Graphene-based glucose sensors with an attomolar limit of detection

Graphene-based glucose sensors with an attomolar limit of detection | 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 Article Graphene-based glucose sensors with an attomolar limit of detection Vicente Lopes, Tiago Abreu, Mafalda Abrantes, Siva Nemala, Francesco De Boni, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5581426/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 Diabetes mellitus , a prevalent metabolic disorder affecting hundreds of millions worldwide, demands continuous glucose monitoring for effective management. Current blood glucose monitoring methods, such as commercial glucometers, though accurate, are invasive and uncomfortable, highlighting the need for non-invasive, ultra-sensitive alternatives. Here, we present a glucose sensing platform based on electrolyte-gated graphene field-effect transistors (EG-GFETs) functionalized with glucose oxidase enzymes for ultra-sensitive detection. Detailed material characterization by Raman and X-ray photoelectron spectroscopies confirms successful enzyme immobilization, with a marked increase in nitrogen content from 0.9% to 7.6% atomic concentration on the graphene surface, indicating substantial glucose oxidase coverage. Raman analysis reveals significant p-type doping and tensile strain on the graphene channel directly correlating with glucose concentration from 1 nanomolar to 1 millimolar. The EG-GFETs demonstrate an ultra-low limit-of-detection of 1 attomolar, with a consistent Dirac point voltage shift of +26 ± 4 mV and a linear response across six orders of magnitude (up to 1 picomolar, with a sensitivity of 10.6 mV/decade). The sensor maintains high selectivity in complex media, such as artificial tears (with a limit-of-detection of 100 attomolar), underscoring its potential for non-invasive continuous glucose monitoring applications, also in wearable format. Physical sciences/Nanoscience and technology/Nanoscale devices/Biosensors Physical sciences/Nanoscience and technology/Graphene/Electronic properties and devices Biological sciences/Biotechnology/Nanobiotechnology/Biosensors diabetes glucose monitoring non-invasiveness graphene field-effect transistors selectivity Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Glucose is a primary source of energy for cellular activity in the body, and maintaining its optimal concentration in the blood is crucial. However, certain patients suffer from metabolic disorders that affect glucose processing. Diabetes mellitus , a major metabolic disorder that impairs glucose regulation, affects more than 400 million people worldwide and is projected to impact up to ~ 700 million people by 2045 1,2 . This condition is characterized by hyperglycemia resulting from defects in insulin secretion or action 3 , 4 . Without proper management, diabetes can lead to severe complications such as blindness, cardiovascular diseases, nerve damage, and even cancer 3 . Many glucose measurement approaches exist, and efforts are being devoted to achieving functional continuous glucose monitoring. Glucose sensors range from optical (by using tilted fibers, photonic crystals or liquid crystals) 5 , 6 to electrochemical (through functionalized graphene derivatives and modified or bare electrodes) 7 , 8 . Essentially, they can be classified as non-enzymatic or enzymatic. Non-enzymatic sensors rely on the direct electrochemical oxidation of glucose, induced by the intrinsic catalytic properties of the electrode materials 9 , which can be metallic ( e.g. , Pt, Au, or Cu), polymeric or carbon-based 10 , 11 . These sensors are conceptually simple, versatile, low-cost, highly stable and durable 12 , 13 . However, they lack biorecognition probes, which are crucial when sensing any analyte in highly complex media such as body fluids 12 . Although more costly and susceptible to degradation over time due to environmental factors 14 , enzymatic glucose sensors offer unmatched sensitivity and selectivity toward glucose. The integration of a glucose-recognition element, such as glucose oxidase (GOx) enzymes, allows selective transducing only when exposed to glucose 15 , 16 . Enzymatic finger-pricking sensors are suitable and for accurate at-home monitoring of blood glucose levels (between 4.4 and 6.7 mM in the daytime) 17 , 18 , but these can be perceived as invasive, uncomfortable, and risky, leading to reluctance or rejection in 30 % of patients 19 . Other biofluids, suchas tears and saliva (normal glucose levels of 0.1–0.2 mM for both), have been proposed as diagnostic biofluids to replace blood. They are considered ideal for non-invasive glucose monitoring but would require high sensitivity, since their fasting glucose concentrations are lower than those of blood (averaging > 0.4 mM for tears and > 0.8 mM for saliva vs . > 7 mM for diabetic patients) 20 – 24 . At present, tear- and saliva-based glucose sensors have not progressed enough to enter the market. In recent years, graphene emerged as a key material for electronics and sensing technologies. Capitalizing on high surface-to-volume ratio, excellent carrier mobility and ease in functionalization 25 , 26 , graphene represents an ideal platform to interact efficiently with the surrounding environment and react to external stimuli with high sensitivity. In particular, the ability to transduce biological interactions into measurable electrical signals is valuable for biosensing. Graphene field-effect transistors (GFETs) have been implemented as biosensors able to detect a wide array of analytes, ranging from gases to biological molecules, at extremely low concentrations and with exceptional precision 27 – 29 . Modification with specific recognition elements allows fine-tuning the selectivity of GFETs, even in complex body fluids 25 , 30 , 31 . Electrolyte-gated graphene field-effect transistors (EG-GFETs) exploit a highly efficient local gating mechanism based on the direct interaction between charge carriers in the electrolyte and the graphene layer. This interaction induces the formation of an electrical double-layer (EDL), which significantly increases the capacitance and transconductance of the device. The changes in ionic concentration or charge distribution within the electrolyte, confined to the Debye screening length, are effectively transduced to the graphene channel, resulting in a high sensitivity 26 , 32 – 34 . In this work, we propose a glucose detection system based on EG-GFETs functionalized with GOx. Each step of the graphene functionalization is investigated via Raman spectroscopy, X-ray photoelectron spectroscopy and water contact angle measurements. The glucose sensing signal is a shift in the EG-GFET transfer curve charge neutrality point (V DIRAC ) toward more positive values, a consequence of graphene p-type doping, proportional to the glucose concentration. The sensors achieve record limit-of-detection (LOD) of 1 aM glucose in 1 x PBS and 100 aM in artificial tears. These advancements will pave the way to replace the invasive glucose monitoring protocols, potentially through wearable technology. 2. Results and discussion The effectiveness and reliability of biosensing via EG-GFETs highly depend on the successful accomplishment of the graphene functionalization process. Our devices' glucose-sensing mechanism relies on immobilizing the glucose oxidase (GOx) enzyme on graphene. We chose this enzyme for its high specificity for glucose and large turnover ( i.e. , the number of substrate molecules that an enzyme can convert to the product of reaction per active site per unit time before the enzyme is fully saturated with the substrate, which is essentially an indication of the enzyme’s catalytic efficiency) 35 – 37 . The process required to immobilize GOx on graphene is schematically shown in Fig. 1 a. Considering that covalent modifications can alter the electrical properties of graphene, we selected a pyrene linker, PBASE, that π-π stacks non-covalently to the graphene to anchor the enzyme via an amine bond, which is facilitated by the nucleophilic substitution of N-hydroxysuccinimide (NHS) 38 . Raman spectroscopy can assess the evolution of graphene's morphologic and electronic properties in response to the functionalization process. All three spectra of as-transferred graphene (Gr), Gr/PBASE, and Gr/PBASE/GOx show the typical features of high-quality, monolayer graphene, with I 2D /I G ratios of 2.30, 1.47, and 1.45, respectively (Fig. 1 b). In Gr/PBASE, new peaks at 1230.8 cm - 1 , 1384.5 cm - 1 , and 1621.2 cm - 1 appear, which can be assigned to pyrene-based molecules 39 – 41 . The latter peak is attributed to the pyrene group resonance 42 , indicating that PBASE is stacked on the graphene surface. The D peak (~ 1342 cm - 1 ), which is absent in the Gr spectrum, is found to increase in the Gr/PBASE spectrum due to pyrene immobilization ( via orbital hybridization of the PBASE molecules with the graphene plane) 42 . Gr/PBASE/GOx shows a slight increase in the intensity of the D peak concerning the previous stage. We measured the Raman spectra of GOx as a powder and in a water solution (Figure S1a), which, to our knowledge, have not been previously reported. This was performed to identify any GOx-related modes in the Gr/PBASE/GOx spectrum. The GOx spectra have two main regions of interest: 1100–1800 cm - 1 and 2800–3100 cm - 1 . We fitted the latter region (Figure S1b), where the most intense and wide peak appears at 2932 cm - 1 . By zooming in on the Gr/PBASE/GOx spectrum, a small and wide feature at ~ 2950 cm⁻¹ can be observed, which could further corroborate enzyme immobilization (no features exist in the corresponding Gr/PBASE spectral range; Figure S1c). We analyzed the G and 2D peak positions and full width at half maximum (FWHM) in the three cases. The distributions of the G and 2D peak FWHMs for each case are shown in Figure S2. To further aid in discriminating against the emergence of charge doping and/or mechanical strain 43 , 44 , we also prepared a 2D vs G frequency plot (Fig. 1 c) and a corresponding linearly transformed plot (Figure S3), which provides information on potential variations in the mechanical strain (Y-axis) and charge (hole) density (X-axis) 45 . In this plot, a higher (lower) charge density represents greater (lower) hole doping in graphene, whereas a higher (lower) mechanical strain represents tensile (compressive) strain. In Gr, the extracted values are centered at a G peak position of 1584.2 cm - 1 (average FWHM of ~ 22 cm - 1 ) and a 2D peak position of 2667.5 cm - 1 (average FWHM of ~ 40 cm - 1 ). Figure S3 shows a significant hole density, indicating p-type doping, as well as a state of tensile strain, likely due to the PMMA transfer process 45 , 46 . In Gr/PBASE, the G and 2D peak positions are centered at 1586.9 cm - 1 (average FWHM ~ 23 cm - 1 ) and 2671.6 cm - 1 (average FWHM ~ 40 cm - 1 ), resulting in ~ 3–4 cm - 1 blueshifts, respectively, with respect to Gr. Figure S3 shows a slight increase in the hole density of graphene, which further confirms the stacking of pyrene molecules on graphene due to its electron-withdrawing properties 40 , 47 , 48 . The observed minor increase in the compressive strain might result from the interaction between the immobilized pyrene molecules and graphene. Finally, in Gr/PBASE/GOx, the G and 2D peak position values are centered at 1585.3 cm - 1 (average FWHM of ~ 26 cm - 1 ) and 2671.7 cm - 1 (average FWHM of ~ 42 cm - 1 ), respectively. There is a slight redshift of the G peak (1.3 cm - 1 ) with respect to Gr/PBASE, whereas the 2D peak position remains unchanged. However, the FWHM 2D and the FWHM G increase by ~ 2 cm - 1 and ~ 3 cm - 1 , respectively, which is often related to doping effects. Figure S3 shows a reduced hole density and further increase in compressive strain, which confirms the binding of GOx to PBASE. Figure 1 d depicts the evolution of the water contact angle (WCA) after functionalization. The WCA on Gr (82.07°) increases by ~ 9° with respect to the Si/SiO 2 substrate (73.5°). PBASE decreases the WCA to 77.4°, as expected for NHS-ester ligands upon surface immobilization 41 , 49 , 50 . The immobilization of GOx, a hydrophilic molecule 51 , significantly decreases the WCA by ~ 23°. Furthermore, charge doping (either n- or p-type) increases the wettability of graphene 52 , 53 , which would also sustain the successive decreases in the WCA of graphene after functionalization with PBASE and GOx. The biofunctionalization was analyzed by XPS. The C 1s and N 1s high-resolution spectra are shown in Fig. 1 e and 1 f, respectively, while the survey spectra are reported in Figure S4. Tables S1 and S2 specify the relative atomic concentration (at. %) of the main components. The immobilization of GOx on graphene was further confirmed by the progressive intensity increase of the N-related peaks in the C 1s and N 1s regions of the three samples. The C 1s fittings show the main features of CVD graphene (Table S1): an asymmetric peak centered at 284.5 eV with the corresponding shake-up satellite peak at 290.9 eV, and a minor C-C sp 3 component. Additionally, three other components appear, which can be assigned to C-O/C-N, C = O, and C = OO/N-C = O bonds. These three components increase in the functionalized samples. In particular, the C-O/C-N passed from an initial 11.7 at. % to 18.2 at. % in Gr/PBASE/GOx, owing to the increased amine and amide functional groups 54 , 55 . Meanwhile, the main N 1 s peak at ~ 400 eV 56 passes from 0.9 at. % to 7.6 at. % (Table S2). The small feature appearing at ~ 398.5 eV is likely due to the formation of pyridinic rings from side reactions involving PBASE 54 , 57 . To achieve a biosensing system based on EG-GFETs, both the graphene FET channel and the Au gate need to be properly functionalized. The complete EG-GFET functionalization method is depicted in Figure S5 and consists of four steps (for details, refer to Section 4.4). Each functionalization stage is verified with electrical measurements by plotting the transfer curves (Fig. 2 a) and measuring the Dirac point shift (ΔV DIRAC ) with respect to the previous functionalization step (Fig. 2 b). Before any functionalization stage (Gr), the sensors are measured to provide a baseline for the shifts of the next stage. Shifts from the baseline transfer curve measurements indicate that charge carrier redistribution in the graphene channel is due to electrostatic potential changes from surface modification. Initially, the V DIRAC value is located at positive gate voltage values (+ 0.45 V), indicating unintentional p-type doping (Stage 0). Adding DDT to the sensors (Stage 1) causes the V DIRAC to shift by ~ -310 mV owing to the formation of a self-assembled monolayer covering the gold electrode, creating an excess of positive charges in the solution from the dipole moment reorientation of the alkanethiols 25 . Graphene doping level is not altered, as confirmed by Raman spectroscopy (Figure S6). The functionalization of graphene is then achieved with PBASE (Stage 2). We observe only a slight shift of approximately + 30 mV, indicating additional graphene p-type doping upon π‒π stacking 58 . This small variation of the V DIRAC can be explained by a doping competition between pyrene molecules and the solvent used to dilute them. Dimethyl solvents ( e.g. , DMF, DMSO) have been reported to n-dope graphene 58 , 59 . This was further proved by Raman spectroscopy, which shows that G and 2D peak blueshift (Figure S7a) after incubation with bare DMSO (corresponding to a decrease in hole density; Figure S7b). Additionally, at the PBASE functionalization stage, both the electron and hole branches of the transfer curve become slightly less steep, indicating a small decrease in the mobility of both types of carriers. The NHS ester group of PBASE remains free to form a covalent bond with the biorecognition probe. GOx is immobilized on graphene (Stage 3) by binding to the free ester group of PBASE. The immobilization of the enzyme results in a V DIRAC shift of -30 mV, in agreement with the Raman shift indicating n-type doping. Finally, ETA (Stage 4) causes the V DIRAC to shift by -5 mV. Once fully functionalized (Gr/PBASE/GOx/ETA), the EG-GFET devices were tested for glucose sensing (Fig. 3 a). A first V DIRAC shift of + 26 ± 4 mV occurs at 1 aM and then the values continue to increase with the glucose concentration for six orders of magnitude, with a sensitivity of 10.6 mV/decade. The trend reaches a plateau at 1 pM (ΔV DIRAC ~ 100 mV), which remain rather constant up to 1 mM (Figure S8, red circles). The corresponding transfer curves are reported in Fig. 3 b. These results demonstrate a marked p-type doping effect due to increasing glucose concentration in fully functionalized devices. For a more comprehensive analysis, we have also tested partially functionalized devices to understand the sensing mechanism. The Gr and Gr/PBASE devices behave very differently in the same glucose concentration range (Fig. 3 a). The first, apparent difference is that the V DIRAC shifts are negative in both cases, demonstrating n-type doping. Also, no clear trend is visible for concentrations < 10 fM. At higher concentrations, the values tend to stabilize or change marginally (Figure S8). Simulations via density functional theory method demonstrated that glucose can absorb on pristine graphene, inducing electron transfer and n-type doping 60 , 61 . However, in the case of Gr/PBASE, the doping effect becomes weaker since the PBASE layer can interfere with the electron transfer. The Gr/PBASE/GOx/ETA devices were analyzed by Raman spectroscopy in the 1 nM-1 mM glucose concentration range (Fig. 3 c). The 2D and G peak positions upshift significantly at 1 nM and then increase marginally at higher concentrations. At 1 mM, the two peaks upshift by + 6.6 ± 0.5 cm − 1 and + 8.6 ± 0.5 cm − 1 (with respect to the pristine case at 0 M), respectively. We evaluated the corresponding contributions of both mechanical strain and hole doping (Figure S9), which further confirmed the p-type doping effect of glucose on the fully functionalized device. Building on the experimental findings, we propose an interpretation of the underlying sensing mechanism. In solution, GOx catalyzes the oxidation of glucose and yields two byproducts: gluconic acid and H 2 O 2 . This two-step catalysis involves the transfer of electrons from glucose to the enzyme's prosthetic group, typically a flavin adenine dinucleotide (FAD) cofactor. FAD oxidizes glucose into gluconolactone and gets reduced to FADH 2 (Eq. 1). Gluconolactone is hydrolyzed in the presence of water to form gluconic acid (Eq. 2). The FAD subunit is regenerated from FADH 2 through the reduction of O 2 to H 2 O 2 (Eq. 3). Upon regeneration, FAD continues to oxidize glucose, resulting in cyclic interactions for continuous H 2 O 2 and gluconic acid production. GOx (FAD) + glucose \(\:\to\:\) GOx (FADH 2 ) + gluconolactone (1) gluconolactone + H 2 O \(\:\to\:\) gluconic acid (2) GOx (FADH 2 ) + O 2 \(\:\to\:\) GOx (FAD) + H 2 O 2 (3) Considering these chemical reactions, we propose that the p-type doping occurring in the devices could stem from the interaction between H 2 O 2 and the functionalized graphene. In similar sensing systems, H 2 O 2 typically undergoes a voltage-driven decomposition, which releases molecular oxygen, protons and free electrons 38 , 62 , 63 : H 2 O 2 \(\:\to\:\) O 2 + 2H + + 2e − (4) In principle, free electrons should transfer to graphene (especially at defect sites, like vacancies) and induce n-type doping 64 . We tested this hypothesis by exposing the Gr and Gr/PBASE/GOx/ETA devices to increasing H 2 O 2 concentrations (Figure S10). The Gr devices do indeed exhibit consistent negative ΔV DIRAC shifts at increasing concentrations when H 2 O 2 is added (up to -130 mV at 1 mM, Figure S10b). By contrast, the Gr/PBASE/GOx/ETA devices show positive V DIRAC shifts, up to + 80 ± 4 mV at 1 mM, indicating strong p-type doping (Figure S10b). This could be explained by two reasons: i) the pyrene monolayer with bound enzymes can shield the graphene and mitigate the direct electron transfer; ii) in proximity of graphene, H 2 O 2 might decompose into hydroxide (OH − ) ions 62 , 65 . In slightly basic conditions (the PBS solution has pH = 7.4), the OH − concentration can further increase and upshift the V DIRAC by an accumulation of positive counter-ions from the electrolyte 66 , 67 . We evaluated the response to glucose of the Gr/PBASE/GOx/ETA devices after direct exposure to H 2 O 2 (see Section 4.4). As shown in Figure S10b, after washing away the H 2 O 2 and cleaning the sensor’s surface in PBS (corresponding to “Glu 0 M”), the device does not respond to glucose in the 1 nM-1 mM concentration range. This can be explained by an inhibitory effect of high concentration of H 2 O 2 on the catalytic activity of GOx 68 – 71 , which in turn would also clarify the plateau in the response to glucose of the regular EG-GFET devices after 1 pM (Fig. 3 a). To assess the selectivity of the sensors, we tested their response to lactate (a compound produced by glycolysis 72 ) in the same concentration range of glucose (Figure S11). Figure 4 a compares the response of the sensors to glucose and lactate. Differently from the linear ΔV DIRAC trend for glucose, the response to lactate exhibits a minor and random trend due to non-specific interactions or noise. The selectivity for glucose was further evaluated in a simulated biological fluid ( i.e. , an undiluted commercial artificial tear), to test the potential clinical application of the sensors. As shown in Fig. 4 b, the ΔV DIRAC shifts exhibit a clear and consistent response across the tested concentration range, with a linear trend (4.9 mV/decade) and a remarkable LOD of 100 aM. The sensors exhibit p-type doping with increasing concentrations of glucose for six orders of magnitude (until saturating at 0.1 nM, with a ΔV DIRAC ~ 45 mV). The reduction in sensitivity might be explained by the composition of the artificial tear, which contains several analytes (electrolytes, buffers, acids) at high concentration that can hinder the direct interaction between glucose and GOx. Several sensing strategies have been developed to achieve high sensitivity and low LOD for glucose sensing (Table 1 ). Non-enzymatic sensors can leverage the catalytic activity of different electrode systems, having achieved LODs up to the nM range 73 – 75 . Fiber-based sensors pushed beyond this limit and reached LOD in the pM-fM range by using tilted fiber Bragg grating combined with surface plasmonic resonance 76 – 78 . Enzymatic sensors, particularly those using GOx, can reach top-level sensitivity and selectivity by perfecting the enzyme immobilization through functional materials, such as Nafion, liquid crystals, nanowires, nanorods and graphene derivatives 79 – 92 . These systems have achieved LODs in the nM-pM level. Our sensors reached the aM level, which is 14 orders of magnitude lower than the lowest reported for configurations based on Gr/PBASE/GOx 88 , 89 . Such LOD level is also at least three orders of magnitude lower than that ever reported for the best-performing glucose sensors (fM level) 78 . Devices enabling aM-level detection of glucose could pave the way for next-generation non-invasive glucose monitoring. Table 1 State-of-the-art glucose sensing, considering their sensing system, detection method, LOD, linear range, sensitivity and selectivity. Type Sensing system Detection method LOD Linear range Sensitivity Selectivity Ref Non-Enzymatic Cu-Gr-COOH-Au electrode CV and chronoamperometry 7.96 nM 0.1 µM-5.48 mM 1142 µA mM − 1 cm − 2 Yes 73 CuO-NiO-MFs/FTO AM 1 nM 3 M-0.51 mM 3165.53 A mM − 1 cm − 2 Yes 74 Gr/Cu Electrode AM 0.5 µM 0.5 µM–4.5 mM NR Yes 75 TFBG-SPR-AuNPs-PMBA SPR 295 pM 1 nM-10 mM NR Yes 76 TFBG/GO/PBA SPR 1 fM 1 fM–10 pM NR NR 78 Enzymatic PET/CNTs/GOx Accumulation mode and AM 4.7 ± 1.4 nM 4 nM–5 µM 0.33 ± 0.04 nC/nM NR 79 TFBG/GO/EDC-NHS/GOx SPR 1 mM 0 mM–8 mM 0.24 nm/mM Yes 80 CNT NEEs/GOx AM 0.08 mM 0.08 mM-30 mM NR Yes 81 Silk/Gr/Silk-GOx TC 0.1 mM 0.1 mM–10 mM 2.5 µA/mM Yes 82 GCE-RGO-GOx CV and AM 0.1 mM 0.1 mM–27 mM 1.85 µA mM − 1 cm − 2 Yes 83 Gr/CNT/ZnO/GOx CV 4.5 ± 0.08 µM 10 µM-6.5 mM 5.362 (± 0.072) µA mM − 1 cm − 2 NR 84 AuNWs/GOx CV 3 µM 10 µM–100 mM 7.2 mA mM − 1 cm − 2 Yes 85 ZNA/GOx Field emission 1 nM 1 nM-50 µM NR NR 86 PB-modified GCE/Nafion/GOx AM 1 µM 1 µM–5 mM NR NR 87 PET/Gr/PBASE/GOx TC 3.3 mM 3.3 mM-10.9 mM NR NR 88 Gr/PBASE/GOx TC 0.1 mM NR NR NR 89 Nafion/Pt-xGnP/GOx AM 1 µM 1 µM-20 mM 61.5 ± 0.6 µA mM − 1 cm 2 NR 90 Gr/PtNPs/Nafion/GOx-CHIT TC 0.5 µM 0.5 µM-1 mM 173 mV/decade Yes 91 UV- 5CB/Au-grid/GOx Optical Response 1 pM 1 pM-50 nM NR Yes 92 Gr/PBASE/GOx/ETA TC 1 aM 1 aM-1 pM 10.6 mV/decade Yes This work CV – Cyclic voltammetry; SPR – surface plasmonic resonance; AM – amperometry; TC – transconductance; TFBG – tilted fiber Bragg grating; GO – graphene oxide; PBA – pyrene-1-boric acid; AuNPs – gold nanoparticles; PMBA – p-mercaptophenylboronic acid; PB – Prussian blue; MFs – microfibers; FTO – fluorine tin oxide; GCE – glass carbon electrode; PET – polyethylene terephthalate; ZNA – ZnO nanorod arrays; EDC – 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide; AuNWs – gold nanowires; CNT – carbon nanotubes; xGnP – exfoliated graphite nanoplatelets; UV-5CB – UV-treated 4-cyano-4′-pentylbiphenyl; NEEs – nanoelectrode ensembles; NR – not reported. 3. Conclusions Diabetes affects millions of lives, but current glucose monitoring methods are still time-consuming, painful and invasive. We presented a highly sensitive glucose sensing platform based on electrolyte-gated graphene field-effect transistors functionalized with glucose oxidase, achieving unprecedented limits-of-detection of 1 attomolar in PBS and 100 attomolar in artificial tears. The sensors demonstrated exceptional sensitivity and selectivity, even in an artificial tear. These features, along with compatibility and stability in complex media, pave the way for non-invasive continuous glucose monitoring in realistic biofluids. This study sets a benchmark for glucose biosensing technology, highlighting the adaptability of this kind of sensors for broad diagnostic applications. Although blood remains the gold standard for diabetes management, this sensing platform unlocks tears as viable diagnostic biofluid. Ultimately, this graphene-based sensing technology could enable wearable devices, such as smart contact lenses, for real-time glucose monitoring with minimal invasiveness. 4. Materials and Methods 4.1. Materials D-(+)-Glucose (≥ 99.5 %), Hydrogen peroxide (H 2 O 2 , 30 % (w/w) in H 2 O), Lactic acid solution (≥ 85 %), Phosphate-buffered saline (PBS) tablets (1814.5-2005.5 mg/tab), Dimethyl sulfoxide (DMSO) (99.9 % HPLC), 1-Pyrenebutyric acid N-hydroxysuccinimide ester (PBASE) (95 %), 1-Dodecanethiol (DDT) (98 %), Ethanolamine (ETA) (98 %), Hydrochloric acid (HCl) (37 %) and Poly(methyl(meth)acrylate) (PMMA) (15k M.W.) were purchased from Sigma Aldrich. Poly(methyl(meth)acrylate) (PMMA) (550k M.W.) was purchased from Alfa Aesar. Glucose oxidase (GOx, from Aspergillus niger, 248878 units/g) was purchased from Merck. Acetone (99.5 %), ethanol (99.8 %), and 2-propanol (99.8 % GC) were purchased from Honeywell. The photoresist AZ1505 (AZ) was purchased from MicroChemicals GmbH. RTV silicone elastomer (3140, Dowsil) and superglue (Loctite) were acquired from Farnel. Systane artificial tears (containing sodium hyaluronate, polyethylene glycol, propylene glycol, hydroxypropyl guar, sorbitol, aminomethyl propanol, boric acid, potassium chloride, sodium chloride, sodium borate, and purified water) were purchased from Lentes de Contacto 365. 4.2. Graphene growth and transfer Monolayer graphene growth was performed in an EasyTube ET3000 chemical vapor deposition (CVD) system (CVD Corp, USA). Graphene was grown on copper (Cu) foils/substrates that act as catalysts. The 5 × 5 cm 2 Cu foils (99.99+% purity) were initially chemically treated in a solution of FeCl 3 , HCl, and DI water for 1 min under ultrasound to reduce rugosities and organic contamination and then heated on a hot plate at 250°C in air for 20 min for partial oxidation. The oxidized Cu foils were placed into a three-zone quartz tube furnace, and the chamber was evacuated to approximately 10 mTorr and then filled with 250 sccm argon (Ar, 99.999 % purity) and 170 sccm hydrogen (H, 99.999 % purity) gas mixtures. Once the growth temperature and pressure were reached, 1.25 sccm CH 4 , the carbon precursor, was introduced into the chamber. Monolayer graphene growth was carried out at 1040°C and 6 Torr for 1 h on both sides of the Cu foil. Graphene was transferred via an optimized poly(methyl methacrylate) (PMMA) solution designed to provide mechanical support during the transfer cycles while suppressing potential contamination 93 . The optimized PMMA is a mixture of PMMA-550k (550,000 average molecular weight) and PMMA-15k (15,000 average molecular weight) in anisole at a 2:1 ratio (3 wt. %). PMMA was spin-coated (3000 rpm, 30 s) on the top side of the graphene/Cu/graphene, followed by drying in a fume hood at room temperature (RT) overnight. Plasma ashing (PVA TePla GiGAbatch, O 2 :Ar 1:1, 0.75 mbar, 230 W, 25°C, 2 min) was performed to remove graphene from the back side of the sample. To dissolve the Cu, the sample was floated in a 0.5 M FeCl 3 solution for 2 h. After full etching, the graphene/PMMA membrane was rinsed in cycles of DI water and 2% HCl solution baths for 30 min to remove etchant impurities. Finally, the graphene/PMMA was rinsed in DI water two times and transferred to the target substrate. 4.3. EG-GFET microfabrication Our electrolyte-gated graphene field-effect transistor chip microfabrication is described elsewhere 25 . Briefly, a 200 mm silicon (Si) wafer (p-type doped with boron, B) with 100 nm of thermal oxide was sputter-coated with chromium (Cr, 3 nm) as the adhesion layer, gold (Au, 35 nm) as the conductive layer, and alumina (Al 2 O 3 , 100 nm) capping. The source, drain and gate Au electrodes were patterned through optical lithography and ion milling. A sacrificial layer (TiWN, 5 nm; AlSiCu, 100 nm; TiWN, 15 nm) was sputtered and patterned by lift-off, leaving only open the GFET channel areas to transfer the graphene. This sacrificial layer protected the wafer from potential contamination from graphene transfer processes. After transfer, the graphene was patterned by optical lithography and oxygen plasma etching, followed by wet etching of the sacrificial layer. A protective layer of Nickel (Ni, 100 nm) was sputtered and patterned by lift-off to work as a stopping layer for ion milling. The multistack passivation layers of SiO 2 (50 nm) and Si 3 N 4 (50 nm) (total thickness of 300 nm) were then grown by plasma-enhanced CVD, patterned via optical lithography and etched to cover the entire sensor area except the graphene channel, the gold gate, and the contact pads. Finally, wet etching of Ni and Al 2 O 3 was performed. The wafer was diced into 729 equal-sized 5 × 5 mm 2 chips. Each chip consists of two gate electrodes and 32 graphene channels (EG-GFETs), each group of 16 with their drains connected to a common source. The chip photoresist was dissolved in acetone and then cleaned with IPA and DI water. The chips were then glued onto a printed circuit board, wire bonded with gold wires and protected with a silicon elastomer. 4.4. Biofunctionalization of the graphene surface The functionalization process of EG-GFETs followed the general procedure developed in 41 , requiring four fundamental steps. Between every functionalization step, the chips were thoroughly cleaned with Milli-Q water, dried with N 2 flow and measured with undiluted phosphate buffer saline (1 x PBS, pH = 7.4). 1 x PBS was prepared by diluting two tablets in 400 mL of Milli-Q water. 1) Au gates were passivated with DDT (2 mM in ethanol), a gold blocking agent, for 2 h. Every 30 minutes, 40 µL were applied to the chips due to ethanol evaporation; 2) 40 µL of PBASE (10 mM in dimethyl sulfoxide; DMSO), a hetero-bifunctional linker binding graphene and GOx, were put on the chips for 2 h in a humid chamber. 3) A solution of GOx (10 mg/mL in Milli-Q water) was applied in 40 µL and incubated overnight (~ 16 h), in a humid chamber, to link to PBASE. iv) 40 µL of ETA was dropped on the chips and incubated for 30 min. To confirm that GOx was correctly immobilized on graphene, we performed a robust characterization of the steps 2) and 3) of the functionalization process on separate graphene samples transferred onto Si/SiO 2 unpatterned substrates. To prepare D-(+)-glucose, lactic acid (lactate) and H 2 O 2 concentrations, stock solutions of 1 mM of each analyte were prepared and diluted in 1 x PBS. To prove the inactivation of GOx by H 2 O 2 , 40 μL of 1 mM of H 2 O 2 was added to the Gr/PBASE/GOx/ETA device and left in contact for 10 min. Afterwards, the chip was thoroughly cleaned with 1 x PBS. For the selectivity test with the artificial tear, glucose concentrations were prepared directly in the artificial tear medium. 4.5. Materials characterization Raman spectroscopy: The monolayer graphene transferred onto Si/SiO 2 substrates cut in 2x2 cm 2 pieces was analyzed by Raman spectroscopy to inspect the graphene features ( e.g., number of layers, defects, wrinkles) before and after PBASE and GOx functionalization steps, to confirm the presence of the added molecules. Measurements were performed with an ALPHA300 R confocal Raman microscope (WITec) using 532 nm laser light for excitation at room temperature. The laser beam was focused on the sample with 50x (NA = 0.7) and 100x (NA = 0.9 DIC) lenses (Zeiss). Single acquisitions and large-area scans were performed using a 600 grooves/mm grating with 3 mW laser power. Raman data analysis was performed with Project FIVE+ software (WITec). Contact angle measurements: Monolayer graphene was transferred to 2x2 cm 2 Si/SiO 2 substrates and then functionalized, as described above. Static wetting angle measurements were conducted with a drop shape analysis-contact angle (DSA-CA) system using DI water as the liquid medium to characterize each functionalization stage. A 3 µL water droplet was deposited on the surface of the sample at a dosing rate of 600 µL/min, and the water contact angle (WCA) was calculated. All the measurements were carried out in air at room temperature. For each analysis, a minimum of 10 measurements were performed, and the average was calculated. Droplet profiles were automatically detected and analyzed by fitting to the Young–Laplace equation in DSA3® software (Kruss). EG-GFET measurements: A signal acquisition platform connected the PCB with the wire-bonded chips to the computer interface and traced the EG-GFET transfer curves. The in-house developed electronic platform has a microcontroller that controls the digital-to-analog converter (DAC), one regulated voltage source generating the gate voltage sweeps (V GS ), and another source that applies a constant source-drain voltage (V DS ) between the source and drain contacts of each device. The source-drain current (I DS ) is converted into a voltage by an analog-to-digital converter (ADC) before the digital data is sent to the computer via a USB connection. The transfer curve of each EG–GFET was measured after each functionalization process step by applying a V DS = 15 mV and measuring I DS in 500 V GS steps between -0.3 and 0.7 V. The signal is the shift of the minimum conductance point, equated to the Dirac point (ΔV DIRAC ), relative to the baseline for each analyte concentration. X-ray photoelectron spectroscopy (XPS): XPS spectra were acquired with an ESCALAB 250 XI (Thermo Fisher Scientific, Source: Al Kα 1486.6 eV, 650 μm spot size, Source Power: 300 W, Pass energy: 160 eV – survey; 10 eV – high resolution) system with an analysis chamber maintained in ultrahigh vacuum (UHV ~ 5 × 10 -10 mbar) conditions where the functionalized monolayer graphene on the 2x2 cm 2 Si/SiO 2 substrates was placed. The spectra were referenced to the graphene C‒C bonds at 284.5 eV. The spectra were analyzed with CasaXPS software (Casa Software Ltd., version 2.3.25) 94 , and the residual background was eliminated via the Shirley method across the binding energy range of the peaks of interest. Declarations Acknowledgements The authors acknowledge Mr. Tiago Pereira from the 2D Materials and Devices group at INL for designing the python code used for linearly transforming the 2D vs G frequency plots into mechanical strain vs charge density plots. Funding We acknowledge the financial support of the project “2DM4EH” with reference DRI/India/0664/2020, funded by FCT—Science and Technology Foundation. References AlMajali, A. S.; Richards, T.; Yusuf, S. W.; Telgenkamp, B. Vascular Service Provision during the COVID-19 Pandemic Worsened Major Amputation Rates in Socially Deprived Diabetic Populations. Front Endocrinol (Lausanne) 2024 , 15 , 1304436. Tsutsui, H.; Tanaka, G.; Kondo, K. Diabetes Mellitus. In Social Determinants of Health in Non-communicable Diseases: Case Studies from Japan ; Kondo, K., Ed.; Springer Singapore: Singapore, 2020; pp 73–86. https://doi.org/10.1007/978-981-15-1831-7_8. Association, A. D. Diagnosis and Classification of Diabetes Mellitus. Diabetes Care 2010 , 33 (Supplement_1), S62–S69. Bruen, D.; Delaney, C.; Florea, L.; Diamond, D. Glucose Sensing for Diabetes Monitoring: Recent Developments. Sensors 2017 , 17 (8), 1866. Gong, P.; Li, X.; Zhou, X.; Zhang, Y.; Chen, N.; Wang, S.; Zhang, S.; Zhao, Y. Optical Fiber Sensors for Glucose Concentration Measurement: A Review. Opt Laser Technol 2021 , 139 , 106981. Kim, J.; Khan, M.; Park, S.