Kinetic profiles and efficiency disparities in biomethane production mediated by indigenous versus exogenous microbial consortia

Kinetic profiles and efficiency disparities in biomethane production mediated by indigenous versus exogenous microbial consortia | 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 Kinetic profiles and efficiency disparities in biomethane production mediated by indigenous versus exogenous microbial consortia Xueru Chen, Yuan Bao, Xingui Wang, Yiliang Hu, Zexi Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9470237/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Biomethane is a clean, renewable, and eco-friendly unconventional natural gas resource that has attracted widespread global attention. However, the differences in biomethane generation and the constraining factors under the drive of indigenous versus exogenous microorganisms remain unclear. To address this, anaerobic fermentation experiments simulating coal‑derived biomethane generation were conducted using two distinct microbial sources: indigenous microorganisms enriched from fresh coal samples from the study area and exogenous microorganisms optimized under laboratory conditions. Five representative coal samples from the Wuguantun and Baode mining areas were used as carbon substrates. The efficiency of biomethane production was evaluated based on gas chromatography and analysis using four kinetic models. By integrating methods including coal petrographic and proximate analyses, 16S rRNA high-throughput sequencing, and Fourier transform infrared spectroscopy, principal component analysis and metabolic pathway analysis were applied to systematically elucidate the main controlling factors and synergistic mechanisms governing biomethane generation. The results indicate that although both indigenous and exogenous microorganisms follow a similar three‑stage process during coal‑degrading methanogenesis, their gas production efficiencies differ significantly. Bioaugmentation with exogenous microbial consortia systematically optimized the gas‑generation process, increasing the maximum methane potential ( A 0 ) by approximately 80% on average, shortening the lag phase ( λ ) by about 55% on average, and significantly enhancing the maximum methane production rate ( µ m ). Coal chemical structure was identified as the primary factor controlling gas‑production variability, with high H/C, and a high aliphatic structures (A al /A ar , CH 2 /CH 3 ), and moderate O/C serving as the most critical predictors, demonstrating excellent bioavailability. Biomethane output is governed by a three‑level synergistic mechanism of “coal physicochemical structure–microbial function–metabolic pathway”: the physicochemical structure of coal sets the upper limit of potential; the functional gene abundance of the microbial community determines substrate degradation efficiency; and the distribution of downstream methanogenic pathways ultimately governs biomethane conversion efficiency. This study not only deepens the understanding of the complex biogeochemical process of coal bioconversion but also provides key scientific evidence for refining theoretical models of biomethane generation. Biomethane Indigenous microorganisms Exogenous microorganisms Kinetic model Constraining factors Methanogenic pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Highlights 1. Exogenous microbial consortia demonstrate markedly higher coal-degrading methanogenic efficiency than indigenous ones. 2. H/C, A/A and CH/CH are key predictors of biomethane generation potential. 3. A three-level synergy—coal structure, microbial function, and metabolic pathway—governs biomethane production. 1. Introduction Coal has always maintained a dominant position in China’s primary energy structure [ 1 ]. The direct combustion of coal leads to significant environmental challenges, driving the global search for cleaner and more efficient utilization technologies as a key research priority [ 2 – 3 ]. It was recognized in the early 20th century that coal can be metabolized by microorganisms to produce methane [ 4 ]. However, it was not until the 1980s that the aerobic biodegradation of coal by bacteria and fungi [ 5 ] and the generation of biomethane by anaerobic microorganisms [ 6 ] were successfully achieved in laboratory settings. Considering that the associated gases in coal are predominantly methane—a renewable and environmentally friendly energy source constituting more than 20% of the global methane reserves [ 7 ] —these gases are classified into primary and secondary biogenic gas closely linked to coalification processes. The majority of gas present in coal seams originates from secondary biogenic methane [ 8 ]. To further quantify these resource, Hu et al. applied a binary mixing discrimination model for calculation, revealing that secondary biogenic methane constitutes between 10% and 27% of global coalbed methane resources [ 8 ]. Currently, it has been established that domesticating indigenous microorganisms in laboratory environments is feasible. Studies have found that adding trace elements or nutrients can enhance microbial activity and significantly improve the degradation efficiency of coal [ 9 , 10 ]. In the United States, companies such as Luca and Ciris Energy have successively conducted industrial trials in basins like the Sydney Basin and the Powder River Basin [ 11 , 12 ]. Notably, the abundance and activity of indigenous microorganisms in coal seams are generally low. Therefore, some researchers have proposed using exogenous microorganisms to enhance biomethane production [ 13 – 15 ]. For instance, Luo et al. analyzed the microbial gasification processes and methane production differences from five coal samples under four microbial sources, indicating certain differences in microbial community composition between indigenous and exogenous microorganisms. Compared to exogenous microorganisms, indigenous microorganisms showed no significant advantage in methane production efficiency across different coal samples [ 16 ]. However, Guo et al. found that acclimated exogenous microorganisms outperformed indigenous microorganisms in methane production from Gansu coal and Inner Mongolia coal. Furthermore, in the later stages of coal biodegradation, the exogenous microorganisms exhibited a more pronounced utilization of the CO 2 reduction methanogenesis pathway [ 17 ]. The above research has demonstrated that both exogenous and indigenous microorganisms can degrade coal and produce methane, although they differ in methanogenic efficiency and microbial community structure. While issues concerning biogas production processes and microbial succession have been largely addressed, several key scientific questions remain: (1) Why does the same efficient exogenous microbial consortium exhibit significant differences in methane production potential and kinetics when degrading different coal samples, and what underlying mechanisms control these variations? (2) Previous studies often correlate methane yield with isolated coal parameters such as coal rank or vitrinite reflectance, lacking a systematic assessment of coal chemical structure—particularly at the molecular functional group level—which hinders the establishment of reliable predictive indicators for methane production potential. (3) Current understanding of coal–microorganism interactions remains largely descriptive, correlating methane production with community composition. How coal chemical structure influences microbial functional gene expression and regulates methanogenic pathways is still unclear. To address these questions, this study simulated anaerobic fermentation using two microbial sources: indigenous consortia enriched from fresh coal samples of the studied seams, and an acclimated exogenous consortium from other regions. Anaerobic degradation experiments were conducted on coals from different seams in the Wuguantun (Datong Basin) and Baode (Ordos Basin) blocks in China. By integrating methane production kinetics, comprehensive coal characterization (including ultimate, proximate, and quantitative functional group analyses), and microbial functional gene prediction, this study applied Principal Component Analysis (PCA) to reveal how coal physicochemical structure—especially aromatic, aliphatic, and oxygen-containing functional groups—controls biomethane production. The research aims to identify the main factors causing differences in methanogenic efficiency between indigenous and exogenous microorganisms, and to clarify the synergistic mechanism of “coal physicochemical structure – microbial degradation function – methanogenic metabolic pathway” under different microbial drives. The findings are expected to provide key structural indicators for assessing coal seam biomethane potential and to offer theoretical and data support for coal bioconversion engineering. 2. Materials and methods 2.1 Coal sample preparation and bacterial sources The coal samples used in this study were collected from the Nos. 7, 11, and 12 coal seams of the Datong Formation in the Wuguantun mining area, and the Nos. 4 + 5 and 8 + 9 coal seams of the Shanxi and Taiyuan Formations in the Baode block, respectively. After crushing and sieving, samples with a particle size of 60–80 mesh were collected and dried for subsequent use. Each sample was split into two portions using the quartering method. One portion was used for petrographic maceral analysis, proximate analysis, ultimate analysis, and Fourier transform infrared spectroscopy (FTIR) of the raw coal. The other portion was reserved for microbial degradation experiments. The raw coal samples were designated as WGT-1, WGT-2, WGT-3, BD-1, and BD-2. Samples subjected to degradation by indigenous and exogenous microorganisms were labeled as B-X and Y-X, respectively, where “X” corresponds to the raw coal sample designation. Five distinct indigenous microbial consortia were enriched from fresh coal samples of their respective seams within the study area and were labeled WGT1, WGT2, WGT3, BD1, and BD2. One exogenous microbial consortium, labeled HN1, consisted of a mixed fermentation culture from other regions that had been acclimated long-term in our laboratory. The methods for enrichment and cultivation of these microbial consortia followed the procedure described in reference [ 18 ]. Successful enrichment was confirmed at the end of the enrichment and scale-up cultivation phases by detecting methane in the headspace of the anaerobic bottles using gas chromatography, verifying the consortia were active and ready for subsequent experiments. 2.2 Anaerobic fermentation experiment Anaerobic fermentation experiments were performed on each coal sample using its corresponding indigenous and exogenous microbial consortia. The experimental procedure was as follows: raw coal samples were exposed to ultraviolet light for 30 minutes in a clean bench. A methanogenic culture medium was prepared according to reference [ 19 ]. The prepared medium and all required experimental materials (anaerobic bottles, conical flasks, glass rods, beakers, etc.) were sterilized by autoclaving at 121°C for 30 minutes. After cooling, 10 g of coal sample, 30 mL of the indigenous or exogenous microbial inoculum, and 300 mL of the cooled sterile medium were aseptically transferred into separate 500 mL anaerobic bottles. All inoculation steps were performed inside an anaerobic glove box. The headspace of each bottle was purged with high-purity nitrogen gas (99.99%) for 5–10 minutes before sealing with butyl rubber stoppers. The sealed bottles were then incubated in a constant-temperature incubator at 35°C for 49 days to simulate biomethane production. For each experimental condition, three parallel sample sets and three control sets were prepared. The control sets contained all components except the coal sample under otherwise identical conditions. A schematic diagram of the experimental workflow is presented in Fig. 1 . 