\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCombustible\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;15\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePromiscuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlcohol, Hydrogen, CO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePellistor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%LEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;30\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-Selective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAny combustible gas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCity Cell\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;30\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSelective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrogen (unless filtered), Ethylene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoke/LPG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;3\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery Promiscuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlcohol, Propane, Hydrogen, Methane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;4\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePromiscuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBenzene, Methane, LPG, CO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;4\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSemi-Selective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlcohol, LPG, Cooking Fumes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPG/Nat Gas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;4\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSemi-Selective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlcohol (Low), Methane, Smoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPG/Butane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;4\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSemi-Selective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlcohol, Smoke, Isobutane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSemi-Selective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrogen (Major), LPG, Alcohol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;5\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePromiscuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlcohol (See note below), LPG, CO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCO \u0026amp; Gas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;6\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePromiscuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLPG triggers CO side; CO triggers Gas side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-131\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOzone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;20\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSemi-Selective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCl₂, NO₂ (Oxidising gases)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-135\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAir Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;4\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery Promiscuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCO₂, Alcohol, Smoke, CO, Ammonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-136\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH₂S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;15\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSemi-Selective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSO₂, Alcohol (Ethanol)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-137\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmmonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;15\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSemi-Selective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCO, Alcohol, Organic Amines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMQ-138\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVOCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;15\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePromiscuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlcohol, Acetone, Formaldehyde\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBME680\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVOCs (IAQ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;15\u0026ndash;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBroad Spectrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHumidity, CO, Hydrogen, Ethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBME688\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVOCs\u0026thinsp;+\u0026thinsp;AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;20\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBroad (AI Filtered)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Same as above, AI filters some)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBME690\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVOCs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;14\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBroad Spectrum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHumidity, CO, Hydrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnlike digital integrated circuits which can be electrically validated in milliseconds, gas sensors operate in the chemical domain and are subject to unavoidable stochastic variations in film thickness and grain structure during the sintering process [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This requires a \"calibration tax,\" where every individual sensor unit must be exposed to known concentrations of target gas to establish a reliable baseline, requiring expensive gas mixing infrastructure that limits production throughput [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Furthermore, gas sensors present an engineering challenge. The sensing element must be hermetically sealed to protect delicate wire bonds and heater electronics from corrosion. However, it needs to be exposed to the external environment to facilitate gas diffusion. This requirement forces the adoption of specialised packages often capped with permeable membranes or steel mesh [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. ISEs mechanism of action\u003c/h2\u003e \u003cp\u003eISEs represent the gold standard for liquid-phase sensing, functioning as potentiometric transducers. ISEs operate near zero current (high impedance), and rely on the generation of a phase boundary potential at the interface between a sample solution and a selective membrane [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This membrane, often composed of doped glass, crystalline lattice, or plasticised PVC containing specific ionophores, selectively binds or exchanges the target ion, establishing a charge separation that adheres to the Nernstian principle.