Effect of alkali, acid and thermal pretreatment techniques on biohydrogen production from organic fraction of municipal solid waste

Effect of alkali, acid and thermal pretreatment techniques on biohydrogen production from organic fraction of municipal solid waste | 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 Research Article Effect of alkali, acid and thermal pretreatment techniques on biohydrogen production from organic fraction of municipal solid waste S Shreyas, Ghurupreya Ramesh, Mohanakrishnan Logan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6906247/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract With the rising need for sustainable energy solutions and efficient waste management, converting organic waste into biohydrogen offers a dual benefit. This study explores the potential of the Organic Fraction of Municipal Solid Waste for hydrogen production, applying thermal, acid, and alkali pretreatment techniques to enhance substrate digestibility. Prior to the application of these techniques, a heat-shock pretreatment was employed on the anaerobic inoculum to suppress hydrogen-consuming microbes and favour hydrogenogenic activity. Among the strategies tested, alkali (followed by acid and thermal) pretreatment resulted in significant improvement in substrate solubilization, with the highest sCOD and volatile solids reduction. In Biochemical Hydrogen Potential assays, it yielded a peak value of about 190 mL H₂/g VS, nearly six times higher than the untreated control. GC-TCD analysis revealed rapid hydrogen evolution within the first 24 hours, while VFA profiling indicated acetic acid dominance, a known precursor for hydrogen production. These findings suggest that alkaline pretreatment combined with inoculum conditioning is a promising route for maximising hydrogen yields. Biohydrogen pre-treatment organic fractions of municipal solid waste anaerobic fermentation volatile fatty acids Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The global demand for energy is escalating rapidly due to population growth, industrialisation, and technological advancements. Currently, about 80% of primary energy is sourced from fossil fuels, contributing to serious environmental challenges, including greenhouse gas emissions, global warming, and air pollution. These issues are linked to around 7 million deaths annually and economic losses exceeding $ 225 billion (Sharma et al., 2021 ). Since the reliance on energy cannot be completely withdrawn, a sustainable shift to renewable energy allows tackling of economic and environmental challenges, simultaneously. In this context, hydrogen has emerged as a clean, high-energy-content alternative that can be produced globally on an industrial scale, independent of weather or season. With water as its only by-product, hydrogen is increasingly viewed as a critical component in achieving sustainable energy transitions and mitigating the adverse effects of climate change. In spite of their theoretical potential to meet global needs, renewable energy sources are limited by practical challenges such as inconsistent availability, high production costs, and geographical variability, also limiting their effective utilisation (Le et al., 2023 ). Hydrogen production methods are broadly classified into conventional and renewable technologies. Conventional methods, such as steam methane reforming (SMR), partial oxidation (POx), and coal gasification, rely on fossil fuels like natural gas and hydrocarbons (Kumar et al., 2021 ; Osman et al., 2022 ). In contrast, renewable technologies use biomass or water as feedstocks. Water-based techniques include electrolysis, thermolysis, and photo-electrolysis, which use water as the sole input. Electrolysis stands out for its simplicity and ability to produce high-purity hydrogen with zero CO₂ emissions when powered by renewable energy, making it suitable for on-site hydrogen generation. However, the high production cost remains a major barrier compared to fossil fuel-based hydrogen (Megía et al., 2021 ). These varied methods offer both opportunities and challenges depending on energy source, cost, and environmental impact. Based on the environmental impact of the sources and production method, a colour-based classification of hydrogen is followed. Grey hydrogen, the most common, is made from fossil fuels like natural gas or coal without carbon capture, releasing high CO₂ emissions (Newborough & Cooley, 2020 ). Brown and black hydrogen come from lignite and bituminous coal, respectively, and are among the least eco-friendly. Blue hydrogen uses similar methods but incorporates carbon capture and storage (CCS), reducing emissions. Turquoise hydrogen, made via methane pyrolysis, emits solid carbon instead of CO₂, offering a lower carbon footprint (Arcos & Santos, 2023 ). Green hydrogen, the cleanest form, is produced by electrolysis powered by renewables, making it vital for a low-carbon future. Biohydrogen is also viewed as green hydrogen derived from biogases and biomass. Biohydrogen is a promising renewable fuel due to its high energy density and carbon-neutral nature. Biomass-based hydrogen production can be achieved via a thermochemical or biochemical route. Biological processes such as dark fermentation and photo-fermentation constitute the biochemical route, where the former is generally more effective than the latter. Utilisation of organic biomass facilitates a sustainable production pathway by directing carbon captured to biomass growth. Operating under anaerobic conditions, dark fermentation includes breakdown of complex organic matter to simpler forms by fermentative and hydrolytic bacteria. The simple molecules are further converted to volatile fatty acids (VFAs), hydrogen, CO₂, and other intermediates by acidogenic bacteria. Hydrogen is a by-product of the acidification phase and is often consumed by hydrogenotrophic methanogens in a normal anaerobic process to produce methane. Hence, inhibiting methanogenesis by suppression of methanogens is essential for efficient biohydrogen production. This is typically achieved by the heat shock pretreatment of anaerobic inoculum at a temperature range of 90–100°C, which allows selective inactivation of non-spore-forming methanogens and enrichment of spore-forming hydrogen-producing bacteria. The resulting microbial community is usually dominated by obligate and facultative anaerobes that efficiently yield higher volumes of hydrogen via the acetic and butyric acid pathways. This pretreatment also inhibits homoacetogens, which compete with hydrogen producers in utilising hydrogen for acetate synthesis. Therefore, a dark fermentation system with heat-pretreated inoculum forms a more stable, efficient, and high-yield fermentation system. Overall, biohydrogen offers a safer, low-energy alternative to thermochemical methods, as it operates at low temperatures and pressures, and aligns well with goals for clean, decentralized energy production (Ananthi et al., 2024 ; Kannaiah Goud et al., 2014 ; Show et al., 2012 ; Woo & Song, 2010 ). Though biohydrogen bridges the shift to sustainable and clean energy systems, incorporating novel mechanisms to enhance the process can ensure higher yield. Apart from heat pretreatment of the inoculum, focus on the substrate can also contribute to an enhanced process. Exploiting the typical advantage of using waste biomass in anaerobic processes, Organic Fraction of Municipal Solid Waste (OFMSW) is used as the substrate for fermentation in this study. This offers dual advantages of bioenergy generation and waste management. Further, substrate pretreatment can also potentially decrease the hydrolytic load on the microbes. Thermal, acid, and alkali pretreatments are employed to enhance the efficiency of dark fermentation by improving the digestibility of complex food-based substrates. Thermal pretreatment involves heating the substrate to disrupt cellular structures and solubilize organic matter, increasing microbial accessibility to intracellular compounds. Acid pretreatment, using strong acids like H₂SO₄ or HCl, primarily hydrolyzes hemicellulose and disrupts lignocellulosic bonds, releasing fermentable sugars but potentially generating inhibitory compounds such as furfural. In contrast, alkali pretreatment (using agents like NaOH or Ca(OH)₂) breaks down lignin and improves cellulose digestibility, especially effective for substrates with high lignin content. The primary difference lies in their mode of action: thermal focuses on physical disintegration, acid promotes hydrolysis of polysaccharides, while alkali targets lignin degradation. (Gbiete et al., 2024 ). Hence, a comparative analysis of the effect of acid, alkali and thermal pre-treatments on the substrate properties (sCOD, VS, biomolecules), hydrogen yield and VFA production is presented in this work. By evaluating solubilization efficiency and substrate digestibility under controlled pH and temperature, the research identifies optimal conditions for maximising hydrogen yield from OFMSW. 2. Materials and methods 2.1. Substrate and Inoculum Characterisation The composition of the OFMSW used in this study was determined based on various literature sources with data related to Indian OFMSW characteristics, to closely mimic the real-world OFMSW. The composition includes 50% of food waste (cooked rice 30%, cooked vegetable 27%, bread/roti 22%, cooked dal 15%, cooked oil 3%, coffee/tea residue 3%), 37% of fruit and vegetable waste (banana 17%, beet 12%, papaya, apple, sapota, carrot, potato 11% each, onion 7%, cabbage 4%, brinjal 3%, citrus 2%) and 3% of yard waste (grass 65%, leaves 33%, wood 2%). Anaerobic sludge (AS) was used as the inoculum and was procured from a biogas plant in Chennai, India. The AS and OFMSW were characterised prior to the Biohydrogen Potential (BHP) tests. The parameters analysed include soluble Chemical Oxygen Demand (sCOD), Total Solids (TS), Volatile Solids (VS), and pH. All measurements were conducted in triplicate, and the mean values along with their standard deviations are presented in Table 1 . Table 1 OFMSW and AS characteristics Parameter AS OFMSW Units TS 0.061 ± 0.004 0.316 ± 0.008 g / g VS 0.038 ± 0.035 0.293 ± 0.026 g / g sCOD 1460 ± 60 85333 ± 250 mg / l pH 6.81 ± 0.12 5.34 0.16 - 2.2. Chemical Reagents The chemicals used in this study include sodium hydroxide (NaOH), sulfuric acid (H₂SO₄), potassium dichromate (K₂Cr₂O₇), ferrous ammonium sulfate ((NH 4 ) 2 Fe(SO 4 ) 2 ⋅6H 2 O), mercuric sulfate (HgSO₄), and silver sulfate (Ag₂SO₄). All chemicals were of analytical grade and were procured from SRL Chemicals, India 2.3. Pretreatment methods 20 g of OFMSW made up to 100 mL slurry was subjected to pre-treatment. In thermal pretreatment, 100 mL of OFMSW slurry was treated at 70°C and 90°C for durations of 20, 40, and 60 minutes. Acid pretreatment was carried out by adjusting the pH to 1 and 3 using 1M H 2 SO 4 . The treatment durations include 1, 3, and 5 hours. For the alkali pretreatment, the pH was adjusted to 9, 11, and 13 using 1M NaOH, and the treatment was carried out for 1, 3, and 5 hours. The pretreated samples were centrifuged at 6000 rpm, filtered using 0.45-micron syringe filter, and the supernatant was used for sCOD, soluble protein and carbohydrate estimation. Along with the remaining VS estimation, these parameters were used to assess the extent of solubilization. Based on the sCOD and VS results, soluble carbohydrate and protein solubilization analyses were carried out specifically for the optimised conditions. All experiments were performed in triplicate to ensure reliability. 2.4. Biohydrogen Potential Test Biochemical hydrogen potential assay of the pretreated samples was carried out using 120 mL glass bottles with a working volume of 80 mL. The substrate to inoculum (S:I) ratio was maintained at 0.5, which minimised disturbances and provided optimal conditions for hydrogen production. Anaerobic conditions were established by purging nitrogen gas for 5 minutes in each flask. All flasks were incubated at 37°C with an initial pH of 7 and agitated at 150 rpm in an orbital incubator shaker. Gas production was monitored every 8 hours, and the experiment was concluded after 3 days, once saturation was observed. All tests were performed in triplicate, and statistical analyses ensured the reliability and accuracy of the results. 