2.3 Experimental testing methods 2.3.1 Gas composition measurement Gas analysis was performed using an Agilent 8860 gas chromatograph (GC) equipped with a 50 m × 320 µm × 8 µm column, a TCD detector, and an FID flame ionization detector. A Hamilton 1710 gas-tight syringe was used for manual injection with an injection volume of 2 mL. The operating conditions were as follows: injector temperature 200°C, oven temperature 30°C, and detector temperature 250°C. High-purity nitrogen served as the carrier gas, with a makeup gas flow rate of 5 mL/min at the front detector. Every 7 days, 2 mL of gas was manually extracted from the headspace of the anaerobic bottles using a syringe and quantitatively analyzed by GC. The retention time for methane was 2.03 min. Methane yield was calculated using the following formula: where: n is the molar quantity of methane per gram of coal (µmol/g); S sample and S standard are the chromatographic peak areas of the sample and the standard gas, respectively; P is atmospheric pressure (1.01325 × 10 5 Pa); V is the headspace volume of the anaerobic bottle (mL); c is the known concentration of methane in the standard gas (1.00597%); R is the ideal gas constant [8.31441 J/(mol·K)]; T is the thermodynamic temperature of the experimental environment (K); and M coal is the mass of the coal sample (g). 2.3.2 DNA extraction and sequencing DNA was extracted from microbial samples collected after inoculation using the MoBio DNeasy PowerSoil kit. The extracted DNA was then evaluated by 0.8% agarose gel electrophoresis to confirm fragment size, followed by quantification via UV-Vis spectrophotometry. Qualified DNA extracts were subjected to PCR amplification. Bacterial 16S rRNA gene fragments were amplified using primers 338F and 806R, while archaeal fragments were amplified using primers 1106F and 1378R [ 20 ]. Microbiome bioinformatic analysis of the sequencing data was performed using QIIME2 2019.4 and the R package (v3.2.0), following the tutorial available at https://docs.qiime2.org/2019.4/tutorials/ . Taxonomic composition and abundance were visualized using MEGAN [ 21 ] and GraPhlAn [ 22 ]. 2.3.3 Micro-Fourier transform infrared spectroscopy FTIR measurements for all coal samples were conducted on a WQF-530 micro-Fourier transform infrared spectrometer. Each sample was mixed with KBr at a mass ratio of 1:100, thoroughly ground, and pressed into a thin pellet. FTIR spectra were acquired by accumulating 16 scans at a resolution of 2 cm − 1 over a wavenumber range of 400–4400cm − 1 [ 8 , 23 ]. To quantitatively characterize the chemical structure of the coal, curve-fitting analysis of the FTIR spectra was performed using Origin software. A combination of Gaussian peaks was applied to model the band shapes and areas. The fitting parameters were adjusted to minimize the variance between the experimental and fitted curves. 3. Results and analysis 3.1 Proximate and ultimate analysis The results of proximate analysis, ultimate analysis, and vitrinite reflectance measurements for the five raw coal samples are presented in Table 1 . Vitrinite reflectance, proximate analysis, and ultimate analysis were performed in accordance with ISO 7404-5, ISO 17246, and ISO 17247 [ 24 – 26 ], respectively. The maximum vitrinite reflectance ( R o, max ) of all samples ranged from 0.76% to 0.83%, classifying them as medium-rank coals. The R o, max values for the WGT samples (0.73%–0.79%) were slightly lower than those for the BD samples (0.80%–0.81%). The average moisture content (1.39 wt.%) and ash yield (avg. 8.49 wt.%) of the WGT samples were lower than those of the BD samples (moisture: 1.73 wt.%; ash: 23.14 wt.%). In contrast, the average volatile matter yield of the WGT samples (avg. 34.03 wt.%) was higher than that of the BD samples (avg. 29.13 wt.%), and the atomic hydrogen-to-carbon (H/C) ratio of the WGT samples (0.059–0.062) was also slightly higher than that of the BD samples (0.050–0.051). The total sulfur content (St, daf) of all coal samples ranged from 0.19 wt.% to 0.91 wt.%, classifying them as low-sulfur coals on a dry basis. Table 1 Ultimate/Proximate analysis results of raw coal samples Sample numbers R o, max (%) Proximate analysis (wt %) Ultimate analysis (wt %) H/C O/C M ad A ad V daf Q gr,d FC ad C daf H daf N daf O daf S t,daf WGT-1 0.79 1.36 7.64 34.43 29.85 56.57 79.58 4.75 1.93 10.06 0.19 0.723 24.292 WGT-2 0.76 1.44 8.93 33.57 30.48 56.06 82.50 4.64 2.33 10.57 0.47 0.681 24.620 WGT-3 0.73 1.36 8.89 34.08 27.94 55.67 77.41 4.78 1.68 13.32 0.42 0.748 33.066 BD-1 0.81 1.54 34.06 34.08 19.85 42.45 81.38 4.15 1.40 12.00 0.30 0.617 28.201 BD-2 0.80 1.92 11.46 33.57 29.18 57.54 81.77 4.23 1.52 10.94 0.91 0.626 25.833 Note: R o, max is maximum vitrinite reflectance. M ad and A ad are moisture content and ash yield of air-dried basis. V daf is volatile yield of dry ash-free basis. Q gr,d is higher calorific value on a dry basis. FC ad is fixed carbon content of air-dried basis. S t,daf is total sulfur of dry ash-free basis. 3.2 Microbial community composition 3.2.1 Bacteria The operational taxonomic unit (OTU) results for the six microbial consortia indicated that bacteria constituted over 85% of the microbial community composition, while archaea accounted for only 6%–8% of the total microbiota. Bacterial sequencing revealed that at the phylum level, the bacterial communities in all samples were predominantly composed of Firmicutes and Proteobacteria. Functional groups within these phyla, involved in hydrolysis, fermentation, and acidogenesis, are the primary participants in coal structure breakdown, representing 77.82%–99.99% of all bacteria at the phylum level (Fig. 2 a). Significant differences were observed at the genus level (Fig. 3 b). In sample WGT1, Acinetobacter was the dominant bacterial genus, accounting for 64.11%. In WGT2, Sedimentibacter was the primary dominant genus, comprising 86.45%. For WGT3, Enterobacter and Clostridium_sensu_stricto_13 were the dominant bacterial genera, representing 33.25% and 21.02%, respectively. In BD1 and BD2 samples, Azonexus and Muribaculaceae were the dominant bacterial genera, accounting for 47.50% and 10.68%, respectively. The exogenous consortium HN1 was dominated by Lysinibacillus as the primary bacterial genus, constituting 80.72%. The high abundance of Lysinibacillus in the exogenous consortium, which can utilize complex organic compounds such as methyl pyruvate and acetoacetate [ 27 ], suggests that the exogenous microorganisms may exhibit a strong hydrolytic capacity. 3.2.2 Archaeal Archaeal sequencing results revealed that at the phylum level, all samples were dominated by the phylum Euryarchaeota , accounting for 75.7%, 91.28%, 94.16%, 97.03%, 95.65%, and 99.86%, respectively (Fig. 2 c). At the genus level (Fig. 2 d), the results showed that WGT1 and WGT3 were dominated by the hydrogenotrophic genus Methanomicrobia , representing 66.38% and 93.51%, respectively; WGT2 was co-dominated by the acetoclastic genus Methanothrix and the hydrogenotrophic genus Methanomicrobia , accounting for 63.24% and 28.03%, respectively. In BD1, the hydrogenotrophic genus Methanobacterium was dominant, representing 60.23%. Sample BD2 was dominated by the hydrogenotrophic genus Methanoregula , accounting for 93.31%. The exogenous consortium HN1 was dominated by Methanosarcina , Methanobacterium , and the acetoclastic genus Methanosaeta [ 28 ], representing 32.53%, 32.14%, and 29.92%, respectively. 3.3 Characteristics of biomethane production The biomethane cumulative yield, after subtracting the control group, is shown in Fig. 3 . All coal samples produced a certain amount of methane when degraded by either indigenous or exogenous microorganisms, but significant differences in biomethane yield were observed between different samples and microbial sources. Overall, the efficiency of methane production from coal degradation was higher with exogenous microorganisms than with indigenous ones. For example, the maximum cumulative methane yield from WGT-1 coal reached 7.37 m 3 /t and 12.47 m 3 /t under degradation by indigenous and exogenous microorganisms, respectively, representing an increase of 69.21%. The highest daily methane production rates were 2.29 m 3 /t/day and 4.15 m 3 /t/day, respectively. The WGT-2 coal showed the highest increase in cumulative methane yield between indigenous and exogenous degradation, reaching 140.45% (with maximum cumulative methane yields of 5.30 m 3 /t and 12.85 m 3 /t for B-WGT-2 and Y-WGT-2, respectively). The highest daily methane production rates for these samples were 3.06 m 3 /t/day and 3.56 m 3 /t/day, respectively. For B-WGT-3 and Y-WGT-3, the maximum cumulative methane yields were 7.07 m 3 /t and 11.69 m 3 /t, respectively, an increase of 63.35%, with peak daily methane production rates of 3.01 m 3 /t/day and 4.10 m 3 /t/day. The increases in cumulative methane yield for BD-1 and BD-2 coals under indigenous versus exogenous degradation were 24.68% and 33.63%, respectively. Compared to the WGT coals, the overall increase in cumulative methane yield for BD coals under both microbial treatments was significantly lower. This difference clearly indicates that the physicochemical properties of the coal itself are one of the core intrinsic factors determining its bioconversion potential. By examining the daily biomethane yield curves, we observed that methane production exhibited multiphasic characteristics, with one or more cycles of increase and decrease. This pattern aligns with the multiphase nature of biomethane production reported in previous studies [ 29 ]. The timing of methane production peaks (e.g., at 14, 21, and 35 days) may mark critical transitions from the “acidification phase” to the “methanogenic phase” [ 23 ]. As the biological reaction progressed, fluctuations in daily methane production gradually stabilized, reflecting an overall decline in microbial activity. This further illustrates that coal bioconversion is not a linear process but rather a dynamically evolving complex of microbial ecology and metabolism over time. Additionally, the cumulative biomethane yield curves revealed that, under the influence of different microbial consortia, all five coal samples underwent three distinct phases: a slow methane production phase, a rapid methane production phase, and a plateau/cessation phase. The most favorable period for microbial degradation and methane production was Phase II. For Y-X and B-WGT samples, Phase II lasted from approximately 7 to 35 days, while B-BD samples entered Phase II later and had a shorter duration, typically between 21 and 42 days. The specific methane production process is as follows: I) Slow Methane Production Phase: Methane production was low initially, with a relatively slow rate. This is primarily because microbial communities require an adaptation period upon entering a new coal matrix environment [ 28 ]. Additionally, coal is a complex macromolecular organic substance, and microorganisms need time to synthesize and secrete specific extracellular enzymes for initial degradation. Notably, Y-X samples exhibited higher methane production and faster rates than B-X samples during this phase, mainly because long-term acclimation of exogenous microbes reduces their adaptation time to coal [ 30 ]. II) Rapid Methane Production Phase: During this period, all experimental groups established a relatively stable methane production process. Hydrolytic and fermentative bacteria continuously broke down coal macromolecules into intermediate products, which methanogens efficiently converted into methane. Y-X samples still demonstrated higher methane production than B-X samples in this phase. III) Plateau/Cessation Phase: In the later stage, methane production gradually declined and oscillated toward equilibrium, with no further increase in cumulative yield. The final cumulative methane yields ranged from 10.65 m 3 /t to 12.85 m 3 /t for exogenous groups and from 5.30 m 3 /t to 98.59 m 3 /t for indigenous groups, with exogenous groups consistently outperforming indigenous ones overall. These three stages of coal bioconversion have also been identified in other studies [ 31 – 33 ]. Variations in the timing of the first two phases [ 34 , 35 ] are likely attributable to differences in coal samples and microbial consortia used across experiments. 