\u003c/p\u003e \u003cp\u003eThe thermodynamic basis for this measurement is described by the Nernst equation. In an ideal scenario, the potential difference developed across the membrane is logarithmically proportional to the activity of the specific ion in the solution. For every decade change in concentration of a monovalent ion (i.e. K\u003csup\u003e+\u003c/sup\u003e, F\u003csup\u003e\u0026minus;\u003c/sup\u003e), the sensor theoretically yields a potential shift of approximately 59.16 mV at 25 \u003csup\u003eo\u003c/sup\u003eC. Unlike MOS sensors which require thermal excitation to create conductivity, ISEs are generally passive devices, though their response slope is strictly temperature-dependent. To achieve specificity, manufacturers engineer the membrane composition to favor the thermodynamic partitioning of a single ion type. For instance, Valinomycin is embedded in PVC membranes to create a cavity specifically sized to trap Potassium ions K\u003csup\u003e+\u003c/sup\u003e, excluding smaller Na\u003csup\u003e+\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTechnical specifications and electrochemical characteristics of representative Ion-Selective Electrodes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensor Type / Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMembrane Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epH Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLinear Range (M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eResponse Time (t90​)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTemp Range (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSlope (mV/dec)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard pH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDoped Glass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u0026ndash; 10\u003csup\u003e\u0026minus;\u0026thinsp;14\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30 s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5 to 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFluoride (F\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolid State (LaF\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e0\u003c/sup\u003e \u0026ndash; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60 s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 to 80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePotassium (K\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePVC (Valinomycin)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e0\u003c/sup\u003e \u0026ndash; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60 s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 to 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCalcium (Ca\u003c/b\u003e\u003csup\u003e\u003cb\u003e2+\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePVC (Ionophore)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e0\u003c/sup\u003e \u0026ndash; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30 s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 to 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNitrate (NO\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePVC (Ion Exchange)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u0026ndash; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60 s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 to 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmmonium (NH\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePVC (Nonactin)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u0026ndash; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60 s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 to 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSodium (Na+)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlass / PVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u0026ndash; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60 s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 to 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChloride (Cl\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolid State (AgCl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e0\u003c/sup\u003e \u0026ndash; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60 s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 to 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLead (Pb\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSolid State (PbS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e10\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u0026ndash; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60 s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 to 80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e+\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePerfect selectivity is thermodynamically impossible. All ISEs suffer from interferences by ions of similar charge or radius, quantified by the Nicolsky-Eisenman coefficient [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Furthermore, unlike the raw semiconductor materials of gas sensors, the fabrication of robust ISE membranes adds significant cost and reduces operational lifespan. Selectivity coefficients and estimated unit prices are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of sensor performance metrics, including target ion specificity, selectivity coefficients, and economic analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensor Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrice (\u0026euro;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSelectivity Profile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMain Interfering Ions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStandard pH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;20\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHighly Selective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e (Alkaline Error\u0026thinsp;\u0026gt;\u0026thinsp;pH 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFluoride\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;150\u0026ndash;250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSelective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOH\u003csup\u003e\u0026minus;\u003c/sup\u003e (at high pH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePotassium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;100\u0026ndash;180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSemi-Selective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCs\u003csup\u003e+\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, Na\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCalcium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;100\u0026ndash;180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSemi-Selective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMg\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNitrate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;120\u0026ndash;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePromiscuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, I\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmmonium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;120\u0026ndash;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSemi-Selective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e (Major interference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSodium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;100\u0026ndash;180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSemi-Selective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAg\u003csup\u003e+\u003c/sup\u003e, H\u003csup\u003e+\u003c/sup\u003e (pH sensitive)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChloride\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCl\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;100\u0026ndash;180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePromiscuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS\u003csup\u003e2\u0026minus;\u003c/sup\u003e, CN\u003csup\u003e\u0026minus;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLead\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePb\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026euro;180\u0026ndash;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSemi-Selective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHg\u003csup\u003e2+\u003c/sup\u003e, Cu\u003csup\u003e2+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Historical Market Evolution of sensors\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.5.1 History of gas sensors\u003c/h2\u003e \u003cp\u003eThe economic trajectory of the MOS sensor market offers a clear roadmap of the commoditization of devices. Naoyoshi Taguchi\u0026rsquo;s discovered in 1962 that the electrical resistance of heated Tin Dioxide (SnO\u003csub\u003e2\u003c/sub\u003e) decreased in the presence of reducing gases. Motivated by a series of fatal domestic gas explosions in Japan, Taguchi patented the technology and founded Figaro Engineering Inc. in 1969. The early TGS (Taguchi Gas Sensor) series, specifically the TGS 109 and later the TGS 813, established the global standard for leak detection. During this \"Proprietary Era\" (1970\u0026ndash;1990), the technology was protected by a number of international patents, allowing Figaro to maintain a monopoly pricing structure where a single sensor unit retailed for approximately \u003cspan\u003e$\u003c/span\u003e50\u0026ndash;\u003cspan\u003e$\u003c/span\u003e80 (inflation-adjusted). Value during this period was locked in the proprietary chemical composition of the ceramic paste and the manual fabrication processes required to ensure consistency, restricting the technology to industrial safety equipment and high-end consumer alarms.