-Y. Glucose Sensor Using Liquid-Crystal Droplets Made by Microfluidics. ACS Appl Mater Interfaces 2013 , 5 (24), 13135–13139. Viswanathan, S.; Narayanan, T. N.; Aran, K.; Fink, K. D.; Paredes, J.; Ajayan, P. M.; Filipek, S.; Miszta, P.; Tekin, H. C.; Inci, F. Graphene–Protein Field Effect Biosensors: Glucose Sensing. Materials Today 2015 , 18 (9), 513–522. Teymourian, H.; Barfidokht, A.; Wang, J. Electrochemical Glucose Sensors in Diabetes Management: An Updated Review (2010–2020). Chem Soc Rev 2020 , 49 (21), 7671–7709. Wang, G.; He, X.; Wang, L.; Gu, A.; Huang, Y.; Fang, B.; Geng, B.; Zhang, X. Non-Enzymatic Electrochemical Sensing of Glucose. Microchimica Acta 2013 , 180 , 161–186. Wei, L.; Li, J.-H.; Chen, C.; Lv, Q.; Lv, R.; Ma, L.; Shen, W.; Kang, F.; Huang, Z.-H. Ultrasensitive Non-Enzymatic Glucose Sensors Based on Hybrid Reduced Graphene Oxide and Carbonized Silk Fabric Electrodes Decorated with Cu Nanoflowers. J Electrochem Soc 2020 , 167 (12), 127501. Guo, M. Q.; Hong, H. S.; Tang, X. N.; Fang, H. D.; Xu, X. H. Ultrasonic Electrodeposition of Platinum Nanoflowers and Their Application in Nonenzymatic Glucose Sensors. Electrochim Acta 2012 , 63 , 1–8. Karami, P.; Babapoor, A.; Shoghi, B. M.; Johari, A. M. Comparison of Electrochemical Enzymatic and Non-Enzymatic Glucose Sensors Based on Literature Review. 2021 . Zhu, H.; Li, L.; Zhou, W.; Shao, Z.; Chen, X. Advances in Non-Enzymatic Glucose Sensors Based on Metal Oxides. J Mater Chem B 2016 , 4 (46), 7333–7349. Miya, N.; Machogo-Phao, L. F. E.; Ntsendwana, B. Exploring Copper Oxide and Copper Sulfide for Non-Enzymatic Glucose Sensors: Current Progress and Future Directions. Micromachines (Basel) 2023 , 14 (10), 1849. Lee, H.; Hong, Y. J.; Baik, S.; Hyeon, T.; Kim, D. Enzyme‐based Glucose Sensor: From Invasive to Wearable Device. Adv Healthc Mater 2018 , 7 (8), 1701150. Kim, I.; Kim, C.; Lee, D.; Lee, S. W.; Lee, G.; Yoon, D. S. A Bio-Inspired Highly Selective Enzymatic Glucose Sensor Using a Red Blood Cell Membrane. Analyst 2020 , 145 (6), 2125–2132. Johnston, L.; Wang, G.; Hu, K.; Qian, C.; Liu, G. Advances in Biosensors for Continuous Glucose Monitoring towards Wearables. Front Bioeng Biotechnol 2021 , 9 , 733810. Solnica, B.; Naskalski, J. W.; Sieradzki, J. Analytical Performance of Glucometers Used for Routine Glucose Self-Monitoring of Diabetic Patients. Clinica Chimica Acta 2003 , 331 (1–2), 29–35. Shlomowitz, A.; Feher, M. D. Anxiety Associated with Self Monitoring of Capillary Blood Glucose. British Journal of Diabetes 2014 , 14 (2), 60–63. Sen, D. K.; Sarin, G. S. Tear Glucose Levels in Normal People and in Diabetic Patients. Br J Ophthalmol 1980 , 64 (9), 693. Sacks, D. B.; Arnold, M.; Bakris, G. L.; Bruns, D. E.; Horvath, A. R.; Lernmark, Å.; Metzger, B. E.; Nathan, D. M.; Kirkman, M. S. Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus. Clin Chem 2023 , 69 (8), 808–868. Lane, J. D.; Krumholz, D. M.; Sack, R. A.; Morris, C. Tear Glucose Dynamics in Diabetes Mellitus. Curr Eye Res 2006 , 31 (11), 895–901. Kim, I. S.; Kim, H. T.; Kim, E.-J.; Lee, E. J. A Comparative Study of the Concentration of Salivary and Blood Glucose in Normal and Diabetic Subjects. Biomedical Science Letters 2013 , 19 (2), 105–111. Vasconcelos, A. C. U.; Soares, M. S. M.; Almeida, P. C.; Soares, T. C. Comparative Study of the Concentration of Salivary and Blood Glucose in Type 2 Diabetic Patients. J Oral Sci 2010 , 52 (2), 293–298. Abrantes, M.; Rodrigues, D.; Domingues, T.; Nemala, S. S.; Monteiro, P.; Borme, J.; Alpuim, P.; Jacinto, L. Ultrasensitive Dopamine Detection with Graphene Aptasensor Multitransistor Arrays. J Nanobiotechnology 2022 , 20 (1), 495. Béraud, A.; Sauvage, M.; Bazán, C. M.; Tie, M.; Bencherif, A.; Bouilly, D. Graphene Field-Effect Transistors as Bioanalytical Sensors: Design, Operation and Performance. Analyst 2021 , 146 (2), 403–428. Peña-Bahamonde, J.; Nguyen, H. N.; Fanourakis, S. K.; Rodrigues, D. F. Recent Advances in Graphene-Based Biosensor Technology with Applications in Life Sciences. J Nanobiotechnology 2018 , 16 , 1–17. Song, Y.; Luo, Y.; Zhu, C.; Li, H.; Du, D.; Lin, Y. Recent Advances in Electrochemical Biosensors Based on Graphene Two-Dimensional Nanomaterials. Biosens Bioelectron 2016 , 76 , 195–212. Bai, Y.; Xu, T.; Zhang, X. Graphene-Based Biosensors for Detection of Biomarkers. Micromachines (Basel) 2020 , 11 (1), 60. Fakih, I.; Durnan, O.; Mahvash, F.; Napal, I.; Centeno, A.; Zurutuza, A.; Yargeau, V.; Szkopek, T. Selective Ion Sensing with High Resolution Large Area Graphene Field Effect Transistor Arrays. Nat Commun 2020 , 11 (1), 3226. Wu, G.; Dai, Z.; Tang, X.; Lin, Z.; Lo, P. K.; Meyyappan, M.; Lai, K. W. C. Graphene Field‐effect Transistors for the Sensitive and Selective Detection of Escherichia Coli Using Pyrene‐tagged DNA Aptamer. Adv Healthc Mater 2017 , 6 (19), 1700736. Forsyth, R.; Devadoss, A.; Guy, O. J. Graphene Field Effect Transistors for Biomedical Applications: Current Status and Future Prospects. Diagnostics 2017 , 7 (3), 45. Yan, F.; Zhang, M.; Li, J. Solution‐gated Graphene Transistors for Chemical and Biological Sensors. Adv Healthc Mater 2014 , 3 (3), 313–331. Ohno, Y.; Maehashi, K.; Yamashiro, Y.; Matsumoto, K. Electrolyte-Gated Graphene Field-Effect Transistors for Detecting PH and Protein Adsorption. Nano Lett 2009 , 9 (9), 3318–3322. https://doi.org/10.1021/nl901596m. Bauer, J. A.; Zámocká, M.; Majtán, J.; Bauerová-Hlinková, V. Glucose Oxidase, an Enzyme “Ferrari”: Its Structure, Function, Production and Properties in the Light of Various Industrial and Biotechnological Applications. Biomolecules 2022 , 12 (3), 472. Smejkal, G. B.; Kakumanu, S. Enzymes and Their Turnover Numbers. Expert Rev Proteomics 2019 , 16 (7), 543–544. Roskoski, R. Michaelis-Menten Kinetics. xPharm: The Comprehensive Pharmacology Reference 2007 , 1–10. https://doi.org/10.1016/B978-008055232-3.60041-8. Park, J.; Kim, J.; Kim, S.-Y.; Cheong, W. H.; Jang, J.; Park, Y.-G.; Na, K.; Kim, Y.-T.; Heo, J. H.; Lee, C. Y. Soft, Smart Contact Lenses with Integrations of Wireless Circuits, Glucose Sensors, and Displays. Sci Adv 2018 , 4 (1), eaap9841. Leyton, P.; Gómez-Jeria, J. S.; Sanchez-Cortes, S.; Domingo, C.; Campos-Vallette, M. Carbon Nanotube Bundles as Molecular Assemblies for the Detection of Polycyclic Aromatic Hydrocarbons: Surface-Enhanced Resonance Raman Spectroscopy and Theoretical Studies. J Phys Chem B 2006 , 110 (13), 6470–6474. Liu, Y.; Yuan, L.; Yang, M.; Zheng, Y.; Li, L.; Gao, L.; Nerngchamnong, N.; Nai, C. T.; Sangeeth, C. S. S.; Feng, Y. P. Giant Enhancement in Vertical Conductivity of Stacked CVD Graphene Sheets by Self-Assembled Molecular Layers. Nat Commun 2014 , 5 (1), 5461. Fernandes, E.; Cabral, P. D.; Campos, R.; Machado, G.; Cerqueira, M. F.; Sousa, C.; Freitas, P. P.; Borme, J.; Petrovykh, D. Y.; Alpuim, P. Functionalization of Single-Layer Graphene for Immunoassays. Appl Surf Sci 2019 , 480 , 709–716. https://doi.org/10.1016/j.apsusc.2019.03.004. Wu, G.; Tang, X.; Meyyappan, M.; Lai, K. W. C. Doping Effects of Surface Functionalization on Graphene with Aromatic Molecule and Organic Solvents. Appl Surf Sci 2017 , 425 , 713–721. Yang, K. L.; Lee, J. O.; Choo, H.; Yang, F. Can Raman Shift Be Used To Characterize the Mechanical Property of Graphene? The Journal of Physical Chemistry C 2018 , 122 (42), 24467–24474. Ni, Z. H.; Yu, T.; Lu, Y. H.; Wang, Y. Y.; Feng, Y. P.; Shen, Z. X. Uniaxial Strain on Graphene: Raman Spectroscopy Study and Band-Gap Opening. ACS Nano 2008 , 2 (11), 2301–2305. Lee, J. E.; Ahn, G.; Shim, J.; Lee, Y. S.; Ryu, S. Optical Separation of Mechanical Strain from Charge Doping in Graphene. Nat Commun 2012 , 3 (1), 1024. Barin, G. B.; Song, Y.; de Fátima Gimenez, I.; Souza Filho, A. G.; Barreto, L. S.; Kong, J. Optimized Graphene Transfer: Influence of Polymethylmethacrylate (PMMA) Layer Concentration and Baking Time on Graphene Final Performance. Carbon N Y 2015 , 84 , 82–90. Dong, X.; Fu, D.; Fang, W.; Shi, Y.; Chen, P.; Li, L. J. Doping Single-Layer Graphene with Aromatic Molecules. Small 2009 . Das, A.; Pisana, S.; Chakraborty, B.; Piscanec, S.; Saha, S. K.; Waghmare, U. V; Novoselov, K. S.; Krishnamurthy, H. R.; Geim, A. K.; Ferrari, A. C. Monitoring Dopants by Raman Scattering in an Electrochemically Top-Gated Graphene Transistor. Nat Nanotechnol 2008 , 3 (4), 210–215. Cherré, S.; Fernandes, E.; Germano, J.; Dias, T.; Cardoso, S.; Piedade, M. S.; Rozlosnik, N.; Oliveira, M. I.; Freitas, P. P. Rapid and Specific Detection of Cell-Derived Microvesicles Using a Magnetoresistive Biochip. Analyst 2017 , 142 (6), 979–986. Fernandes, E.; Martins, V. C.; Nóbrega, C.; Carvalho, C. M.; Cardoso, F. A.; Cardoso, S.; Dias, J.; Deng, D.; Kluskens, L. D.; Freitas, P. P. A Bacteriophage Detection Tool for Viability Assessment of Salmonella Cells. Biosens Bioelectron 2014 , 52 , 239–246. Kamyshny, A.; Feldman, A.; Baszkin, A.; Boissonnade, M. M.; Rosilio, V.; Magdassi, S. Chemically Modified Glucose Oxidase with Enhanced Hydrophobicity: Adsorption at Polystyrene, Silica, and Silica Coated by Lipid Monolayers. J Colloid Interface Sci 1999 , 218 (1), 300–308. https://doi.org/https://doi.org/10.1006/jcis.1999.6440. Goniszewski, S.; Adabi, M.; Shaforost, O.; Hanham, S. M.; Hao, L.; Klein, N. Correlation of P-Doping in CVD Graphene with Substrate Surface Charges. Sci Rep 2016 , 6 (1), 22858. Ashraf, A.; Wu, Y.; Wang, M. C.; Yong, K.; Sun, T.; Jing, Y.; Haasch, R. T.; Aluru, N. R.; Nam, S. Doping-Induced Tunable Wettability and Adhesion of Graphene. Nano Lett 2016 , 16 (7), 4708–4712. Wagner, C. D.; Naumkin, A. V; Kraut-Vass, A.; Allison, J. W.; Powell, C. J.; Rumble Jr, J. R. NIST Standard Reference Database 20. Version 2003. Silvestri, A.; Zayas-Arrabal, J.; Vera-Hidalgo, M.; Di Silvio, D.; Wetzl, C.; Martinez-Moro, M.; Zurutuza, A.; Torres, E.; Centeno, A.; Maestre, A. Ultrasensitive Detection of SARS-CoV-2 Spike Protein by Graphene Field-Effect Transistors. Nanoscale 2023 , 15 (3), 1076–1085. Kwong Hong Tsang, D.; Lieberthal, T. J.; Watts, C.; Dunlop, I. E.; Ramadan, S.; del Rio Hernandez, A. E.; Klein, N. Chemically Functionalised Graphene FET Biosensor for the Label-Free Sensing of Exosomes. Sci Rep 2019 , 9 (1), 1–10. Faisal, S. N.; Haque, E.; Noorbehesht, N.; Zhang, W.; Harris, A. T.; Church, T. L.; Minett, A. I. Pyridinic and Graphitic Nitrogen-Rich Graphene for High-Performance Supercapacitors and Metal-Free Bifunctional Electrocatalysts for ORR and OER. RSC Adv 2017 , 7 (29), 17950–17958. Wu, G.; Tang, X.; Meyyappan, M.; Lai, K. W. C. Chemical Functionalization of Graphene with Aromatic Molecule. In 2015 IEEE 15th International Conference on Nanotechnology (IEEE-NANO) ; IEEE, 2015; pp 1324–1327. Xia, Y.; Sun, L.; Eyley, S.; Daelemans, B.; Thielemans, W.; Seibel, J.; De Feyter, S. Grafting Ink for Direct Writing: Solvation Activated Covalent Functionalization of Graphene. Advanced Science 2022 , 9 (19). https://doi.org/10.1002/advs.202105017. Zhang, W.; Wu, L.; Li, Z.; Liu, Y. Doped Graphene: Synthesis, Properties and Bioanalysis. RSC Adv 2015 , 5 (61), 49521–49533. Singh, K. K. A DFT Studies on Absorbing and Sensing Possibilities of Glucose on Graphene Surface Doped with Ag, Au, Cu, Ni & Pt Atoms. Biosens Bioelectron X 2023 , 13 , 100287. Ahmadianyazdi, A.; Nguyen, N. H. L.; Xu, J.; Berry, V. Glucose Measurement via Raman Spectroscopy of Graphene: Principles and Operation. Nano Res 2022 , 15 (10), 8697–8704. Kwak, Y. H.; Choi, D. S.; Kim, Y. N.; Kim, H.; Yoon, D. H.; Ahn, S.-S.; Yang, J.-W.; Yang, W. S.; Seo, S. Flexible Glucose Sensor Using CVD-Grown Graphene-Based Field Effect Transistor. Biosens Bioelectron 2012 , 37 (1), 82–87. Kislenko, V. A.; Pavlov, S. V; Kislenko, S. A. Influence of Defects in Graphene on Electron Transfer Kinetics: The Role of the Surface Electronic Structure. Electrochim Acta 2020 , 341 , 136011. You, X.; Pak, J. J. Graphene-Based Field Effect Transistor Enzymatic Glucose Biosensor Using Silk Protein for Enzyme Immobilization and Device Substrate. Sens Actuators B Chem 2014 , 202 , 1357–1365. Salvo, P.; Melai, B.; Calisi, N.; Paoletti, C.; Bellagambi, F.; Kirchhain, A.; Trivella, M. G.; Fuoco, R.; Di Francesco, F. Graphene-Based Devices for Measuring PH. Sens Actuators B Chem 2018 , 256 , 976–991. https://doi.org/https://doi.org/10.1016/j.snb.2017.10.037. Fenoy, G. E.; Marmisollé, W. A.; Azzaroni, O.; Knoll, W. Acetylcholine Biosensor Based on the Electrochemical Functionalization of Graphene Field-Effect Transistors. Biosens Bioelectron 2020 , 148 , 111796. Bao, J.; Furumoto, K.; Yoshimoto, M.; Fukunaga, K.; Nakao, K. Competitive Inhibition by Hydrogen Peroxide Produced in Glucose Oxidation Catalyzed by Glucose Oxidase. Biochem Eng J 2003 , 13 (1), 69–72. Kleppe, K. The Effect of Hydrogen Peroxide on Glucose Oxidase from Aspergillus Niger. Biochemistry 1966 , 5 (1), 139–143. Von Woedtke, T.; Fischer, U.; Abel, P. Glucose Oxidase Electrodes: Effect of Hydrogen Peroxide on Enzyme Activity? Biosens Bioelectron 1994 , 9 (1), 65–71. Greenfield, P. F.; Kittrell, J. R.; Laurence, R. L. Inactivation of Immobilized Glucose Oxidase by Hydrogen Peroxide. Anal Biochem 1975 , 65 (1–2), 109–124. Richards, J. E.; Mazzeffi, M. A.; Massey, M. S.; Rock, P.; Galvagno, S. M.; Scalea, T. M. The Bitter and the Sweet: Relationship of Lactate, Glucose, and Mortality after Severe Blunt Trauma. Anesth Analg 2021 , 133 (2), 455–461. Wu, S.; Zhang, Y.; Liu, L.; Fan, W. Conifer-like Copper-Carboxylated Graphene Nanocomposites Modified Electrode for Sensitive Nonenzymatic Glucose Biosensing with Very Low Limit of Detection. Mater Lett 2020 , 276 , 128253. https://doi.org/https://doi.org/10.1016/j.matlet.2020.128253. Cao, F.; Guo, S.; Ma, H.; Yang, G.; Yang, S.; Gong, J. Highly Sensitive Nonenzymatic Glucose Sensor Based on Electrospun Copper Oxide-Doped Nickel Oxide Composite Microfibers. Talanta 2011 , 86 (1), 214–220. https://doi.org/10.1016/j.talanta.2011.09.003. Luo, J.; Jiang, S.; Zhang, H.; Jiang, J.; Liu, X. A Novel Non-Enzymatic Glucose Sensor Based on Cu Nanoparticle Modified Graphene Sheets Electrode. Anal Chim Acta 2012 , 709 , 47–53. https://doi.org/https://doi.org/10.1016/j.aca.2011.10.025. Wang, F.; Lu, M.; Yuan, H.; Zhang, Y.; Ji, W.; Sun, C.; Peng, W. PM Level and Large Dynamic Range Glucose Detection Based on a Sandwich Type Plasmonic Fiber Sensor. Journal of Lightwave Technology 2021 , 39 (12), 3882–3889. https://doi.org/10.1109/JLT.2020.3039776. Hernaez, M.; Acevedo, B.; Mayes, A. G.; Melendi-Espina, S. High-Performance Optical Fiber Humidity Sensor Based on Lossy Mode Resonance Using a Nanostructured Polyethylenimine and Graphene Oxide Coating. Sens Actuators B Chem 2019 , 286 , 408–414. Chen, X.; Lin, W.; Xu, P.; Chen, L.; Meng, W.; Hu, X.; qu, H.; Sun, J.; Cui, Y. FM-Level Detection of Glucose Using a Grating Based Sensor Enhanced With Graphene Oxide. Journal of Lightwave Technology 2022 , PP , 1. https://doi.org/10.1109/JLT.2022.3174039. Oja, S. M.; Feldman, B.; Eshoo, M. W. Method for Low Nanomolar Concentration Analyte Sensing Using Electrochemical Enzymatic Biosensors. Anal Chem 2018 , 90 (3), 1536–1541. https://doi.org/10.1021/acs.analchem.7b04075. Jiang, B.; Zhou, K.; Wang, C.; Sun, Q.; Yin, G.; Tai, Z.; Wilson, K.; Zhao, J.; Zhang, L. Label-Free Glucose Biosensor Based on Enzymatic Graphene Oxide-Functionalized Tilted Fiber Grating. Sens Actuators B Chem 2018 , 254 , 1033–1039. https://doi.org/https://doi.org/10.1016/j.snb.2017.07.109. Lin, Y.; Lu, F.; Tu, Y.; Ren, Z. Glucose Biosensors Based on Carbon Nanotube Nanoelectrode Ensembles. Nano Lett 2004 , 4 (2), 191–195. https://doi.org/10.1021/nl0347233. You, X.; Pak, J. J. Graphene-Based Field Effect Transistor Enzymatic Glucose Biosensor Using Silk Protein for Enzyme Immobilization and Device Substrate. Sens Actuators B Chem 2014 , 202 , 1357–1365. https://doi.org/https://doi.org/10.1016/j.snb.2014.04.079. Unnikrishnan, B.; Palanisamy, S.; Chen, S.-M. A Simple Electrochemical Approach to Fabricate a Glucose Biosensor Based on Graphene–Glucose Oxidase Biocomposite. Biosens Bioelectron 2013 , 39 (1), 70–75. https://doi.org/https://doi.org/10.1016/j.bios.2012.06.045. Hwa, K.-Y.; Subramani, B. Synthesis of Zinc Oxide Nanoparticles on Graphene–Carbon Nanotube Hybrid for Glucose Biosensor Applications. Biosens Bioelectron 2014 , 62 , 127–133. https://doi.org/https://doi.org/10.1016/j.bios.2014.06.023. Dawson, K.; Baudequin, M.; O’Riordan, A. Single On-Chip Gold Nanowires for Electrochemical Biosensing of Glucose. Analyst 2011 , 136 (21), 4507–4513. https://doi.org/10.1039/C1AN15279C. Liu, H.; Qian, X.; Wang, S.; Li, Y.; Song, Y.; Zhu, D. Ultra-Sensitivity Glucose Sensor Based on Field Emitters. Nanoscale Res Lett 2009 , 4 (10), 1141. https://doi.org/10.1007/s11671-009-9372-0. Karyakin, A. A.; Gitelmacher, O. V; Karyakina, E. E. Prussian Blue-Based First-Generation Biosensor. A Sensitive Amperometric Electrode for Glucose. Anal Chem 1995 , 67 (14), 2419–2423. https://doi.org/10.1021/ac00110a016. Kwak, Y. H.; Choi, D. S.; Kim, Y. N.; Kim, H.; Yoon, D. H.; Ahn, S.-S.; Yang, J.-W.; Yang, W. S.; Seo, S. Flexible Glucose Sensor Using CVD-Grown Graphene-Based Field Effect Transistor. Biosens Bioelectron 2012 , 37 (1), 82–87. https://doi.org/https://doi.org/10.1016/j.bios.2012.04.042. Huang, Y.; Dong, X.; Shi, Y.; Li, C. M.; Li, L.-J.; Chen, P. Nanoelectronic Biosensors Based on CVD Grown Graphene. Nanoscale 2010 , 2 (8), 1485–1488. https://doi.org/10.1039/C0NR00142B. Lu, J.; Do, I.; Drzal, L. T.; Worden, R. M.; Lee, I. Nanometal-Decorated Exfoliated Graphite Nanoplatelet Based Glucose Biosensors with High Sensitivity and Fast Response. ACS Nano 2008 , 2 (9), 1825–1832. https://doi.org/10.1021/nn800244k. Zhang, M.; Liao, C.; Mak, C. H.; You, P.; Mak, C. L.; Yan, F. Highly Sensitive Glucose Sensors Based on Enzyme-Modified Whole-Graphene Solution-Gated Transistors. Sci Rep 2015 , 5 (1), 8311. https://doi.org/10.1038/srep08311. Zhong, S.; Jang, C.-H. Highly Sensitive and Selective Glucose Sensor Based on Ultraviolet-Treated Nematic Liquid Crystals. Biosens Bioelectron 2014 , 59 , 293–299. https://doi.org/https://doi.org/10.1016/j.bios.2014.03.070. Liao, C.