3.4 Functional group structure of coal The FTIR spectra of the coal samples are primarily divided into four absorption bands. The regions 700–900 cm − 1 , 1000–1800 cm − 1 , 2800–3000 cm − 1 , and 3000–3600 cm − 1 correspond to the characteristic absorption peaks of aromatic structures, oxygen-containing functional groups, aliphatic hydrocarbon structures, and hydroxyl structures, respectively [ 36 ]. The baseline-corrected FTIR spectra and their deconvoluted peak-fitting diagrams for all samples are shown in Fig. 4 . It is evident that the overall framework of characteristic absorption bands is similar across all coal samples, though the intensities of the peaks differ, indicating similar surface structures and functional groups but variations in their relative abundances. Within the 700–900 cm − 1 region, associated with aromatic structures, four main absorption bands appear, representing different aromatic ring substitution patterns. The WGT samples are dominated by di- and tri-substitutions, whereas the BD samples are characterized by tri- and tetra-substitutions. This clearly shows that the BD samples have a higher proportion of highly substituted aromatics, implying a greater degree of aromatic condensation and more stable structures. The absorption peaks in the 1033–1350 cm − 1 region belong to C–O stretching vibrations in ethers, alcohols, and phenols. The BD samples exhibit high-intensity peaks in this region, suggesting these coals may possess higher bioreactivity. These functional groups can provide effective initial sites for microbial attack, potentially shortening the methane production lag phase and enhancing overall methane yield. All samples show a prominent peak around 1600 cm − 1 , representing the aromatic C = C stretching vibration. Due to structural similarities and interactions between functional groups, the C = O stretching vibration bands overlap. Therefore, the 1650–1728 cm − 1 region is assigned to the characteristic C = O double bond vibrations, including those from carbonyl groups in carboxylic acids and esters. The absorption peaks at 2850 and 2920 cm − 1 represent the symmetric and asymmetric stretching vibrations of aliphatic CH 2 groups, while peaks at 2870 and 2950 cm − 1 correspond to the symmetric and asymmetric stretching of aliphatic CH 3 groups. Similarly, the WGT samples are rich in aliphatic components, which is a positive indicator for assessing their biodegradability. In the hydroxyl band, a strong absorption peak around 3400 cm − 1 corresponds to hydrogen bonds formed by self-associated hydroxyl groups. A pronounced hydroxyl peak signifies a significant interaction interface between the coal sample and microorganisms. However, whether this ultimately promotes or constrains biodegradation efficiency depends on the specific chemical environment of the hydroxyl groups and their overall role within the macromolecular structure of the coal. By performing peak deconvolution on the FTIR spectra of different coal samples and calculating functional group structural parameters [ 37 ], the differences in surface functional groups among the samples were further investigated, with the results presented in Table 2 . The functional group characteristics are primarily analyzed in three aspects: first, parameters related to aromatic structures, including the degree of aromatic ring condensation (DOC), aromaticity (AR), and aromatic carbon content ( fa ); second, parameters related to aliphatic structures, including the abundance of aliphatic structures (A al /A ar ) and the length/branching degree of aliphatic side chains (CH 2 /CH 3 ); and third, the ratio of oxygen-containing functional groups (C = O/C = C). The results show that, overall, the AR and fa values are lower for WGT coals (AR avg. 0.36, fa avg. 0.62) than for BD coals (AR avg. 1.26, fa avg. 0.84). The CH 2 /CH 3 and A al /A ar ratios show minimal variation across all samples. The CH 2 /CH 3 values range from 2.16 to 2.20 for WGT coals and from 2.17 to 2.21 for BD coals. The A al /A ar values range from 0.51 to 0.59 for WGT coals and from 0.45 to 0.47 for BD coals. WGT-2 and WGT-1 exhibit relatively high C = O/C = C ratios (0.20 and 0.19, respectively), indicating the presence of abundant carbonyl/carboxyl groups in these coals. Previous research has shown that such groups serve as crucial initial sites for microbial enzymatic attack [ 38 ]. Table 2 The semi-quantitative parameters of coal samples derived from FTIR analysis Sample numbers DOC AR fa A al /A ar CH 2 /CH 3 C = O/C = C WGT-1 0.37 0.13 0.53 0.59 2.16 0.19 WGT-2 0.18 0.60 0.77 0.51 2.19 0.20 WGT-3 0.21 0.34 0.55 0.58 2.20 0.14 BD-1 0.27 1.41 0.80 0.45 2.17 0.16 BD-2 0.23 1.11 0.88 0.47 2.21 0.18 4. Discussion 4.1 Biomethane generation dynamics during coal biogasification To accurately evaluate and compare biomethane generation efficiency among different samples and under different microbial consortia, four kinetic models—the Modified Gompertz [ 39 , 40 ], Logistic [ 41 ], DoseResp [ 42 ] and SRichards [ 43 ] models—were applied to fit the methane production data. By comparing the correlation coefficients (R 2 ) (Table 3 ), the Modified Gompertz model, demonstrating the highest applicability, was selected (Eq. 2) to simulate biomethane yield and production rates from different coal seams degraded by different microbial sources (Figs. 5 a, 5 b, 5 c, 5 d, 5 e). The Modified Gompertz model is formulated as follows: where: f (t) is the cumulative methane yield (µmol/g); A 0 is the maximum methane potential (µmol/g), typically characterizing the substrate’s hydrolysability; λ is the lag phase (d), evaluating microbial adaptability to the substrate—a lower λ indicates stronger adaptability; µ m is the maximum methane production rate (µmol/g·d), representing the slope of the fitted curve and characterizing the substrate degradation rate; t is the anaerobic fermentation time (d); and T max is the time at which µ m occurs (d) [ 40 ]. Table 3 Fitting coefficient R 2 of different hydrocarbon generation kinetics models Modified Gompertz B-WGT-1 Y-WGT-1 B-WGT-2 Y-WGT-2 B-WGT-3 Y-WGT-3 B-BD-1 Y-BD-1 B-BD-2 Y-BD-2 0.980 0.979 0.896 0.981 0.950 0.977 0.895 0.989 0.985 0.991 Logistic 0.975 0.970 0.866 0.982 0.949 0.979 0.894 0.988 0.984 0.994 DoseResp 0.981 0.969 0.865 0.978 0.943 0.978 0.893 0.980 0.985 0.987 SRichards 0.979 0.956 0.867 0.972 0.925 0.979 0.887 0.989 0.984 0.993 Analysis revealed that WGT and BD samples exhibited similar simulated curves within their respective groups. Notably, degradation by the exogenous consortium demonstrated significant advantages in both “quantity” ( A 0 ↑) and “rate” ( λ ↓, T max ↓), achieving comprehensive enhancement of the bioconversion process. The increase in A 0 (Δ A 0 ) was greater for WGT coals (5.07–7.88) compared to BD coals (1.91–2.41). This likely relates to the inherently more biodegradable structure of WGT coals. Their low A 0 under indigenous degradation is primarily due to the functional limitations of the indigenous microbiome. Once high-efficiency exogenous consortia were introduced, this “high-quality” substrate was rapidly utilized, releasing its substantial potential, with a maximum increase of 147.76% observed for WGT-2. Furthermore, the exogenous consortium exhibited higher methanogenic efficiency than the indigenous one. This is because, during methane production using laboratory-acclimated mixed microbes, the microorganisms can directly utilize organic matter from the nutrient solution. Additionally, long-term acclimation reduces their adaptation time to the coal substrate [ 28 , 30 ], a finding consistent with the microbial sequencing results. The methane production rates ( µ m ) for all five coals degraded by either indigenous or exogenous microbes followed a trend of initial increase followed by decrease. The Y-X samples exhibited a shorter lag phase ( λ ) compared to the B-X samples, and the methane production peak for B-X samples was consequently delayed ( T max : B-X > Y-X). This indicates that exogenous microorganisms likely possess more efficient metabolic pathways or a more robust enzymatic system for decomposing organic matter in coal, thus requiring less time for initiation and optimization. Their superior adaptability to the coal environment allows them to adjust their metabolic activity more rapidly to maximize methanogenic efficiency. 4.2 Analysis of constraining factors for biomethane production efficiency The physicochemical properties of coal, controlled by depositional environment and coalification processes, are key factors influencing coal bioconversion and consequently constrain the efficiency of biomethane generation [ 2 , 3 ]. When evaluating samples, it is insufficient to consider only single or a few specific characteristics; instead, a comprehensive, systematic, and scientific assessment is essential [ 44 ]. Principal Component Analysis (PCA) serves as a convenient tool for this purpose, utilizing dimensionality reduction to transform multiple indicators into a few comprehensive components while retaining a substantial portion of the information from the original variables. PCA was performed based on five sets of gas production data obtained from the degradation of five different coals (WGT-1, WGT-2, WGT-3, BD-1, and BD-2) by the exogenous microbial consortium. This analysis integrated various physicochemical parameters of the raw coals—including elemental content (C, H, O, N, S), proximate analysis (moisture, ash, volatile matter, fixed carbon), and functional group indices (DOC, AR, f a, A al /A ar , CH 2 /CH 3 , C = O/C = C) —as well as corresponding kinetic parameters ( A 0 , λ , µ m ). The results identified three principal components with a cumulative variance contribution rate of 91.52%, integrating most of the physicochemical information. The contribution rates of the first three principal components were 44.64%, 25.57%, and 21.25%, respectively (Fig. 6 a). PC1 exhibited the highest contribution, representing the most influential factors in the coal matrix. All WGT samples had positive scores on PC1 (0.27–1.10), indicating homogeneity in the properties represented by this component. In contrast, BD samples had negative scores (-1.14 to -0.95) (Fig. 6 b), demonstrating that PC1 is the primary factor distinguishing gas production potential between WGT and BD coals. Here, positive scores can be interpreted as indicating the “easily degradable” end of the spectrum, while negative scores represent the “difficult to degrade” end. Based on loading analysis, the high positive loadings (promoting degradation) on PC1 were H daf (0.316), H/C (0.308), and A al /A ar (0.314) (Fig. 6 c). A high H/C ratio signifies abundant aliphatic hydrogen, which serves as a primary hydrogen source for methanogenesis. A high Aal/Aar indicates a relatively rich aliphatic structure [ 2 , 36 ]. The high negative loadings (inhibiting degradation) on PC1 were AR (-0.314), f a (-0.299), and A ad (-0.206) (Fig. 6 c). Highly aromatic molecular frameworks are exceptionally stable and difficult for microbial enzyme systems to cleave or oxidize [ 45 ]. Higher ash content reduces the proportion of bioavailable organic carbon in coal, leading to insufficient carbon supply for microorganisms and consequently restricting their growth and metabolism [ 46 ]. Concurrently, the lag phase ( λ ) showed a strong negative correlation with PC1 (-0.198), meaning that coals with higher PC1 scores also initiated biodegradation more rapidly (smaller λ ). WGT samples had higher H/C and richer aliphatic structures, corresponding to higher maximum methane production potential and shorter lag time; in contrast, BD samples had higher aromaticity and poorer biodegradability, manifesting as a longer lag time. In the PC2 dimension, the high positive loadings included CH 2 /CH 3 (0.402), O daf (0.286), and O/C (0.308), while µ m (-0.429) and DOC (-0.251) exhibited negative loadings (Fig. 6 c). Sample scores showed that WGT-3 (1.360) was much higher than the other samples, WGT-2 (-0.39) and WGT-1 (-0.80) were negative, and BD-1 (-0.90) and BD-2 (0.74) were located in the negative and positive regions, respectively. Considering the loading directions, coals with high CH 2 /CH 3 and high O/C (represented by WGT-3) corresponded to higher µ m , while coals dominated by aromatic structures (represented by BD-1) exhibited the lowest µ m . Therefore, structures with longer aliphatic chains and lower degrees of aromatic condensation may be more readily degraded via pathways such as β -oxidation and exhibit relatively open physical structures [ 47 ], which also confirms the understanding that aliphatic side chains are the priority attack sites for extracellular enzymes of hydrolytic fermentative bacteria. In the PC3 dimension, the positive loadings included C = O/C = C (0.372), Q gr,d (0.334), and FC (0.299), while the negative loadings included O/C (-0.306) and V daf (-0.280). Meanwhile, A 0 exhibited a positive loading (0.182) on PC3, indicating that the positive end of PC3 contributes positively to gas production potential. Therefore, PCA analysis reduced the multidimensional coal quality