2.5. Analytical methods The physicochemical characterization of the substrate was carried out to determine Total Solids (TS), Volatile Solids (VS), Fixed Solids (FS), Moisture Content, Soluble Chemical Oxygen Demand (sCOD), Carbohydrates, Proteins, and Volatile Fatty Acids (VFAs). These analyses were conducted in accordance with the Standard Methods for the Examination of Water and Wastewater (APHA, 2017). Soluble carbohydrate content estimation was performed using the phenol-sulfuric acid method, and soluble protein estimation was performed using the Bradford assay. Volatile Fatty Acid (VFA) composition analysis was carried out by collecting the liquid sample from the batch reactor, filtering using a 0.45-micron syringe filter, adjusting the pH of the solution between 3 and 4 using 96% orthophosphoric acid and analysing in a gas chromatographic system with Flame Ionisation Detector (GC-FID). Gas volume measurement was carried out using the manometric method. Gas composition analysis was performed using gas chromatographic system with a Thermal Conductivity Detector (GC-TCD). The gas and hydrogen volumes were estimated based on equations (1) and (2). \(\:n{H}_{2}=\:\frac{{P}_{m}*\:{Vol}_{head}}{R*\:{T}_{exp}}*\:\frac{\%\:{H}_{2}}{100}\) …….. (1) \(\:Vol\:{H}_{2}=\:\frac{n{H}_{2}*R*\:{T}_{STP}}{{P}_{STP}}*1000\) ……… (2) (Where, nH 2 - Moles of hydrogen produced, P m - Pressure accumulated inside the flask during the “n” hours, Vol head - Head space volume maintained in the flask (ml), T exp - Temperature maintained during the batch assay, %H - Percentage of Hydrogen in the produced gas (From GC-TCD), Vol H 2 - Volume of hydrogen produced at STP conditions, R - Universal Gas constant (0.0820574 L bar mol -1 K -1 ) The experimental data of biohydrogen production were fitted using the Modified-Gompertz model as follows: $$\:{G}_{\begin{array}{c}t\\\:\:\end{array}}=\:{G}_{m\:}*\text{e}\text{x}\text{p}(-\text{exp}\left({R}_{m}*\frac{2.71828}{{G}_{m}}\right)*\left(L-t\right)+1))\dots\:\dots\:.(3)$$ Where, G t = Biohydrogen production at time ‘t” (mL), G m = Maximum biohydrogen production (mL), R m = Biohydrogen production rate (mL/hour), L = Lag phase (hour), t = time (hour). The Origin software was used to derive the kinetic parameters such as G m , R m and L from the Modified-Gompertz model. 3. Results 3.1. Effect of pretreatment on substrate characteristics 3.1.1. Effect on sCOD and VS of OFMSW Substrate pretreatment primarily focuses on causing effective solubilisation, thereby reducing the load on the hydrolysis step in dark fermentation. Hence, sCOD, VS, soluble carbohydrates and proteins are taken as metrics to predict pretreatment effect on biohydrogen production. The sCOD results, represented in Fig. 1 , clearly demonstrate distinct patterns for each pretreatment technique. In the thermal pretreatment, the highest solubilization is observed at 70°C for 60 minutes, which shows a 3.8x increase in sCOD compared to the untreated sample. Interestingly, this value is higher than that at 90°C by 9.29% for the same duration, suggesting that moderate heating is more effective than higher temperatures. Furthermore, at all the tested time intervals (20, 40, and 60 minutes), the sCOD values remain consistently higher at 70°C than at 90°C, indicating better solubilization at this moderate temperature throughout the experimental period. For the acid pretreatment, a maximum of 1.75x increase in sCOD is achieved at pH 1 after 3 hours of treatment over the untreated OFMSW, representing a significant release of soluble organics. Further, at pH 1.5x and 1.63x increase in sCOD is observed after 1 hour and 5 hours of treatment, respectively, showing a clear peak at the 3-hour mark. At pH 3, the maximum sCOD obtained was also 1.5x the control, but this is comparatively lower than the values observed at the more acidic pH 1. Lastly, in the alkali pretreatment, 5 times increase in sCOD is achieved at pH 11 for 3 hours, indicating the most efficient solubilisation of organics among all tested conditions. This is followed by 4.6x increase at pH 13 and 3.75x increase at pH 9 for the same 3-hour duration. Interestingly, across all pH levels tested in alkali conditions, the 3-hour duration consistently yielded higher sCOD values compared to 1 hour and 5 hours, confirming it as the optimal reaction time for solubilization. These results collectively highlight how specific combinations of temperature, pH, and duration significantly influence the extent of organic matter solubilization during pretreatment. The VS remaining as shown in Fig. 2 , depicts clear variations depending on the type and severity of pretreatment conditions, reflecting the degree of organic matter breakdown. Based on the trend in sCOD, 60 minutes is supported as the optimal time for observing VS reduction using thermal pretreatment. After 60 minutes, 85.18% of the VS remained at 70°C, whereas a slightly higher 90.4% remained at 90°C, indicating that greater solid reduction occurs at the moderate temperature, likely due to more effective solubilization. In the case of acid pretreatment, the lowest VS% remaining was observed at pH 1 after 3 hours, with 95.22% of the initial VS retained, compared to 97.58% at pH 3 for the same duration. In contrast, alkali pretreatment exhibited more significant VS reduction, with the lowest VS% remaining of 79.85% observed at pH 11 for 3 hours. At the same 3-hour duration, VS remaining was 81.28% and 91.16% at pH 13 and 9, respectively, clearly indicating that pH 11 provided the most effective condition for converting solid organics into soluble form. These values also align with the corresponding peaks in sCOD, reaffirming that alkaline pretreatment, particularly at moderate strength and optimal duration, resulted in the most substantial disruption of particulate organic matter across all conditions tested. 3.1.2. Effect on Carbohydrate and Protein Concentration of OFMSW The soluble carbohydrate and protein concentration in the control and pretreated samples are represented in Fig. 3 . Carbohydrate release after pretreatment varied significantly across different conditions. The control sample (no pretreatment) exhibited the lowest soluble carbohydrate concentration of 5.71 g/L. Among acidic pretreatments, a concentration of 112% and 84% increase was observed at pH 1 and pH 3. Under alkaline conditions, carbohydrate concentrations were markedly higher than acid pretreatment. pH 9 resulted in 85% increase, followed by 398% increase in pH 13. Highest carbohydrate solubilisation was achieved at pH 11, which showed 562% increase. In the case of thermal pretreatments, heating at 70°C and 90°C resulted in 431% and 167% increase, respectively. These values show that the carbohydrate content solubilised into the solution varied from a minimum of 5.71 g/L (control) to a maximum of 37.86 g/L (pH 11) across all the experimental conditions. Protein concentrations post-pretreatment also displayed significant variation. The control condition measured a baseline protein concentration of 4.7 g/L. Under acidic conditions, 77% increase in soluble proteins was achieved at pH 1 and 104% at pH 3, exhibiting the highest protein release at 9.58 g/L. In the case of alkaline pretreatments, pH 9 yielded 94% increase, further declining to 60% and 36% increase at higher pH levels of 11 and 13, respectively. For thermal conditions, pretreatment at 70°C resulted in 71%, and 90°C gave a slightly higher value of 94% increase. Overall, protein concentrations ranged from 4.27 g/L (pH 13) to 9.58 g/L (pH 3), depending on the pretreatment method used. 3.2. Biohydrogen Potential Assay 3.2.1. Total gas production from pretreated OFMSW The total gas production during the 72-hour Biochemical Hydrogen Potential (BHP) test is illustrated in Fig. 4 A. Overall, a clear enhancement with pretreatment was observed when compared with the control. The untreated OFMSW (control) produced 322 mL/g VS of gas. In comparison, thermal pretreatment at 70°C for 60 minutes resulted in 47% increase in gas yield, acid pretreatment at pH 1 for 3 hours yielded 62% increase, and alkaline pretreatment at pH 11 for 3 hours achieved the highest cumulative gas output of 749 mL/g VS accounting for a 133% increase over the control. The gas accumulation pattern shows rapid generation in the first 48 hours across all conditions, with alkali-treated samples reaching around 600 mL/g VS by that point, indicating fast initial conversion. Although gas production continued beyond 48 hours, the rate of increase declined after 56 hours, as reflected in the gradually plateauing cumulative gas curves for all samples. 3.2.2. Cumulative hydrogen production from pretreated OFMSW The Biohydrogen Potential (BHP) test results show a significant enhancement in cumulative hydrogen yields due to pretreatment as depicted in Fig. 4 B. The control (untreated OFMSW) yielded only 32.85 mL H₂/g VS over the 72-hour test period, serving as the baseline for estimating the pretreatment effects. Thermal pretreatment at 70°C for 60 minutes increased the hydrogen yield by 130% over the control, reflecting a 2.3-fold improvement, while acid pretreatment using 0.1M H₂SO₄ at pH 1 for 3 hours resulted in a 3.1-fold increase compared to the control. However, the most substantial enhancement was observed with alkali pretreatment, conducted at pH 11 for 3 hours, which achieved a peak hydrogen yield of 189.25 mL H₂/g VS, marking a 5.76-fold increase over the control. Across all tested conditions, the majority of hydrogen production occurred within the first 24 hours, accounting for approximately 80–90% of the total hydrogen yield, indicating that readily fermentable substrates formed during pretreatment were rapidly consumed by hydrogen-producing microorganisms in the early fermentation phase. The rate of hydrogen release peaked within the first 8 to 16 hours, especially in the alkali-pretreated samples, and then gradually tapered off, reaching a plateau by the end of the 72-hour period. Table 2 shows the kinetic parameters of biohydrogen production for control (untreated), thermal, acid and alkali pretreated OFMSW samples. Table 2 Kinetic parameters of the biohydrogen production estimated using modified-Gompertz equation Control Thermal Acid Alkali G m mL 32.2355 ± 0.5150 73.6241 ± 0.6500 101.0319 ± 1.0180 187.3373 ± 1.3310 R m mL/hour 1.8874 ± 0.2935 5.8259 ± 0.9930 4.6709 ± 0.5627 9.1586 ± 1.1362 L hour 2.8686 + 1.7043 2.9626 + 1.8830 1.5959 + 1.9613 1.4677 + 2.2721 R 2 - 0.9986 0.9987 0.9988 0.9995 3.2.3. VFA profile of pretreated OFMSW before and after dark fermentation The VFA profiles measured during the Biohydrogen Potential (BHP) test on day 0 and day 3 are presented in Fig. 5 , illustrating distinct patterns across all pretreatment conditions. At day 0, the acid pretreated sample exhibited the highest initial total VFA concentration of 13,558 mg/L, with acetic acid (43%) being the major contributor and iso-butyric acid (10%) being the least, indicating substantial soluble organic presence even before fermentation began. The thermal and alkali pretreated groups had lower starting VFA concentrations, registering 2463 mg/L and 6583 mg/L, respectively. The control group recorded the lowest initial VFA level at 1819 mg/L. By day 3, a clear increase in VFA accumulation was observed in all groups. The alkali pretreatment led to the highest total VFA production with a concentration of 24,224 mg/L. Acetic acid dominated the VFA mixture, comprising 52% concentration, followed by n-butyric acid (24%). 6% each of propionic acid, iso-butyric acid, iso-valeric acid and n-valeric acid were present in the mixture. Following this was the thermal pretreatment, with a total VFA accumulation of 20,314 mg/L, including acetic acid (56%), butyric acid (27%), and moderate levels of propionic acid (11%) and valeric acids (6%). The acid pretreated group reached 18,851 mg/L total VFAs by day 3, composed of acetic acid (40%), n-butyric acid (31%), and iso-butyric acid (7%), with moderate amounts of propionic (8%) and valeric acids (15%). The control group, though the slowest to build up, reached a final total VFA concentration of 14,510 mg/L, predominantly composed of acetic acid (56%), butyric acid (27%), and propionic acid (11%), with minimal branched-chain VFAs. This data highlights the temporal evolution of VFA concentrations and their respective compositions across different pretreatment strategies over the three-day anaerobic dark fermentation period. 