-D.; Capasso, A.; Queirós, T.; Domingues, T.; Cerqueira, F.; Nicoara, N.; Borme, J.; Freitas, P.; Alpuim, P. Optimizing PMMA Solutions to Suppress Contamination in the Transfer of CVD Graphene for Batch Production. Beilstein Journal of Nanotechnology 2022 , 13 , 796–806. https://doi.org/10.3762/bjnano.13.70. Fairley, N.; Fernandez, V.; Richard‐Plouet, M.; Guillot-Deudon, C.; Walton, J.; Smith, E.; Flahaut, D.; Greiner, M.; Biesinger, M.; Tougaard, S. Systematic and Collaborative Approach to Problem Solving Using X-Ray Photoelectron Spectroscopy. Applied Surface Science Advances 2021 , 5 , 100112. Additional Declarations There is NO Competing Interest. Supplementary Files CapassoGlucosesensingSI.pdf Supplementary Information Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5581426","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":387911964,"identity":"d3d6fbd2-a218-4af1-8923-d498f6ad095a","order_by":0,"name":"Vicente Lopes","email":"","orcid":"","institution":"International Iberian Nanotechnology Laboratory, 4715-330 Braga, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Vicente","middleName":"","lastName":"Lopes","suffix":""},{"id":387911965,"identity":"18570f6d-3637-43d5-baf3-1302983d0631","order_by":1,"name":"Tiago Abreu","email":"","orcid":"","institution":"International Iberian Nanotechnology Laboratory, 4715-330 Braga, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Tiago","middleName":"","lastName":"Abreu","suffix":""},{"id":387911966,"identity":"77d4bc77-3d96-4de4-aab2-e82d4e36206d","order_by":2,"name":"Mafalda Abrantes","email":"","orcid":"","institution":"International Iberian Nanotechnology Laboratory, 4715-330 Braga, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Mafalda","middleName":"","lastName":"Abrantes","suffix":""},{"id":387911967,"identity":"7d594a12-da3c-417d-b175-36e85c4320c2","order_by":3,"name":"Siva Nemala","email":"","orcid":"","institution":"International Iberian Nanotechnology Laboratory, 4715-330 Braga, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Siva","middleName":"","lastName":"Nemala","suffix":""},{"id":387911968,"identity":"df4af5cc-f589-497f-bf60-4e3d957e6bdd","order_by":4,"name":"Francesco De Boni","email":"","orcid":"https://orcid.org/0000-0001-5285-1008","institution":"Materials Characterization Facility, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"De Boni","suffix":""},{"id":387911969,"identity":"f95e48f9-9f07-4a8e-bdc8-9eb7454236e2","order_by":5,"name":"Mirko Prato","email":"","orcid":"https://orcid.org/0000-0002-2188-8059","institution":"Materials Characterization Facility, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy","correspondingAuthor":false,"prefix":"","firstName":"Mirko","middleName":"","lastName":"Prato","suffix":""},{"id":387911970,"identity":"03463e29-8d18-4bf1-a01e-5f71a1550e43","order_by":6,"name":"Pedro Alpuim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYHACAxAhw8DA2ABiyLExMDcwJBChhQemxZgNxEiAiBLSAgGJDRC9uLWYszdv+/CBwY7HXCK5dQNjW116n3RjA8PDHX9warHsOVY8cwZDMo/ljMS2G4xth3PbZA42MCSewW2LwY0cY2YeBmYegxtgLQdy2ySAbktsw6Pl/huQlnqYlrp0NoJabvCAtByGaWFOIKjFsietmHGGwXEey56HbTcSzh02BDnsQGKbMU4t5uyHNzN8qKiWM2dPf3bjQ1mdvPyM5IMPf7bJ4XYYjAQzEqCiB3CqZ0CKMbyxPQpGwSgYBSMbAAD+a1ASRUQZhgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-9875-6188","institution":"International Iberian Nanotechnology Laboratory, 4715-330 Braga, Portugal","correspondingAuthor":true,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Alpuim","suffix":""},{"id":387911963,"identity":"2a1e1b37-48a9-4e43-80c9-90f17398d749","order_by":7,"name":"Andrea Capasso","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYHACA2SODRAzNh4gRUsaSEsDSVoOg0m8WvhnN2/78KGGQR7IePzxR8V5u7Xth4G21NhE49IicedY8cwZxxgMZ9w5ZibNc+Z28rYziUAtx9JyG3DpuZFjzMzDxpDAcCPBjJmx7Xay2QGgFsaGwzi1yIO0/PnHkCB/I/3zx59t55LNzj/Er8UApIWxjSEByDCQ4G07YGd2g4AthjfSihl7+yQMN945Uwb0S3KC2Q2gLQl4/CJ3I3kzw49vNvJyt9s3A0PMzt7sfPrDBx9qbHB7HwIkwAgEEsEqE/ArR+gCAXuiFI+CUTAKRsGIAgCnmWPZcCqfxwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0299-6764","institution":"International Iberian Nanotechnology Laboratory, 4715-330 Braga, Portugal","correspondingAuthor":true,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Capasso","suffix":""}],"badges":[],"createdAt":"2024-12-04 16:35:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5581426/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5581426/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71728563,"identity":"5a3f34fe-1534-4586-ae54-e1a6d7d868e9","added_by":"auto","created_at":"2024-12-18 06:32:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2185239,"visible":true,"origin":"","legend":"\u003cp\u003eFunctionalization of graphene for glucose detection. (a) Schematic of the functionalization process. (b) Raman spectra of each stage involved in the immobilization of GOx molecules on graphene: pristine graphene (Gr, black line), PBASE modification (Gr/PBASE, red line) and GOx immobilization (Gr/PBASE/GOx, blue line). Each spectrum is an average of 1500 measurements. (c) 2D vs G frequency plot of the 1500 measurements of Gr, Gr/PBASE and Gr/PBASE/GOx used to calculate the average spectra in (b). The red dashed line is an average of (ω\u003csub\u003eG\u003c/sub\u003e, ω\u003csub\u003e2D\u003c/sub\u003e) for strain-free graphene with varying density of holes (n, whereas the black dashed line represents a prediction of (ω\u003csub\u003eG\u003c/sub\u003e,ω\u003csub\u003e2D\u003c/sub\u003e) for charge-neutral graphene under randomly oriented uniaxial stress. (d) Water contact angle (WCA) measurements for each graphene functionalization stage, showing an increase in hydrophobicity after graphene transfer and a subsequent decrease upon functionalization. Each WCA value is an average of 10 measurements. (e) Deconvoluted high-resolution C 1s and (f) N 1s spectra of Gr, Gr/PBASE and Gr/PBASE/GOx.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5581426/v1/7c73b373718fb2ed171d0366.png"},{"id":71727908,"identity":"72f94b91-539a-45b1-829d-f18145fa835e","added_by":"auto","created_at":"2024-12-18 06:24:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":409544,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eElectrical characterization of the EG-GFETs after each step of the functionalization process employed for glucose sensing. (a) Transfer curves of one EG-GFET measured after each functionalization stage and (b) their respective average value of the V\u003c/em\u003e\u003csub\u003e\u003cem\u003eDIRAC\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e for 50 transistors (data are presented as the mean ± standard error mean (sem)). The schematic of an EG-GFET is depicted in the inset of (b). The transfer curve of graphene exhibits symmetry centered around a minimum I\u003c/em\u003e\u003csub\u003e\u003cem\u003eDS\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e point, occurring at a gate voltage V\u003c/em\u003e\u003csub\u003e\u003cem\u003eDIRAC\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e. This is the point where the channel’s conductivity reaches its minimum. The sharp branches in the curve represent hole transport (on the left branch) and electron transport (on the right branch).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5581426/v1/29436d955177b739501887ac.png"},{"id":71727911,"identity":"f20e8f6b-769b-42ff-bc61-320fc17f89cc","added_by":"auto","created_at":"2024-12-18 06:24:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":654224,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGlucose sensing via EG-GFETs and Raman spectroscopy in 1x PBS. (a) Comparison of the ΔV\u003c/em\u003e\u003csub\u003e\u003cem\u003eDIRAC\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e as a function of glucose concentration for Gr\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e, Gr\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/PBASE and Gr\u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/PBASE/GOx/ETA. In the latter, V\u003c/em\u003e\u003csub\u003e\u003cem\u003eDIRAC\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e positively shifts as the glucose concentration increases, resulting in p-type doping with the lowest reported glucose LOD of 1 aM. The negative control tests show the opposite response. The data are presented as mean ± sem. (b) Transfer curve trend of one transistor for the different glucose concentrations in the linear range (1 aM-1 pM). (c) 2D vs G frequency plot of Gr/PBASE/GOx/ETA with varying glucose concentration (1 nM-1 mM). For each concentration, a distribution of the peaks position within 300 measurements (over a total area of 300 μm²) is displayed with the corresponding average point. \u003c/em\u003e\u003csup\u003e\u003cem\u003e*\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eThe surface was treated with DDT to passivate the Au electrodes.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5581426/v1/763d1b8f1d3e988ef3f01d94.png"},{"id":71729970,"identity":"f0f6f164-66fb-4a83-8266-83913d83c243","added_by":"auto","created_at":"2024-12-18 06:40:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":241838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSelectivity of the EG-GFETs with Gr/PBASE/GOx/ETA configuration. (a) Comparison of the sensors’ response to glucose and lactate in 1 x PBS, showing linear and erratic trends, respectively. (b) Calibration curve of the ΔV\u003c/em\u003e\u003csub\u003e\u003cem\u003eDIRAC\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e as a function of glucose concentrations in an artificial tear, showcasing the sensitivity and selectivity of the sensors in a complex medium.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5581426/v1/d2cf3407c22808294ceabf92.png"},{"id":72208912,"identity":"53de9759-eb7d-4d7f-bcdd-c7691f9b3549","added_by":"auto","created_at":"2024-12-23 17:17:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4510912,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5581426/v1/24bb3407-f32e-4d02-81cc-31fc3078c0d8.pdf"},{"id":71727925,"identity":"11344725-6fc6-4b2f-9b0b-fcf5d1c81f69","added_by":"auto","created_at":"2024-12-18 06:24:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":985530,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"CapassoGlucosesensingSI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5581426/v1/3c5549c8037d62165d0932c7.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Graphene-based glucose sensors with an attomolar limit of detection","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlucose is a primary source of energy for cellular activity in the body, and maintaining its optimal concentration in the blood is crucial. However, certain patients suffer from metabolic disorders that affect glucose processing. \u003cem\u003eDiabetes mellitus\u003c/em\u003e, a major metabolic disorder that impairs glucose regulation, affects more than 400\u0026nbsp;million people worldwide and is projected to impact up to ~\u0026thinsp;700\u0026nbsp;million people by 2045 \u003csup\u003e1,2\u003c/sup\u003e. This condition is characterized by hyperglycemia resulting from defects in insulin secretion or action \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Without proper management, diabetes can lead to severe complications such as blindness, cardiovascular diseases, nerve damage, and even cancer \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Many glucose measurement approaches exist, and efforts are being devoted to achieving functional continuous glucose monitoring. Glucose sensors range from optical (by using tilted fibers, photonic crystals or liquid crystals) \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e to electrochemical (through functionalized graphene derivatives and modified or bare electrodes) \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Essentially, they can be classified as non-enzymatic or enzymatic. Non-enzymatic sensors rely on the direct electrochemical oxidation of glucose, induced by the intrinsic catalytic properties of the electrode materials \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, which can be metallic (\u003cem\u003ee.g.\u003c/em\u003e, Pt, Au, or Cu), polymeric or carbon-based \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. These sensors are conceptually simple, versatile, low-cost, highly stable and durable \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, they lack biorecognition probes, which are crucial when sensing any analyte in highly complex media such as body fluids \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Although more costly and susceptible to degradation over time due to environmental factors \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, enzymatic glucose sensors offer unmatched sensitivity and selectivity toward glucose. The integration of a glucose-recognition element, such as glucose oxidase (GOx) enzymes, allows selective transducing only when exposed to glucose \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Enzymatic finger-pricking sensors are suitable and for accurate at-home monitoring of blood glucose levels (between 4.4 and 6.7 mM in the daytime) \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, but these can be perceived as invasive, uncomfortable, and risky, leading to reluctance or rejection in 30 % of patients \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Other biofluids, suchas tears and saliva (normal glucose levels of 0.1\u0026ndash;0.2 mM for both), have been proposed as diagnostic biofluids to replace blood. They are considered ideal for non-invasive glucose monitoring but would require high sensitivity, since their fasting glucose concentrations are lower than those of blood (averaging\u0026thinsp;\u0026gt;\u0026thinsp;0.4 mM for tears and \u0026gt;\u0026thinsp;0.8 mM for saliva \u003cem\u003evs\u003c/em\u003e. \u0026gt; 7 mM for diabetic patients) \u003csup\u003e\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. At present, tear- and saliva-based glucose sensors have not progressed enough to enter the market.\u003c/p\u003e \u003cp\u003eIn recent years, graphene emerged as a key material for electronics and sensing technologies. Capitalizing on high surface-to-volume ratio, excellent carrier mobility and ease in functionalization \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, graphene represents an ideal platform to interact efficiently with the surrounding environment and react to external stimuli with high sensitivity. In particular, the ability to transduce biological interactions into measurable electrical signals is valuable for biosensing. Graphene field-effect transistors (GFETs) have been implemented as biosensors able to detect a wide array of analytes, ranging from gases to biological molecules, at extremely low concentrations and with exceptional precision \u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Modification with specific recognition elements allows fine-tuning the selectivity of GFETs, even in complex body fluids \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Electrolyte-gated graphene field-effect transistors (EG-GFETs) exploit a highly efficient local gating mechanism based on the direct interaction between charge carriers in the electrolyte and the graphene layer. This interaction induces the formation of an electrical double-layer (EDL), which significantly increases the capacitance and transconductance of the device. The changes in ionic concentration or charge distribution within the electrolyte, confined to the Debye screening length, are effectively transduced to the graphene channel, resulting in a high sensitivity \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In this work, we propose a glucose detection system based on EG-GFETs functionalized with GOx. Each step of the graphene functionalization is investigated via Raman spectroscopy, X-ray photoelectron spectroscopy and water contact angle measurements. The glucose sensing signal is a shift in the EG-GFET transfer curve charge neutrality point (V\u003csub\u003eDIRAC\u003c/sub\u003e) toward more positive values, a consequence of graphene p-type doping, proportional to the glucose concentration. The sensors achieve record limit-of-detection (LOD) of 1 aM glucose in 1 x PBS and 100 aM in artificial tears. These advancements will pave the way to replace the invasive glucose monitoring protocols, potentially through wearable technology.\u003c/p\u003e"},{"header":"2. Results and discussion","content":"\u003cp\u003eThe effectiveness and reliability of biosensing \u003cem\u003evia\u003c/em\u003e EG-GFETs highly depend on the successful accomplishment of the graphene functionalization process. Our devices' glucose-sensing mechanism relies on immobilizing the glucose oxidase (GOx) enzyme on graphene. We chose this enzyme for its high specificity for glucose and large turnover (\u003cem\u003ei.e.