\u003c/p\u003e \u003cp\u003eTaguchi\u0026rsquo;s core patents expired in the late 1980s, allowing competitors to reverse-engineer the ceramic sintering process, leading to the emergence of manufacturers, who introduced the \"MQ\" series (i.e., MQ-2, MQ-4) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These devices were functional clones of the TGS architecture. The resulting flood of supply drove unit prices down to the \u003cspan\u003e$\u003c/span\u003e10\u0026ndash;\u003cspan\u003e$\u003c/span\u003e25 range. While these sensors lacked the rigorous calibration of their Japanese counterparts, their price-performance ratio was sufficient to expand the market beyond industry and into general residential air quality monitoring.\u003c/p\u003e \u003cp\u003eThe final and most disruptive shift (2010\u0026ndash;Present), was driven by the economies of scale demanded by IoT. The explosion of open-source hardware (i.e., Arduino, Raspberry Pi) created a massive demand for low-cost breakout boards, pushing manufacturers to optimise the production of sensor modules. Today, the raw sensor component is effectively demonetised, selling for less than \u003cspan\u003e$\u003c/span\u003e2.00. The value has shifted entirely from the hardware to the software algorithms required to interpret its noisy output [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. A comparison of sensor prices is shown in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. This collapse in unit pricing has fuelled an explosion in total market valuation. By transitioning from a low-volume industrial instrument to a ubiquitous component in smart home devices and automotive systems, the global gas sensor market has reached a valuation of approximately \u003cspan\u003e$\u003c/span\u003e2.7\u0026nbsp;billion in 2024, with projected growth to \u003cspan\u003e$\u003c/span\u003e4.5\u0026nbsp;billion by 2030 [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. This stands in contrast to the ISE market, which remains estimated at \u003cspan\u003e$\u003c/span\u003e578\u0026nbsp;million [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHistorical price evolution, market valuation, and technological drivers of MOS gas sensors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEra / Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDominant Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnit Price (Adjusted)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlobal Market Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEconomic / Technological Driver\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1970\u0026ndash;1990\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFigaro TGS 813\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e$50.00 \u0026ndash; $80.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eIP Monopoly\u003c/b\u003e: Protected by Taguchi patents; manual fabrication; low global competition.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1990\u0026ndash;2010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEarly MQ Series\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e$10.00 \u0026ndash; $25.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e~ \u003cspan\u003e$\u003c/span\u003e800 Million\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePatent Expiration\u003c/b\u003e: Generic manufacturers reverse-engineer sintering process; market stratification begins.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2010 \u0026ndash; Present\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMQ-x Modules\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e$1.50 \u0026ndash; $5.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e~ \u003cspan\u003e$\u003c/span\u003e2.5 Billion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCommoditisation\u003c/b\u003e: Rise of Shenzhen supply chain; lower quality materials used to meet demand for non-critical uses.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2015 \u0026ndash; Present\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMEMS (BME680)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e$5.00 \u0026ndash; $15.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(Included above)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMiniaturisation\u003c/b\u003e: Shift from ceramic tubes to MEMS wafers; price reflects integration of digital logic (ASIC).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.5.2 History of ISEs\u003c/h2\u003e \u003cp\u003eIn contrast to the MOS trajectory, the commercialisation of ISEs followed a clinical-first model. In the mid-1960s, Frant and Ross\u0026rsquo;s invented of the fluoride electrode (using a single-crystal Lanthanum Fluoride membrane) [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e] and Simon\u0026rsquo;s discovery that antibiotics like Valinomycin could serve as highly selective ionophores for K\u003csup\u003e+\u003c/sup\u003e [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. This era, saw a rapid expansion of detectable analytes (Ca\u0026sup2;⁺, Na⁺, Cl⁻). However, the ISE remained a liquid membrane-based device, requiring an internal filling solution and a glass or polymer membrane to function. This physical constraint tethered the technology to high-value laboratory and clinical settings, specifically the blood gas analysers that became standard in hospitals by the 1980s. As such, the liquid sensor industry suffers from path dependence, evolving to prioritise accuracy over cost. On the contrary, the gas industry started in domestic leak detection prioritising low cost over accuracy.\u003c/p\u003e \u003cp\u003eConsequently, the ISE market never experienced the demonetisation seen in the gas sensor industry. While MOS sensors were driven by the volume of the smoke alarm and IoT markets, ISEs were driven by the precision of the medical market. The complexity of maintaining a stable reference potential in a miniaturised format acted as a technological barrier to commoditisatio. Because these sensors require constant hydration and frequent two-point calibration with liquid buffers, they could not be easily integrated into the \"dry\" world of consumer electronics or smartphones. This effectively capped the ISE market at its current ~\u003cspan\u003e$\u003c/span\u003e578\u0026nbsp;million valuation [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.5.3 Strip sensors\u003c/h2\u003e \u003cp\u003eA third, distinct market trajectory is found in disposable colorimetric test strips. Unlike the digital architecture of MOS sensors or the fragile liquid-contact format of clinical ISEs, this sector is built on simple, single-use chemical pads. These \"dipsticks\" dominate the manual water quality market\u0026mdash;from residential aquariums to field environmental monitoring\u0026mdash;because of their ability to multiplex. A single paper strip can simultaneously assay Nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), Nitrite (\u003cspan\u003e$\u003c/span\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), pH, and hardness (Ca\u003csup\u003e2+\u003c/sup\u003e/Mg\u003csup\u003e2+\u003c/sup\u003e) within 60 seconds. With a unit price often falling between \u003cspan\u003e$\u003c/span\u003e0.20 \u0026ndash; \u003cspan\u003e$\u003c/span\u003e0.50 per strip, they represent the most accessible form of chemical sensing globally.