parameters affecting microbial gas production to three independent principal components and revealed a hierarchical control model. Within this model: PC1 is the “bioavailability” component. Its high scores are significantly correlated with high H/C, high aliphatic structures (A al /A ar ), as well as low ash content (A ad ) and low aromaticity (AR, f a). It is the key dimension distinguishing WGT coals (easily degradable) from BD coals (difficult to degrade) and controlling their maximum gas production potential ( A 0 ) and initiation speed ( λ ). PC2 is the “gas production rate regulation” component. Its high scores are significantly correlated with high aliphatic chain length (CH 2 /CH 3 ) and high O/C. This component determines the peak intensity ( µ m ) of the gas production process, with coals characterized by high aromatic structures (e.g., BD-1) scoring the lowest on this dimension,exhibiting a restricted rate. PC3 fine-tunes the degradation process at a more detailed chemical structure level, involving influences such as a high proportion of reactive oxygen-containing functional groups (C = O/C = C), high calorific value (Q gr,d ), and low volatile matter (V daf ), providing an auxiliary positive contribution to gas production potential ( A 0 ). This hierarchical model clarifies that the biogenic gas production performance of coal is not controlled by a single parameter but is governed by the hierarchical characteristics of its chemical structure: the foundational potential is established by its “bioavailability” (PC1), the gas production rate is subsequently regulated by “gas production rate regulation” (PC2), and finally fine-tuned by PC3. Together, these three components constitute the core evaluation system for assessing the microbial gasification potential of coal, among which high H/C, rich aliphatic chains, high CH 2 /CH 3 , and moderate O/C are the key indicators for medium-rank coal selection. 4.3 The pathways and coordination mechanisms of biomethane generation Extensive research has confirmed the crucial role of microorganisms in converting coal into biomethane [ 48 , 49 ], a process that requires the synergistic efforts of various organisms, including hydrolytic and fermentative bacteria, acetogenic bacteria, and methanogenic archaea [ 50 – 53 ]. The bioconversion of coal primarily involves two major phases: the acidification phase and the methanogenic phase [ 29 ]. The acidification phase encompasses key pathways such as glycolysis, saturated hydrocarbon degradation, fatty acid synthesis and consumption, and aromatic hydrocarbon degradation [ 54 , 55 ]. The subsequent methanogenic phase converts the methane precursors generated during acidification into biomethane via hydrogenotrophic, acetoclastic, and methylotrophic pathways [ 56 – 58 ]. Based on 16S rRNA gene sequencing results, the PICRUSt software was used to predict the metabolic functions of the microbial communities. PICRUSt predictions for metabolic functions have an accuracy of approximately 84%–95%, providing a good reflection of functional gene composition and enabling the exploration of differences in methane generation pathways from coal degraded by different microbial sources. Figure 7 illustrates the changes in the abundance of key functional genes related to the generation of methanogenic precursors during the acidification phase of coal bioconversion. Glycolysis, as the core pathway for microbial energy metabolism, shows gene abundances that directly reflect the initial capacity of the microbial consortium to utilize soluble organic matter and carbohydrates from coal. Analysis of genes for key glycolytic steps (from glucose to pyruvate) indicates the occurrence of glycolysis at critical time points during bioconversion. The exogenous consortium HN1 exhibited significantly higher abundances for most of these genes (e.g., Hexokinase EC 2.7.1.2, 6-Phosphofructokinase EC 2.7.1.11, Pyruvate kinase EC 2.7.1.40, etc.) compared to all indigenous consortia. This provides a genetic basis for the observation in gas production experiments that coal degradation by the exogenous consortium yields higher gas volumes than by indigenous consortia. The initiation of saturated hydrocarbon degradation primarily follows two modes: terminal oxidation of alkanes and alkyl side chains (EC 1.14.15.3) [ 59 ] and hydrolysis by lipases (EC: 3.1.1.3) [ 60 ]. Both reactions produce fatty acids, which are rapidly degraded via β -oxidation, ultimately yielding methane and carbon dioxide [ 47 ]. Among the indigenous consortia, gene abundances related to these pathways were markedly higher for samples from the BD area than for those from the WGT area, with BD1 showing particularly high abundances. Polycyclic or monocyclic aromatic hydrocarbons (e.g., toluene, ethylbenzene) undergo a series of β -oxidation-like reactions where side chains are modified and shortened, ultimately converging in their conversion to benzoyl-CoA. Here, benzoyl-CoA reductase (EC: 1.3.8.4) acts as a key activation enzyme [ 61 ]. This leads to the formation of a linear fatty acid derivative, which then enters the conventional β -oxidation pathway and is completely degraded to acetyl-CoA, providing precursors for methane generation [ 47 ]. The exogenous consortium HN1 demonstrated substantially higher abundances of these core anaerobic aromatic degradation genes, which is likely key to its efficient gas production across all coal samples. In contrast, the aromatic degradation capacity of indigenous consortia was generally limited and exhibited regional variation. In summary, comparison of the functional gene profiles of the original microbial consortia clearly indicates that the exogenous consortium HN1 possesses the most comprehensive genetic arsenal for substrate degradation, which aligns with its superior gas production performance. Conversely, the inherent advantages of BD1 in glycolysis and hydrocarbon degradation potential accurately predicted its status as the best-performing indigenous consortium. Table 4 presents the gene coding for metabolic enzymes related to methanogenic pathways in the six microbial consortia. As shown in Fig. 8 , the methanogenic phase can be categorized into three types based on substrate: hydrogenotrophic, acetoclastic, and methylotrophic. Among these three methanogenic pathways, enzyme abundances related to the hydrogenotrophic pathway were the highest, while gene abundances for the acetoclastic pathway were the lowest. Enzymes associated with the methylotrophic pathway were found in substantial quantities, intermediate between the other two. Within the indigenous microbial systems, enzyme counts related to the hydrogenotrophic pathway were generally higher for BD area samples than for WGT samples. The opposite trend was observed for the methylotrophic pathway, where WGT samples exhibited higher abundances. Concurrently, WGT samples also showed an advantage in the acetoclastic pathway, evidenced by higher abundances of genes such as [EC: 6.2.1.1], [EC: 2.7.2.1], [EC: 2.3.1.8], and [EC: 1.5.98.2]. This pattern in functional gene abundance is entirely consistent with the dominant genera identified through microbial sequencing: hydrogenotrophic Methanobacterium and Methanoregula were dominant in the BD area, while the WGT area hosted hydrogenotrophic Methanomicrobia acetoclastic Methanothrix , and the metabolically versatile Methanosarcina . This indicates that the simultaneous presence of all three methanogenic pathways does not necessarily guarantee higher gas production; sometimes, a single dominant pathway can also yield high output. Furthermore, we observed that the exogenous microbial system generally harbored a greater diversity and higher relative abundance of these functional genes, belonging to well-acclimated consortia with complete metabolic capabilities. This is a primary reason why the same coal, when degraded, yielded better gas production with the exogenous consortium than with the indigenous ones. Table 4 Abundance of biological enzymes related to methanogenic metabolism Methanogenic metabolic pathway Enzyme number Description Enzyme abundance WGT-1 WGT-2 WGT-3 BD-1 BD-2 HN-1 Hydrogenotrophic methanogenesis 1.1.1.302 2,5-diamino-6-(ribosylamino)-4(3H)-pyrimidinone 5’-phosphate reductase 2303.88 2515.55 2613.23 2647.69 3401.92 2906.2 2.3.1.101 Formylmethanofuran–tetrahydromethanopterin N-formyltransferase 1874.31 1348.06 1301.68 2716.78 1711.32 2758.85 3.5.4.27 Methenyltetrahydromethanopterin cyclohydrolase 1562.4 1441.56 1297.79 1800.15 1707.08 1648.72 1.5.98.1 Methylenetetrahydromethanopterin dehydrogenase 1520.72 1265.42 1297.07 1793.33 1704.36 1753.71 2.1.2.1 Glycine hydroxymethyltransferase 1784.6 1569.61 1326.35 1794.6 1758.25 1522.37 2.1.1.86 Tetrahydromethanopterin S-methyltransferase 16135.8 11781.7 12968.7 14914.6 13649.9 15078.7 Methylotrophic methanogenesis 2.1.1.248 [Trimethylamine-corrinoid protein] Co-methyltransferase 1659.78 2263.76 3914.37 190.586 46.9258 2626.41 2.1.1.249 [Dimethylamine-corrinoid protein] Co-methyltransferase 1784.91 1614.35 1326.28 190.586 79.5912 1522.37 2.1.1.250 [Methylamine-corrinoid protein] Co-methyltransferase 1077.17 2180.17 2593.37 127.057 84.2503 2689.48 Aceticlastic methanogenesis 6.2.1.1 Acetate-CoA ligase 4742.38 3716.59 1490.77 5787.09 6856.08 4583.64 2.7.2.1 Acetate kinase 17.3314 522.529 1289.81 105.157 50.8433 552.32 2.3.1.8 Phosphate acetyltransferase 17.2725 499.812 1289.94 101.574 69.8788 552.32 1.5.98.2 5,10-methylenetetrahydromethanopterin reductase 1648.72 1266.22 1297.39 1792.17 1705.99 1521.77 Common pathway 1.8.98.1 CoB-CoM heterodisulfide reductase 9905.86 8518.85 11458.4 7553.81 6846.51 13039.8 2.8.4.1 Coenzyme-B sulfoethylthiotransferase 5726.74 4565.4 3905.55 7004.23 5124.45 6880.95 Building upon the comprehensive analysis and discussion, this study proposes a synergistic mechanism of “physicochemical structure–microbial function–metabolic pathway” driven by indigenous and exogenous microorganisms. First, the physicochemical structure of coal acts as the primary screening net, determining the scale and accessibility of substrates available for microbial utilization. This constitutes the potential control tier. Second, the inoculated microbial community (indigenous or exogenous) serves as the “bioreactor”, whose completeness and abundance of functional genes dictate the rate and extent of substrate degradation. The introduction of highly efficient exogenous consortia can effectively overcome the functional limitations inherent to the indigenous flora. This represents the efficiency modulation tier. Finally, the acidification products drive specific methanogenic metabolic pathways, whose flux matching and conversion efficiency accomplish the final energy transformation from chemical energy to biomethane. This is the terminal conversion tier. The ultimate output of the entire process ( A 0 , λ , µ m ) is the result of the dynamic coupling, stepwise transmission, and amplification across these three tiers. This mechanism clarifies why the same microbial consortium performs differently on various coal samples and provides a theoretical basis for optimizing coalbed bioconversion processes. 