\u003c/em\u003e, the number of substrate molecules that an enzyme can convert to the product of reaction per active site per unit time before the enzyme is fully saturated with the substrate, which is essentially an indication of the enzyme\u0026rsquo;s catalytic efficiency) \u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The process required to immobilize GOx on graphene is schematically shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea. Considering that covalent modifications can alter the electrical properties of graphene, we selected a pyrene linker, PBASE, that π-π stacks non-covalently to the graphene to anchor the enzyme via an amine bond, which is facilitated by the nucleophilic substitution of N-hydroxysuccinimide (NHS) \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Raman spectroscopy can assess the evolution of graphene's morphologic and electronic properties in response to the functionalization process. All three spectra of as-transferred graphene (Gr), Gr/PBASE, and Gr/PBASE/GOx show the typical features of high-quality, monolayer graphene, with I\u003csub\u003e2D\u003c/sub\u003e/I\u003csub\u003eG\u003c/sub\u003e ratios of 2.30, 1.47, and 1.45, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). In Gr/PBASE, new peaks at 1230.8 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, 1384.5 cm\u003csup\u003e\u003cb\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e, and 1621.2 cm\u003csup\u003e\u003cb\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e appear, which can be assigned to pyrene-based molecules \u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The latter peak is attributed to the pyrene group resonance \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, indicating that PBASE is stacked on the graphene surface. The D peak (~\u0026thinsp;1342 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e), which is absent in the Gr spectrum, is found to increase in the Gr/PBASE spectrum due to pyrene immobilization (\u003cem\u003evia\u003c/em\u003e orbital hybridization of the PBASE molecules with the graphene plane) \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Gr/PBASE/GOx shows a slight increase in the intensity of the D peak concerning the previous stage. We measured the Raman spectra of GOx as a powder and in a water solution (Figure S1a), which, to our knowledge, have not been previously reported. This was performed to identify any GOx-related modes in the Gr/PBASE/GOx spectrum. The GOx spectra have two main regions of interest: 1100\u0026ndash;1800 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and 2800\u0026ndash;3100 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. We fitted the latter region (Figure S1b), where the most intense and wide peak appears at 2932 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. By zooming in on the Gr/PBASE/GOx spectrum, a small and wide feature at ~\u0026thinsp;2950 cm⁻\u0026sup1; can be observed, which could further corroborate enzyme immobilization (no features exist in the corresponding Gr/PBASE spectral range; Figure S1c). We analyzed the G and 2D peak positions and full width at half maximum (FWHM) in the three cases. The distributions of the G and 2D peak FWHMs for each case are shown in Figure S2. To further aid in discriminating against the emergence of charge doping and/or mechanical strain \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, we also prepared a 2D \u003cem\u003evs\u003c/em\u003e G frequency plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec) and a corresponding linearly transformed plot (Figure S3), which provides information on potential variations in the mechanical strain (Y-axis) and charge (hole) density (X-axis) \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. In this plot, a higher (lower) charge density represents greater (lower) hole doping in graphene, whereas a higher (lower) mechanical strain represents tensile (compressive) strain. In Gr, the extracted values are centered at a G peak position of 1584.2 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e (average FWHM of ~\u0026thinsp;22 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) and a 2D peak position of 2667.5 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e (average FWHM of ~\u0026thinsp;40 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e). Figure S3 shows a significant hole density, indicating p-type doping, as well as a state of tensile strain, likely due to the PMMA transfer process \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In Gr/PBASE, the G and 2D peak positions are centered at 1586.9 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e (average FWHM\u0026thinsp;~\u0026thinsp;23 cm\u003csup\u003e\u003cb\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e) and 2671.6 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e (average FWHM\u0026thinsp;~\u0026thinsp;40 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e), resulting in ~\u0026thinsp;3\u0026ndash;4 cm\u003csup\u003e\u003cb\u003e-\u003c/b\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e blueshifts, respectively, with respect to Gr. Figure S3 shows a slight increase in the hole density of graphene, which further confirms the stacking of pyrene molecules on graphene due to its electron-withdrawing properties \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. The observed minor increase in the compressive strain might result from the interaction between the immobilized pyrene molecules and graphene. Finally, in Gr/PBASE/GOx, the G and 2D peak position values are centered at 1585.3 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e (average FWHM of ~\u0026thinsp;26 cm\u003csup\u003e\u003cb\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e) and 2671.7 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e (average FWHM of ~\u0026thinsp;42 cm\u003csup\u003e\u003cb\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e), respectively. There is a slight redshift of the G peak (1.3 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e) with respect to Gr/PBASE, whereas the 2D peak position remains unchanged. However, the FWHM\u003csub\u003e2D\u003c/sub\u003e and the FWHM\u003csub\u003e\u003cb\u003eG\u003c/b\u003e\u003c/sub\u003e increase by ~\u0026thinsp;2 cm\u003csup\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and ~\u0026thinsp;3 cm\u003csup\u003e\u003cb\u003e-\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e, respectively, which is often related to doping effects. Figure S3 shows a reduced hole density and further increase in compressive strain, which confirms the binding of GOx to PBASE. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed depicts the evolution of the water contact angle (WCA) after functionalization. The WCA on Gr (82.07\u0026deg;) increases by ~\u0026thinsp;9\u0026deg; with respect to the Si/SiO\u003csub\u003e2\u003c/sub\u003e substrate (73.5\u0026deg;). PBASE decreases the WCA to 77.4\u0026deg;, as expected for NHS-ester ligands upon surface immobilization \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. The immobilization of GOx, a hydrophilic molecule \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, significantly decreases the WCA by ~\u0026thinsp;23\u0026deg;. Furthermore, charge doping (either n- or p-type) increases the wettability of graphene \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, which would also sustain the successive decreases in the WCA of graphene after functionalization with PBASE and GOx. The biofunctionalization was analyzed by XPS. The C 1s and N 1s high-resolution spectra are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef, respectively, while the survey spectra are reported in Figure S4. Tables S1 and S2 specify the relative atomic concentration (at. %) of the main components. The immobilization of GOx on graphene was further confirmed by the progressive intensity increase of the N-related peaks in the C 1s and N 1s regions of the three samples. The C 1s fittings show the main features of CVD graphene (Table S1): an asymmetric peak centered at 284.5 eV with the corresponding shake-up satellite peak at 290.9 eV, and a minor C-C sp\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e component. Additionally, three other components appear, which can be assigned to C-O/C-N, C\u0026thinsp;=\u0026thinsp;O, and C\u0026thinsp;=\u0026thinsp;OO/N-C\u0026thinsp;=\u0026thinsp;O bonds. These three components increase in the functionalized samples. In particular, the C-O/C-N passed from an initial 11.7 at. % to 18.2 at. % in Gr/PBASE/GOx, owing to the increased amine and amide functional groups \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Meanwhile, the main N 1 s peak at ~\u0026thinsp;400 eV \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e passes from 0.9 at. % to 7.6 at. % (Table S2). The small feature appearing at ~\u0026thinsp;398.5 eV is likely due to the formation of pyridinic rings from side reactions involving PBASE \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo achieve a biosensing system based on EG-GFETs, both the graphene FET channel and the Au gate need to be properly functionalized. The complete EG-GFET functionalization method is depicted in Figure S5 and consists of four steps (for details, refer to Section 4.4). Each functionalization stage is verified with electrical measurements by plotting the transfer curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) and measuring the Dirac point shift (ΔV\u003csub\u003eDIRAC\u003c/sub\u003e) with respect to the previous functionalization step (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Before any functionalization stage (Gr), the sensors are measured to provide a baseline for the shifts of the next stage. Shifts from the baseline transfer curve measurements indicate that charge carrier redistribution in the graphene channel is due to electrostatic potential changes from surface modification. Initially, the V\u003csub\u003eDIRAC\u003c/sub\u003e value is located at positive gate voltage values (+\u0026thinsp;0.45 V), indicating unintentional p-type doping (Stage 0). Adding DDT to the sensors (Stage 1) causes the V\u003csub\u003eDIRAC\u003c/sub\u003e to shift by ~ -310 mV owing to the formation of a self-assembled monolayer covering the gold electrode, creating an excess of positive charges in the solution from the dipole moment reorientation of the alkanethiols \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Graphene doping level is not altered, as confirmed by Raman spectroscopy (Figure S6). The functionalization of graphene is then achieved with PBASE (Stage 2). We observe only a slight shift of approximately\u0026thinsp;+\u0026thinsp;30 mV, indicating additional graphene p-type doping upon π‒π stacking \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. This small variation of the V\u003csub\u003eDIRAC\u003c/sub\u003e can be explained by a doping competition between pyrene molecules and the solvent used to dilute them. Dimethyl solvents (\u003cem\u003ee.g.\u003c/em\u003e, DMF, DMSO) have been reported to n-dope graphene \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. This was further proved by Raman spectroscopy, which shows that G and 2D peak blueshift (Figure S7a) after incubation with bare DMSO (corresponding to a decrease in hole density; Figure S7b). Additionally, at the PBASE functionalization stage, both the electron and hole branches of the transfer curve become slightly less steep, indicating a small decrease in the mobility of both types of carriers. The NHS ester group of PBASE remains free to form a covalent bond with the biorecognition probe. GOx is immobilized on graphene (Stage 3) by binding to the free ester group of PBASE. The immobilization of the enzyme results in a V\u003csub\u003eDIRAC\u003c/sub\u003e shift of -30 mV, in agreement with the Raman shift indicating n-type doping. Finally, ETA (Stage 4) causes the V\u003csub\u003eDIRAC\u003c/sub\u003e to shift by -5 mV.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOnce fully functionalized (Gr/PBASE/GOx/ETA), the EG-GFET devices were tested for glucose sensing (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). A first V\u003csub\u003eDIRAC\u003c/sub\u003e shift of +\u0026thinsp;26\u0026thinsp;\u0026plusmn;\u0026thinsp;4 mV occurs at 1 aM and then the values continue to increase with the glucose concentration for six orders of magnitude, with a sensitivity of 10.6 mV/decade. The trend reaches a plateau at 1 pM (ΔV\u003csub\u003eDIRAC\u003c/sub\u003e ~ 100 mV), which remain rather constant up to 1 mM (Figure S8, red circles). The corresponding transfer curves are reported in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb. These results demonstrate a marked p-type doping effect due to increasing glucose concentration in fully functionalized devices. For a more comprehensive analysis, we have also tested partially functionalized devices to understand the sensing mechanism. The Gr and Gr/PBASE devices behave very differently in the same glucose concentration range (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The first, apparent difference is that the V\u003csub\u003eDIRAC\u003c/sub\u003e shifts are negative in both cases, demonstrating n-type doping. Also, no clear trend is visible for concentrations\u0026thinsp;\u0026lt;\u0026thinsp;10 fM. At higher concentrations, the values tend to stabilize or change marginally (Figure S8). Simulations via density functional theory method demonstrated that glucose can absorb on pristine graphene, inducing electron transfer and n-type doping \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. However, in the case of Gr/PBASE, the doping effect becomes weaker since the PBASE layer can interfere with the electron transfer. The Gr/PBASE/GOx/ETA devices were analyzed by Raman spectroscopy in the 1 nM-1 mM glucose concentration range (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The 2D and G peak positions upshift significantly at 1 nM and then increase marginally at higher concentrations. At 1 mM, the two peaks upshift by +\u0026thinsp;6.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and +\u0026thinsp;8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (with respect to the pristine case at 0 M), respectively. We evaluated the corresponding contributions of both mechanical strain and hole doping (Figure S9), which further confirmed the p-type doping effect of glucose on the fully functionalized device.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBuilding on the experimental findings, we propose an interpretation of the underlying sensing mechanism. In solution, GOx catalyzes the oxidation of glucose and yields two byproducts: gluconic acid and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e. This two-step catalysis involves the transfer of electrons from glucose to the enzyme's prosthetic group, typically a flavin adenine dinucleotide (FAD) cofactor. FAD oxidizes glucose into gluconolactone and gets reduced to FADH\u003csub\u003e2\u003c/sub\u003e (Eq.\u0026nbsp;1). Gluconolactone is hydrolyzed in the presence of water to form gluconic acid (Eq.\u0026nbsp;2). The FAD subunit is regenerated from FADH\u003csub\u003e2\u003c/sub\u003e through the reduction of O\u003csub\u003e2\u003c/sub\u003e to H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (Eq.\u0026nbsp;3). Upon regeneration, FAD continues to oxidize glucose, resulting in cyclic interactions for continuous H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and gluconic acid production.\u003c/p\u003e \u003cp\u003eGOx (FAD)\u0026thinsp;+\u0026thinsp;glucose \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e GOx (FADH\u003csub\u003e2\u003c/sub\u003e)\u0026thinsp;+\u0026thinsp;gluconolactone (1)\u003c/p\u003e \u003cp\u003egluconolactone\u0026thinsp;+\u0026thinsp;H\u003csub\u003e2\u003c/sub\u003eO \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e gluconic acid (2)\u003c/p\u003e \u003cp\u003eGOx (FADH\u003csub\u003e2\u003c/sub\u003e)\u0026thinsp;+\u0026thinsp;O\u003csub\u003e2\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e GOx (FAD)\u0026thinsp;+\u0026thinsp;H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (3)\u003c/p\u003e \u003cp\u003eConsidering these chemical reactions, we propose that the p-type doping occurring in the devices could stem from the interaction between H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and the functionalized graphene. In similar sensing systems, H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e typically undergoes a voltage-driven decomposition, which releases molecular oxygen, protons and free electrons \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\to\\:\\)\u003c/span\u003e\u003c/span\u003e O\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;2H\u003csup\u003e+\u003c/sup\u003e + 2e\u003csup\u003e\u0026minus;\u003c/sup\u003e (4)\u003c/p\u003e \u003cp\u003eIn principle, free electrons should transfer to graphene (especially at defect sites, like vacancies) and induce n-type doping \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. We tested this hypothesis by exposing the Gr and Gr/PBASE/GOx/ETA devices to increasing H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e concentrations (Figure S10). The Gr devices do indeed exhibit consistent negative ΔV\u003csub\u003eDIRAC\u003c/sub\u003e shifts at increasing concentrations when H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e is added (up to -130 mV at 1 mM, Figure S10b). By contrast, the Gr/PBASE/GOx/ETA devices show positive V\u003csub\u003eDIRAC\u003c/sub\u003e shifts, up to +\u0026thinsp;80\u0026thinsp;\u0026plusmn;\u0026thinsp;4 mV at 1 mM, indicating strong p-type doping (Figure S10b). This could be explained by two reasons: i) the pyrene monolayer with bound enzymes can shield the graphene and mitigate the direct electron transfer; ii) in proximity of graphene, H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e might decompose into hydroxide (OH\u003csup\u003e\u0026minus;\u003c/sup\u003e) ions \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. In slightly basic conditions (the PBS solution has pH\u0026thinsp;=\u0026thinsp;7.4), the OH\u003csup\u003e\u0026minus;\u003c/sup\u003e concentration can further increase and upshift the V\u003csub\u003eDIRAC\u003c/sub\u003e by an accumulation of positive counter-ions from the electrolyte \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. We evaluated the response to glucose of the Gr/PBASE/GOx/ETA devices after direct exposure to H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e (see Section 4.4). As shown in Figure S10b, after washing away the H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and cleaning the sensor\u0026rsquo;s surface in PBS (corresponding to \u0026ldquo;Glu 0 M\u0026rdquo;), the device does not respond to glucose in the 1 nM-1 mM concentration range. This can be explained by an inhibitory effect of high concentration of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e on the catalytic activity of GOx \u003csup\u003e\u003cspan additionalcitationids=\"CR69 CR70\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, which in turn would also clarify the plateau in the response to glucose of the regular EG-GFET devices after 1 pM (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eTo assess the selectivity of the sensors, we tested their response to lactate (a compound produced by glycolysis \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e) in the same concentration range of glucose (Figure S11). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea compares the response of the sensors to glucose and lactate. Differently from the linear ΔV\u003csub\u003eDIRAC\u003c/sub\u003e trend for glucose, the response to lactate exhibits a minor and random trend due to non-specific interactions or noise. The selectivity for glucose was further evaluated in a simulated biological fluid (\u003cem\u003ei.e.\u003c/em\u003e, an undiluted commercial artificial tear), to test the potential clinical application of the sensors. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, the ΔV\u003csub\u003eDIRAC\u003c/sub\u003e shifts exhibit a clear and consistent response across the tested concentration range, with a linear trend (4.9 mV/decade) and a remarkable LOD of 100 aM. The sensors exhibit p-type doping with increasing concentrations of glucose for six orders of magnitude (until saturating at 0.1 nM, with a ΔV\u003csub\u003eDIRAC\u003c/sub\u003e ~ 45 mV). The reduction in sensitivity might be explained by the composition of the artificial tear, which contains several analytes (electrolytes, buffers, acids) at high concentration that can hinder the direct interaction between glucose and GOx.