\u003c/p\u003e \u003cp\u003eHowever, the \"dipstick\" model is incompatible with the IoT economy due to the \"human-in-the-loop\" requirement. While convenient for spot-checks, these strips provide only a semi-quantitative\"snapshot of water quality, relying on subjective visual comparison against a colour chart. Recent studies have highlighted that this manual interpretation frequently leads to data bias, with user error rates in citizen science projects often exceeding 30% when compared to laboratory spectroscopy [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Furthermore, replicating the temporal resolution of a digital sensor (which logs data every minute) using strips is economically impossible; obtaining a comparable 24-hour data stream would require 1,440 manual tests, costing over \u003cspan\u003e$\u003c/span\u003e400 daily. Consequently, the global market for water quality test strips remains valued at approximately \u003cspan\u003e$\u003c/span\u003e233\u0026nbsp;million (2024) [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Diversification and thematic evolution of the gas sensing field\u003c/h2\u003e \u003cp\u003eThe diversification of the gas sensing research landscape was examined through a longitudinal analysis of term co-occurrence networks constructed for four distinct eras, defined by inflection points in the first derivative of annual publication counts. To ensure distinct thematic comparison across these historical phases, a standardised sample of 2,500 articles was extracted for analysis from each era. These periods corresponded to phases of accelerated thematic expansion and were used to capture shifts in research focus as the field evolved (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Across the four eras, a clear progression was observed from materials-centric investigations toward application-driven, data-centric sensing systems. In the earliest period (1981\u0026ndash;1984), the co-occurrence network (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) exhibited a sparse and tightly clustered structure, indicative of a field in its formative stage where implementation was largely restricted to the automotive and heavy industrial sectors. Dominant terms were associated with fundamental materials science and device physics, including tin dioxide, semiconductor, adsorption, oxygen, temperature, and conductivity. These terms formed a highly interconnected core, reflecting a research emphasis on elucidating the basic mechanisms of gas sensing in metal-oxide semiconductors for the binary detection of hazardous leaks, such as LPG or methane, rather than precise quantification.\u003c/p\u003e \u003cp\u003eBy the mid-1990s (1995\u0026ndash;1999), the term networks expanded in both size and complexity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), marking the technology's transition from leak detection to environmental monitoring. Research focus during this period shifted toward engineering selectivity and fabrication strategies, as evidenced by the prominence of terms such as thin film, oxide, doping, catalyst, and selectivity. The appearance of fabrication-related keywords, including sol\u0026ndash;gel and sputtering, reflected growing interest in controllable deposition techniques and microstructural tuning. Concurrently, early signal-processing concepts such as pattern recognition and analysis began to co-occur with materials terms, indicating the initial adoption of algorithmic methods to mitigate cross-sensitivity. Although materials science remained dominant, early references to environmental monitoring and volatile organic compounds suggested the beginnings of application-driven research in fixed-station air quality networks and process control.\u003c/p\u003e \u003cp\u003eA pronounced diversification was observed during the 2005\u0026ndash;2009 period, where the co-occurrence maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) revealed multiple well-defined clusters corresponding to the technology's entry into the healthcare and diagnostic sectors. Nanostructuring emerged as a central theme, with nanowire, nanoparticle, and surface modification forming a dense cluster linked to enhanced sensitivity and lower detection limits. In parallel, application-focused clusters associated with air quality monitoring, breath analysis, medical diagnostics, and environmental sensing became prominent, indicating a shift toward real-world use cases. Systems-level concepts such as instrument, calibration, detection limit, and sensor array also gained centrality, reflecting increased attention to deployable sensing platforms rather than isolated sensing elements. This period marked a transition from predominantly laboratory-scale studies to integrated sensor systems tailored for specific applications, moving beyond industrial safety to non-invasive medical screening.\u003c/p\u003e \u003cp\u003eIn the most recent era (2019\u0026ndash;2024), the co-occurrence networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed) displayed the highest degree of thematic fragmentation and structural complexity, consistent with a mature field now embedded within IoT and wearable ecosystems. Central nodes were increasingly associated with data-centric and computational concepts, including model, algorithm, dataset, machine learning, and classification. These terms formed strong links with system-level descriptors such as monitoring, value, and network, indicating a shift toward software-defined sensing architectures in which sensor hardware served primarily as a data source for downstream analytics. Application clusters related to healthcare, patient monitoring, wearables, and IoT deployments were strongly represented, highlighting the integration of gas sensors into broader cyber-physical systems. While materials-related terms persisted, they occupied more peripheral positions in the network, suggesting that sensing hardware had become standardised and commoditised, with innovation increasingly occurring at the level of data interpretation and system integration. Taken together, the term co-occurrence analysis revealed a clear longitudinal trajectory in which the gas sensing field evolved from a narrowly focused materials science discipline into a diversified, application-driven, and data-oriented research ecosystem. This thematic expansion closely paralleled the economic commoditisation of gas sensors described in the preceding section, supporting the interpretation that reductions in hardware cost and increased accessibility were key enablers of intellectual diversification and cross-disciplinary adoption.\u003c/p\u003e \u003cp\u003eThe delineation of the four eras was further supported by the presence of four pronounced peaks in annual publication rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(e\u0026ndash;g)), which were interpreted as successive waves of accelerated research activity occurring over distinct historical intervals. The first peak, observed in the early 1980s (approximately 1981\u0026ndash;1984), coincided with the consolidation of metal-oxide semiconductor gas sensing as a viable analytical technology. This period followed the commercialisation of Taguchi-type sensors in the 1970s and reflected intensified academic interest in elucidating the fundamental surface chemistry and charge-transport mechanisms underlying chemoresistive sensing. The associated growth in publications was therefore attributed primarily to foundational materials science and device physics rather than to external system-level enablers.