5. Conclusions This study examined the kinetics, controlling factors, and synergistic mechanisms of biomethane production from regionally distinct coals degraded by indigenous and exogenous microorganisms. The main conclusions are as follows: (1) Both indigenous and exogenous consortia exhibited similar gas-generation trends across three consistent stages: adaptation and rapid production (0–14 d), stable and slow production (14–35 d), and plateau/cessation (35–49 d). Bioaugmentation with an efficient exogenous consortium (HN1) systematically enhanced the methane‑producing process, increasing the maximum methane potential ( A 0 ↑), shortening the lag phase ( λ ↓), and advancing the gas‑production peak ( T max ↓). This improvement was most pronounced for easily degradable coals with high aliphatic and low aromatic contents (e.g., WGT-2), where methane yield increased by up to 147.76%. (2) The chemical structure of medium-rank coals, particularly H/C, A al /A ar , and CH 2 /CH 3 , serves as the primary and quantifiable key factor controlling their biogenic gas potential. Principal Component Analysis (PCA) revealed a hierarchical model governing coal biogenic gas production efficiency: the primary tier is “Bioavailability”, which is mainly dominated by high H/C, high aliphatic structures (A al /A ar ), as well as low ash content (A ad ) and low aromaticity (AR, f a). Its scores distinguish easily degradable (WGT) from recalcitrant (BD) coals and determine the upper limits of gas potential ( A 0 ) and initiation speed ( λ ). The secondary tier achieves regulation of the maximum methane production rate ( μ m ) via CH 2 /CH 3 and O/C. The tertiary tier involves fine-tuning through fine chemical structural parameters such as oxygen-containing functional groups and calorific value. (3) Coal biodegradation and methanogenesis result from a synergistic interplay among coal physicochemical structure, microbial function, and metabolic pathways. Coal structure predetermines substrate bioavailability and potential limits. The abundance and completeness of microbial functional genes (e.g., acidogenesis and aromatic degradation genes in the exogenous consortium) govern the efficiency of substrate breakdown. The resulting degradation precursors then channel carbon flux into specific methanogenic pathways (hydrogenotrophic, acetoclastic, etc.), whose coordination ultimately determines methane conversion efficiency. This mechanistic framework provides a theoretical basis for enhancing coal bioconversion. Declarations CRediT authorship contribution statement Xueru Chen : Conceptualization, Methodology, Data curation, Writing – original draft, Writing – review & editing. Yuan Bao : Conceptualization, Formal analysis, Visualization, Investigation, Writing – review & editing, Supervision, Funding acquisition. Xingui Wang : Visualization, Writing – review & editing. Yiliang Hu : Methodology, Software, Validation. Zexi Zhu : Investigation. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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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-9470237","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634839593,"identity":"4f90226f-c033-4c0c-adbc-1ef310ccc2ce","order_by":0,"name":"Xueru Chen","email":"","orcid":"","institution":"Xi'an University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xueru","middleName":"","lastName":"Chen","suffix":""},{"id":634839597,"identity":"2b10c571-5b3e-46ed-9e66-9be733f5a7a4","order_by":1,"name":"Yuan Bao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYFACHiCukAMzDzwgXssZY4iWBKK1MLZBtDAQpcXgAO/BDx/nGcgZXDv8EGiLnZxuA0EtfMmSM7cZGEvOTjMAakk2NjtAUAuPgTTvtj+J/dIJIC0HErcRocX4N+8cg8Q26fQPRGsxk+ZtMADakkOkLZKH+dIsZxwD+SWn4ECCARF+4Tvee/jGhxpgiN1O3/zhQ4WdHEEtCodR3UlAOQjINxChaBSMglEwCkY4AABaSkLVc+CzfgAAAABJRU5ErkJggg==","orcid":"","institution":"Xi'an University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Bao","suffix":""},{"id":634839600,"identity":"d05e45a5-9069-49a7-a1b4-08f0a9460aa2","order_by":2,"name":"Xingui Wang","email":"","orcid":"","institution":"Datong Wuguantun Coal Industry Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Xingui","middleName":"","lastName":"Wang","suffix":""},{"id":634839601,"identity":"139e4a5d-3b86-4d70-bba2-9e77e1f50918","order_by":3,"name":"Yiliang Hu","email":"","orcid":"","institution":"Xi'an University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yiliang","middleName":"","lastName":"Hu","suffix":""},{"id":634839603,"identity":"6d2358d1-3409-464c-9381-80ca3b36b6e7","order_by":4,"name":"Zexi Zhu","email":"","orcid":"","institution":"Xi'an University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zexi","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2026-04-20 09:53:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9470237/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9470237/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108805582,"identity":"1289f7af-ac06-4192-91e4-6b4620c35986","added_by":"auto","created_at":"2026-05-08 15:26:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":426457,"visible":true,"origin":"","legend":"\u003cp\u003eThe experimental procedure and methodology\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9470237/v1/996c3100a767a933d9f43bb6.png"},{"id":108625183,"identity":"a2ceadb1-f81b-481a-aabc-1946ca5312e8","added_by":"auto","created_at":"2026-05-06 15:26:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":441012,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial diversity of bacteria at phylum level (a) and genus level (b) and archaea at phylum level (c) and genus level (d)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9470237/v1/b2da148bdb1edb4bdadddd83.png"},{"id":108625185,"identity":"a8166f20-c9b9-4c3e-a2bd-bbfb450c6f96","added_by":"auto","created_at":"2026-05-06 15:26:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":884326,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative biomethane production curves of differential coal samples degraded by indigenous and exogenous microorganisms\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9470237/v1/2cf7f9823732700cd842ecd0.png"},{"id":108625268,"identity":"3c0108f4-d04b-4d19-a77a-967f34dda350","added_by":"auto","created_at":"2026-05-06 15:26:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":359708,"visible":true,"origin":"","legend":"\u003cp\u003eFTIR spectra (a) and peak fitting diagrams (b, example by WGT-1 sample)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9470237/v1/e855621141ec8486c302d571.png"},{"id":108625305,"identity":"0b7f2484-a7bb-4984-a001-bf7ff59b42bf","added_by":"auto","created_at":"2026-05-06 15:26:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":803831,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative methane yield and production rates during coal degradation by indigenous and exogenous microorganisms\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9470237/v1/f81c422c3d2d636484f889e9.png"},{"id":108625308,"identity":"c88069b3-f32c-4886-833f-f5379edc10d2","added_by":"auto","created_at":"2026-05-06 15:27:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":421776,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of explanatory variables in anaerobic fermentation system\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9470237/v1/82dfd2c0676a8d0bf6937c54.png"},{"id":108625189,"identity":"ee6cb43e-9c8c-4440-9961-a8fe1f68c6e1","added_by":"auto","created_at":"2026-05-06 15:26:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":455312,"visible":true,"origin":"","legend":"\u003cp\u003eExpression profiles of functional genes associated with methane precursor pathways during coal biogasification\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9470237/v1/523f63c0723efffcb22979ba.png"},{"id":108625186,"identity":"e1b5b6cc-ac66-4472-8093-0d711beff10e","added_by":"auto","created_at":"2026-05-06 15:26:45","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":348691,"visible":true,"origin":"","legend":"\u003cp\u003eMethane metabolic pathway with comparative abundance of key functional genes\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9470237/v1/17248af28f67b6c104ac94f3.png"},{"id":108809985,"identity":"164c2b7f-2dd1-4e0c-8e0a-a5cf715023a4","added_by":"auto","created_at":"2026-05-08 15:56:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4766969,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9470237/v1/8598d1ab-c2e4-4a85-b951-c5ea1a65d0df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Kinetic profiles and efficiency disparities in biomethane production mediated by indigenous versus exogenous microbial consortia","fulltext":[{"header":"Highlights","content":"\u003cp\u003e1. Exogenous microbial consortia demonstrate markedly higher coal-degrading methanogenic efficiency than indigenous ones.\u003c/p\u003e\u003cp\u003e2. H/C, A/A and CH/CH are key predictors of biomethane generation potential.\u003c/p\u003e\u003cp\u003e3. A three-level synergy\u0026mdash;coal structure, microbial function, and metabolic pathway\u0026mdash;governs biomethane production.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eCoal has always maintained a dominant position in China\u0026rsquo;s primary energy structure [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The direct combustion of coal leads to significant environmental challenges, driving the global search for cleaner and more efficient utilization technologies as a key research priority [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It was recognized in the early 20th century that coal can be metabolized by microorganisms to produce methane [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, it was not until the 1980s that the aerobic biodegradation of coal by bacteria and fungi [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and the generation of biomethane by anaerobic microorganisms [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] were successfully achieved in laboratory settings. Considering that the associated gases in coal are predominantly methane\u0026mdash;a renewable and environmentally friendly energy source constituting more than 20% of the global methane reserves [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] \u0026mdash;these gases are classified into primary and secondary biogenic gas closely linked to coalification processes. The majority of gas present in coal seams originates from secondary biogenic methane [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To further quantify these resource, Hu et al. applied a binary mixing discrimination model for calculation, revealing that secondary biogenic methane constitutes between 10% and 27% of global coalbed methane resources [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, it has been established that domesticating indigenous microorganisms in laboratory environments is feasible. Studies have found that adding trace elements or nutrients can enhance microbial activity and significantly improve the degradation efficiency of coal [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In the United States, companies such as Luca and Ciris Energy have successively conducted industrial trials in basins like the Sydney Basin and the Powder River Basin [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Notably, the abundance and activity of indigenous microorganisms in coal seams are generally low. Therefore, some researchers have proposed using exogenous microorganisms to enhance biomethane production [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For instance, Luo et al. analyzed the microbial gasification processes and methane production differences from five coal samples under four microbial sources, indicating certain differences in microbial community composition between indigenous and exogenous microorganisms. Compared to exogenous microorganisms, indigenous microorganisms showed no significant advantage in methane production efficiency across different coal samples [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, Guo et al. found that acclimated exogenous microorganisms outperformed indigenous microorganisms in methane production from Gansu coal and Inner Mongolia coal. Furthermore, in the later stages of coal biodegradation, the exogenous microorganisms exhibited a more pronounced utilization of the CO\u003csub\u003e2\u003c/sub\u003e reduction methanogenesis pathway [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe above research has demonstrated that both exogenous and indigenous microorganisms can degrade coal and produce methane, although they differ in methanogenic efficiency and microbial community structure. While issues concerning biogas production processes and microbial succession have been largely addressed, several key scientific questions remain: (1) Why does the same efficient exogenous microbial consortium exhibit significant differences in methane production potential and kinetics when degrading different coal samples, and what underlying mechanisms control these variations? (2) Previous studies often correlate methane yield with isolated coal parameters such as coal rank or vitrinite reflectance, lacking a systematic assessment of coal chemical structure\u0026mdash;particularly at the molecular functional group level\u0026mdash;which hinders the establishment of reliable predictive indicators for methane production potential. (3) Current understanding of coal\u0026ndash;microorganism interactions remains largely descriptive, correlating methane production with community composition. How coal chemical structure influences microbial functional gene expression and regulates methanogenic pathways is still unclear.