4. Discussions 4.1. Alkali pretreatment maximises biohydrogen yield The effectiveness of pretreatment methods was evaluated by analysing soluble chemical oxygen demand (sCOD) and percentage of volatile solids (VS) remaining, as both metrics are key indicators of solubilization efficiency and particulate organic matter degradation (Preethi et al., 2022 ; Ma et al., 2018 ). Alkaline pretreatment improves substrate solubilisation due to chemical reactions like saponification (of acetyl esters and carboxylic acid) and degradation of lignocellulose, which breaks down complex biochemical bonds. Furthermore, it also causes decrystallisation of polymers and increases surface area and accessibility (Ahmadi-Pirlou et al., 2017 ; Dasgupta & Chandel, 2020 ). Alkaline pretreatment at pH 11 for 3 hours showed superior solubilisation performance as indicated by a 5-fold increase in sCOD over the control and maximum VS reduction by ~ 20%, strongly supporting the most efficient breakdown of solid organic matter into fermentable soluble organics. As such, pH 11 outperformed other alkaline conditions tested. Beyond a critical point, exposure to strong alkaline conditions (pH 13) could lead to secondary reactions that hinder further solubilization and allow re-polymerisation or formation of humic-like recalcitrant substances. The stronger alkaline conditions can chemically stabilise intermediates, slowing further conversion of solids (Xie et al., 2014 ). At pH 9, the hydroxide strength is insufficient for the hydrolysis of complex organic structures, resulting in lower solubilisation (De Sousa et al., 2021 ). The alkaline hydrolysis process shows time-dependent solubilisation mechanisms and hence was considered in this study. Hence, the effect of time was also critical in assessing the pretreatment performance, as shorter duration (1 hour) allowed in incomplete/partial breakdown and longer (5 hour) duration allowed degradation of solubilised intermediates over time, resulting in lower sCOD values (J. Kim et al., 2013 ). Protein solubilisation at pH 11 was less than at pH 9. Though high alkalinity results in better cell disruption and protein release, the observed pattern could be due to high protein denaturation and precipitation at high hydroxide ion activity. The protein denaturation could have released amino acids in the system, which could not be detected using the Bradford assay performed in this work for protein determination. This is also confirmed by the lowest protein solubilisation at pH 13 (Gao et al., 2020 ; Hadinoto et al., 2024 ). Carbohydrate solubilisation was maximum at pH 11 due to enhanced solvation and saponification reactions, breakdown of ester linkages in lignocellulosic structures, and starch gelatinisation, whereas these reactions are limited at pH 9 due to hydroxide availability. At pH 13, excessive reactions might have promoted refractory and insoluble compound formation, resulting in low soluble carbohydrates (Bali et al., 2014 ; Ragheb et al., 1995 ). Based on these comparative results, alkaline pretreatment at pH 11 for 3 hours was the most efficient method due to its superior solubilization, highest sCOD release, and VS reduction, supporting a greater theoretical biohydrogen production potential. The solubilisation effect of pretreatment was studied for mapping it to hydrogen production during dark fermentation. Cumulative gas production (total gas and hydrogen) was monitored over a 72-hour biohydrogen potential test. Results clearly show that pretreatment significantly enhances fermentative gas yields, with alkali pretreatment delivering the highest improvement, with a 2.4x and 5.8x increase in total gas and hydrogen production, respectively, over the control. The superior performance of alkali pretreatment is attributed to the disruption of lignocellulosic and protein matrices, facilitating the release of soluble compounds such as amino acids and sugars. These are easily fermentable by hydrogen-producing bacteria, leading to enhanced gas yields. Parthiba Karthikeyan et al. ( 2018 ) emphasised that slightly acidic substrates like food waste respond well to alkaline hydrolysis, leading to a significant rise in hydrogen production. The rate of gas production was rapid during the first 48 hours, with alkali-treated samples reaching 590 mL/g VS by that point, indicating an accelerated microbial conversion. This observation of rapid gas evolution aligns with the solubilisation pattern observed, suggesting improved substrate accessibility and microbial activity due to increase in the specific surface area of the substrate (Ahmadi-Pirlou et al., 2017 ). Kinetic modelling also predicts the lowest lag time and the highest hydrogen production rate in alkali pretreated system. However, gas accumulation slowed after 56 hours, suggesting either substrate depletion or the onset of product inhibition, a phenomenon corroborated by prior work (J. K. Kim et al., 2006 ; Mata-Alvarez et al., 2014 ) that attributes such tapering to the build-up of inhibitory metabolites like volatile fatty acids (VFAs) or ammonia during prolonged batch fermentation. Supporting this observation, hydrogen concentration data in this study showed that 80–90% of the total hydrogen was produced within the first 24 hours, especially under alkali pretreatment, with a decline thereafter, as carbon dioxide became more dominant. This pattern is consistent with typical dark fermentation pathways, where hydrogenogenic fermentation peaks early, followed by acidogenesis and CO₂ production in later stages (Hallenbeck & Ghosh, 2009 ; Kapdan & Kargi, 2006 ). The absence of methane and trace nitrogen confirmed that the dominant gases were hydrogen and carbon dioxide, ruling out methanogenesis and indicating a clean dark fermentation process. The VFA dynamics offer critical insights into microbial fermentation behaviour, pretreatment-induced solubilization, and pathway dominance, especially regarding hydrogen-producing routes. Alkali pretreated groups showed moderate initial VFA levels, suggesting that these pretreatments required microbial action to further convert solubilised substrates into VFAs. As proposed by Pan et al. ( 2006 ), alkali pretreatment likely targeted more recalcitrant lignin fractions, thus yielding fewer immediately fermentable intermediates at this stage. By Day 3, a sharp increase in total VFAs was evident in the alkali-treated system reaching ~ 24 g/L – the highest among all treatments. The molecular hydrogen production is catalysed by hydrogenase enzymes that reduce surplus electrons with protons (H⁺) as electron acceptors. The biochemical route for hydrogen production from glucose can be acetate or butyrate pathway as represented by Eqs. (4) and (5). The theoretical hydrogen yield with acetate formation is 4 moles of hydrogen per mole of glucose, and with butyrate formation, it is 2 moles of hydrogen per mole of glucose (Becerra-Quiroz et al., 2024 ). Acetate pathway: C₆H₁₂O₆ + 2H₂O → 2CH₃COOH + 4H₂ + 2CO₂ …. (4) Butyrate pathway: C₆H₁₂O₆ → CH₃CH₂CH₂COOH + 2H₂ + 2CO₂ … (5) The dominance of acetic acid in this system indicates a strong acetogenic fermentation route, which is closely tied to hydrogen production. This finding is consistent with Parthiba Karthikeyan et al. ( 2018 ), who emphasised that acetic and butyric acids are primary products of hydrogen-producing bacteria (HPB) and represent a desirable VFA profile for biohydrogen synthesis. Furthermore, the presence of significant amounts of branched-chain isovaleric acid suggests active amino acid degradation, especially leucine. The speculations on the observed protein solubilisation effects driven by alkali-induced denaturation are hence supported. n-valeric acid production is predominantly via chain elongation of short-chain fatty acids (like propionate) and is primarily carbohydrate-dependent. Since OFMSW contains significant amounts of proteins and carbohydrates, alkali-based solubilisation has enhanced the dark fermentation via multiple mechanisms. This observation aligns with Akunna, ( 2000 ), who reported that alkaline environments facilitate the breakdown of nitrogen-rich biomass, enhancing mixed-acid fermentation and increasing biohydrogen precursors. The consistent and high VFA load in the alkali group also implies minimal inhibitory effects, suggesting that the microbial community functioned under relatively stable conditions despite elevated acid concentrations. This can also be attributed to the buffering provided by the residual alkali in the system (Ahmadi-Pirlou et al., 2017 ). 4.2. Biohydrogen recovery through acid and thermal pretreatment techniques Acid pretreatment was carried out at pH 1 and 3 only, as the control (untreated substrate) had an initial pH of 5.34 rendering acid pretreatment at this pH unnecessary for consideration. The results also suggested strength and time-dependent effects. pH 1 showed better results than pH 3, and the incubation time of 3 hours showed better performance than 1 and 5 hours. Solubilization efficiency dropped slightly at longer durations, likely due to the formation of inhibitory compounds or re-polymerisation of intermediates (Carrère et al., 2010 ). However, in comparison to the other pretreatments, alkali pretreatment shows the lowest VS degradation of ~ 4.8%, sCOD (1.75x) and biomolecule concentration. Despite the VS remaining slightly lower at pH 3 (97.58%) than at pH 1 (95.22%), the higher sCOD at pH 1 implies that more particulate organic matter was effectively broken down into soluble compounds, which is more critical for fermentative biohydrogen production. This apparent contradiction between higher VS at pH 3 and higher sCOD at pH 1 is also observed in similar studies, where strong acid conditions enhance solubilization despite leaving behind slightly more undissolved solids (Appels et al., 2008 ). It underscores the fact that sCOD increase and VS loss do not always follow a linear relationship, especially in complex organic mixtures. Acid pretreatment resulted in a 1.69x and 3.1x increase in biogas and hydrogen concentration, respectively, with a cumulative yield greater than that of thermal pretreatment. (Cui & Shen, 2012 ) also reported that acid-treated substrates underperform compared to alkali-treated ones in hydrogen fermentation, echoing the observed trends. Despite the high cumulative yield and low lag phase observed in comparison to thermal treatment, the hydrogen production rate is lower in the case of acid pretreatment. This can be attributed to the presence of inhibitors and high initial VFA concentration. At the onset of the test (Day 0), the acid-pretreated system already exhibited a high total VFA concentration (~ 13.6 g/L). The profile was notably rich in acetic acid and n-butyric acid, suggesting significant pre-fermentation solubilization. This observation aligns with the findings of Wang & Wan, ( 2009 ), who emphasised that intermediates such as acetic and butyric acids play a pivotal role in driving dark fermentative hydrogen production. n-butyric acid usually presents feedback inhibition to fermentative hydrogen production due to pH-dependent disruption of microbial cells (Albuquerque et al., 2024 ; Chen et al., 2021 ). The VFA composition clearly indicates an increase in n-butyric acid concentration that could have resulted in a lower hydrogen production rate. The data imply acid pretreatment-initiated pre-hydrolysis of the lignocellulosic matrix, releasing soluble sugars and VFAs, even before inoculation. This effect is further corroborated by Mosier ( 2005 ), who noted the presence of VFAs prior to fermentation in acid-treated lignocellulosic substrates, attributing it to hemicellulose degradation, which directly generates VFAs, particularly acetic acid. Dutta et al. ( 2022 ) also support this mechanism by reinforcing the fact that acid hydrolysis solubilises carbohydrates