\u003c/p\u003e \u003cp\u003eA second peak was identified in the mid-to-late 1990s (approximately 1995\u0026ndash;1999), temporally aligned with two converging developments: the widespread commoditisation of MOS gas sensors following patent expirations and the emergence of early electronic nose concepts. The term \u0026ldquo;electronic nose\u0026rdquo; was formalised in the late 1980s and early 1990s, with seminal work demonstrating that arrays of broadly selective sensors, combined with multivariate pattern recognition, could achieve functional selectivity. During this period, advances in personal computing, data acquisition hardware, and statistical classification methods lowered the barrier to implementing sensor arrays, leading to increased research output focused on selectivity enhancement through system-level approaches rather than material specificity alone.\u003c/p\u003e \u003cp\u003eThe third publication surge occurred during the mid-2000s (approximately 2005\u0026ndash;2009) and coincided with the rapid expansion of open-source electronics and low-cost embedded computing. The introduction of the Arduino platform in 2005, followed by the early development of the Raspberry Pi project (publicly announced in 2006 and released in 2012), substantially democratised access to microcontrollers, analogue-to-digital conversion, and sensor interfacing. This period also overlapped with declining unit costs of gas sensors driven by globalised manufacturing. Collectively, these factors enabled gas sensing to move beyond specialised laboratories into educational, hobbyist, and interdisciplinary research contexts. The observed increase in publications during this era was therefore interpreted as a consequence of reduced economic and technical barriers to experimentation, facilitating application-driven research in environmental monitoring, portable instrumentation, and early IoT systems.\u003c/p\u003e \u003cp\u003eThe most recent and largest peak was observed in the period from approximately 2019 to 2024 and was temporally associated with the widespread adoption of machine learning and edge-AI techniques in sensor research. This era followed major advances in deep learning frameworks during the 2010s and the subsequent emergence of resource-efficient inference methods, often referred to as TinyML, which enabled machine-learning models to be deployed directly on microcontrollers. In parallel, commercially available \u0026ldquo;smart\u0026rdquo; gas sensors with integrated processing and digital interfaces became widely accessible. These developments transformed gas sensors into software-defined sensing platforms, where application-specific value was increasingly extracted through data-driven models rather than improvements in intrinsic selectivity. The magnitude and breadth of the publication peak in this period were therefore attributed to the convergence of mature sensing hardware, open-source software ecosystems, and embedded AI capabilities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen deconstructing these trends at the component level, distinct adoption dynamics emerge between proprietary industrial standards and commodity hardware (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(f\u0026ndash;g)). While the industrial TGS-813 anchored the field with a prominent peak in 1981, the cyclical waves of interest manifested differently in the commodity sector. Notably, the MQ series exhibited intermediate peaks in 1986, 1996, and 2004, often preceding the secondary resurgences of the industrial standard (observed in 1997 and 2010). During the current artificial intelligence era, the growth trajectory of the proprietary TGS sensor appears less consistent compared to the robust, exponential rise of the MQ series. This steady acceleration in commodity sensor research is likely attributable to their wider availability, extensive public documentation, and negligible unit cost. Our network analysis reveals two distinct epistemic communities: the Gas community, which is open and application-focused, and the Liquid community, which is closed and material-focused.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Bibliometric Analysis of Ion-Selective Electrodes\u003c/h2\u003e \u003cp\u003eTo quantify the divergence in research trajectories between gas and liquid-phase sensing, a parallel longitudinal analysis was performed on the ISE and pH sensors literature. While the gas sensor domain witnessed an explosive expansion, accumulating a total of 1,313,700 publications since 1974 with 115,469 outputs in 2024 alone, the liquid-phase sensing field has remained comparatively restricted. In the same period, research into ISEs produced a total of 109,016 manuscripts, with only 6,070 published in 2024, a nearly twenty-fold disparity in annual output. This magnitude gap suggests that, while gas sensing has successfully transitioned into a ubiquitous, data-driven commodity discipline, ISE research remains a specialised niche.\u003c/p\u003e \u003cp\u003eTo understand the qualitative evolution of this field, term co-occurrence networks were constructed for four distinct eras using a standardised sample of 2,500 articles per period, mirroring the methodology applied to the gas sensor landscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-d)). In contrast to the rapid, chaotic expansion of the gas sensor networks, the ISE landscape exhibits a more linear and chemically focused evolutionary path. In the earliest period (1983\u0026ndash;1988), the network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) is dominated by foundational terms such as calcium, potassium, selectivity coefficient, and neutral carrier. This cluster reflects an era focused on the discovery and synthesis of primary ionophores (e.g., valinomycin) and the establishment of the theoretical frameworks governing potentiometric response. Unlike the early gas sensor networks which focused on industrial leak detection, the primary drivers here were clinical analysis and serum electrolyte monitoring.\u003c/p\u003e \u003cp\u003eBy the second era (1993\u0026ndash;1998), the network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) reveals a consolidation around membrane engineering. Dominant nodes such as PVC, membrane, plasticiser, and lipophilicity indicate a shift from ionophore discovery to the optimisation of the polymeric matrix. This period corresponds to the maturation of the classical liquid-contact ISE, where research prioritised the suppression of transmembrane fluxes and the improvement of sensor lifetime. A significant shift occurred in the third era (2003\u0026ndash;2008) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec), where the network structure pivots toward performance limits. The emergence of terms such as detection limit, trace, and environmental coincides with the \"trace-level revolution\" in the ISE field, pioneered by groups demonstrating that optimising the inner filling solution could push detection limits from micromolar to picomolar levels. However, the relatively sparse connectivity of this network compared to the equivalent gas sensor era (which was exploding with \"electronic nose\" concepts) suggests a field deepening its fundamental understanding rather than broadening its application base.\u003c/p\u003e \u003cp\u003eIn the most recent era (2019\u0026ndash;2024), the ISE network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed) finally exhibits signs of the \"wearable shift\" observed earlier in gas sensing, though at a smaller scale. New clusters emerge around solid-contact, wearable, sweat, and real-time, reflecting the modern effort to eliminate the liquid reference electrode and integrate ISEs into flexible electronics. However, distinct from the gas sensor network where machine learning and AI became central nodes, the modern ISE network remains anchored in materials science (i.e., conducting polymer, carbon nanotube), highlighting that the primary bottleneck in liquid sensing remains the physical transducer rather than data interpretation.