\u003c/p\u003e \u003cp\u003eTo address these questions, this study simulated anaerobic fermentation using two microbial sources: indigenous consortia enriched from fresh coal samples of the studied seams, and an acclimated exogenous consortium from other regions. Anaerobic degradation experiments were conducted on coals from different seams in the Wuguantun (Datong Basin) and Baode (Ordos Basin) blocks in China. By integrating methane production kinetics, comprehensive coal characterization (including ultimate, proximate, and quantitative functional group analyses), and microbial functional gene prediction, this study applied Principal Component Analysis (PCA) to reveal how coal physicochemical structure\u0026mdash;especially aromatic, aliphatic, and oxygen-containing functional groups\u0026mdash;controls biomethane production. The research aims to identify the main factors causing differences in methanogenic efficiency between indigenous and exogenous microorganisms, and to clarify the synergistic mechanism of \u0026ldquo;coal physicochemical structure \u0026ndash; microbial degradation function \u0026ndash; methanogenic metabolic pathway\u0026rdquo; under different microbial drives. The findings are expected to provide key structural indicators for assessing coal seam biomethane potential and to offer theoretical and data support for coal bioconversion engineering.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Coal sample preparation and bacterial sources\u003c/h2\u003e\n \u003cp\u003eThe coal samples used in this study were collected from the Nos. 7, 11, and 12 coal seams of the Datong Formation in the Wuguantun mining area, and the Nos. 4\u0026thinsp;+\u0026thinsp;5 and 8\u0026thinsp;+\u0026thinsp;9 coal seams of the Shanxi and Taiyuan Formations in the Baode block, respectively. After crushing and sieving, samples with a particle size of 60\u0026ndash;80 mesh were collected and dried for subsequent use. Each sample was split into two portions using the quartering method. One portion was used for petrographic maceral analysis, proximate analysis, ultimate analysis, and Fourier transform infrared spectroscopy (FTIR) of the raw coal. The other portion was reserved for microbial degradation experiments. The raw coal samples were designated as WGT-1, WGT-2, WGT-3, BD-1, and BD-2. Samples subjected to degradation by indigenous and exogenous microorganisms were labeled as B-X and Y-X, respectively, where \u0026ldquo;X\u0026rdquo; corresponds to the raw coal sample designation.\u003c/p\u003e\n \u003cp\u003eFive distinct indigenous microbial consortia were enriched from fresh coal samples of their respective seams within the study area and were labeled WGT1, WGT2, WGT3, BD1, and BD2. One exogenous microbial consortium, labeled HN1, consisted of a mixed fermentation culture from other regions that had been acclimated long-term in our laboratory. The methods for enrichment and cultivation of these microbial consortia followed the procedure described in reference [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Successful enrichment was confirmed at the end of the enrichment and scale-up cultivation phases by detecting methane in the headspace of the anaerobic bottles using gas chromatography, verifying the consortia were active and ready for subsequent experiments.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Anaerobic fermentation experiment\u003c/h2\u003e\n \u003cp\u003eAnaerobic fermentation experiments were performed on each coal sample using its corresponding indigenous and exogenous microbial consortia. The experimental procedure was as follows: raw coal samples were exposed to ultraviolet light for 30 minutes in a clean bench. A methanogenic culture medium was prepared according to reference [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The prepared medium and all required experimental materials (anaerobic bottles, conical flasks, glass rods, beakers, etc.) were sterilized by autoclaving at 121\u0026deg;C for 30 minutes. After cooling, 10 g of coal sample, 30 mL of the indigenous or exogenous microbial inoculum, and 300 mL of the cooled sterile medium were aseptically transferred into separate 500 mL anaerobic bottles. All inoculation steps were performed inside an anaerobic glove box. The headspace of each bottle was purged with high-purity nitrogen gas (99.99%) for 5\u0026ndash;10 minutes before sealing with butyl rubber stoppers. The sealed bottles were then incubated in a constant-temperature incubator at 35\u0026deg;C for 49 days to simulate biomethane production. For each experimental condition, three parallel sample sets and three control sets were prepared. The control sets contained all components except the coal sample under otherwise identical conditions. A schematic diagram of the experimental workflow is presented in Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Experimental testing methods\u003c/h2\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.1 Gas composition measurement\u003c/h2\u003e\n \u003cp\u003eGas analysis was performed using an Agilent 8860 gas chromatograph (GC) equipped with a 50 m \u0026times; 320 \u0026micro;m \u0026times; 8 \u0026micro;m column, a TCD detector, and an FID flame ionization detector. A Hamilton 1710 gas-tight syringe was used for manual injection with an injection volume of 2 mL. The operating conditions were as follows: injector temperature 200\u0026deg;C, oven temperature 30\u0026deg;C, and detector temperature 250\u0026deg;C. High-purity nitrogen served as the carrier gas, with a makeup gas flow rate of 5 mL/min at the front detector. Every 7 days, 2 mL of gas was manually extracted from the headspace of the anaerobic bottles using a syringe and quantitatively analyzed by GC. The retention time for methane was 2.03 min.\u003c/p\u003e\n \u003cp\u003eMethane yield was calculated using the following formula:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAo8AAABSCAYAAAA1rz6jAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAAFiUAABYlAUlSJPAAABX9SURBVHhe7d1tTFvn2Qfwvx/1w1TZoPUgLRbpNGwRvmwFg4hE5UYUgwKBdlvLWho1ECmLiWDV0phWoKyKoqhK1GBKpNKUkEik2RJXXSVaICQC3LRBnRQWm2SaJuzFVFvAaOKsyo7Vr/fzYfjMvrHBEPP+/0lHaq/7xa6PUl25Xw1CCAEiIiIiohT8nxwgIiIiIkqGySMRERERpYzJIxERERGljMkjEREREaWMySMRERERpYzJIxERERGljMnjKgoGg3A6nTAYDPrjdDoRDAYRiURw6tQpRCIRuRkRERHRhsXkcZX4/X6UlJQgFAohEAhACAEhBMrKylBSUgKTyYQnn3wSRqNRbkpERES0YTF5XCWdnZ0oKChAX18fcnNz9XhdXR2Gh4ehKAry8vLi2hARERFtdEweV0E4HMbY2Jgc1uXm5uKll15CUVGRXERp0tDQELdcQH6ysrJQVVUFv98vNwUARCIRlJeXL2hnMBjgdrv1em63e0H5wMBAXF9ERERbCZPHVTQxMYFgMCiHAQBPP/00TCaTHKY0uXz5MjRNw7FjxwAADocDmqZBCAFN0/DBBx9gfHwchYWFcLlccnMYjUaMjIzA5/NBURQAwLFjx6BpWlx9l8uFa9euAfOfEQgEUFNTo5cTERFtNUweV4HZbIbdboeqqqitrV2QQBqNRrzzzjtc77jKjEYjnn/+eTkMo9GIuro69Pb2AgA6OjriRhNj5ebmoqCgAIqi4PXXX0/4zqanp1FfX79giQIREdFWxORxlZw5cwbFxcUIhUIoKSlJOj1K66e0tBQOhwMAMDQ0lHDnu6ZpmJqaQmZmJnbs2CEXY2BgAENDQ+jq6kqYWBIREW01TB5Xidlsxueff47i4mKoqoqKigquhdtgjEYjsrOzAQBTU1PQNE2usii/3w+Xy4WzZ88ycSQiom2DyeMqMpvN8Hq9cDgcUFUVL7zwAhPIDSQSiWB6eloOx5mdncWjR4+Qk5MTt0Y1EongzJkzcLvdsNlscW2iIpEIPB4P8vLy9M00Tqcz4QgnERHRZsHkcZUZjUb09fXp06MHDx7kFPYGEQwGMTExAQBoamqC2WyWq2B6ehqqqiI7OztudLG7uxs/+MEPkm6OCQaDKCsrQ0dHB95//30IITAzM4OJiQl0d3fL1YmIiDYNJo9pFIlEcOnSJTkMo9GIK1euwGKxQFVVeL1euQqtgdhpaY/Hg4qKCqiqCofDgcbGxri6UZOTkwCAZ555Ro8NDAzgzp076Orqiqn5P9ED4jMyMuD1erFv3z4AwNWrVxEKhVBWViY3ISIi2jSYPKaRpml4+PChHAbmp7CbmpoAAPfv35eLaZVNTEygtrZWnz5+7bXXoCgKOjo60NfXl/KaxXA4jK6uLrS2tiZsEw6HUVtbi8zMTFy5ciWujsvlwtzcXNJpbiIios2AyWMa3b17F7dv315yTVvsKNZaC4fD2L1797abOi8oKMDNmzf1ayKFEJicnMSbb76ZMAmMiib6eXl5iEQiOHr0KJqbm5MmgNHRxWTT4ERERJsdk8c0mpycxOjoKG7duiUXIRKJYGhoCIqirNu0ZSQSwYEDBxAKheQiSiC6oUZRFGRnZ6O7uxs7d+5Mus4xHA7jww8/hMViwf79++ViIiKiLYHJYxpFR6kOHjwIj8ejx2dnZ9HQ0IDR0VG0tbUlHbVabfLmHVpc7BmP4XAYd+7cwcmTJ+VqumQ7s4mIiLYSJo9pEg6HUVBQAE3T8Kc//QlerxdZWVkwGAwwm834/vvvEQgEFlyFF72D2Wq14ty5cwiHwwvuVW5oaNA/w2q1wuPx6OUNDQ16PHrvcnRq+vr163HxZPx+v/5do59F/xMKhfDmm28mXedIRES0nTB5TBOz2ayvn8vNzcWFCxcwNzenr68bGhpacHWd3+/Hc889ByEExsbG9DMgo9Pemqahv78fY2NjCAaD+pTzb37zG5w9exY+nw+Dg4Ow2+0YGxtDf38/Lly4gJ/97GcYHx/HG2+8ocdPnz6dcJ1jOBzG+fPn8e2330LTNExPT/MsynnRkUQAi57nmKqTJ08mfAdERESbCZPHddba2gq32w2z2Yzh4WGYzWbU1NRgZGREvwHliSeeQCQS0aece3t7YbPZsGPHDlgsFvzxj3+E2WxGdnY2MjMzMTw8jOLiYj1eWlqKgoKChEcEXb16FT09PTCZTDCZTBgdHcWnn34qV9uUIpEIvvzyS2B+t7V8x/hSomc81tfXJ13nGMtms6G6uhqjo6M4ceKEvnEqGAzirbfewosvvvjYCSgREdF6Y/K4jmw2G4aHh3H69OmEU8tutxuFhYVQVTUunm7t7e1xu5AvX74sV9l0GhoaYDKZ0NHRAQBQVRWFhYUwGAwpj6xOTk7C4XAkPc8xka6uLhw7dgwdHR0wmUzIysrCRx99hBMnTjBxJCKiLYHJ4zqz2WyYm5tDf38/WlpaMDAwoK9BBACfzwdFUeRmK5KXlyeHAOncyXA4vOimkM3i8uXLcQlx7JPKKCLmz2WMjgCnymg0wu126581NzcHt9u9rD6IiIg2MiaP68jv9+PixYsAgJqaGrS3twMAvF4vqqurF2yuWY7Ym2xu3bqFqakpFBUVydVQVlaGwcFBfTRucHAQL774olyNiIiI6L8ErZtAICAOHz4sAAgAor6+XgghhM/nE4qiCABi165dQlEUYbFYRHl5uQAgFEURX3/9tXA4HAKAsFgswufzCYvFIgCI8vJy/Z+j9X0+nxBCiPr6+gWx/v5+vW57e3vcd1wpTdOEw+EQ9fX1QtM0uZiIiIg2KYMQQsgJJW1u4XAYP//5z9Hd3b1u6+zC4TDsdjtycnKWdf0fERERbWwbfto6Eong+vXrqKqqgtVqRTgchsfjQV5eHgwGw7a8am+jiq7VdLlcMJlMsNvtyM7OBgA4nU5YrdZl73gmIiKijWXDJ4/Nzc2or6/HjRs3AAAdHR3IyMjA5OQkfD4fQqEQamtrEQ6H5abbUvQKwvHxcVRUVKxLYt3R0YHm5mZUVVWhqqoKzc3N6OnpkasRERHRJrQppq39fj8qKiqQmZmJsbExmM1mYD5R+sUvfoGJiQkMDw8vOkXrdrvR0tIih5Nqb29/rA0r29nt27fxxRdf4JtvvgEAPPvss3j++eexb98+uSoRERFtMht+5DEVqqpienpaDsdxuVwLjmxZ7GHiuDKRSAThcBj//Oc/kZGRocdNJpN+aDYRERFtXlsieaSNwe/34yc/+QleffVVFBcXo7y8HG+88Qb+9a9/Yc+ePcjPz+fyAiIiok2OyeMKGAyGbf8s5tixY3C5XLh//z6GhobQ1dWFw4cPA/MjkzK5bz4re4iIiNYCk8cVkKe4t+OTSPS2HLfbjXA4jLGxMX05wYULF/DgwQPk5ubKzRb0zWdlDxER0VrYNsmj2+1eMFKz2CPfM03LYzKZkJOTox/VQ0RERFvDtkkeuWFmbRmNRoyMjODy5cs8IJyIiGgL2RTJ4+TkJFRVxaNHjzA7O6vHw+Ewpqam9DpEREREtLo2dPIYiURQXl6O1157DZg/kqewsBANDQ1wu93YtWsXQqEQAKClpUW/gYaIiIg2h3v37uG9996TwxvO8ePH9Zxju9sUh4QTERHR1uPxePCXv/wF7777LjB/+kYq9u7dq988l04ejwe/+93v0NvbC7vdLhejqakJjY2NyM/Pl4u2lQ098khERERb07179+B2u/XEEfOnb1y7di3u3+Xno48+0svT5d69e+ju7kZTUxMePHggF+tOnTqFX//617h3755ctK0weSQiIqI1paoqXn755YSbU+vq6uRQnMbGRvzyl7+Uw48lPz8fjY2NaG1tlYviKIoCl8uFl19+GaqqysXbBpPHNAoGg3A6nXFH/jidTgSDQUQiEZw6dSrhIdn0v/Wt8pFJsU9WVhaqqqpw/fp1uTkwf8NNVlbWgnbJnqysLPj9/oSfvdz1sw0NDXHty8vL+a6JiJK4dOkS/v3vfy+ZKCbT2Ngoh9ZMXV0dHjx4gEuXLslF2waTxzTx+/0oKSlBKBRCIBDQh9fLyspQUlICk8mEJ598ksfWJBE92kfTNDgcDgBAfX29/jtqmoYPPvgA4+PjqK6uRkNDg9wFMP+3Qp/Pp7ebmZmBxWKBxWLBzMyMHvf5fACA6elp/bMDgYD+2aFQCFevXpV6Tyx6IDoAOBwOaJqGkZERvmsioiTOnDmD3bt3y+GUJNtc4/F4UFxcDIPBgKeeegrHjx9HZWWl/v/ndNq7dy8uXLggh7cNJo9p0tnZiYKCAvT19cXdolJXV4fh4WEoioK8vLy4NrSQ0WhEVVWVHIbRaERdXR16e3sBAB9//HHCg9zfeust2Gw2ObyAzWbT+4rKzc2N++wPP/wwpdHHq1ev6jvwqqqqmDQSES1ibGwM3333HcrKyuSiJXV3d8shYH4ji9vtxvvvvw8hBL744gucP38eN2/elKumRVFRER48eLBt1z4yeUyD2JGnRHJzc/HSSy+hqKhILqJlKi0t1UcHh4aG4qaGNU1b1m+cnZ2NjIwMOQyXywWLxZLS6GM4HMaNGzcSrtshIqKFvvnmGzmUlLzc6MiRI3IVeDwenD9/Hp988om+Q9puty+5fvFxZGZmAsCqJacbHZPHNJqYmEAwGJTDAICnn34aJpNJDlMa7dmzJ6VRxyibzYY9e/bIYZjNZjQ1NQEJElTZ3bt3UVlZCbPZLBcREdEifvrTn8qhBVLZae12u7F3715YLBa5aNX8+Mc/lkPbCpPHNDCbzbDb7VBVFbW1tQsSSKPRiHfeeYfTmWmgaZp+q1B2dvaq/ab79++HxWLB6Ogobt26JRcD85t8rly5sqKpFyKi7crr9QJAwpmfpSTaKPPnP/9ZDq26nTt3yqFthcljmpw5cwbFxcUIhUIoKSmB3++Xq1AaxK4v/NWvfiUXp03s6GNnZ2fC0cdbt25h586dyxrtJCLa7qJ/4X748KFclJK3335bDtEaY/KYJmazGZ9//jmKi4uhqioqKiowMDAgV6MV8vv9cDqdaGlpgaIouHbtGmpqauRqaVVWVgZFURKOPkZHHV9//fW4OBERpeYf//iHHFqW2F3Xd+7ciStbbctZt7kVMXlMI7PZDK/XC4fDAVVV8cILLzCBfAwff/yxvki6sLAQX331FS5evIhvv/12xWeDLYfNZkN1dTWQYPQxujQhdmc9EREt7dlnn5VDy9bd3a2vO3z11Vfx3XffLdiJHT2SLZaqqotucF2udPy3bEZMHtPMaDSir69P3xF88OBBTmGvUPScx+jZj4FAAD/60Y9WbZ1jIkePHoWiKAs2Q/3+97/HgQMH1vS7EBFtBXa7HVarVV/7KItNAhPd4tLd3Y22tjb9nMi2tjb88Ic/xJEjR1BZWYn33nsv6QBDc3MznnvuuQWJZlQ04fzrX/8qF8Xxer2wWq0J77/eDpg8PoZIJJLwhHmj0YgrV67AYrFAVdWkf0AoNbG/529/+9uUzl5Ml+joo6qq6OzsBOan0B8+fIjS0lK5OhERpcDpdCY85kY+jifRrWFHjhyB1WrVd1fn5+fjyy+/hNVqxc2bN+H1etHW1obCwsKYnv/LarUC8yegxBobG4PBYMAnn3wCADhy5AgMBkNcnVh37tyB0+mUw9sGk8fHoGla0gW/sRsu7t+/LxfTMpnNZpw7dw6hUAgHDhxIuIFltURHHwcHB+H3++H1ejnqSET0GA4dOgSr1QqPxxMXl4/mSfaMj4/HtcvPz8ff//53CCFw48YN5Ofnx5VHvfvuuxBCYN++fXFxu92+4DOEEHF1oq5fv46nnnoKhw4dkou2DSaPj+Hu3bu4ffv2konMM888I4dWnd/vx+7dux9rlC4dfaRTTU0N6uvrMTo6iubmZrl41eTm5qKgoACqquLtt9/GjRs3lnUYebqEw2Hs3r2byyCIaNNTFAWfffYZ3G53wqnpjezEiRP47LPPoCiKXLRtMHl8DJOTkwl34mJ+SntoaAiKoqR0DuDAwEDaNtf4/X5UVFQ81h/IdPSxGrq6uuBwOJJeTyibnZ3Fo0eP5PCi5JFio9GIo0ePAgBGRkbW5VDwSCSCAwcO6McUERFtdvn5+bh48SI6OjrkorRIdf3ichw/fhwnT55MOrK5XTB5fAzRJOPgwYNxQ++zs7NoaGjA6Ogo2traUjoH8NNPP5VDK2az2fT7tFcqHX2sRDTpBoC//e1vCQ9cP3v2LBRFQUtLC5xO56IjcZOTk1BVFY8ePcJXX30lF8eJfvb09PSC0eTotYgWiwX79++PK4v9zkvdSLNS8kYsIqKtID8/H4cOHYo7dicd5PWLlZWVcpVl6+7uxiuvvLJgyntbErQiMzMzoqOjQ2iaJgKBgDh8+LBQFEUAEABEZWWlCAQCcjNRX18vAAiLxSI6OzvFzMyMaG9v19s5HA4RDoeFw+HQY/X19fpnWiwWce3aNb28vb1d7ztaHm1nsVjEzMyM0DQtYX/ReE9Pj7BYLMLhcIhAIJCwj9Umf8fYJ9F36O/vX7SOz+eLex/RR1EU4fP54vpK9tkOh0NomqbX6+/vj/u9k7WT28r15Pcpf69k9TVNE6+88sqC709ERLSWmDyuIZ/PJ3p6eoSYTxzKy8vFzMyM8Pl8wuFwiP7+fiHmk5Ro8tHf3y8sFosIBAJ6QhFNNKJlsQlitI/29na9bKn+UumDVkb+TaNJb09PjyguLtYTwaXeV/T9MHkkIqL1xmnrNdba2gq32w2z2Yzh4WGYzeYFB03X1NRgZGQERqMR2dnZeOKJJxCJRPRpy97eXthsNmRnZ+vTytF1l9HjY6K3o6TS37lz52A2mxftg1YmGAxCURT9N62pqYEQAjt27EBGRob+7qNT4Xfv3k34vmZnZ+P6JSIiWi9MHtdQdB3h6dOnYTAYltzw4Xa7UVhYmNKmlcnJSTm0wFL9pdIHLc/09HTC31v+rU0mE3JycuLiS70vIiKi9cDkcY3ZbDbMzc2hv78fLS0tCXdY+/1+ZGVlAfO7xR539C/d/dHyTE1NQdO0uFheXl7SON8XERFtZEwe15Df78fFixeB+enL9vZ2uQowf+1RdXU1XC6XXJRUWVkZJiYm4q5cCoVCsNvt+MMf/pBSf4v1sVHOetxsSktLkZOTg9bWVj3m9/vx/fffA/PLGDA/vf2f//wHRUVFK3r/REREa0ZeBEmrJ7orW95FK2J2YTscDvH111/rO4V37dolFEURFotFlJeX6xtmBgcH9Z26sZsron3HbqyJ3XmcqD8ACzZ0yH3Qysm74KPvK/a9xG5MWup98Z0QEdF6Mohk9+8QEREREUk4bU1EREREKWPySEREREQpY/JIRERERClj8khEREREKWPySEREREQpY/JIRERERClj8khEREREKWPySEREREQpY/JIRERERClj8khEREREKWPySEREREQpY/JIRERERClj8khEREREKWPySEREREQpY/JIRERERCn7f7b0GLiEGMlBAAAAAElFTkSuQmCC\"\u003e\u003c/p\u003e\n \u003cp\u003ewhere: \u003cem\u003en\u003c/em\u003e is the molar quantity of methane per gram of coal (\u0026micro;mol/g); \u003cem\u003eS\u003c/em\u003e\u003csub\u003esample\u003c/sub\u003e and \u003cem\u003eS\u003c/em\u003e\u003csub\u003estandard\u003c/sub\u003e are the chromatographic peak areas of the sample and the standard gas, respectively; \u003cem\u003eP\u003c/em\u003e is atmospheric pressure (1.01325 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e Pa); \u003cem\u003eV\u003c/em\u003e is the headspace volume of the anaerobic bottle (mL); \u003cem\u003ec\u003c/em\u003e is the known concentration of methane in the standard gas (1.00597%); \u003cem\u003eR\u003c/em\u003e is the ideal gas constant [8.31441 J/(mol\u0026middot;K)]; \u003cem\u003eT\u003c/em\u003e is the thermodynamic temperature of the experimental environment (K); and \u003cem\u003eM\u003c/em\u003e\u003csub\u003ecoal\u003c/sub\u003e is the mass of the coal sample (g).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.2 DNA extraction and sequencing\u003c/h2\u003e\n \u003cp\u003eDNA was extracted from microbial samples collected after inoculation using the MoBio DNeasy PowerSoil kit. The extracted DNA was then evaluated by 0.8% agarose gel electrophoresis to confirm fragment size, followed by quantification via UV-Vis spectrophotometry. Qualified DNA extracts were subjected to PCR amplification. Bacterial 16S rRNA gene fragments were amplified using primers 338F and 806R, while archaeal fragments were amplified using primers 1106F and 1378R [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Microbiome bioinformatic analysis of the sequencing data was performed using QIIME2 2019.4 and the R package (v3.2.0), following the tutorial available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://docs.qiime2.org/2019.4/tutorials/\u003c/span\u003e\u003c/span\u003e. Taxonomic composition and abundance were visualized using MEGAN [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and GraPhlAn [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.3 Micro-Fourier transform infrared spectroscopy\u003c/h2\u003e\n \u003cp\u003eFTIR measurements for all coal samples were conducted on a WQF-530 micro-Fourier transform infrared spectrometer. Each sample was mixed with KBr at a mass ratio of 1:100, thoroughly ground, and pressed into a thin pellet. FTIR spectra were acquired by accumulating 16 scans at a resolution of 2 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e over a wavenumber range of 400\u0026ndash;4400cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. To quantitatively characterize the chemical structure of the coal, curve-fitting analysis of the FTIR spectra was performed using Origin software. A combination of Gaussian peaks was applied to model the band shapes and areas. The fitting parameters were adjusted to minimize the variance between the experimental and fitted curves.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results and analysis","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Proximate and ultimate analysis\u003c/h2\u003e \u003cp\u003eThe results of proximate analysis, ultimate analysis, and vitrinite reflectance measurements for the five raw coal samples are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Vitrinite reflectance, proximate analysis, and ultimate analysis were performed in accordance with ISO 7404-5, ISO 17246, and ISO 17247 [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], respectively. The maximum vitrinite reflectance (\u003cem\u003eR\u003c/em\u003e\u003csub\u003eo, max\u003c/sub\u003e) of all samples ranged from 0.76% to 0.83%, classifying them as medium-rank coals. The \u003cem\u003eR\u003c/em\u003e\u003csub\u003eo, max\u003c/sub\u003e values for the WGT samples (0.73%\u0026ndash;0.79%) were slightly lower than those for the BD samples (0.80%\u0026ndash;0.81%). The average moisture content (1.39 wt.%) and ash yield (avg. 8.49 wt.%) of the WGT samples were lower than those of the BD samples (moisture: 1.73 wt.%; ash: 23.14 wt.%). In contrast, the average volatile matter yield of the WGT samples (avg. 34.03 wt.%) was higher than that of the BD samples (avg. 29.13 wt.%), and the atomic hydrogen-to-carbon (H/C) ratio of the WGT samples (0.059\u0026ndash;0.062) was also slightly higher than that of the BD samples (0.050\u0026ndash;0.051). The total sulfur content (St, daf) of all coal samples ranged from 0.19 wt.% to 0.91 wt.%, classifying them as low-sulfur coals on a dry basis.\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\u003eUltimate/Proximate analysis results of raw coal samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003cp\u003enumbers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csub\u003eo, max\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eProximate analysis (wt %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003eUltimate analysis (wt %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eH/C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eO/C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003csub\u003ead\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eA\u003c/em\u003e\u003csub\u003ead\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eV\u003c/em\u003e\u003csub\u003edaf\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eQ\u003c/em\u003e\u003csub\u003egr,d\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eFC\u003c/em\u003e\u003csub\u003ead\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eC\u003csub\u003edaf\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eH\u003csub\u003edaf\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN\u003csub\u003edaf\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eO\u003csub\u003edaf\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eS\u003csub\u003et,daf\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWGT-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e79.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e24.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWGT-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e82.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e24.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWGT-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e77.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e13.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e33.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e81.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e28.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e81.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e25.833\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"14\"\u003eNote: \u003cem\u003eR\u003c/em\u003e\u003csub\u003eo, max\u003c/sub\u003e is maximum vitrinite reflectance. \u003cem\u003eM\u003c/em\u003e\u003csub\u003ead\u003c/sub\u003e and \u003cem\u003eA\u003c/em\u003e\u003csub\u003ead\u003c/sub\u003e are moisture content and ash yield of air-dried basis. \u003cem\u003eV\u003c/em\u003e\u003csub\u003edaf\u003c/sub\u003e is volatile yield of dry ash-free basis. \u003cem\u003eQ\u003c/em\u003e\u003csub\u003egr,d\u003c/sub\u003e is higher calorific value on a dry basis. \u003cem\u003eFC\u003c/em\u003e\u003csub\u003ead\u003c/sub\u003e is fixed carbon content of air-dried basis. S\u003csub\u003et,daf\u003c/sub\u003e is total sulfur of dry ash-free basis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Microbial community composition\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Bacteria\u003c/h2\u003e \u003cp\u003eThe operational taxonomic unit (OTU) results for the six microbial consortia indicated that bacteria constituted over 85% of the microbial community composition, while archaea accounted for only 6%\u0026ndash;8% of the total microbiota. Bacterial sequencing revealed that at the phylum level, the bacterial communities in all samples were predominantly composed of Firmicutes and Proteobacteria. Functional groups within these phyla, involved in hydrolysis, fermentation, and acidogenesis, are the primary participants in coal structure breakdown, representing 77.82%\u0026ndash;99.99% of all bacteria at the phylum level (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Significant differences were observed at the genus level (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). In sample WGT1, \u003cem\u003eAcinetobacter\u003c/em\u003e was the dominant bacterial genus, accounting for 64.11%. In WGT2, \u003cem\u003eSedimentibacter\u003c/em\u003e was the primary dominant genus, comprising 86.45%. For WGT3, \u003cem\u003eEnterobacter\u003c/em\u003e and \u003cem\u003eClostridium_sensu_stricto_13\u003c/em\u003e were the dominant bacterial genera, representing 33.25% and 21.02%, respectively. In BD1 and BD2 samples, \u003cem\u003eAzonexus\u003c/em\u003e and \u003cem\u003eMuribaculaceae\u003c/em\u003e were the dominant bacterial genera, accounting for 47.50% and 10.68%, respectively. The exogenous consortium HN1 was dominated by \u003cem\u003eLysinibacillus\u003c/em\u003e as the primary bacterial genus, constituting 80.72%. The high abundance of \u003cem\u003eLysinibacillus\u003c/em\u003e in the exogenous consortium, which can utilize complex organic compounds such as methyl pyruvate and acetoacetate [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], suggests that the exogenous microorganisms may exhibit a strong hydrolytic capacity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Archaeal\u003c/h2\u003e \u003cp\u003eArchaeal sequencing results revealed that at the phylum level, all samples were dominated by the phylum \u003cem\u003eEuryarchaeota\u003c/em\u003e, accounting for 75.7%, 91.28%, 94.16%, 97.03%, 95.65%, and 99.86%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). At the genus level (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed), the results showed that WGT1 and WGT3 were dominated by the hydrogenotrophic genus \u003cem\u003eMethanomicrobia\u003c/em\u003e, representing 66.38% and 93.51%, respectively; WGT2 was co-dominated by the acetoclastic genus \u003cem\u003eMethanothrix\u003c/em\u003e and the hydrogenotrophic genus \u003cem\u003eMethanomicrobia\u003c/em\u003e, accounting for 63.24% and 28.03%, respectively. In BD1, the hydrogenotrophic genus \u003cem\u003eMethanobacterium\u003c/em\u003e was dominant, representing 60.23%. Sample BD2 was dominated by the hydrogenotrophic genus \u003cem\u003eMethanoregula\u003c/em\u003e, accounting for 93.31%. The exogenous consortium HN1 was dominated by \u003cem\u003eMethanosarcina\u003c/em\u003e, \u003cem\u003eMethanobacterium\u003c/em\u003e, and the acetoclastic genus \u003cem\u003eMethanosaeta\u003c/em\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], representing 32.53%, 32.14%, and 29.92%, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Characteristics of biomethane production\u003c/h2\u003e \u003cp\u003eThe biomethane cumulative yield, after subtracting the control group, is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. All coal samples produced a certain amount of methane when degraded by either indigenous or exogenous microorganisms, but significant differences in biomethane yield were observed between different samples and microbial sources. Overall, the efficiency of methane production from coal degradation was higher with exogenous microorganisms than with indigenous ones. For example, the maximum cumulative methane yield from WGT-1 coal reached 7.37 m\u003csup\u003e3\u003c/sup\u003e/t and 12.47 m\u003csup\u003e3\u003c/sup\u003e/t under degradation by indigenous and exogenous microorganisms, respectively, representing an increase of 69.21%. The highest daily methane production rates were 2.29 m\u003csup\u003e3\u003c/sup\u003e/t/day and 4.15 m\u003csup\u003e3\u003c/sup\u003e/t/day, respectively. The WGT-2 coal showed the highest increase in cumulative methane yield between indigenous and exogenous degradation, reaching 140.45% (with maximum cumulative methane yields of 5.30 m\u003csup\u003e3\u003c/sup\u003e/t and 12.85 m\u003csup\u003e3\u003c/sup\u003e/t for B-WGT-2 and Y-WGT-2, respectively). The highest daily methane production rates for these samples were 3.06 m\u003csup\u003e3\u003c/sup\u003e/t/day and 3.56 m\u003csup\u003e3\u003c/sup\u003e/t/day, respectively. For B-WGT-3 and Y-WGT-3, the maximum cumulative methane yields were 7.07 m\u003csup\u003e3\u003c/sup\u003e/t and 11.69 m\u003csup\u003e3\u003c/sup\u003e/t, respectively, an increase of 63.35%, with peak daily methane production rates of 3.01 m\u003csup\u003e3\u003c/sup\u003e/t/day and 4.10 m\u003csup\u003e3\u003c/sup\u003e/t/day. The increases in cumulative methane yield for BD-1 and BD-2 coals under indigenous versus exogenous degradation were 24.68% and 33.63%, respectively. Compared to the WGT coals, the overall increase in cumulative methane yield for BD coals under both microbial treatments was significantly lower. This difference clearly indicates that the physicochemical properties of the coal itself are one of the core intrinsic factors determining its bioconversion potential.