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSeveral sensing strategies have been developed to achieve high sensitivity and low LOD for glucose sensing (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Non-enzymatic sensors can leverage the catalytic activity of different electrode systems, having achieved LODs up to the nM range \u003csup\u003e\u003cspan additionalcitationids=\"CR74\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. Fiber-based sensors pushed beyond this limit and reached LOD in the pM-fM range by using tilted fiber Bragg grating combined with surface plasmonic resonance \u003csup\u003e\u003cspan additionalcitationids=\"CR77\" citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Enzymatic sensors, particularly those using GOx, can reach top-level sensitivity and selectivity by perfecting the enzyme immobilization through functional materials, such as Nafion, liquid crystals, nanowires, nanorods and graphene derivatives \u003csup\u003e\u003cspan additionalcitationids=\"CR80 CR81 CR82 CR83 CR84 CR85 CR86 CR87 CR88 CR89 CR90 CR91\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. These systems have achieved LODs in the nM-pM level. Our sensors reached the aM level, which is 14 orders of magnitude lower than the lowest reported for configurations based on Gr/PBASE/GOx \u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e,\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e. Such LOD level is also at least three orders of magnitude lower than that ever reported for the best-performing glucose sensors (fM level) \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Devices enabling aM-level detection of glucose could pave the way for next-generation non-invasive glucose monitoring.\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\u003eState-of-the-art glucose sensing, considering their sensing system, detection method, LOD, linear range, sensitivity and selectivity.\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\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensing system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDetection method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLinear range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSelectivity\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\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eNon-Enzymatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCu-Gr-COOH-Au electrode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCV and chronoamperometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.96 nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 \u0026micro;M-5.48 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1142 \u0026micro;A mM\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026nbsp;cm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCuO-NiO-MFs/FTO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 M-0.51 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3165.53 A mM\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e cm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGr/Cu Electrode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5 \u0026micro;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5 \u0026micro;M\u0026ndash;4.5 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTFBG-SPR-AuNPs-PMBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e295 pM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 nM-10 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTFBG/GO/PBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 fM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 fM\u0026ndash;10 pM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003eEnzymatic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET/CNTs/GOx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccumulation mode and AM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 nM\u0026ndash;5 \u0026micro;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u0026nbsp;\u0026plusmn;\u0026nbsp;0.04\u0026nbsp;nC/nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTFBG/GO/EDC-NHS/GOx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 mM\u0026ndash;8\u0026nbsp;mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u0026nbsp;nm/mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCNT NEEs/GOx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08 mM-30 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSilk/Gr/Silk-GOx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 mM\u0026ndash;10\u0026nbsp;mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u0026nbsp;\u0026micro;A/mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCE-RGO-GOx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCV and AM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1 mM\u0026ndash;27\u0026nbsp;mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.85\u0026nbsp;\u0026micro;A\u0026nbsp;mM\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026nbsp;cm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGr/CNT/ZnO/GOx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u0026nbsp;\u0026micro;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u0026nbsp;\u0026micro;M-6.5\u0026nbsp;mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.362 (\u0026plusmn;\u0026thinsp;0.072)\u0026nbsp;\u0026micro;A\u0026nbsp;mM\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026nbsp;cm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAuNWs/GOx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 \u0026micro;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 \u0026micro;M\u0026ndash;100 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.2 mA\u0026nbsp;mM\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u0026nbsp;cm\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZNA/GOx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eField emission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u0026nbsp;nM-50 \u0026micro;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePB-modified GCE/Nafion/GOx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 \u0026micro;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 \u0026micro;M\u0026ndash;5 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET/Gr/PBASE/GOx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.3 mM-10.9 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGr/PBASE/GOx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNafion/Pt-xGnP/GOx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 \u0026micro;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 \u0026micro;M-20 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 \u0026micro;A mM\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e cm\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGr/PtNPs/Nafion/GOx-CHIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5 \u0026micro;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5 \u0026micro;M-1 mM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e173 mV/decade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUV- 5CB/Au-grid/GOx\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptical Response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 pM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 pM-50 nM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGr/PBASE/GOx/ETA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 aM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 aM-1 pM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.6 mV/decade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eThis work\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\u003e \u003cem\u003eCV \u0026ndash; Cyclic voltammetry; SPR \u0026ndash; surface plasmonic resonance; AM \u0026ndash; amperometry; TC \u0026ndash; transconductance; TFBG \u0026ndash; tilted fiber Bragg grating; GO \u0026ndash; graphene oxide; PBA \u0026ndash; pyrene-1-boric acid; AuNPs \u0026ndash; gold nanoparticles; PMBA \u0026ndash; p-mercaptophenylboronic acid; PB \u0026ndash; Prussian blue; MFs \u0026ndash; microfibers; FTO \u0026ndash; fluorine tin oxide; GCE \u0026ndash; glass carbon electrode; PET \u0026ndash; polyethylene terephthalate; ZNA \u0026ndash; ZnO nanorod arrays; EDC \u0026ndash; 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide; AuNWs \u0026ndash; gold nanowires; CNT \u0026ndash; carbon nanotubes; xGnP \u0026ndash; exfoliated graphite nanoplatelets; UV-5CB \u0026ndash; UV-treated 4-cyano-4\u0026prime;-pentylbiphenyl; NEEs \u0026ndash; nanoelectrode ensembles; NR \u0026ndash; not reported.\u003c/em\u003e \u003c/p\u003e"},{"header":"3. Conclusions","content":"\u003cp\u003eDiabetes affects millions of lives, but current glucose monitoring methods are still time-consuming, painful and invasive. We presented a highly sensitive glucose sensing platform based on electrolyte-gated graphene field-effect transistors functionalized with glucose oxidase, achieving unprecedented limits-of-detection of 1 attomolar in PBS and 100 attomolar in artificial tears. The sensors demonstrated exceptional sensitivity and selectivity, even in an artificial tear. These features, along with compatibility and stability in complex media, pave the way for non-invasive continuous glucose monitoring in realistic biofluids. This study sets a benchmark for glucose biosensing technology, highlighting the adaptability of this kind of sensors for broad diagnostic applications. Although blood remains the gold standard for diabetes management, this sensing platform unlocks tears as viable diagnostic biofluid. Ultimately, this graphene-based sensing technology could enable wearable devices, such as smart contact lenses, for real-time glucose monitoring with minimal invasiveness.\u003c/p\u003e"},{"header":"4. Materials and Methods","content":"\u003cp\u003e4.1. Materials\u003c/p\u003e\n\u003cp\u003eD-(+)-Glucose (≥ 99.5 %), Hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, 30 % (w/w) in H\u003csub\u003e2\u003c/sub\u003eO), Lactic acid solution (≥ 85 %), Phosphate-buffered saline (PBS) tablets (1814.5-2005.5 mg/tab), Dimethyl sulfoxide (DMSO) (99.9 % HPLC), 1-Pyrenebutyric acid N-hydroxysuccinimide ester (PBASE) (95 %), 1-Dodecanethiol (DDT) (98 %), Ethanolamine (ETA) (98 %), Hydrochloric acid (HCl) (37 %) and Poly(methyl(meth)acrylate) (PMMA) (15k M.W.) were purchased from Sigma Aldrich. Poly(methyl(meth)acrylate) (PMMA) (550k M.W.) was purchased from Alfa Aesar. Glucose oxidase (GOx, from \u003cem\u003eAspergillus niger, \u003c/em\u003e248878 units/g) was purchased from Merck. Acetone (99.5 %), ethanol (99.8 %), and 2-propanol (99.8 % GC) were purchased from Honeywell. The photoresist AZ1505 (AZ) was purchased from MicroChemicals GmbH. RTV silicone elastomer (3140, Dowsil) and superglue (Loctite) were acquired from Farnel. Systane artificial tears (containing sodium hyaluronate, polyethylene glycol, propylene glycol, hydroxypropyl guar, sorbitol, aminomethyl propanol, boric acid, potassium chloride, sodium chloride, sodium borate, and purified water) were purchased from Lentes de Contacto 365.\u003c/p\u003e\n\u003cp\u003e4.2. Graphene growth and transfer\u003c/p\u003e\n\u003cp\u003eMonolayer graphene growth was performed in an EasyTube ET3000 chemical vapor deposition (CVD) system (CVD Corp, USA). Graphene was grown on copper (Cu) foils/substrates that act as catalysts. The 5 × 5 cm\u003csup\u003e2\u003c/sup\u003e Cu foils (99.99+% purity) were initially chemically treated in a solution of FeCl\u003csub\u003e3\u003c/sub\u003e, HCl, and DI water for 1 min under ultrasound to reduce rugosities and organic contamination and then heated on a hot plate at 250°C in air for 20 min for partial oxidation. The oxidized Cu foils were placed into a three-zone quartz tube furnace, and the chamber was evacuated to approximately 10 mTorr and then filled with 250 sccm argon (Ar, 99.999 % purity) and 170 sccm hydrogen (H, 99.999 % purity) gas mixtures. Once the growth temperature and pressure were reached, 1.25 sccm CH\u003csub\u003e4\u003c/sub\u003e, the carbon precursor, was introduced into the chamber. Monolayer graphene growth was carried out at 1040°C and 6 Torr for 1 h on both sides of the Cu foil. Graphene was transferred via an optimized poly(methyl methacrylate) (PMMA) solution designed to provide mechanical support during the transfer cycles while suppressing potential contamination \u003csup\u003e93\u003c/sup\u003e. The optimized PMMA is a mixture of PMMA-550k (550,000 average molecular weight) and PMMA-15k (15,000 average molecular weight) in anisole at a 2:1 ratio (3 wt. %). PMMA was spin-coated (3000 rpm, 30 s) on the top side of the graphene/Cu/graphene, followed by drying in a fume hood at room temperature (RT) overnight. Plasma ashing (PVA TePla GiGAbatch, O\u003csub\u003e2\u003c/sub\u003e:Ar 1:1, 0.75 mbar, 230 W, 25°C, 2 min) was performed to remove graphene from the back side of the sample. To dissolve the Cu, the sample was floated in a 0.5 M FeCl\u003csub\u003e3 \u003c/sub\u003esolution for 2 h. After full etching, the graphene/PMMA membrane was rinsed in cycles of DI water and 2% HCl solution baths for 30 min to remove etchant impurities. Finally, the graphene/PMMA was rinsed in DI water two times and transferred to the target substrate.\u003c/p\u003e\n\u003cp\u003e4.3. EG-GFET microfabrication\u003c/p\u003e\n\u003cp\u003eOur electrolyte-gated graphene field-effect transistor chip microfabrication is described elsewhere \u003csup\u003e25\u003c/sup\u003e. Briefly, a 200 mm silicon (Si) wafer (p-type doped with boron, B) with 100 nm of thermal oxide was sputter-coated with chromium (Cr, 3 nm) as the adhesion layer, gold (Au, 35 nm) as the conductive layer, and alumina (Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, 100 nm) capping. The source, drain and gate Au electrodes were patterned through optical lithography and ion milling. A sacrificial layer (TiWN, 5 nm; AlSiCu, 100 nm; TiWN, 15 nm) was sputtered and patterned by lift-off, leaving only open the GFET channel areas to transfer the graphene. This sacrificial layer protected the wafer from potential contamination from graphene transfer processes. After transfer, the graphene was patterned by optical lithography and oxygen plasma etching, followed by wet etching of the sacrificial layer. A protective layer of Nickel (Ni, 100 nm) was sputtered and patterned by lift-off to work as a stopping layer for ion milling. The multistack passivation layers of SiO\u003csub\u003e2\u003c/sub\u003e (50 nm) and Si\u003csub\u003e3\u003c/sub\u003eN\u003csub\u003e4\u003c/sub\u003e (50 nm) (total thickness of 300 nm) were then grown by plasma-enhanced CVD, patterned via optical lithography and etched to cover the entire sensor area except the graphene channel, the gold gate, and the contact pads. Finally, wet etching of Ni and Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e was performed. The wafer was diced into 729 equal-sized 5 × 5 mm\u003csup\u003e2\u003c/sup\u003e chips. Each chip consists of two gate electrodes and 32 graphene channels (EG-GFETs), each group of 16 with their drains connected to a common source. The chip photoresist was dissolved in acetone and then cleaned with IPA and DI water. The chips were then glued onto a printed circuit board, wire bonded with gold wires and protected with a silicon elastomer.\u003c/p\u003e\n\u003cp\u003e4.4. Biofunctionalization of the graphene surface\u003c/p\u003e\n\u003cp\u003eThe functionalization process of EG-GFETs followed the general procedure developed in \u003csup\u003e41\u003c/sup\u003e, requiring four fundamental steps. Between every functionalization step, the chips were thoroughly cleaned with Milli-Q water, dried with N\u003csub\u003e2\u003c/sub\u003e flow and measured with undiluted phosphate buffer saline (1 x PBS, pH = 7.4). 1 x PBS was prepared by diluting two tablets in 400 mL of Milli-Q water. 1) Au gates were passivated with DDT (2 mM in ethanol), a gold blocking agent, for 2 h. Every 30 minutes, 40 µL were applied to the chips due to ethanol evaporation; 2) 40 µL of PBASE (10 mM in dimethyl sulfoxide; DMSO), a hetero-bifunctional linker binding graphene and GOx, were put on the chips for 2 h in a humid chamber. 3) A solution of GOx (10 mg/mL in Milli-Q water) was applied in 40 µL and incubated overnight (~ 16 h), in a humid chamber, to link to PBASE. iv) 40 µL of ETA was dropped on the chips and incubated for 30 min. To confirm that GOx was correctly immobilized on graphene, we performed a robust characterization of the steps 2) and 3) of the functionalization process on separate graphene samples transferred onto Si/SiO\u003csub\u003e2 \u003c/sub\u003eunpatterned substrates. To prepare D-(+)-glucose, lactic acid (lactate) and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e concentrations, stock solutions of 1 mM of each analyte were prepared and diluted in 1 x PBS. To prove the inactivation of GOx by H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, 40 μL of 1 mM of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e was added to the Gr/PBASE/GOx/ETA device and left in contact for 10 min. Afterwards, the chip was thoroughly cleaned with 1 x PBS. For the selectivity test with the artificial tear, glucose concentrations were prepared directly in the artificial tear medium.\u003c/p\u003e\n\u003cp\u003e4.5. Materials characterization\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRaman spectroscopy:\u003c/strong\u003e The monolayer graphene transferred onto Si/SiO\u003csub\u003e2\u003c/sub\u003e substrates cut in 2x2 cm\u003csup\u003e2\u003c/sup\u003e pieces was analyzed by Raman spectroscopy to inspect the graphene features (\u003cem\u003ee.g., \u003c/em\u003enumber of layers, defects, wrinkles) before and after PBASE and GOx functionalization steps, to confirm the presence of the added molecules. Measurements were performed with an ALPHA300 R confocal Raman microscope (WITec) using 532 nm laser light for excitation at room temperature. The laser beam was focused on the sample with 50x (NA = 0.7) and 100x (NA = 0.9 DIC) lenses (Zeiss). Single acquisitions and large-area scans were performed using a 600 grooves/mm grating with 3 mW laser power. Raman data analysis was performed with Project FIVE+ software (WITec).