\u003c/p\u003e \u003cp\u003eThe historical publication rates further evidence this divergence (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(e\u0026ndash;f)). While the curve for pH sensors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef) displays a smooth exponential growth similar to commodity gas sensors, the trajectory for general ISEs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee) is far more complex. A notable plateau and slight decline in publication activity is observable between approximately 2000 and 2010. This \"period of stagnation\" contrasts with the explosive growth of MOS sensors during the same decade (driven by the Arduino/IoT boom). It implies that while gas sensors were benefiting from the democratisation of microelectronics, ISE research was stalled by the inherent limitations of the classical liquid-filled architecture. The subsequent resurgence in ISE publications post-2015 aligns with the breakthrough of robust solid-contact transducers, yet the growth remains linear rather than exponential, constrained by the high cost of materials and the lack of a \"digital-native\" sensor architecture equivalent to the MEMS gas sensor.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhile the gas sensing lexicon transitioned from device physics (i.e., sintering, grain boundary) to broad application domains (i.e., breath analysis, IoT), indicating a commoditisation of the hardware, the ISE literature displays a distinct semantic inertia. Rather than migrating toward application-centric terminology, the network vocabulary has cycled through iterative generations of materials science, shifting from the neutral carriers of the 1980s to the conducting polymers and carbon nanotubes of the modern era. This persistent dominance of fabrication and material descriptors implies that ISE technology has not yet achieved the maturity of its gas-sensing counterparts. Instead of expanding into deployment and data interpretation, the field remains locked in a cycle of transducer optimisation, still seeking the ideal material composition to overcome inherent stability limitations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Sensors and the Open Source community\u003c/h2\u003e \u003cp\u003eThe expansion of gas sensing technology was paired with the growth of open-source hardware and maker communities, which acted as amplifiers for adoption, experimentation, and downstream commercial demand. An analysis of major community-driven platforms revealed a substantial volume of publicly documented gas sensor projects, underscoring the role of participatory innovation in shaping the modern gas sensing ecosystem.\u003c/p\u003e \u003cp\u003eOn Hackster.io, a total of 905 projects related to gas sensing were identified at the time of analysis. These projects spanned a wide range of applications, including smoke and fire detection, indoor air quality monitoring, industrial safety alarms, environmental sensing stations, and early electronic nose implementations. Engagement metrics indicated sustained and broad interest. For example, one of the most referenced tutorials, \u0026ldquo;Smoke Detection using MQ-2 Gas Sensor\u0026rdquo;, accumulated over 606,000 views and more than 330 user endorsements. A complementary pattern was observed within manufacturer-hosted community ecosystems. On the DFRobot community platform, 101 distinct gas sensor\u0026ndash;related projects were identified. These projects were typically structured as step-by-step build logs, incorporating detailed hardware schematics, firmware examples, and deployment guidance. Compared to general-purpose maker platforms, manufacturer communities exhibited a stronger alignment with specific product families and breakout boards, suggesting a tighter coupling between community experimentation and commercial offerings. The clustering of projects around particular sensor modules indicated that community engagement was not evenly distributed across the sensing landscape but instead concentrated on devices that were inexpensive, readily available, and well-supported by example code and documentation.\u003c/p\u003e \u003cp\u003eQuantitative searches conducted on Hackster.io revealed a near-total absence of community-driven development for specific electrolytes. While the broader term \"Ion sensor\" returned 158 results, a qualitative review indicated these were predominantly news aggregations regarding academic breakthroughs rather than the reproducible hardware manuals found in the gas sector. Even the ubiquitous pH probe, the standard for liquid sensing, appeared in only 82 projects, less than 10% of the volume observed for gas sensors. The few identified projects were predominantly instructional demonstrations of basic pH probing involving off-the-shelf laboratory probes, with minimal evidence of the derivative innovation or \"long-tail\" replication observed for MQ-series or MEMS gas sensors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Patent Landscape and Commercial Proprietary Trends\u003c/h2\u003e \u003cp\u003eTo triangulate the academic trends with proprietary commercial activity, a patent landscape analysis was performed using the Google Patents database. Search queries were constructed to isolate the two dominant sensing modalities: gas sensors were queried using the string (\"Metal Oxide\" OR \"MOS\") AND \"sensor\" AND \"gas\", while liquid-phase sensors were targeted with (((\"ion\" OR \"electrolyte\") AND \"sensor\") OR \"ion-selective electrode\"). Datasets were analysed for total volume, priority date distribution, and assignee composition to assess the \"IP age\" and commercial focus of the respective fields.\u003c/p\u003e \u003cp\u003eThe search yielded 12,393 results for gas sensors, with the earliest relevant priority date identified in 2002, and 14,856 results for ion-selective sensors, dating from 2012. A qualitative audit of the metadata reveals a profound structural divergence that mirrors the \"horizontal vs. vertical\" split observed in the bibliometric analysis. The gas sensor patent landscape is characterised by high-level integration into consumer electronics. Dominant assignees include major consumer technology conglomerates and specialised semiconductor manufacturers. Moreover, patent titles frequently reference \"electronic devices,\" \"display devices,\" and \"mobile communications,\" confirming that gas sensing IP has moved beyond the transducer level to focus on system-level integration into mass-market hardware.\u003c/p\u003e \u003cp\u003eIn contrast, the ion-selective sensor dataset displays significant semantic contamination from adjacent, high-capital industries. A substantial portion of the search results are not sensing patents but energy storage innovations (i.e., secondary batteries), where the terms \"ion\" and \"electrolyte\" are ubiquitous. When these artefacts are filtered out, the remaining genuine sensing IP is heavily concentrated in the \"vertical\" domain of clinical diagnostics. Key assignees in this space are predominantly large medical device and pharmaceutical entities, with patents related to \"continuous analyte sensors\" and \"in vivo monitoring.\" Unlike the gas sector, where IP is distributed across a broad \"long tail\" of consumer applications, liquid sensing IP is locked within deep corporate silos focused on high-value, regulated medical devices.