efficiently, leading to early metabolite release that may impact downstream microbial activity. Comparison of sCOD values in thermal pretreatment suggests that moderate heat is more effective for solubilising organic compounds, likely because excessively high temperatures may promote Maillard reactions between amino acids and reducing sugars or thermal degradation, leading to recalcitrant compounds that are not captured as sCOD (Taherdanak et al., 2017 ). Supporting this trend, the VS remaining, proteins and carbohydrates were also lower at 70°C, confirming that more particulate matter was converted to soluble form under moderate heat. This aligns with findings by Liu et al. ( 2018 ), who reported that excessive heat can stabilise or re-polymerise breakdown products, reducing the net conversion of solids into soluble organics. Thermal pretreatment at 70°C for 60 minutes showed a moderate but significant enhancement of sCOD (a 3.83x) and VS reduction (14.49%), confirming its effectiveness with shorter treatment time. Thermal pretreatment shows a 1.55x and 2.3x increase in biogas and hydrogen production, respectively. Thermal pretreatment has a higher production rate but lower maximum production and a higher lag phase than acid pretreatment. This can be attributed to the fact that acid pretreatment hydrolyses the OFMSW into simpler forms; however, thermal treatment largely contributes to increased accessibility of the substrate to microbes, without much reduction in the biochemical complexity. Thermal pretreatment, although moderate in performance, still offered a reliable increase without introducing inhibitory compounds. This is consistent with the previous report (Rajesh Banu et al., 2020 ), which shows that moderate heat enhances hydrogen production by partial solubilization of complex organics. VFA composition after thermal treatment was comparable to the control, possibly as the physical pretreatment technique did not facilitate chemical reactions governing biomolecule conversion to VFA, but rather accelerated solubilisation only. However, a strong inclination towards acetic and butyric acid fermentation is suggested based on the final VFA concentration. 5. Conclusion This study demonstrates the feasibility of producing biohydrogen from the OFMSW using novel pretreatment techniques. Heat treatment of the inoculum at 100°C for 1 hour effectively suppressed hydrogen-consuming microbes, leading to a significant increase in hydrogen yield—10.74 mL H₂/g VS compared to just 1.04 mL H₂/g VS in the control. Most of the hydrogen was generated within 36 hours, with GC-TCD analysis showing a peak hydrogen concentration of 32% at 24 hours in the treated group, while the control remained below 4%. These findings highlight the importance of inoculum pretreatment in enhancing both the rate and purity of hydrogen production. Further, among the different substrate pretreatments tested—thermal, acid, and alkali—the alkali pretreatment (pH 11 for 3 hours) was most effective, yielding about 190 mL H₂/g VS, which is a 5.76-fold improvement over the control. It also showed the highest hydrogen content (~ 45%) at 16 hours, indicating faster and more efficient hydrogenogenic activity. In contrast, acid and thermal pretreatments were less effective, with thermal pretreatment producing more propionic acid, a known hydrogen sink, which likely reduced hydrogen yield. Overall, alkali pretreatment emerged as the most promising strategy for enhancing biohydrogen production from OFMSW through dark fermentation. Declarations Acknowledgements The analytical facility availed at the Environmental Engineering Division, Department of Civil Engineering, IIT Madras is gratefully acknowledged. Funding declaration This research work has emanated from the Trendsetter Award of the Energy Consortium at IIT Madras. Declaration of Competing Interests The authors have no conflicts of interest to declare that are relevant to the content of this article. Author information and contributions Authors and Affiliations Environmental Engineering Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India – 600 036. Author contributions Shreyas S – Conceptualisation, data curation, formal analysis, investigation, writing – original draft Ghurupreya Ramesh - Conceptualisation, data visualisation, formal analysis, investigation, writing – original draft Mohanakrishnan Logan – Conceptualisation, Funding acquisition, Project Administration, Supervision, Writing – review & editing Corresponding author Dr. Mohanakrishnan Logan ( [email protected] ) Data Availability The datasets generated during and/or analysed during the current study are available in the Zenodo repository [https://doi.org/10.5281/zenodo.15592895]. References Ahmadi-Pirlou, M., Ebrahimi-Nik, M., Khojastehpour, M., & Ebrahimi, S. H. (2017). Mesophilic co-digestion of municipal solid waste and sewage sludge: Effect of mixing ratio, total solids, and alkaline pretreatment. International Biodeterioration & Biodegradation, 125, 97–104. https://doi.org/10.1016/j.ibiod.2017.09.004 Akunna, J. (2000). 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G., Tyagi, V. K., Bajhaiya, A. K., Gugulothu, P., & Gunasekaran, M. (2022). Biohydrogen production from waste activated sludge through thermochemical mechanical pretreatment. Bioresource Technology, 358, 127301. https://doi.org/10.1016/j.biortech.2022.127301 Ragheb, A. A., Abdel‐Thalouth, I., & Tawfik, S. (1995). Gelatinization of starch in aqueous alkaline solutions. Starch - Stärke, 47(9), 338–345. https://doi.org/10.1002/star.19950470904 Rajesh Banu, J., Merrylin, J., Mohamed Usman, T. M., Yukesh Kannah, R., Gunasekaran, M., Kim, S.-H., & Kumar, G. (2020). Impact of pretreatment on food waste for biohydrogen production: A review. International Journal of Hydrogen Energy, 45(36), 18211–18225. https://doi.org/10.1016/j.ijhydene.2019.09.176 Sharma, S., Agarwal, S., & Jain, A. (2021). Significance of Hydrogen as Economic and Environmentally Friendly Fuel. Energies, 14(21), 7389. https://doi.org/10.3390/en14217389 Show, K. Y., Lee, D. J., Tay, J. H., Lin, C. Y., & Chang, J. S. (2012). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6906247","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":483404420,"identity":"1ce8a407-365b-47be-a663-2463f4a84b8e","order_by":0,"name":"S Shreyas","email":"","orcid":"","institution":"Indian Institute of Technology Madras","correspondingAuthor":false,"prefix":"","firstName":"S","middleName":"","lastName":"Shreyas","suffix":""},{"id":483404421,"identity":"b514e61e-5baf-44d8-9a56-1a7b1f77820d","order_by":1,"name":"Ghurupreya Ramesh","email":"","orcid":"","institution":"Indian Institute of Technology 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13:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6906247/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6906247/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86529792,"identity":"a301fd9b-6b0d-4e39-8ae7-b082e2b2354d","added_by":"auto","created_at":"2025-07-11 16:47:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143376,"visible":true,"origin":"","legend":"\u003cp\u003esCOD profile for thermal (A), acid (B) and alkali (C) pretreatment at different durations\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6906247/v1/2825a81cbd62d060b00269bf.png"},{"id":86530603,"identity":"2ef6286d-bfd3-4882-97c7-61846b9d5ee4","added_by":"auto","created_at":"2025-07-11 16:55:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131005,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Remaining VS (%) after thermal, acid and alkali pretreatment at optimised time\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6906247/v1/9e9f75ea7d5af191d4ac4595.png"},{"id":86529795,"identity":"788029aa-9a8c-4bdc-9afa-718580ab5990","added_by":"auto","created_at":"2025-07-11 16:47:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139345,"visible":true,"origin":"","legend":"\u003cp\u003eCarbohydrate and protein analysis of OFMSW after being subjected to pretreatment at optimised time\u003c/p\u003e","description":"","filename":"floatimage41.png","url":"https://assets-eu.researchsquare.com/files/rs-6906247/v1/ce8e7412770dfd04ad335d1d.png"},{"id":86530604,"identity":"77ed7c07-0b1a-47e3-a191-2df09334936f","added_by":"auto","created_at":"2025-07-11 16:55:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":145972,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative gas production (A) and Cumulative hydrogen production (B) observed during BHP assay of the pretreated OFMSW\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6906247/v1/4e34fba0fcf5acbe7bcee3ed.png"},{"id":86530609,"identity":"1d9fbbcd-dafd-43b1-bd2b-d91a0121c348","added_by":"auto","created_at":"2025-07-11 16:55:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5580760,"visible":true,"origin":"","legend":"\u003cp\u003eVFA production profile on Day 0 and Day 3 of the BHP test of pretreated OFMSW\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6906247/v1/9307a0a52c42dd601a11874b.png"},{"id":86531894,"identity":"9607e711-43e4-4b32-a59a-1912ac22a0a1","added_by":"auto","created_at":"2025-07-11 17:11:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6959685,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6906247/v1/8c8bb95f-c39e-4c1c-9b3a-705f7911d28c.pdf"},{"id":86529800,"identity":"03ca693c-cde8-4f54-a323-c731e03f5e16","added_by":"auto","created_at":"2025-07-11 16:47:20","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":491072,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstract\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6906247/v1/d5b78b4bde4a4b880d91a48b.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effect of alkali, acid and thermal pretreatment techniques on biohydrogen production from organic fraction of municipal solid waste","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global demand for energy is escalating rapidly due to population growth, industrialisation, and technological advancements. Currently, about 80% of primary energy is sourced from fossil fuels, contributing to serious environmental challenges, including greenhouse gas emissions, global warming, and air pollution. These issues are linked to around 7\u0026nbsp;million deaths annually and economic losses exceeding \u003cspan\u003e$\u003c/span\u003e225\u0026nbsp;billion (Sharma et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Since the reliance on energy cannot be completely withdrawn, a sustainable shift to renewable energy allows tackling of economic and environmental challenges, simultaneously. In this context, hydrogen has emerged as a clean, high-energy-content alternative that can be produced globally on an industrial scale, independent of weather or season. With water as its only by-product, hydrogen is increasingly viewed as a critical component in achieving sustainable energy transitions and mitigating the adverse effects of climate change. In spite of their theoretical potential to meet global needs, renewable energy sources are limited by practical challenges such as inconsistent availability, high production costs, and geographical variability, also limiting their effective utilisation (Le et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHydrogen production methods are broadly classified into conventional and renewable technologies. Conventional methods, such as steam methane reforming (SMR), partial oxidation (POx), and coal gasification, rely on fossil fuels like natural gas and hydrocarbons (Kumar et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Osman et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, renewable technologies use biomass or water as feedstocks. Water-based techniques include electrolysis, thermolysis, and photo-electrolysis, which use water as the sole input. Electrolysis stands out for its simplicity and ability to produce high-purity hydrogen with zero CO₂ emissions when powered by renewable energy, making it suitable for on-site hydrogen generation. However, the high production cost remains a major barrier compared to fossil fuel-based hydrogen (Meg\u0026iacute;a et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These varied methods offer both opportunities and challenges depending on energy source, cost, and environmental impact.