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContact angle measurements:\u003c/strong\u003e Monolayer graphene was transferred to 2x2 cm\u003csup\u003e2\u003c/sup\u003e Si/SiO\u003csub\u003e2\u003c/sub\u003e substrates and then functionalized, as described above. Static wetting angle measurements were conducted with a drop shape analysis-contact angle (DSA-CA) system using DI water as the liquid medium to characterize each functionalization stage. A 3 µL water droplet was deposited on the surface of the sample at a dosing rate of 600 µL/min, and the water contact angle (WCA) was calculated. All the measurements were carried out in air at room temperature. For each analysis, a minimum of 10 measurements were performed, and the average was calculated. Droplet profiles were automatically detected and analyzed by fitting to the Young–Laplace equation in DSA3® software (Kruss).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEG-GFET measurements: \u003c/strong\u003eA signal acquisition platform connected the PCB with the wire-bonded chips to the computer interface and traced the EG-GFET transfer curves. The in-house developed electronic platform has a microcontroller that controls the digital-to-analog converter (DAC), one regulated voltage source generating the gate voltage sweeps (V\u003csub\u003eGS\u003c/sub\u003e), and another source that applies a constant source-drain voltage (V\u003csub\u003eDS\u003c/sub\u003e) between the source and drain contacts of each device. The source-drain current (I\u003csub\u003eDS\u003c/sub\u003e) is converted into a voltage by an analog-to-digital converter (ADC) before the digital data is sent to the computer via a USB connection. The transfer curve of each EG–GFET was measured after each functionalization process step by applying a V\u003csub\u003eDS\u003c/sub\u003e = 15 mV and measuring I\u003csub\u003eDS\u003c/sub\u003e in 500 V\u003csub\u003eGS\u003c/sub\u003e steps between -0.3 and 0.7 V. The signal is the shift of the minimum conductance point, equated to the Dirac point (ΔV\u003csub\u003eDIRAC\u003c/sub\u003e), relative to the baseline for each analyte concentration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eX-ray photoelectron spectroscopy (XPS): \u003c/strong\u003eXPS spectra were acquired with an ESCALAB 250 XI (Thermo Fisher Scientific, Source: Al Kα 1486.6 eV, 650 μm spot size, Source Power: 300 W, Pass energy: 160 eV – survey; 10 eV – high resolution) system with an analysis chamber maintained in ultrahigh vacuum (UHV ~ 5 × 10\u003csup\u003e-10\u003c/sup\u003e mbar) conditions where the functionalized monolayer graphene on the 2x2 cm\u003csup\u003e2\u003c/sup\u003e Si/SiO\u003csub\u003e2\u003c/sub\u003e substrates was placed. The spectra were referenced to the graphene C‒C bonds at 284.5 eV. The spectra were analyzed with CasaXPS software (Casa Software Ltd., version 2.3.25) \u003csup\u003e94\u003c/sup\u003e, and the residual background was eliminated via the Shirley method across the binding energy range of the peaks of interest.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge Mr. Tiago Pereira from the 2D Materials and Devices group at INL for designing the python code used for linearly transforming the 2D \u003cem\u003evs\u003c/em\u003e G frequency plots into mechanical strain \u003cem\u003evs\u003c/em\u003e charge density plots.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eWe acknowledge the financial support of the project “2DM4EH” with reference DRI/India/0664/2020, funded by FCT—Science and Technology Foundation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlMajali, A. S.; Richards, T.; Yusuf, S. W.; Telgenkamp, B. Vascular Service Provision during the COVID-19 Pandemic Worsened Major Amputation Rates in Socially Deprived Diabetic Populations. \u003cem\u003eFront Endocrinol (Lausanne)\u003c/em\u003e\u003cstrong\u003e2024\u003c/strong\u003e, \u003cem\u003e15\u003c/em\u003e, 1304436.\u003c/li\u003e\n \u003cli\u003eTsutsui, H.; Tanaka, G.; Kondo, K. Diabetes Mellitus. In \u003cem\u003eSocial Determinants of Health in Non-communicable Diseases: Case Studies from Japan\u003c/em\u003e; Kondo, K., Ed.; Springer Singapore: Singapore, 2020; pp 73\u0026ndash;86. https://doi.org/10.1007/978-981-15-1831-7_8.\u003c/li\u003e\n \u003cli\u003eAssociation, A. D. Diagnosis and Classification of Diabetes Mellitus. \u003cem\u003eDiabetes Care\u003c/em\u003e\u003cstrong\u003e2010\u003c/strong\u003e, \u003cem\u003e33\u003c/em\u003e (Supplement_1), S62\u0026ndash;S69.\u003c/li\u003e\n \u003cli\u003eBruen, D.; Delaney, C.; Florea, L.; Diamond, D. Glucose Sensing for Diabetes Monitoring: Recent Developments. \u003cem\u003eSensors\u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e17\u003c/em\u003e (8), 1866.\u003c/li\u003e\n \u003cli\u003eGong, P.; Li, X.; Zhou, X.; Zhang, Y.; Chen, N.; Wang, S.; Zhang, S.; Zhao, Y. Optical Fiber Sensors for Glucose Concentration Measurement: A Review. \u003cem\u003eOpt Laser Technol\u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e139\u003c/em\u003e, 106981.\u003c/li\u003e\n \u003cli\u003eKim, J.; Khan, M.; Park, S.-Y. Glucose Sensor Using Liquid-Crystal Droplets Made by Microfluidics. \u003cem\u003eACS Appl Mater Interfaces\u003c/em\u003e\u003cstrong\u003e2013\u003c/strong\u003e, \u003cem\u003e5\u003c/em\u003e (24), 13135\u0026ndash;13139.\u003c/li\u003e\n \u003cli\u003eViswanathan, S.; Narayanan, T. N.; Aran, K.; Fink, K. D.; Paredes, J.; Ajayan, P. M.; Filipek, S.; Miszta, P.; Tekin, H. C.; Inci, F. Graphene\u0026ndash;Protein Field Effect Biosensors: Glucose Sensing. \u003cem\u003eMaterials Today\u003c/em\u003e\u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e18\u003c/em\u003e (9), 513\u0026ndash;522.\u003c/li\u003e\n \u003cli\u003eTeymourian, H.; Barfidokht, A.; Wang, J. Electrochemical Glucose Sensors in Diabetes Management: An Updated Review (2010\u0026ndash;2020). \u003cem\u003eChem Soc Rev\u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e49\u003c/em\u003e (21), 7671\u0026ndash;7709.\u003c/li\u003e\n \u003cli\u003eWang, G.; He, X.; Wang, L.; Gu, A.; Huang, Y.; Fang, B.; Geng, B.; Zhang, X. Non-Enzymatic Electrochemical Sensing of Glucose. \u003cem\u003eMicrochimica Acta\u003c/em\u003e\u003cstrong\u003e2013\u003c/strong\u003e, \u003cem\u003e180\u003c/em\u003e, 161\u0026ndash;186.\u003c/li\u003e\n \u003cli\u003eWei, L.; Li, J.-H.; Chen, C.; Lv, Q.; Lv, R.; Ma, L.; Shen, W.; Kang, F.; Huang, Z.-H. Ultrasensitive Non-Enzymatic Glucose Sensors Based on Hybrid Reduced Graphene Oxide and Carbonized Silk Fabric Electrodes Decorated with Cu Nanoflowers. \u003cem\u003eJ Electrochem Soc\u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e167\u003c/em\u003e (12), 127501.\u003c/li\u003e\n \u003cli\u003eGuo, M. Q.; Hong, H. S.; Tang, X. N.; Fang, H. D.; Xu, X. H. Ultrasonic Electrodeposition of Platinum Nanoflowers and Their Application in Nonenzymatic Glucose Sensors. \u003cem\u003eElectrochim Acta\u003c/em\u003e\u003cstrong\u003e2012\u003c/strong\u003e, \u003cem\u003e63\u003c/em\u003e, 1\u0026ndash;8.\u003c/li\u003e\n \u003cli\u003eKarami, P.; Babapoor, A.; Shoghi, B. M.; Johari, A. M. Comparison of Electrochemical Enzymatic and Non-Enzymatic Glucose Sensors Based on Literature Review. \u003cstrong\u003e2021\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eZhu, H.; Li, L.; Zhou, W.; Shao, Z.; Chen, X. Advances in Non-Enzymatic Glucose Sensors Based on Metal Oxides. \u003cem\u003eJ Mater Chem B\u003c/em\u003e\u003cstrong\u003e2016\u003c/strong\u003e, \u003cem\u003e4\u003c/em\u003e (46), 7333\u0026ndash;7349.\u003c/li\u003e\n \u003cli\u003eMiya, N.; Machogo-Phao, L. F. E.; Ntsendwana, B. Exploring Copper Oxide and Copper Sulfide for Non-Enzymatic Glucose Sensors: Current Progress and Future Directions. \u003cem\u003eMicromachines (Basel)\u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e14\u003c/em\u003e (10), 1849.\u003c/li\u003e\n \u003cli\u003eLee, H.; Hong, Y. J.; Baik, S.; Hyeon, T.; Kim, D. Enzyme‐based Glucose Sensor: From Invasive to Wearable Device. \u003cem\u003eAdv Healthc Mater\u003c/em\u003e\u003cstrong\u003e2018\u003c/strong\u003e, \u003cem\u003e7\u003c/em\u003e (8), 1701150.\u003c/li\u003e\n \u003cli\u003eKim, I.; Kim, C.; Lee, D.; Lee, S. W.; Lee, G.; Yoon, D. S. A Bio-Inspired Highly Selective Enzymatic Glucose Sensor Using a Red Blood Cell Membrane. \u003cem\u003eAnalyst\u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e145\u003c/em\u003e (6), 2125\u0026ndash;2132.\u003c/li\u003e\n \u003cli\u003eJohnston, L.; Wang, G.; Hu, K.; Qian, C.; Liu, G. Advances in Biosensors for Continuous Glucose Monitoring towards Wearables. \u003cem\u003eFront Bioeng Biotechnol\u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e9\u003c/em\u003e, 733810.\u003c/li\u003e\n \u003cli\u003eSolnica, B.; Naskalski, J. W.; Sieradzki, J. Analytical Performance of Glucometers Used for Routine Glucose Self-Monitoring of Diabetic Patients. \u003cem\u003eClinica Chimica Acta\u003c/em\u003e\u003cstrong\u003e2003\u003c/strong\u003e, \u003cem\u003e331\u003c/em\u003e (1\u0026ndash;2), 29\u0026ndash;35.\u003c/li\u003e\n \u003cli\u003eShlomowitz, A.; Feher, M. D. Anxiety Associated with Self Monitoring of Capillary Blood Glucose. \u003cem\u003eBritish Journal of Diabetes\u003c/em\u003e\u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e14\u003c/em\u003e (2), 60\u0026ndash;63.\u003c/li\u003e\n \u003cli\u003eSen, D. K.; Sarin, G. S. Tear Glucose Levels in Normal People and in Diabetic Patients. \u003cem\u003eBr J Ophthalmol\u003c/em\u003e\u003cstrong\u003e1980\u003c/strong\u003e, \u003cem\u003e64\u003c/em\u003e (9), 693.\u003c/li\u003e\n \u003cli\u003eSacks, D. B.; Arnold, M.; Bakris, G. L.; Bruns, D. E.; Horvath, A. R.; Lernmark, \u0026Aring;.; Metzger, B. E.; Nathan, D. M.; Kirkman, M. S. Guidelines and Recommendations for Laboratory Analysis in the Diagnosis and Management of Diabetes Mellitus. \u003cem\u003eClin Chem\u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e69\u003c/em\u003e (8), 808\u0026ndash;868.\u003c/li\u003e\n \u003cli\u003eLane, J. D.; Krumholz, D. M.; Sack, R. A.; Morris, C. Tear Glucose Dynamics in Diabetes Mellitus. \u003cem\u003eCurr Eye Res\u003c/em\u003e\u003cstrong\u003e2006\u003c/strong\u003e, \u003cem\u003e31\u003c/em\u003e (11), 895\u0026ndash;901.\u003c/li\u003e\n \u003cli\u003eKim, I. S.; Kim, H. T.; Kim, E.-J.; Lee, E. J. A Comparative Study of the Concentration of Salivary and Blood Glucose in Normal and Diabetic Subjects. \u003cem\u003eBiomedical Science Letters\u003c/em\u003e\u003cstrong\u003e2013\u003c/strong\u003e, \u003cem\u003e19\u003c/em\u003e (2), 105\u0026ndash;111.\u003c/li\u003e\n \u003cli\u003eVasconcelos, A. C. U.; Soares, M. S. M.; Almeida, P. C.; Soares, T. C. Comparative Study of the Concentration of Salivary and Blood Glucose in Type 2 Diabetic Patients. \u003cem\u003eJ Oral Sci\u003c/em\u003e\u003cstrong\u003e2010\u003c/strong\u003e, \u003cem\u003e52\u003c/em\u003e (2), 293\u0026ndash;298.\u003c/li\u003e\n \u003cli\u003eAbrantes, M.; Rodrigues, D.; Domingues, T.; Nemala, S. S.; Monteiro, P.; Borme, J.; Alpuim, P.; Jacinto, L. Ultrasensitive Dopamine Detection with Graphene Aptasensor Multitransistor Arrays. \u003cem\u003eJ Nanobiotechnology\u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e20\u003c/em\u003e (1), 495.\u003c/li\u003e\n \u003cli\u003eB\u0026eacute;raud, A.; Sauvage, M.; Baz\u0026aacute;n, C. M.; Tie, M.; Bencherif, A.; Bouilly, D. Graphene Field-Effect Transistors as Bioanalytical Sensors: Design, Operation and Performance. \u003cem\u003eAnalyst\u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e146\u003c/em\u003e (2), 403\u0026ndash;428.\u003c/li\u003e\n \u003cli\u003ePe\u0026ntilde;a-Bahamonde, J.; Nguyen, H. N.; Fanourakis, S. K.; Rodrigues, D. F. Recent Advances in Graphene-Based Biosensor Technology with Applications in Life Sciences. \u003cem\u003eJ Nanobiotechnology\u003c/em\u003e\u003cstrong\u003e2018\u003c/strong\u003e, \u003cem\u003e16\u003c/em\u003e, 1\u0026ndash;17.\u003c/li\u003e\n \u003cli\u003eSong, Y.; Luo, Y.; Zhu, C.; Li, H.; Du, D.; Lin, Y. Recent Advances in Electrochemical Biosensors Based on Graphene Two-Dimensional Nanomaterials. \u003cem\u003eBiosens Bioelectron\u003c/em\u003e\u003cstrong\u003e2016\u003c/strong\u003e, \u003cem\u003e76\u003c/em\u003e, 195\u0026ndash;212.\u003c/li\u003e\n \u003cli\u003eBai, Y.; Xu, T.; Zhang, X. Graphene-Based Biosensors for Detection of Biomarkers. \u003cem\u003eMicromachines (Basel)\u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e11\u003c/em\u003e (1), 60.\u003c/li\u003e\n \u003cli\u003eFakih, I.; Durnan, O.; Mahvash, F.; Napal, I.; Centeno, A.; Zurutuza, A.; Yargeau, V.; Szkopek, T. Selective Ion Sensing with High Resolution Large Area Graphene Field Effect Transistor Arrays. \u003cem\u003eNat Commun\u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e11\u003c/em\u003e (1), 3226.\u003c/li\u003e\n \u003cli\u003eWu, G.; Dai, Z.; Tang, X.; Lin, Z.; Lo, P. K.; Meyyappan, M.; Lai, K. W. C. Graphene Field‐effect Transistors for the Sensitive and Selective Detection of Escherichia Coli Using Pyrene‐tagged DNA Aptamer. \u003cem\u003eAdv Healthc Mater\u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e6\u003c/em\u003e (19), 1700736.\u003c/li\u003e\n \u003cli\u003eForsyth, R.; Devadoss, A.; Guy, O. J. Graphene Field Effect Transistors for Biomedical Applications: Current Status and Future Prospects. \u003cem\u003eDiagnostics\u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e7\u003c/em\u003e (3), 45.\u003c/li\u003e\n \u003cli\u003eYan, F.; Zhang, M.; Li, J. Solution‐gated Graphene Transistors for Chemical and Biological Sensors. \u003cem\u003eAdv Healthc Mater\u003c/em\u003e\u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e3\u003c/em\u003e (3), 313\u0026ndash;331.\u003c/li\u003e\n \u003cli\u003eOhno, Y.; Maehashi, K.; Yamashiro, Y.; Matsumoto, K. Electrolyte-Gated Graphene Field-Effect Transistors for Detecting PH and Protein Adsorption. \u003cem\u003eNano Lett\u003c/em\u003e\u003cstrong\u003e2009\u003c/strong\u003e, \u003cem\u003e9\u003c/em\u003e (9), 3318\u0026ndash;3322. https://doi.org/10.1021/nl901596m.\u003c/li\u003e\n \u003cli\u003eBauer, J. A.; Z\u0026aacute;mock\u0026aacute;, M.; Majt\u0026aacute;n, J.; Bauerov\u0026aacute;-Hlinkov\u0026aacute;, V. Glucose Oxidase, an Enzyme \u0026ldquo;Ferrari\u0026rdquo;: Its Structure, Function, Production and Properties in the Light of Various Industrial and Biotechnological Applications. \u003cem\u003eBiomolecules\u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e12\u003c/em\u003e (3), 472.\u003c/li\u003e\n \u003cli\u003eSmejkal, G. B.; Kakumanu, S. Enzymes and Their Turnover Numbers. \u003cem\u003eExpert Rev Proteomics\u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e16\u003c/em\u003e (7), 543\u0026ndash;544.\u003c/li\u003e\n \u003cli\u003eRoskoski, R. Michaelis-Menten Kinetics. \u003cem\u003exPharm: The Comprehensive Pharmacology Reference\u003c/em\u003e\u003cstrong\u003e2007\u003c/strong\u003e, 1\u0026ndash;10. https://doi.org/10.1016/B978-008055232-3.60041-8.\u003c/li\u003e\n \u003cli\u003ePark, J.; Kim, J.; Kim, S.-Y.; Cheong, W. H.; Jang, J.; Park, Y.-G.; Na, K.; Kim, Y.-T.; Heo, J. H.; Lee, C. Y. Soft, Smart Contact Lenses with Integrations of Wireless Circuits, Glucose Sensors, and Displays. \u003cem\u003eSci Adv\u003c/em\u003e\u003cstrong\u003e2018\u003c/strong\u003e, \u003cem\u003e4\u003c/em\u003e (1), eaap9841.\u003c/li\u003e\n \u003cli\u003eLeyton, P.; G\u0026oacute;mez-Jeria, J. S.; Sanchez-Cortes, S.; Domingo, C.; Campos-Vallette, M. Carbon Nanotube Bundles as Molecular Assemblies for the Detection of Polycyclic Aromatic Hydrocarbons: Surface-Enhanced Resonance Raman Spectroscopy and Theoretical Studies. \u003cem\u003eJ Phys Chem B\u003c/em\u003e\u003cstrong\u003e2006\u003c/strong\u003e, \u003cem\u003e110\u003c/em\u003e (13), 6470\u0026ndash;6474.\u003c/li\u003e\n \u003cli\u003eLiu, Y.; Yuan, L.; Yang, M.; Zheng, Y.; Li, L.; Gao, L.; Nerngchamnong, N.; Nai, C. T.; Sangeeth, C. S. S.; Feng, Y. P. Giant Enhancement in Vertical Conductivity of Stacked CVD Graphene Sheets by Self-Assembled Molecular Layers. \u003cem\u003eNat Commun\u003c/em\u003e\u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e5\u003c/em\u003e (1), 5461.\u003c/li\u003e\n \u003cli\u003eFernandes, E.; Cabral, P. D.; Campos, R.; Machado, G.; Cerqueira, M. F.; Sousa, C.; Freitas, P. P.; Borme, J.; Petrovykh, D. Y.; Alpuim, P. Functionalization of Single-Layer Graphene for Immunoassays. \u003cem\u003eAppl Surf Sci\u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e480\u003c/em\u003e, 709\u0026ndash;716. https://doi.org/10.1016/j.apsusc.2019.03.004.\u003c/li\u003e\n \u003cli\u003eWu, G.; Tang, X.; Meyyappan, M.; Lai, K. W. C. Doping Effects of Surface Functionalization on Graphene with Aromatic Molecule and Organic Solvents. \u003cem\u003eAppl Surf Sci\u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e425\u003c/em\u003e, 713\u0026ndash;721.\u003c/li\u003e\n \u003cli\u003eYang, K. L.; Lee, J. O.; Choo, H.; Yang, F. Can Raman Shift Be Used To Characterize the Mechanical Property of Graphene? \u003cem\u003eThe Journal of Physical Chemistry C\u003c/em\u003e\u003cstrong\u003e2018\u003c/strong\u003e, \u003cem\u003e122\u003c/em\u003e (42), 24467\u0026ndash;24474.\u003c/li\u003e\n \u003cli\u003eNi, Z. H.; Yu, T.; Lu, Y. H.; Wang, Y. Y.; Feng, Y. P.; Shen, Z. X. Uniaxial Strain on Graphene: Raman Spectroscopy Study and Band-Gap Opening. \u003cem\u003eACS Nano\u003c/em\u003e\u003cstrong\u003e2008\u003c/strong\u003e, \u003cem\u003e2\u003c/em\u003e (11), 2301\u0026ndash;2305.\u003c/li\u003e\n \u003cli\u003eLee, J. E.; Ahn, G.; Shim, J.; Lee, Y. S.; Ryu, S. Optical Separation of Mechanical Strain from Charge Doping in Graphene. \u003cem\u003eNat Commun\u003c/em\u003e\u003cstrong\u003e2012\u003c/strong\u003e, \u003cem\u003e3\u003c/em\u003e (1), 1024.\u003c/li\u003e\n \u003cli\u003eBarin, G. B.; Song, Y.; de F\u0026aacute;tima Gimenez, I.; Souza Filho, A. G.; Barreto, L. S.; Kong, J. Optimized Graphene Transfer: Influence of Polymethylmethacrylate (PMMA) Layer Concentration and Baking Time on Graphene Final Performance. \u003cem\u003eCarbon N Y\u003c/em\u003e\u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e84\u003c/em\u003e, 82\u0026ndash;90.\u003c/li\u003e\n \u003cli\u003eDong, X.; Fu, D.; Fang, W.; Shi, Y.; Chen, P.; Li, L. J. Doping Single-Layer Graphene with Aromatic Molecules. \u003cem\u003eSmall\u003c/em\u003e\u003cstrong\u003e2009\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eDas, A.; Pisana, S.; Chakraborty, B.; Piscanec, S.; Saha, S. K.; Waghmare, U. V; Novoselov, K. S.; Krishnamurthy, H. R.; Geim, A. K.; Ferrari, A. C. Monitoring Dopants by Raman Scattering in an Electrochemically Top-Gated Graphene Transistor. \u003cem\u003eNat Nanotechnol\u003c/em\u003e\u003cstrong\u003e2008\u003c/strong\u003e, \u003cem\u003e3\u003c/em\u003e (4), 210\u0026ndash;215.