\u003c/p\u003e \u003cp\u003eFurthermore, comparing these patent volumes against the bibliometric output exposes a massive disparity in the \"Openness Ratio\" of the two fields. The gas sensor field, with its\u0026thinsp;~\u0026thinsp;1.3\u0026nbsp;million academic publications and ~\u0026thinsp;12,000 patents, exhibits a ratio of roughly 100 papers for every patent. Conversely, the ISE field, with ~\u0026thinsp;100,000 papers and a chemically-inflated patent count, operates under a much tighter ratio. This suggests a less open ecosystem.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Discussion\u003c/h2\u003e \u003cp\u003eThe comparative analysis of the gas and liquid-phase sensing landscapes reveals a divergence in technological maturity, driven by the economics of data acquisition rather than the utility of the chemical information. The horizon scan exposed a gas sensor market that has successfully stratified into three distinct tiers, digital MEMS innovators, industrial incumbents, and commodity aggregators. This creates a supply chain of accessibility that caters to users across all levels of technical expertise. Devices such as the Bosch BME688 or Renesas ZMOD4410 utilise generic metal-oxide micro-hotplates, relying on machine learning algorithms to interpret dynamic conductivity slopes and classify complex odours. In contrast, ISEs market remains technologically conservative, anchored to the macro-scale combination electrode architecture. While gas sensors have evolved into \"Digital Noses\" with integrated I\u0026sup2;C logic, liquid sensors remain analogue, high-impedance components, shifting the burden of signal amplification and calibration entirely to the end-user.\u003c/p\u003e \u003cp\u003eThis technological bifurcation is mirrored in the bibliometric data, which exposes a twenty-fold disparity in annual research output (115,469 publications for gas sensors versus 6,070 for ISEs in 2024). The semantic evolution of the two fields offers a causal explanation for this gap. Longitudinal co-occurrence networks reveal that the gas sensing lexicon has successfully migrated from \"device physics\" terminology (i.e., sintering, grain boundary) in the 1980s to \"application\" terminology (i.e., breath analysis, IoT, wearable) in the modern era. Conversely, the ISE literature displays a distinct semantic inertia. Rather than expanding into application domains, vocabulary has merely cycled through iterative generations of materials science, from the neutral carriers of the 1980s to the conducting polymers and carbon nanotubes of today. This persistent fixation on fabrication implies that ISE technology has not yet breached the maturity threshold required to enable widespread application-layer research.\u003c/p\u003e \u003cp\u003eThe mechanism governing this divergence is the \"commoditisation feedback loop,\" a cycle where the collapse of unit costs triggers an increase in application diversity, which in turn drives total market value. Historical data suggests that the gas sensor industry catalysed this loop when the expiration of key patents allowed low cost sensors to flood the market, pushing unit prices below the critical threshold of \u003cspan\u003e$\u003c/span\u003e5.00. This affordability unlocked a \"long tail\" of horizontal innovation, allowing computer scientists, civil engineers, and hobbyists to integrate gas sensing into non-critical domains. This diversification leads to an expansion of total market valuation. The ubiquity of cheap sensors generates massive volumes of training data, which fuels the development of robust AI, increasing the utility of the hardware and driving a secondary cycle of demand. The ISE market, trapped by the high manufacturing costs, does not seem to have breached this accessibility threshold. Consequently, it remains stuck in a vertical innovation cycle.\u003c/p\u003e \u003cp\u003eThe concept of using sensor arrays to fingerprint complex liquids emerged synchronously with the Electronic Nose in the mid-1990s [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. However, while the electronic nose turned the liability of poor selectivity into a feature through pattern recognition [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], the electronic tongue failed to disrupt the market. This failure was driven by physical constraints that software could not resolve, including the need for Reference Electrodes. Unlike a MOS array, which is a self-contained solid-state system, an electronic tongue relies on a single, fragile liquid-junction reference electrode. If this component drifts or clogs, the entire multivariate pattern collapses. Furthermore, while gas sensor heaters continuously burn off contaminants, liquid sensors operate at room temperature in complex matrices where biofouling causes irreversible drift. These physical realities have prevented the electronic tongue from becoming the commoditised engine of growth for the ISE field, leaving liquid sensing trapped in a cycle of material optimisation while gas sensing has ascended into a software-defined, data-driven ecosystem.\u003c/p\u003e \u003cp\u003eAn examination of the technical specifications of sensors revealed a differences between the perceived and real barriers to commercialisation. The liquid-sensing community frequently cites \"insufficient selectivity,\" \"signal drift,\" and \"conditioning requirements\" as primary obstacles preventing the widespread adoption of ISEs [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, a direct comparison with MOS gas sensors suggests these are not true commercial limitations. In terms of selectivity, MOS sensors are notoriously promiscuous, reacting to broad families of reducing gases rather than specific molecules [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similarly, while ISEs require a hydration period (conditioning) to equilibrate the phase boundary potential, MOS sensors mandate a significant \"burn-in\" period to stabilise the depletion layer. Even signal drift, often framed as a disqualifying limitation for solid-state ISEs, is a chronic issue in MOS technology, known as the \"sleep effect,\" which manufacturers mitigate through algorithmic baseline correction [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. However, three physical limitations of the ISE architecture present genuine barriers that software cannot resolve: material costs, durability and the need of a reference electrode. While a sintered MOS heater can operate continuously for over a decade [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], the lifespan of a typical liquid-contact ISE is often capped at six months [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. This limited longevity is driven by the inevitable desiccation of the internal filling solution and the leaching of plasticisers from the polymeric membrane. Furthermore, every potentiometric measurement relies on a reference electrode to complete the circuit. This component is prone to clogging and potential drift, preventing the true miniaturisation achieved by the self-contained MOS pixel.