\u003c/p\u003e\u003cp\u003eBased on the environmental impact of the sources and production method, a colour-based classification of hydrogen is followed. Grey hydrogen, the most common, is made from fossil fuels like natural gas or coal without carbon capture, releasing high CO₂ emissions (Newborough \u0026amp; Cooley, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Brown and black hydrogen come from lignite and bituminous coal, respectively, and are among the least eco-friendly. Blue hydrogen uses similar methods but incorporates carbon capture and storage (CCS), reducing emissions. Turquoise hydrogen, made via methane pyrolysis, emits solid carbon instead of CO₂, offering a lower carbon footprint (Arcos \u0026amp; Santos, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Green hydrogen, the cleanest form, is produced by electrolysis powered by renewables, making it vital for a low-carbon future. Biohydrogen is also viewed as green hydrogen derived from biogases and biomass.\u003c/p\u003e\u003cp\u003eBiohydrogen is a promising renewable fuel due to its high energy density and carbon-neutral nature. Biomass-based hydrogen production can be achieved via a thermochemical or biochemical route. Biological processes such as dark fermentation and photo-fermentation constitute the biochemical route, where the former is generally more effective than the latter. Utilisation of organic biomass facilitates a sustainable production pathway by directing carbon captured to biomass growth. Operating under anaerobic conditions, dark fermentation includes breakdown of complex organic matter to simpler forms by fermentative and hydrolytic bacteria. The simple molecules are further converted to volatile fatty acids (VFAs), hydrogen, CO₂, and other intermediates by acidogenic bacteria. Hydrogen is a by-product of the acidification phase and is often consumed by hydrogenotrophic methanogens in a normal anaerobic process to produce methane. Hence, inhibiting methanogenesis by suppression of methanogens is essential for efficient biohydrogen production. This is typically achieved by the heat shock pretreatment of anaerobic inoculum at a temperature range of 90\u0026ndash;100\u0026deg;C, which allows selective inactivation of non-spore-forming methanogens and enrichment of spore-forming hydrogen-producing bacteria. The resulting microbial community is usually dominated by obligate and facultative anaerobes that efficiently yield higher volumes of hydrogen via the acetic and butyric acid pathways. This pretreatment also inhibits homoacetogens, which compete with hydrogen producers in utilising hydrogen for acetate synthesis. Therefore, a dark fermentation system with heat-pretreated inoculum forms a more stable, efficient, and high-yield fermentation system. Overall, biohydrogen offers a safer, low-energy alternative to thermochemical methods, as it operates at low temperatures and pressures, and aligns well with goals for clean, decentralized energy production (Ananthi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kannaiah Goud et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Show et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Woo \u0026amp; Song, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThough biohydrogen bridges the shift to sustainable and clean energy systems, incorporating novel mechanisms to enhance the process can ensure higher yield. Apart from heat pretreatment of the inoculum, focus on the substrate can also contribute to an enhanced process. Exploiting the typical advantage of using waste biomass in anaerobic processes, Organic Fraction of Municipal Solid Waste (OFMSW) is used as the substrate for fermentation in this study. This offers dual advantages of bioenergy generation and waste management. Further, substrate pretreatment can also potentially decrease the hydrolytic load on the microbes. Thermal, acid, and alkali pretreatments are employed to enhance the efficiency of dark fermentation by improving the digestibility of complex food-based substrates. Thermal pretreatment involves heating the substrate to disrupt cellular structures and solubilize organic matter, increasing microbial accessibility to intracellular compounds. Acid pretreatment, using strong acids like H₂SO₄ or HCl, primarily hydrolyzes hemicellulose and disrupts lignocellulosic bonds, releasing fermentable sugars but potentially generating inhibitory compounds such as furfural. In contrast, alkali pretreatment (using agents like NaOH or Ca(OH)₂) breaks down lignin and improves cellulose digestibility, especially effective for substrates with high lignin content. The primary difference lies in their mode of action: thermal focuses on physical disintegration, acid promotes hydrolysis of polysaccharides, while alkali targets lignin degradation. (Gbiete et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHence, a comparative analysis of the effect of acid, alkali and thermal pre-treatments on the substrate properties (sCOD, VS, biomolecules), hydrogen yield and VFA production is presented in this work. By evaluating solubilization efficiency and substrate digestibility under controlled pH and temperature, the research identifies optimal conditions for maximising hydrogen yield from OFMSW.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Substrate and Inoculum Characterisation\u003c/h2\u003e\u003cp\u003eThe composition of the OFMSW used in this study was determined based on various literature sources with data related to Indian OFMSW characteristics, to closely mimic the real-world OFMSW. The composition includes 50% of food waste (cooked rice 30%, cooked vegetable 27%, bread/roti 22%, cooked dal 15%, cooked oil 3%, coffee/tea residue 3%), 37% of fruit and vegetable waste (banana 17%, beet 12%, papaya, apple, sapota, carrot, potato 11% each, onion 7%, cabbage 4%, brinjal 3%, citrus 2%) and 3% of yard waste (grass 65%, leaves 33%, wood 2%). Anaerobic sludge (AS) was used as the inoculum and was procured from a biogas plant in Chennai, India. The AS and OFMSW were characterised prior to the Biohydrogen Potential (BHP) tests. The parameters analysed include soluble Chemical Oxygen Demand (sCOD), Total Solids (TS), Volatile Solids (VS), and pH. All measurements were conducted in triplicate, and the mean values along with their standard deviations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOFMSW and AS characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOFMSW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eUnits\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.061\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.316\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eg / g\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.038\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.293\u0026thinsp;\u0026plusmn;\u0026thinsp;0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eg / g\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003esCOD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1460\u0026thinsp;\u0026plusmn;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85333\u0026thinsp;\u0026plusmn;\u0026thinsp;250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emg / l\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003epH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e6.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.34 0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Chemical Reagents\u003c/h2\u003e\u003cp\u003eThe chemicals used in this study include sodium hydroxide (NaOH), sulfuric acid (H₂SO₄), potassium dichromate (K₂Cr₂O₇), ferrous ammonium sulfate ((NH\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003eFe(SO\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e\u0026sdot;6H\u003csub\u003e2\u003c/sub\u003eO), mercuric sulfate (HgSO₄), and silver sulfate (Ag₂SO₄). All chemicals were of analytical grade and were procured from SRL Chemicals, India\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Pretreatment methods\u003c/h2\u003e\u003cp\u003e20 g of OFMSW made up to 100 mL slurry was subjected to pre-treatment. In thermal pretreatment, 100 mL of OFMSW slurry was treated at 70\u0026deg;C and 90\u0026deg;C for durations of 20, 40, and 60 minutes. Acid pretreatment was carried out by adjusting the pH to 1 and 3 using 1M H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e. The treatment durations include 1, 3, and 5 hours. For the alkali pretreatment, the pH was adjusted to 9, 11, and 13 using 1M NaOH, and the treatment was carried out for 1, 3, and 5 hours. The pretreated samples were centrifuged at 6000 rpm, filtered using 0.45-micron syringe filter, and the supernatant was used for sCOD, soluble protein and carbohydrate estimation. Along with the remaining VS estimation, these parameters were used to assess the extent of solubilization. Based on the sCOD and VS results, soluble carbohydrate and protein solubilization analyses were carried out specifically for the optimised conditions. All experiments were performed in triplicate to ensure reliability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Biohydrogen Potential Test\u003c/h2\u003e\u003cp\u003eBiochemical hydrogen potential assay of the pretreated samples was carried out using 120 mL glass bottles with a working volume of 80 mL. The substrate to inoculum (S:I) ratio was maintained at 0.5, which minimised disturbances and provided optimal conditions for hydrogen production. Anaerobic conditions were established by purging nitrogen gas for 5 minutes in each flask. All flasks were incubated at 37\u0026deg;C with an initial pH of 7 and agitated at 150 rpm in an orbital incubator shaker. Gas production was monitored every 8 hours, and the experiment was concluded after 3 days, once saturation was observed. All tests were performed in triplicate, and statistical analyses ensured the reliability and accuracy of the results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Analytical methods\u003c/h2\u003e\u003cp\u003eThe physicochemical characterization of the substrate was carried out to determine Total Solids (TS), Volatile Solids (VS), Fixed Solids (FS), Moisture Content, Soluble Chemical Oxygen Demand (sCOD), Carbohydrates, Proteins, and Volatile Fatty Acids (VFAs). These analyses were conducted in accordance with the Standard Methods for the Examination of Water and Wastewater (APHA, 2017). Soluble carbohydrate content estimation was performed using the phenol-sulfuric acid method, and soluble protein estimation was performed using the Bradford assay.\u003c/p\u003e\u003cp\u003eVolatile Fatty Acid (VFA) composition analysis was carried out by collecting the liquid sample from the batch reactor, filtering using a 0.45-micron syringe filter, adjusting the pH of the solution between 3 and 4 using 96% orthophosphoric acid and analysing in a gas chromatographic system with Flame Ionisation Detector (GC-FID). Gas volume measurement was carried out using the manometric method. Gas composition analysis was performed using gas chromatographic system with a Thermal Conductivity Detector (GC-TCD). The gas and hydrogen volumes were estimated based on equations (1) and (2).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n{H}_{2}=\\:\\frac{{P}_{m}*\\:{Vol}_{head}}{R*\\:{T}_{exp}}*\\:\\frac{\\%\\:{H}_{2}}{100}\\)\u003c/span\u003e\u003c/span\u003e \u0026hellip;\u0026hellip;.. (1) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Vol\\:{H}_{2}=\\:\\frac{n{H}_{2}*R*\\:{T}_{STP}}{{P}_{STP}}*1000\\)\u003c/span\u003e\u003c/span\u003e \u0026hellip;\u0026hellip;\u0026hellip; (2)\u003c/p\u003e\u003cp\u003e(Where, nH\u003csub\u003e2\u003c/sub\u003e - Moles of hydrogen produced, P\u003csub\u003em\u003c/sub\u003e - Pressure accumulated inside the flask during the \u0026ldquo;n\u0026rdquo; hours, Vol \u003csub\u003ehead\u003c/sub\u003e - Head space volume maintained in the flask (ml), T\u003csub\u003eexp\u003c/sub\u003e - Temperature maintained during the batch assay, %H - Percentage of Hydrogen in the produced gas (From GC-TCD), Vol H\u003csub\u003e2\u003c/sub\u003e - Volume of hydrogen produced at STP conditions, R - Universal Gas constant (0.0820574 L bar mol\u003csup\u003e-1\u003c/sup\u003e K\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe experimental data of biohydrogen production were fitted using the Modified-Gompertz model as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{G}_{\\begin{array}{c}t\\\\\\:\\:\\end{array}}=\\:{G}_{m\\:}*\\text{e}\\text{x}\\text{p}(-\\text{exp}\\left({R}_{m}*\\frac{2.71828}{{G}_{m}}\\right)*\\left(L-t\\right)+1))\\dots\\:\\dots\\:.