\u003c/li\u003e\n \u003cli\u003eCherr\u0026eacute;, S.; Fernandes, E.; Germano, J.; Dias, T.; Cardoso, S.; Piedade, M. S.; Rozlosnik, N.; Oliveira, M. I.; Freitas, P. P. Rapid and Specific Detection of Cell-Derived Microvesicles Using a Magnetoresistive Biochip. \u003cem\u003eAnalyst\u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e142\u003c/em\u003e (6), 979\u0026ndash;986.\u003c/li\u003e\n \u003cli\u003eFernandes, E.; Martins, V. C.; N\u0026oacute;brega, C.; Carvalho, C. M.; Cardoso, F. A.; Cardoso, S.; Dias, J.; Deng, D.; Kluskens, L. D.; Freitas, P. P. A Bacteriophage Detection Tool for Viability Assessment of Salmonella Cells. \u003cem\u003eBiosens Bioelectron\u003c/em\u003e\u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e52\u003c/em\u003e, 239\u0026ndash;246.\u003c/li\u003e\n \u003cli\u003eKamyshny, A.; Feldman, A.; Baszkin, A.; Boissonnade, M. M.; Rosilio, V.; Magdassi, S. Chemically Modified Glucose Oxidase with Enhanced Hydrophobicity: Adsorption at Polystyrene, Silica, and Silica Coated by Lipid Monolayers. \u003cem\u003eJ Colloid Interface Sci\u003c/em\u003e\u003cstrong\u003e1999\u003c/strong\u003e, \u003cem\u003e218\u003c/em\u003e (1), 300\u0026ndash;308. https://doi.org/https://doi.org/10.1006/jcis.1999.6440.\u003c/li\u003e\n \u003cli\u003eGoniszewski, S.; Adabi, M.; Shaforost, O.; Hanham, S. M.; Hao, L.; Klein, N. Correlation of P-Doping in CVD Graphene with Substrate Surface Charges. \u003cem\u003eSci Rep\u003c/em\u003e\u003cstrong\u003e2016\u003c/strong\u003e, \u003cem\u003e6\u003c/em\u003e (1), 22858.\u003c/li\u003e\n \u003cli\u003eAshraf, A.; Wu, Y.; Wang, M. C.; Yong, K.; Sun, T.; Jing, Y.; Haasch, R. T.; Aluru, N. R.; Nam, S. Doping-Induced Tunable Wettability and Adhesion of Graphene. \u003cem\u003eNano Lett\u003c/em\u003e\u003cstrong\u003e2016\u003c/strong\u003e, \u003cem\u003e16\u003c/em\u003e (7), 4708\u0026ndash;4712.\u003c/li\u003e\n \u003cli\u003eWagner, C. D.; Naumkin, A. V; Kraut-Vass, A.; Allison, J. W.; Powell, C. J.; Rumble Jr, J. R. NIST Standard Reference Database 20. Version 2003.\u003c/li\u003e\n \u003cli\u003eSilvestri, A.; Zayas-Arrabal, J.; Vera-Hidalgo, M.; Di Silvio, D.; Wetzl, C.; Martinez-Moro, M.; Zurutuza, A.; Torres, E.; Centeno, A.; Maestre, A. Ultrasensitive Detection of SARS-CoV-2 Spike Protein by Graphene Field-Effect Transistors. \u003cem\u003eNanoscale\u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e15\u003c/em\u003e (3), 1076\u0026ndash;1085.\u003c/li\u003e\n \u003cli\u003eKwong Hong Tsang, D.; Lieberthal, T. J.; Watts, C.; Dunlop, I. E.; Ramadan, S.; del Rio Hernandez, A. E.; Klein, N. Chemically Functionalised Graphene FET Biosensor for the Label-Free Sensing of Exosomes. \u003cem\u003eSci Rep\u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e9\u003c/em\u003e (1), 1\u0026ndash;10.\u003c/li\u003e\n \u003cli\u003eFaisal, S. N.; Haque, E.; Noorbehesht, N.; Zhang, W.; Harris, A. T.; Church, T. L.; Minett, A. I. Pyridinic and Graphitic Nitrogen-Rich Graphene for High-Performance Supercapacitors and Metal-Free Bifunctional Electrocatalysts for ORR and OER. \u003cem\u003eRSC Adv\u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e7\u003c/em\u003e (29), 17950\u0026ndash;17958.\u003c/li\u003e\n \u003cli\u003eWu, G.; Tang, X.; Meyyappan, M.; Lai, K. W. C. Chemical Functionalization of Graphene with Aromatic Molecule. In \u003cem\u003e2015 IEEE 15th International Conference on Nanotechnology (IEEE-NANO)\u003c/em\u003e; IEEE, 2015; pp 1324\u0026ndash;1327.\u003c/li\u003e\n \u003cli\u003eXia, Y.; Sun, L.; Eyley, S.; Daelemans, B.; Thielemans, W.; Seibel, J.; De Feyter, S. Grafting Ink for Direct Writing: Solvation Activated Covalent Functionalization of Graphene. \u003cem\u003eAdvanced Science\u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e9\u003c/em\u003e (19). https://doi.org/10.1002/advs.202105017.\u003c/li\u003e\n \u003cli\u003eZhang, W.; Wu, L.; Li, Z.; Liu, Y. Doped Graphene: Synthesis, Properties and Bioanalysis. \u003cem\u003eRSC Adv\u003c/em\u003e\u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e5\u003c/em\u003e (61), 49521\u0026ndash;49533.\u003c/li\u003e\n \u003cli\u003eSingh, K. K. A DFT Studies on Absorbing and Sensing Possibilities of Glucose on Graphene Surface Doped with Ag, Au, Cu, Ni \u0026amp; Pt Atoms. \u003cem\u003eBiosens Bioelectron X\u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e13\u003c/em\u003e, 100287.\u003c/li\u003e\n \u003cli\u003eAhmadianyazdi, A.; Nguyen, N. H. L.; Xu, J.; Berry, V. Glucose Measurement via Raman Spectroscopy of Graphene: Principles and Operation. \u003cem\u003eNano Res\u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e15\u003c/em\u003e (10), 8697\u0026ndash;8704.\u003c/li\u003e\n \u003cli\u003eKwak, Y. H.; Choi, D. S.; Kim, Y. N.; Kim, H.; Yoon, D. H.; Ahn, S.-S.; Yang, J.-W.; Yang, W. S.; Seo, S. Flexible Glucose Sensor Using CVD-Grown Graphene-Based Field Effect Transistor. \u003cem\u003eBiosens Bioelectron\u003c/em\u003e\u003cstrong\u003e2012\u003c/strong\u003e, \u003cem\u003e37\u003c/em\u003e (1), 82\u0026ndash;87.\u003c/li\u003e\n \u003cli\u003eKislenko, V. A.; Pavlov, S. V; Kislenko, S. A. Influence of Defects in Graphene on Electron Transfer Kinetics: The Role of the Surface Electronic Structure. \u003cem\u003eElectrochim Acta\u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e341\u003c/em\u003e, 136011.\u003c/li\u003e\n \u003cli\u003eYou, X.; Pak, J. J. Graphene-Based Field Effect Transistor Enzymatic Glucose Biosensor Using Silk Protein for Enzyme Immobilization and Device Substrate. \u003cem\u003eSens Actuators B Chem\u003c/em\u003e\u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e202\u003c/em\u003e, 1357\u0026ndash;1365.\u003c/li\u003e\n \u003cli\u003eSalvo, P.; Melai, B.; Calisi, N.; Paoletti, C.; Bellagambi, F.; Kirchhain, A.; Trivella, M. G.; Fuoco, R.; Di Francesco, F. Graphene-Based Devices for Measuring PH. \u003cem\u003eSens Actuators B Chem\u003c/em\u003e\u003cstrong\u003e2018\u003c/strong\u003e, \u003cem\u003e256\u003c/em\u003e, 976\u0026ndash;991. https://doi.org/https://doi.org/10.1016/j.snb.2017.10.037.\u003c/li\u003e\n \u003cli\u003eFenoy, G. E.; Marmisoll\u0026eacute;, W. A.; Azzaroni, O.; Knoll, W. Acetylcholine Biosensor Based on the Electrochemical Functionalization of Graphene Field-Effect Transistors. \u003cem\u003eBiosens Bioelectron\u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e148\u003c/em\u003e, 111796.\u003c/li\u003e\n \u003cli\u003eBao, J.; Furumoto, K.; Yoshimoto, M.; Fukunaga, K.; Nakao, K. Competitive Inhibition by Hydrogen Peroxide Produced in Glucose Oxidation Catalyzed by Glucose Oxidase. \u003cem\u003eBiochem Eng J\u003c/em\u003e\u003cstrong\u003e2003\u003c/strong\u003e, \u003cem\u003e13\u003c/em\u003e (1), 69\u0026ndash;72.\u003c/li\u003e\n \u003cli\u003eKleppe, K. The Effect of Hydrogen Peroxide on Glucose Oxidase from Aspergillus Niger. \u003cem\u003eBiochemistry\u003c/em\u003e\u003cstrong\u003e1966\u003c/strong\u003e, \u003cem\u003e5\u003c/em\u003e (1), 139\u0026ndash;143.\u003c/li\u003e\n \u003cli\u003eVon Woedtke, T.; Fischer, U.; Abel, P. Glucose Oxidase Electrodes: Effect of Hydrogen Peroxide on Enzyme Activity? \u003cem\u003eBiosens Bioelectron\u003c/em\u003e\u003cstrong\u003e1994\u003c/strong\u003e, \u003cem\u003e9\u003c/em\u003e (1), 65\u0026ndash;71.\u003c/li\u003e\n \u003cli\u003eGreenfield, P. F.; Kittrell, J. R.; Laurence, R. L. Inactivation of Immobilized Glucose Oxidase by Hydrogen Peroxide. \u003cem\u003eAnal Biochem\u003c/em\u003e\u003cstrong\u003e1975\u003c/strong\u003e, \u003cem\u003e65\u003c/em\u003e (1\u0026ndash;2), 109\u0026ndash;124.\u003c/li\u003e\n \u003cli\u003eRichards, J. E.; Mazzeffi, M. A.; Massey, M. S.; Rock, P.; Galvagno, S. M.; Scalea, T. M. The Bitter and the Sweet: Relationship of Lactate, Glucose, and Mortality after Severe Blunt Trauma. \u003cem\u003eAnesth Analg\u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e133\u003c/em\u003e (2), 455\u0026ndash;461.\u003c/li\u003e\n \u003cli\u003eWu, S.; Zhang, Y.; Liu, L.; Fan, W. Conifer-like Copper-Carboxylated Graphene Nanocomposites Modified Electrode for Sensitive Nonenzymatic Glucose Biosensing with Very Low Limit of Detection. \u003cem\u003eMater Lett\u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e276\u003c/em\u003e, 128253. https://doi.org/https://doi.org/10.1016/j.matlet.2020.128253.\u003c/li\u003e\n \u003cli\u003eCao, F.; Guo, S.; Ma, H.; Yang, G.; Yang, S.; Gong, J. Highly Sensitive Nonenzymatic Glucose Sensor Based on Electrospun Copper Oxide-Doped Nickel Oxide Composite Microfibers. \u003cem\u003eTalanta\u003c/em\u003e\u003cstrong\u003e2011\u003c/strong\u003e, \u003cem\u003e86\u003c/em\u003e (1), 214\u0026ndash;220. https://doi.org/10.1016/j.talanta.2011.09.003.\u003c/li\u003e\n \u003cli\u003eLuo, J.; Jiang, S.; Zhang, H.; Jiang, J.; Liu, X. A Novel Non-Enzymatic Glucose Sensor Based on Cu Nanoparticle Modified Graphene Sheets Electrode. \u003cem\u003eAnal Chim Acta\u003c/em\u003e\u003cstrong\u003e2012\u003c/strong\u003e, \u003cem\u003e709\u003c/em\u003e, 47\u0026ndash;53. https://doi.org/https://doi.org/10.1016/j.aca.2011.10.025.\u003c/li\u003e\n \u003cli\u003eWang, F.; Lu, M.; Yuan, H.; Zhang, Y.; Ji, W.; Sun, C.; Peng, W. PM Level and Large Dynamic Range Glucose Detection Based on a Sandwich Type Plasmonic Fiber Sensor. \u003cem\u003eJournal of Lightwave Technology\u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e39\u003c/em\u003e (12), 3882\u0026ndash;3889. https://doi.org/10.1109/JLT.2020.3039776.\u003c/li\u003e\n \u003cli\u003eHernaez, M.; Acevedo, B.; Mayes, A. G.; Melendi-Espina, S. High-Performance Optical Fiber Humidity Sensor Based on Lossy Mode Resonance Using a Nanostructured Polyethylenimine and Graphene Oxide Coating. \u003cem\u003eSens Actuators B Chem\u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e286\u003c/em\u003e, 408\u0026ndash;414.\u003c/li\u003e\n \u003cli\u003eChen, X.; Lin, W.; Xu, P.; Chen, L.; Meng, W.; Hu, X.; qu, H.; Sun, J.; Cui, Y. FM-Level Detection of Glucose Using a Grating Based Sensor Enhanced With Graphene Oxide. \u003cem\u003eJournal of Lightwave Technology\u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003ePP\u003c/em\u003e, 1. https://doi.org/10.1109/JLT.2022.3174039.\u003c/li\u003e\n \u003cli\u003eOja, S. M.; Feldman, B.; Eshoo, M. W. Method for Low Nanomolar Concentration Analyte Sensing Using Electrochemical Enzymatic Biosensors. \u003cem\u003eAnal Chem\u003c/em\u003e\u003cstrong\u003e2018\u003c/strong\u003e, \u003cem\u003e90\u003c/em\u003e (3), 1536\u0026ndash;1541. https://doi.org/10.1021/acs.analchem.7b04075.\u003c/li\u003e\n \u003cli\u003eJiang, B.; Zhou, K.; Wang, C.; Sun, Q.; Yin, G.; Tai, Z.; Wilson, K.; Zhao, J.; Zhang, L. Label-Free Glucose Biosensor Based on Enzymatic Graphene Oxide-Functionalized Tilted Fiber Grating. \u003cem\u003eSens Actuators B Chem\u003c/em\u003e\u003cstrong\u003e2018\u003c/strong\u003e, \u003cem\u003e254\u003c/em\u003e, 1033\u0026ndash;1039. https://doi.org/https://doi.org/10.1016/j.snb.2017.07.109.\u003c/li\u003e\n \u003cli\u003eLin, Y.; Lu, F.; Tu, Y.; Ren, Z. Glucose Biosensors Based on Carbon Nanotube Nanoelectrode Ensembles. \u003cem\u003eNano Lett\u003c/em\u003e\u003cstrong\u003e2004\u003c/strong\u003e, \u003cem\u003e4\u003c/em\u003e (2), 191\u0026ndash;195. https://doi.org/10.1021/nl0347233.\u003c/li\u003e\n \u003cli\u003eYou, X.; Pak, J. J. Graphene-Based Field Effect Transistor Enzymatic Glucose Biosensor Using Silk Protein for Enzyme Immobilization and Device Substrate. \u003cem\u003eSens Actuators B Chem\u003c/em\u003e\u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e202\u003c/em\u003e, 1357\u0026ndash;1365. https://doi.org/https://doi.org/10.1016/j.snb.2014.04.079.\u003c/li\u003e\n \u003cli\u003eUnnikrishnan, B.; Palanisamy, S.; Chen, S.-M. A Simple Electrochemical Approach to Fabricate a Glucose Biosensor Based on Graphene\u0026ndash;Glucose Oxidase Biocomposite. \u003cem\u003eBiosens Bioelectron\u003c/em\u003e\u003cstrong\u003e2013\u003c/strong\u003e, \u003cem\u003e39\u003c/em\u003e (1), 70\u0026ndash;75. https://doi.org/https://doi.org/10.1016/j.bios.2012.06.045.\u003c/li\u003e\n \u003cli\u003eHwa, K.-Y.; Subramani, B. Synthesis of Zinc Oxide Nanoparticles on Graphene\u0026ndash;Carbon Nanotube Hybrid for Glucose Biosensor Applications. \u003cem\u003eBiosens Bioelectron\u003c/em\u003e\u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e62\u003c/em\u003e, 127\u0026ndash;133. https://doi.org/https://doi.org/10.1016/j.bios.2014.06.023.\u003c/li\u003e\n \u003cli\u003eDawson, K.; Baudequin, M.; O\u0026rsquo;Riordan, A. Single On-Chip Gold Nanowires for Electrochemical Biosensing of Glucose. \u003cem\u003eAnalyst\u003c/em\u003e\u003cstrong\u003e2011\u003c/strong\u003e, \u003cem\u003e136\u003c/em\u003e (21), 4507\u0026ndash;4513. https://doi.org/10.1039/C1AN15279C.\u003c/li\u003e\n \u003cli\u003eLiu, H.; Qian, X.; Wang, S.; Li, Y.; Song, Y.; Zhu, D. Ultra-Sensitivity Glucose Sensor Based on Field Emitters. \u003cem\u003eNanoscale Res Lett\u003c/em\u003e\u003cstrong\u003e2009\u003c/strong\u003e, \u003cem\u003e4\u003c/em\u003e (10), 1141. https://doi.org/10.1007/s11671-009-9372-0.\u003c/li\u003e\n \u003cli\u003eKaryakin, A. A.; Gitelmacher, O. V; Karyakina, E. E. Prussian Blue-Based First-Generation Biosensor. A Sensitive Amperometric Electrode for Glucose. \u003cem\u003eAnal Chem\u003c/em\u003e\u003cstrong\u003e1995\u003c/strong\u003e, \u003cem\u003e67\u003c/em\u003e (14), 2419\u0026ndash;2423. https://doi.org/10.1021/ac00110a016.\u003c/li\u003e\n \u003cli\u003eKwak, Y. H.; Choi, D. S.; Kim, Y. N.; Kim, H.; Yoon, D. H.; Ahn, S.-S.; Yang, J.-W.; Yang, W. S.; Seo, S. Flexible Glucose Sensor Using CVD-Grown Graphene-Based Field Effect Transistor. \u003cem\u003eBiosens Bioelectron\u003c/em\u003e\u003cstrong\u003e2012\u003c/strong\u003e, \u003cem\u003e37\u003c/em\u003e (1), 82\u0026ndash;87. https://doi.org/https://doi.org/10.1016/j.bios.2012.04.042.\u003c/li\u003e\n \u003cli\u003eHuang, Y.; Dong, X.; Shi, Y.; Li, C. M.; Li, L.-J.; Chen, P. Nanoelectronic Biosensors Based on CVD Grown Graphene. \u003cem\u003eNanoscale\u003c/em\u003e\u003cstrong\u003e2010\u003c/strong\u003e, \u003cem\u003e2\u003c/em\u003e (8), 1485\u0026ndash;1488. https://doi.org/10.1039/C0NR00142B.\u003c/li\u003e\n \u003cli\u003eLu, J.; Do, I.; Drzal, L. T.; Worden, R. M.; Lee, I. Nanometal-Decorated Exfoliated Graphite Nanoplatelet Based Glucose Biosensors with High Sensitivity and Fast Response. \u003cem\u003eACS Nano\u003c/em\u003e\u003cstrong\u003e2008\u003c/strong\u003e, \u003cem\u003e2\u003c/em\u003e (9), 1825\u0026ndash;1832. https://doi.org/10.1021/nn800244k.\u003c/li\u003e\n \u003cli\u003eZhang, M.; Liao, C.; Mak, C. H.; You, P.; Mak, C. L.; Yan, F. Highly Sensitive Glucose Sensors Based on Enzyme-Modified Whole-Graphene Solution-Gated Transistors. \u003cem\u003eSci Rep\u003c/em\u003e\u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e5\u003c/em\u003e (1), 8311. https://doi.org/10.1038/srep08311.\u003c/li\u003e\n \u003cli\u003eZhong, S.; Jang, C.-H. Highly Sensitive and Selective Glucose Sensor Based on Ultraviolet-Treated Nematic Liquid Crystals. \u003cem\u003eBiosens Bioelectron\u003c/em\u003e\u003cstrong\u003e2014\u003c/strong\u003e, \u003cem\u003e59\u003c/em\u003e, 293\u0026ndash;299. https://doi.org/https://doi.org/10.1016/j.bios.2014.03.070.\u003c/li\u003e\n \u003cli\u003eLiao, C.-D.; Capasso, A.; Queir\u0026oacute;s, T.; Domingues, T.; Cerqueira, F.; Nicoara, N.; Borme, J.; Freitas, P.; Alpuim, P. Optimizing PMMA Solutions to Suppress Contamination in the Transfer of CVD Graphene for Batch Production. \u003cem\u003eBeilstein Journal of Nanotechnology\u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e13\u003c/em\u003e, 796\u0026ndash;806. https://doi.org/10.3762/bjnano.13.70.\u003c/li\u003e\n \u003cli\u003eFairley, N.; Fernandez, V.; Richard‐Plouet, M.; Guillot-Deudon, C.; Walton, J.; Smith, E.; Flahaut, D.; Greiner, M.; Biesinger, M.; Tougaard, S. Systematic and Collaborative Approach to Problem Solving Using X-Ray Photoelectron Spectroscopy. \u003cem\u003eApplied Surface Science Advances\u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e5\u003c/em\u003e, 100112.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"diabetes, glucose monitoring, non-invasiveness, graphene, field-effect transistors, selectivity","lastPublishedDoi":"10.21203/rs.3.rs-5581426/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5581426/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eDiabetes mellitus\u003c/em\u003e, a prevalent metabolic disorder affecting hundreds of millions worldwide, demands continuous glucose monitoring for effective management. Current blood glucose monitoring methods, such as commercial glucometers, though accurate, are invasive and uncomfortable, highlighting the need for non-invasive, ultra-sensitive alternatives. Here, we present a glucose sensing platform based on electrolyte-gated graphene field-effect transistors (EG-GFETs) functionalized with glucose oxidase enzymes for ultra-sensitive detection. Detailed material characterization by Raman and X-ray photoelectron spectroscopies confirms successful enzyme immobilization, with a marked increase in nitrogen content from 0.9% to 7.6% atomic concentration on the graphene surface, indicating substantial glucose oxidase coverage. Raman analysis reveals significant p-type doping and tensile strain on the graphene channel directly correlating with glucose concentration from 1 nanomolar to 1 millimolar. The EG-GFETs demonstrate an ultra-low limit-of-detection of 1 attomolar, with a consistent Dirac point voltage shift of +26 ± 4 mV and a linear response across six orders of magnitude (up to 1 picomolar, with a sensitivity of 10.6 mV/decade). The sensor maintains high selectivity in complex media, such as artificial tears (with a limit-of-detection of 100 attomolar), underscoring its potential for non-invasive continuous glucose monitoring applications, also in wearable format.\u003c/p\u003e","manuscriptTitle":"Graphene-based glucose sensors with an attomolar limit of detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 06:24:26","doi":"10.21203/rs.3.rs-5581426/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":"d8a0b30d-dffe-4a58-ab6e-27aef3d583a8","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41333425,"name":"Physical sciences/Nanoscience and technology/Nanoscale devices/Biosensors"},{"id":41333426,"name":"Physical sciences/Nanoscience and technology/Graphene/Electronic properties and devices"},{"id":41333427,"name":"Biological sciences/Biotechnology/Nanobiotechnology/Biosensors"}],"tags":[],"updatedAt":"2024-12-23T17:10:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-18 06:24:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5581426","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5581426","identity":"rs-5581426","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}