\u003c/p\u003e \u003cp\u003eEmerging All-Solid-State ISEs (ASS-ISEs) promise to circumvent these physical challenges by replacing the internal solution with a conductive solid transducer (i.e., carbon nanotubes or conducting polymers) [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. This architecture mimics the robust, layer-by-layer fabrication of MEMS devices, theoretically allowing for storage in dry conditions and integration into wearable electronics. However, these devices currently trade durability for stability, given the formation of a microscopic water layer at the solid-contact interface often introduces random potential drifts that compromise the Nernstian response [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe path to ubiquity, therefore, lies in abandoning the pursuit of a highly selective sensor in favour of a more durable, but promiscuous sensor. A commoditised ISE market could leverage AI to compensate for imperfect selectivity. If manufacturing innovations, such as screen-printing [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e] or aerosol deposition [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e] can lower the cost of ionophore-based sensors to a fraction of their current price, the strict requirement for highly specific, expensive ionophores (i.e., Valinomycin) could be relaxed. Moving further, the industry could pivot entirely from fragile polymeric membranes to robust inorganic materials, such as Molybdenum oxide (MoO₃) [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e] or Ruthenium oxide (RuO₂) [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], with proved ion sensing capabilities. Unlike organic ionophores which are prone to leaching and diffusion over time, these metal oxides offer superior durability and can be deposited as solid-state layers. While they typically exhibit lower intrinsic selectivity, often acting as broad-spectrum pH or redox probes, their mechanical resilience makes them ideal for the \"sensor fusion\" approach. A dense array of cheap, less selective sensors, maintaining a Nernstian sensitivity of 59 mV/decade, could allow machine learning models to deconvolute interferences in real-time. This shift would unlock massive application domains currently priced out of the market, from precision agriculture and nutrient monitoring to real-time sweat analysis, effectively replicating the data-driven explosion observed in the gas sensor industry.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe comparative sociotechnical analysis reveals that the trajectory of sensor commercialisation is driven not by the intrinsic quality of analytical data, but by the political economy of data acquisition. The divergence between the ubiquitous, multi-billion-dollar gas sensor industry and the stagnant, niche market for ISEs serves as a testament to the power of the commoditisation feedback loop and technological lock-in. While the gas sensor sector successfully transitioned from a hardware-centric discipline focused on device physics to a software-defined ecosystem driven by application diversity, the ISE field remains hindered by a cycle of material optimisation. This stagnation is quantitatively evidenced by the twenty-fold disparity in annual research output and the semantic inertia of the ISE lexicon, which has failed to migrate from fabrication terminology to deployment use-cases over the last four decades.\u003c/p\u003e \u003cp\u003eWe argue that the fundamental barrier to the ubiquity of liquid sensing is not the lack of selectivity or the presence of signal drift, but the prohibitive \"marginal cost of experimentation\" imposed by the legacy combination electrode architecture. The history of the MOS gas sensor demonstrates that the collapse of unit costs below a critical accessibility threshold (enabled by the expiration of patents and the rise of mass-manufacturing among others) is the primary catalyst for horizontal innovation. By democratising access to hardware, the gas sector unlocked a \"long tail\" of non-expert developers who integrated sensors into broader cyber-physical systems, generating the massive datasets required to train robust machine learning models. In contrast, the high capital cost of ISEs have kept the technology locked within vertical, professional silos, creating an epistemic inequality where only well-funded institutions can generate hydrological data.\u003c/p\u003e \u003cp\u003eTo allow for a more widespread use of ISEs, the liquid sensing community must look beyond the laboratory. Emerging manufacturing paradigms, such as screen-printing and the use of durable metal oxides (e.g., Ruthenium or Molybdenum oxide), offer a viable pathway to overcome the limitations of reference electrodes. As such, the field can shift the burden of analytical resolution from the hardware to the algorithm by enabling lower intrinsic selectivity in exchange for mechanical robustness and negligible unit costs,\u003c/p\u003e \u003cp\u003eUltimately, this study reframes the challenge of liquid sensing not as a chemical problem, but as an economic problem awaiting a better distribution model. This paradigm shift is a prerequisite for the democratisation of environmental stewardship. 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J Solid State Electrochem 6:374\u0026ndash;383\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanumihardja E, Olthuis W, Van den Berg A (2018) \u003cem\u003eRuthenium Oxide Nanorods as Potentiometric pH Sensor for Organs-On-Chip Purposes.\u003c/em\u003e 18(9): p. 2901\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Plantion Ltd","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gas Sensors, Ion-Selective Electrodes, Bibliometrics, Commoditisation, Internet of Things, Artificial Intelligence","lastPublishedDoi":"10.21203/rs.3.rs-9001946/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9001946/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDigital innovation has democratised data processing. However, the physical acquisition of environmental data remains stratified, excluding non-experts and developing regions from water quality stewardship. The Internet of Things (IoT) and Artificial Intelligence (AI) have shifted the economic value of sensing from precision to data density. While gas sensing has successfully transitioned into a ubiquitous, software-defined commodity, liquid-phase sensing, especially Ion-Selective Electrodes (ISEs), remain locked in a high-cost, low-volume niche. This study presents a comparative longitudinal analysis of the economic, technological, and bibliometric trajectories of both fields to elucidate the drivers of this divergence. Using term co-occurrence networks from 1.3\u0026nbsp;million gas sensing publications and 109,000 ISE publications (1980\u0026ndash;2024), we reveal a strong semantic migration in the gas sector from device physics to application-centric domains, contrasting with the ISE field's persistent fixation on material formulation. A \"commoditisation feedback loop\" was identified in the gas market, triggered by the expiration of key patents and the collapse of unit costs below \u003cspan\u003e$\u003c/span\u003e2.00, which unlocked a long tail of open-source innovation and data generation. In contrast, the ISE market remains stifled by the need of a Reference Electrode, and the high capital cost of legacy glass architectures, limiting annual research output to a fraction of its gas counterpart. We argue that the barriers to liquid sensing ubiquity are not intrinsic performance flaws, such as drift or selectivity, but rather the prohibitive marginal cost of experimentation. We conclude that to replicate the gas sensor revolution, the liquid sensing community must pivot from the pursuit of \"perfect\" chemical specificity to \"sufficient\" digital utility, leveraging emerging scalable manufacturing techniques and AI-driven sensor arrays to democratise hydrological data acquisition.\u003c/p\u003e","manuscriptTitle":"Technological Lock-in and the Democratisation of Environmental Data: A Comparative Scientometric Analysis of Gas and Liquid Sensing Trajectories","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 04:48:31","doi":"10.21203/rs.3.rs-9001946/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"59d30cba-9fbd-4478-8fbe-2fd4578d14b7","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63721368,"name":"Other Economics"},{"id":63721369,"name":"General Biochemistry"}],"tags":[],"updatedAt":"2026-03-03T04:48:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 04:48:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9001946","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9001946","identity":"rs-9001946","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}