(3)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere, G\u003csub\u003et\u003c/sub\u003e = Biohydrogen production at time \u0026lsquo;t\u0026rdquo; (mL), G\u003csub\u003em\u003c/sub\u003e = Maximum biohydrogen production (mL), R\u003csub\u003em\u003c/sub\u003e = Biohydrogen production rate (mL/hour), L\u0026thinsp;=\u0026thinsp;Lag phase (hour), t\u0026thinsp;=\u0026thinsp;time (hour).\u003c/p\u003e\u003cp\u003eThe Origin software was used to derive the kinetic parameters such as G\u003csub\u003em\u003c/sub\u003e, R\u003csub\u003em\u003c/sub\u003e and L from the Modified-Gompertz model.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Effect of pretreatment on substrate characteristics\u003c/h2\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1. Effect on sCOD and VS of OFMSW\u003c/h2\u003e\u003cp\u003eSubstrate pretreatment primarily focuses on causing effective solubilisation, thereby reducing the load on the hydrolysis step in dark fermentation. Hence, sCOD, VS, soluble carbohydrates and proteins are taken as metrics to predict pretreatment effect on biohydrogen production. The sCOD results, represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, clearly demonstrate distinct patterns for each pretreatment technique. In the thermal pretreatment, the highest solubilization is observed at 70\u0026deg;C for 60 minutes, which shows a 3.8x increase in sCOD compared to the untreated sample. Interestingly, this value is higher than that at 90\u0026deg;C by 9.29% for the same duration, suggesting that moderate heating is more effective than higher temperatures. Furthermore, at all the tested time intervals (20, 40, and 60 minutes), the sCOD values remain consistently higher at 70\u0026deg;C than at 90\u0026deg;C, indicating better solubilization at this moderate temperature throughout the experimental period.\u003c/p\u003e\u003cp\u003eFor the acid pretreatment, a maximum of 1.75x increase in sCOD is achieved at pH 1 after 3 hours of treatment over the untreated OFMSW, representing a significant release of soluble organics. Further, at pH 1.5x and 1.63x increase in sCOD is observed after 1 hour and 5 hours of treatment, respectively, showing a clear peak at the 3-hour mark. At pH 3, the maximum sCOD obtained was also 1.5x the control, but this is comparatively lower than the values observed at the more acidic pH 1.\u003c/p\u003e\u003cp\u003eLastly, in the alkali pretreatment, 5 times increase in sCOD is achieved at pH 11 for 3 hours, indicating the most efficient solubilisation of organics among all tested conditions. This is followed by 4.6x increase at pH 13 and 3.75x increase at pH 9 for the same 3-hour duration. Interestingly, across all pH levels tested in alkali conditions, the 3-hour duration consistently yielded higher sCOD values compared to 1 hour and 5 hours, confirming it as the optimal reaction time for solubilization. These results collectively highlight how specific combinations of temperature, pH, and duration significantly influence the extent of organic matter solubilization during pretreatment.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe VS remaining as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, depicts clear variations depending on the type and severity of pretreatment conditions, reflecting the degree of organic matter breakdown. Based on the trend in sCOD, 60 minutes is supported as the optimal time for observing VS reduction using thermal pretreatment. After 60 minutes, 85.18% of the VS remained at 70\u0026deg;C, whereas a slightly higher 90.4% remained at 90\u0026deg;C, indicating that greater solid reduction occurs at the moderate temperature, likely due to more effective solubilization. In the case of acid pretreatment, the lowest VS% remaining was observed at pH 1 after 3 hours, with 95.22% of the initial VS retained, compared to 97.58% at pH 3 for the same duration. In contrast, alkali pretreatment exhibited more significant VS reduction, with the lowest VS% remaining of 79.85% observed at pH 11 for 3 hours. At the same 3-hour duration, VS remaining was 81.28% and 91.16% at pH 13 and 9, respectively, clearly indicating that pH 11 provided the most effective condition for converting solid organics into soluble form. These values also align with the corresponding peaks in sCOD, reaffirming that alkaline pretreatment, particularly at moderate strength and optimal duration, resulted in the most substantial disruption of particulate organic matter across all conditions tested.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2. Effect on Carbohydrate and Protein Concentration of OFMSW\u003c/h2\u003e\u003cp\u003eThe soluble carbohydrate and protein concentration in the control and pretreated samples are represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Carbohydrate release after pretreatment varied significantly across different conditions. The control sample (no pretreatment) exhibited the lowest soluble carbohydrate concentration of 5.71 g/L. Among acidic pretreatments, a concentration of 112% and 84% increase was observed at pH 1 and pH 3. Under alkaline conditions, carbohydrate concentrations were markedly higher than acid pretreatment. pH 9 resulted in 85% increase, followed by 398% increase in pH 13. Highest carbohydrate solubilisation was achieved at pH 11, which showed 562% increase. In the case of thermal pretreatments, heating at 70\u0026deg;C and 90\u0026deg;C resulted in 431% and 167% increase, respectively. These values show that the carbohydrate content solubilised into the solution varied from a minimum of 5.71 g/L (control) to a maximum of 37.86 g/L (pH 11) across all the experimental conditions.\u003c/p\u003e\u003cp\u003eProtein concentrations post-pretreatment also displayed significant variation. The control condition measured a baseline protein concentration of 4.7 g/L. Under acidic conditions, 77% increase in soluble proteins was achieved at pH 1 and 104% at pH 3, exhibiting the highest protein release at 9.58 g/L. In the case of alkaline pretreatments, pH 9 yielded 94% increase, further declining to 60% and 36% increase at higher pH levels of 11 and 13, respectively. For thermal conditions, pretreatment at 70\u0026deg;C resulted in 71%, and 90\u0026deg;C gave a slightly higher value of 94% increase. Overall, protein concentrations ranged from 4.27 g/L (pH 13) to 9.58 g/L (pH 3), depending on the pretreatment method used.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Biohydrogen Potential Assay\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1. Total gas production from pretreated OFMSW\u003c/h2\u003e\u003cp\u003eThe total gas production during the 72-hour Biochemical Hydrogen Potential (BHP) test is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. Overall, a clear enhancement with pretreatment was observed when compared with the control. The untreated OFMSW (control) produced 322 mL/g VS of gas. In comparison, thermal pretreatment at 70\u0026deg;C for 60 minutes resulted in 47% increase in gas yield, acid pretreatment at pH 1 for 3 hours yielded 62% increase, and alkaline pretreatment at pH 11 for 3 hours achieved the highest cumulative gas output of 749 mL/g VS accounting for a 133% increase over the control. The gas accumulation pattern shows rapid generation in the first 48 hours across all conditions, with alkali-treated samples reaching around 600 mL/g VS by that point, indicating fast initial conversion. Although gas production continued beyond 48 hours, the rate of increase declined after 56 hours, as reflected in the gradually plateauing cumulative gas curves for all samples.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2. Cumulative hydrogen production from pretreated OFMSW\u003c/h2\u003e\u003cp\u003eThe Biohydrogen Potential (BHP) test results show a significant enhancement in cumulative hydrogen yields due to pretreatment as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB. The control (untreated OFMSW) yielded only 32.85 mL H₂/g VS over the 72-hour test period, serving as the baseline for estimating the pretreatment effects. Thermal pretreatment at 70\u0026deg;C for 60 minutes increased the hydrogen yield by 130% over the control, reflecting a 2.3-fold improvement, while acid pretreatment using 0.1M H₂SO₄ at pH 1 for 3 hours resulted in a 3.1-fold increase compared to the control. However, the most substantial enhancement was observed with alkali pretreatment, conducted at pH 11 for 3 hours, which achieved a peak hydrogen yield of 189.25 mL H₂/g VS, marking a 5.76-fold increase over the control. Across all tested conditions, the majority of hydrogen production occurred within the first 24 hours, accounting for approximately 80\u0026ndash;90% of the total hydrogen yield, indicating that readily fermentable substrates formed during pretreatment were rapidly consumed by hydrogen-producing microorganisms in the early fermentation phase. The rate of hydrogen release peaked within the first 8 to 16 hours, especially in the alkali-pretreated samples, and then gradually tapered off, reaching a plateau by the end of the 72-hour period. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the kinetic parameters of biohydrogen production for control (untreated), thermal, acid and alkali pretreated OFMSW samples.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKinetic parameters of the biohydrogen production estimated using modified-Gompertz equation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThermal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAcid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAlkali\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eG\u003c/b\u003e\u003csub\u003e\u003cb\u003em\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003emL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32.2355\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.5150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.6241\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.6500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e101.0319\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.0180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e187.3373\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.3310\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csub\u003e\u003cb\u003em\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003emL/hour\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.8874\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.2935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.8259\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.9930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.6709\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.5627\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.1586\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.1362\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eL\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ehour\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8686\u0026thinsp;+\u0026thinsp;1.7043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.9626\u0026thinsp;+\u0026thinsp;1.8830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.5959\u0026thinsp;+\u0026thinsp;1.9613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.4677\u0026thinsp;+\u0026thinsp;2.2721\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eR\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3. VFA profile of pretreated OFMSW before and after dark fermentation\u003c/h2\u003e\u003cp\u003eThe VFA profiles measured during the Biohydrogen Potential (BHP) test on day 0 and day 3 are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, illustrating distinct patterns across all pretreatment conditions. At day 0, the acid pretreated sample exhibited the highest initial total VFA concentration of 13,558 mg/L, with acetic acid (43%) being the major contributor and iso-butyric acid (10%) being the least, indicating substantial soluble organic presence even before fermentation began. The thermal and alkali pretreated groups had lower starting VFA concentrations, registering 2463 mg/L and 6583 mg/L, respectively. The control group recorded the lowest initial VFA level at 1819 mg/L. By day 3, a clear increase in VFA accumulation was observed in all groups. The alkali pretreatment led to the highest total VFA production with a concentration of 24,224 mg/L. Acetic acid dominated the VFA mixture, comprising 52% concentration, followed by n-butyric acid (24%). 6% each of propionic acid, iso-butyric acid, iso-valeric acid and n-valeric acid were present in the mixture. Following this was the thermal pretreatment, with a total VFA accumulation of 20,314 mg/L, including acetic acid (56%), butyric acid (27%), and moderate levels of propionic acid (11%) and valeric acids (6%). The acid pretreated group reached 18,851 mg/L total VFAs by day 3, composed of acetic acid (40%), n-butyric acid (31%), and iso-butyric acid (7%), with moderate amounts of propionic (8%) and valeric acids (15%). The control group, though the slowest to build up, reached a final total VFA concentration of 14,510 mg/L, predominantly composed of acetic acid (56%), butyric acid (27%), and propionic acid (11%), with minimal branched-chain VFAs. This data highlights the temporal evolution of VFA concentrations and their respective compositions across different pretreatment strategies over the three-day anaerobic dark fermentation period.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Discussions","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Alkali pretreatment maximises biohydrogen yield\u003c/h2\u003e\u003cp\u003eThe effectiveness of pretreatment methods was evaluated by analysing soluble chemical oxygen demand (sCOD) and percentage of volatile solids (VS) remaining, as both metrics are key indicators of solubilization efficiency and particulate organic matter degradation (Preethi et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Alkaline pretreatment improves substrate solubilisation due to chemical reactions like saponification (of acetyl esters and carboxylic acid) and degradation of lignocellulose, which breaks down complex biochemical bonds. Furthermore, it also causes decrystallisation of polymers and increases surface area and accessibility (Ahmadi-Pirlou et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dasgupta \u0026amp; Chandel, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Alkaline pretreatment at pH 11 for 3 hours showed superior solubilisation performance as indicated by a 5-fold increase in sCOD over the control and maximum VS reduction by ~\u0026thinsp;20%, strongly supporting the most efficient breakdown of solid organic matter into fermentable soluble organics. As such, pH 11 outperformed other alkaline conditions tested. Beyond a critical point, exposure to strong alkaline conditions (pH 13) could lead to secondary reactions that hinder further solubilization and allow re-polymerisation or formation of humic-like recalcitrant substances. The stronger alkaline conditions can chemically stabilise intermediates, slowing further conversion of solids (Xie et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). At pH 9, the hydroxide strength is insufficient for the hydrolysis of complex organic structures, resulting in lower solubilisation (De Sousa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The alkaline hydrolysis process shows time-dependent solubilisation mechanisms and hence was considered in this study. Hence, the effect of time was also critical in assessing the pretreatment performance, as shorter duration (1 hour) allowed in incomplete/partial breakdown and longer (5 hour) duration allowed degradation of solubilised intermediates over time, resulting in lower sCOD values (J. Kim et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Protein solubilisation at pH 11 was less than at pH 9. Though high alkalinity results in better cell disruption and protein release, the observed pattern could be due to high protein denaturation and precipitation at high hydroxide ion activity. The protein denaturation could have released amino acids in the system, which could not be detected using the Bradford assay performed in this work for protein determination. This is also confirmed by the lowest protein solubilisation at pH 13 (Gao et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hadinoto et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Carbohydrate solubilisation was maximum at pH 11 due to enhanced solvation and saponification reactions, breakdown of ester linkages in lignocellulosic structures, and starch gelatinisation, whereas these reactions are limited at pH 9 due to hydroxide availability. At pH 13, excessive reactions might have promoted refractory and insoluble compound formation, resulting in low soluble carbohydrates (Bali et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ragheb et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Based on these comparative results, alkaline pretreatment at pH 11 for 3 hours was the most efficient method due to its superior solubilization, highest sCOD release, and VS reduction, supporting a greater theoretical biohydrogen production potential.\u003c/p\u003e\u003cp\u003eThe solubilisation effect of pretreatment was studied for mapping it to hydrogen production during dark fermentation. Cumulative gas production (total gas and hydrogen) was monitored over a 72-hour biohydrogen potential test. Results clearly show that pretreatment significantly enhances fermentative gas yields, with alkali pretreatment delivering the highest improvement, with a 2.4x and 5.8x increase in total gas and hydrogen production, respectively, over the control. The superior performance of alkali pretreatment is attributed to the disruption of lignocellulosic and protein matrices, facilitating the release of soluble compounds such as amino acids and sugars. These are easily fermentable by hydrogen-producing bacteria, leading to enhanced gas yields. Parthiba Karthikeyan et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) emphasised that slightly acidic substrates like food waste respond well to alkaline hydrolysis, leading to a significant rise in hydrogen production. The rate of gas production was rapid during the first 48 hours, with alkali-treated samples reaching 590 mL/g VS by that point, indicating an accelerated microbial conversion. This observation of rapid gas evolution aligns with the solubilisation pattern observed, suggesting improved substrate accessibility and microbial activity due to increase in the specific surface area of the substrate (Ahmadi-Pirlou et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Kinetic modelling also predicts the lowest lag time and the highest hydrogen production rate in alkali pretreated system. However, gas accumulation slowed after 56 hours, suggesting either substrate depletion or the onset of product inhibition, a phenomenon corroborated by prior work (J. K. Kim et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Mata-Alvarez et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) that attributes such tapering to the build-up of inhibitory metabolites like volatile fatty acids (VFAs) or ammonia during prolonged batch fermentation. Supporting this observation, hydrogen concentration data in this study showed that 80\u0026ndash;90% of the total hydrogen was produced within the first 24 hours, especially under alkali pretreatment, with a decline thereafter, as carbon dioxide became more dominant. This pattern is consistent with typical dark fermentation pathways, where hydrogenogenic fermentation peaks early, followed by acidogenesis and CO₂ production in later stages (Hallenbeck \u0026amp; Ghosh, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kapdan \u0026amp; Kargi, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The absence of methane and trace nitrogen confirmed that the dominant gases were hydrogen and carbon dioxide, ruling out methanogenesis and indicating a clean dark fermentation process.\u003c/p\u003e\u003cp\u003eThe VFA dynamics offer critical insights into microbial fermentation behaviour, pretreatment-induced solubilization, and pathway dominance, especially regarding hydrogen-producing routes. Alkali pretreated groups showed moderate initial VFA levels, suggesting that these pretreatments required microbial action to further convert solubilised substrates into VFAs. As proposed by Pan et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), alkali pretreatment likely targeted more recalcitrant lignin fractions, thus yielding fewer immediately fermentable intermediates at this stage. By Day 3, a sharp increase in total VFAs was evident in the alkali-treated system reaching\u0026thinsp;~\u0026thinsp;24 g/L \u0026ndash; the highest among all treatments. The molecular hydrogen production is catalysed by hydrogenase enzymes that reduce surplus electrons with protons (H⁺) as electron acceptors. The biochemical route for hydrogen production from glucose can be acetate or butyrate pathway as represented by Eqs.\u0026nbsp;(4) and (5). The theoretical hydrogen yield with acetate formation is 4 moles of hydrogen per mole of glucose, and with butyrate formation, it is 2 moles of hydrogen per mole of glucose (Becerra-Quiroz et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAcetate pathway: C₆H₁₂O₆ + 2H₂O \u0026rarr; 2CH₃COOH\u0026thinsp;+\u0026thinsp;4H₂ + 2CO₂ \u0026hellip;. (4)\u003c/p\u003e\u003cp\u003eButyrate pathway: C₆H₁₂O₆ \u0026rarr; CH₃CH₂CH₂COOH\u0026thinsp;+\u0026thinsp;2H₂ + 2CO₂ \u0026hellip; (5)\u003c/p\u003e\u003cp\u003eThe dominance of acetic acid in this system indicates a strong acetogenic fermentation route, which is closely tied to hydrogen production. This finding is consistent with Parthiba Karthikeyan et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who emphasised that acetic and butyric acids are primary products of hydrogen-producing bacteria (HPB) and represent a desirable VFA profile for biohydrogen synthesis. Furthermore, the presence of significant amounts of branched-chain isovaleric acid suggests active amino acid degradation, especially leucine. The speculations on the observed protein solubilisation effects driven by alkali-induced denaturation are hence supported. n-valeric acid production is predominantly via chain elongation of short-chain fatty acids (like propionate) and is primarily carbohydrate-dependent. Since OFMSW contains significant amounts of proteins and carbohydrates, alkali-based solubilisation has enhanced the dark fermentation via multiple mechanisms. This observation aligns with Akunna, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), who reported that alkaline environments facilitate the breakdown of nitrogen-rich biomass, enhancing mixed-acid fermentation and increasing biohydrogen precursors. The consistent and high VFA load in the alkali group also implies minimal inhibitory effects, suggesting that the microbial community functioned under relatively stable conditions despite elevated acid concentrations. This can also be attributed to the buffering provided by the residual alkali in the system (Ahmadi-Pirlou et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Biohydrogen recovery through acid and thermal pretreatment techniques\u003c/h2\u003e\u003cp\u003eAcid pretreatment was carried out at pH 1 and 3 only, as the control (untreated substrate) had an initial pH of 5.34 rendering acid pretreatment at this pH unnecessary for consideration. The results also suggested strength and time-dependent effects. pH 1 showed better results than pH 3, and the incubation time of 3 hours showed better performance than 1 and 5 hours. Solubilization efficiency dropped slightly at longer durations, likely due to the formation of inhibitory compounds or re-polymerisation of intermediates (Carr\u0026egrave;re et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, in comparison to the other pretreatments, alkali pretreatment shows the lowest VS degradation of ~\u0026thinsp;4.8%, sCOD (1.75x) and biomolecule concentration. Despite the VS remaining slightly lower at pH 3 (97.58%) than at pH 1 (95.22%), the higher sCOD at pH 1 implies that more particulate organic matter was effectively broken down into soluble compounds, which is more critical for fermentative biohydrogen production. This apparent contradiction between higher VS at pH 3 and higher sCOD at pH 1 is also observed in similar studies, where strong acid conditions enhance solubilization despite leaving behind slightly more undissolved solids (Appels et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). It underscores the fact that sCOD increase and VS loss do not always follow a linear relationship, especially in complex organic mixtures.\u003c/p\u003e\u003cp\u003eAcid pretreatment resulted in a 1.69x and 3.1x increase in biogas and hydrogen concentration, respectively, with a cumulative yield greater than that of thermal pretreatment. (Cui \u0026amp; Shen, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) also reported that acid-treated substrates underperform compared to alkali-treated ones in hydrogen fermentation, echoing the observed trends. Despite the high cumulative yield and low lag phase observed in comparison to thermal treatment, the hydrogen production rate is lower in the case of acid pretreatment. This can be attributed to the presence of inhibitors and high initial VFA concentration. At the onset of the test (Day 0), the acid-pretreated system already exhibited a high total VFA concentration (~\u0026thinsp;13.6 g/L). The profile was notably rich in acetic acid and n-butyric acid, suggesting significant pre-fermentation solubilization. This observation aligns with the findings of Wang \u0026amp; Wan, (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), who emphasised that intermediates such as acetic and butyric acids play a pivotal role in driving dark fermentative hydrogen production. n-butyric acid usually presents feedback inhibition to fermentative hydrogen production due to pH-dependent disruption of microbial cells (Albuquerque et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The VFA composition clearly indicates an increase in n-butyric acid concentration that could have resulted in a lower hydrogen production rate. The data imply acid pretreatment-initiated pre-hydrolysis of the lignocellulosic matrix, releasing soluble sugars and VFAs, even before inoculation. This effect is further corroborated by Mosier (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), who noted the presence of VFAs prior to fermentation in acid-treated lignocellulosic substrates, attributing it to hemicellulose degradation, which directly generates VFAs, particularly acetic acid. Dutta et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also support this mechanism by reinforcing the fact that acid hydrolysis solubilises carbohydrates efficiently, leading to early metabolite release that may impact downstream microbial activity.\u003c/p\u003e\u003cp\u003eComparison of sCOD values in thermal pretreatment suggests that moderate heat is more effective for solubilising organic compounds, likely because excessively high temperatures may promote Maillard reactions between amino acids and reducing sugars or thermal degradation, leading to recalcitrant compounds that are not captured as sCOD (Taherdanak et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Supporting this trend, the VS remaining, proteins and carbohydrates were also lower at 70\u0026deg;C, confirming that more particulate matter was converted to soluble form under moderate heat. This aligns with findings by Liu et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), who reported that excessive heat can stabilise or re-polymerise breakdown products, reducing the net conversion of solids into soluble organics. Thermal pretreatment at 70\u0026deg;C for 60 minutes showed a moderate but significant enhancement of sCOD (a 3.83x) and VS reduction (14.49%), confirming its effectiveness with shorter treatment time.\u003c/p\u003e\u003cp\u003eThermal pretreatment shows a 1.55x and 2.3x increase in biogas and hydrogen production, respectively. Thermal pretreatment has a higher production rate but lower maximum production and a higher lag phase than acid pretreatment. This can be attributed to the fact that acid pretreatment hydrolyses the OFMSW into simpler forms; however, thermal treatment largely contributes to increased accessibility of the substrate to microbes, without much reduction in the biochemical complexity. Thermal pretreatment, although moderate in performance, still offered a reliable increase without introducing inhibitory compounds. This is consistent with the previous report (Rajesh Banu et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which shows that moderate heat enhances hydrogen production by partial solubilization of complex organics. VFA composition after thermal treatment was comparable to the control, possibly as the physical pretreatment technique did not facilitate chemical reactions governing biomolecule conversion to VFA, but rather accelerated solubilisation only. However, a strong inclination towards acetic and butyric acid fermentation is suggested based on the final VFA concentration.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates the feasibility of producing biohydrogen from the OFMSW using novel pretreatment techniques. Heat treatment of the inoculum at 100\u0026deg;C for 1 hour effectively suppressed hydrogen-consuming microbes, leading to a significant increase in hydrogen yield\u0026mdash;10.74 mL H₂/g VS compared to just 1.04 mL H₂/g VS in the control. Most of the hydrogen was generated within 36 hours, with GC-TCD analysis showing a peak hydrogen concentration of 32% at 24 hours in the treated group, while the control remained below 4%. These findings highlight the importance of inoculum pretreatment in enhancing both the rate and purity of hydrogen production. Further, among the different substrate pretreatments tested\u0026mdash;thermal, acid, and alkali\u0026mdash;the alkali pretreatment (pH 11 for 3 hours) was most effective, yielding about 190 mL H₂/g VS, which is a 5.76-fold improvement over the control. It also showed the highest hydrogen content (~\u0026thinsp;45%) at 16 hours, indicating faster and more efficient hydrogenogenic activity. In contrast, acid and thermal pretreatments were less effective, with thermal pretreatment producing more propionic acid, a known hydrogen sink, which likely reduced hydrogen yield. Overall, alkali pretreatment emerged as the most promising strategy for enhancing biohydrogen production from OFMSW through dark fermentation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe analytical facility availed at the Environmental Engineering Division, Department of Civil Engineering, IIT Madras is gratefully acknowledged.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding declaration\u003c/p\u003e\n\u003cp\u003eThis research work has emanated from the Trendsetter Award of the Energy Consortium at IIT Madras. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDeclaration of Competing Interests\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003eAuthor information and contributions\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEnvironmental Engineering Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, India \u0026ndash; 600 036.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShreyas S \u0026ndash; Conceptualisation, data curation, formal analysis, investigation, writing \u0026ndash; original draft\u003c/p\u003e\n\u003cp\u003eGhurupreya Ramesh - Conceptualisation, data visualisation, formal analysis, investigation, writing \u0026ndash; original draft\u003c/p\u003e\n\u003cp\u003eMohanakrishnan Logan \u0026ndash; Conceptualisation, Funding acquisition, Project Administration, Supervision, Writing \u0026ndash; review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr. Mohanakrishnan Logan ([email protected])\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available in the Zenodo repository [https://doi.org/10.5281/zenodo.15592895].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmadi-Pirlou, M., Ebrahimi-Nik, M., Khojastehpour, M., \u0026amp; Ebrahimi, S. 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Radiation Physics and Chemistry, 97, 349\u0026ndash;353. https://doi.org/10.1016/j.radphyschem.2013.07.026\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"clean-technologies-and-environmental-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctep","sideBox":"Learn more about [Clean Technologies and Environmental Policy](https://www.springer.com/journal/10098)","snPcode":"10098","submissionUrl":"https://submission.nature.com/new-submission/10098/3","title":"Clean Technologies and Environmental Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Biohydrogen, pre-treatment, organic fractions of municipal solid waste, anaerobic fermentation, volatile fatty acids","lastPublishedDoi":"10.21203/rs.3.rs-6906247/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6906247/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the rising need for sustainable energy solutions and efficient waste management, converting organic waste into biohydrogen offers a dual benefit. This study explores the potential of the Organic Fraction of Municipal Solid Waste for hydrogen production, applying thermal, acid, and alkali pretreatment techniques to enhance substrate digestibility. Prior to the application of these techniques, a heat-shock pretreatment was employed on the anaerobic inoculum to suppress hydrogen-consuming microbes and favour hydrogenogenic activity. Among the strategies tested, alkali (followed by acid and thermal) pretreatment resulted in significant improvement in substrate solubilization, with the highest sCOD and volatile solids reduction. In Biochemical Hydrogen Potential assays, it yielded a peak value of about 190 mL H₂/g VS, nearly six times higher than the untreated control. GC-TCD analysis revealed rapid hydrogen evolution within the first 24 hours, while VFA profiling indicated acetic acid dominance, a known precursor for hydrogen production. These findings suggest that alkaline pretreatment combined with inoculum conditioning is a promising route for maximising hydrogen yields.\u003c/p\u003e","manuscriptTitle":"Effect of alkali, acid and thermal pretreatment techniques on biohydrogen production from organic fraction of municipal solid waste","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-11 16:47:16","doi":"10.21203/rs.3.rs-6906247/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-16T14:50:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-19T22:46:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T15:46:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114688910326726489585086879957252499615","date":"2025-11-09T02:11:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47467262285175490811156879751611228654","date":"2025-11-03T17:30:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184777540993482340251050595011399791412","date":"2025-07-14T10:47:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-09T11:50:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-08T23:29:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-17T10:39:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clean Technologies and Environmental Policy","date":"2025-06-16T13:48:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clean-technologies-and-environmental-policy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ctep","sideBox":"Learn more about [Clean Technologies and Environmental Policy](https://www.springer.com/journal/10098)","snPcode":"10098","submissionUrl":"https://submission.nature.com/new-submission/10098/3","title":"Clean Technologies and Environmental Policy","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3e7be846-bbc3-473b-9d18-49a048b431ff","owner":[],"postedDate":"July 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T19:53:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-11 16:47:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6906247","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6906247","identity":"rs-6906247","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}