\u003c/p\u003e \u003cp\u003eBy examining the daily biomethane yield curves, we observed that methane production exhibited multiphasic characteristics, with one or more cycles of increase and decrease. This pattern aligns with the multiphase nature of biomethane production reported in previous studies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The timing of methane production peaks (e.g., at 14, 21, and 35 days) may mark critical transitions from the \u0026ldquo;acidification phase\u0026rdquo; to the \u0026ldquo;methanogenic phase\u0026rdquo; [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. As the biological reaction progressed, fluctuations in daily methane production gradually stabilized, reflecting an overall decline in microbial activity. This further illustrates that coal bioconversion is not a linear process but rather a dynamically evolving complex of microbial ecology and metabolism over time.\u003c/p\u003e \u003cp\u003eAdditionally, the cumulative biomethane yield curves revealed that, under the influence of different microbial consortia, all five coal samples underwent three distinct phases: a slow methane production phase, a rapid methane production phase, and a plateau/cessation phase. The most favorable period for microbial degradation and methane production was Phase II. For Y-X and B-WGT samples, Phase II lasted from approximately 7 to 35 days, while B-BD samples entered Phase II later and had a shorter duration, typically between 21 and 42 days. The specific methane production process is as follows: I) Slow Methane Production Phase: Methane production was low initially, with a relatively slow rate. This is primarily because microbial communities require an adaptation period upon entering a new coal matrix environment [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Additionally, coal is a complex macromolecular organic substance, and microorganisms need time to synthesize and secrete specific extracellular enzymes for initial degradation. Notably, Y-X samples exhibited higher methane production and faster rates than B-X samples during this phase, mainly because long-term acclimation of exogenous microbes reduces their adaptation time to coal [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. II) Rapid Methane Production Phase: During this period, all experimental groups established a relatively stable methane production process. Hydrolytic and fermentative bacteria continuously broke down coal macromolecules into intermediate products, which methanogens efficiently converted into methane. Y-X samples still demonstrated higher methane production than B-X samples in this phase. III) Plateau/Cessation Phase: In the later stage, methane production gradually declined and oscillated toward equilibrium, with no further increase in cumulative yield. The final cumulative methane yields ranged from 10.65 m\u003csup\u003e3\u003c/sup\u003e/t to 12.85 m\u003csup\u003e3\u003c/sup\u003e/t for exogenous groups and from 5.30 m\u003csup\u003e3\u003c/sup\u003e/t to 98.59 m\u003csup\u003e3\u003c/sup\u003e/t for indigenous groups, with exogenous groups consistently outperforming indigenous ones overall. These three stages of coal bioconversion have also been identified in other studies [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Variations in the timing of the first two phases [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] are likely attributable to differences in coal samples and microbial consortia used across experiments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Functional group structure of coal\u003c/h2\u003e \u003cp\u003eThe FTIR spectra of the coal samples are primarily divided into four absorption bands. The regions 700\u0026ndash;900 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 1000\u0026ndash;1800 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 2800\u0026ndash;3000 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 3000\u0026ndash;3600 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e correspond to the characteristic absorption peaks of aromatic structures, oxygen-containing functional groups, aliphatic hydrocarbon structures, and hydroxyl structures, respectively [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The baseline-corrected FTIR spectra and their deconvoluted peak-fitting diagrams for all samples are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. It is evident that the overall framework of characteristic absorption bands is similar across all coal samples, though the intensities of the peaks differ, indicating similar surface structures and functional groups but variations in their relative abundances. Within the 700\u0026ndash;900 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e region, associated with aromatic structures, four main absorption bands appear, representing different aromatic ring substitution patterns. The WGT samples are dominated by di- and tri-substitutions, whereas the BD samples are characterized by tri- and tetra-substitutions. This clearly shows that the BD samples have a higher proportion of highly substituted aromatics, implying a greater degree of aromatic condensation and more stable structures. The absorption peaks in the 1033\u0026ndash;1350 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e region belong to C\u0026ndash;O stretching vibrations in ethers, alcohols, and phenols. The BD samples exhibit high-intensity peaks in this region, suggesting these coals may possess higher bioreactivity. These functional groups can provide effective initial sites for microbial attack, potentially shortening the methane production lag phase and enhancing overall methane yield. All samples show a prominent peak around 1600 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, representing the aromatic C\u0026thinsp;=\u0026thinsp;C stretching vibration. Due to structural similarities and interactions between functional groups, the C\u0026thinsp;=\u0026thinsp;O stretching vibration bands overlap. Therefore, the 1650\u0026ndash;1728 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e region is assigned to the characteristic C\u0026thinsp;=\u0026thinsp;O double bond vibrations, including those from carbonyl groups in carboxylic acids and esters. The absorption peaks at 2850 and 2920 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e represent the symmetric and asymmetric stretching vibrations of aliphatic CH\u003csub\u003e2\u003c/sub\u003e groups, while peaks at 2870 and 2950 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e correspond to the symmetric and asymmetric stretching of aliphatic CH\u003csub\u003e3\u003c/sub\u003e groups. Similarly, the WGT samples are rich in aliphatic components, which is a positive indicator for assessing their biodegradability. In the hydroxyl band, a strong absorption peak around 3400 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e corresponds to hydrogen bonds formed by self-associated hydroxyl groups. A pronounced hydroxyl peak signifies a significant interaction interface between the coal sample and microorganisms. However, whether this ultimately promotes or constrains biodegradation efficiency depends on the specific chemical environment of the hydroxyl groups and their overall role within the macromolecular structure of the coal.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy performing peak deconvolution on the FTIR spectra of different coal samples and calculating functional group structural parameters [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], the differences in surface functional groups among the samples were further investigated, with the results presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The functional group characteristics are primarily analyzed in three aspects: first, parameters related to aromatic structures, including the degree of aromatic ring condensation (DOC), aromaticity (AR), and aromatic carbon content (\u003cem\u003efa\u003c/em\u003e); second, parameters related to aliphatic structures, including the abundance of aliphatic structures (A\u003csub\u003eal\u003c/sub\u003e/A\u003csub\u003ear\u003c/sub\u003e) and the length/branching degree of aliphatic side chains (CH\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e3\u003c/sub\u003e); and third, the ratio of oxygen-containing functional groups (C\u0026thinsp;=\u0026thinsp;O/C\u0026thinsp;=\u0026thinsp;C). The results show that, overall, the AR and \u003cem\u003efa\u003c/em\u003e values are lower for WGT coals (AR avg. 0.36, \u003cem\u003efa\u003c/em\u003e avg. 0.62) than for BD coals (AR avg. 1.26, \u003cem\u003efa\u003c/em\u003e avg. 0.84). The CH\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e3\u003c/sub\u003e and A\u003csub\u003eal\u003c/sub\u003e/A\u003csub\u003ear\u003c/sub\u003e ratios show minimal variation across all samples. The CH\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e3\u003c/sub\u003e values range from 2.16 to 2.20 for WGT coals and from 2.17 to 2.21 for BD coals. The A\u003csub\u003eal\u003c/sub\u003e/A\u003csub\u003ear\u003c/sub\u003e values range from 0.51 to 0.59 for WGT coals and from 0.45 to 0.47 for BD coals. WGT-2 and WGT-1 exhibit relatively high C\u0026thinsp;=\u0026thinsp;O/C\u0026thinsp;=\u0026thinsp;C ratios (0.20 and 0.19, respectively), indicating the presence of abundant carbonyl/carboxyl groups in these coals. Previous research has shown that such groups serve as crucial initial sites for microbial enzymatic attack [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe semi-quantitative parameters of coal samples derived from FTIR analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample numbers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDOC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003efa\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA\u003csub\u003eal\u003c/sub\u003e/A\u003csub\u003ear\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCH\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u0026thinsp;=\u0026thinsp;O/C\u0026thinsp;=\u0026thinsp;C\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWGT-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWGT-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWGT-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBD-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.18\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"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Biomethane generation dynamics during coal biogasification\u003c/h2\u003e\n \u003cp\u003eTo accurately evaluate and compare biomethane generation efficiency among different samples and under different microbial consortia, four kinetic models\u0026mdash;the Modified Gompertz [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], Logistic [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], DoseResp [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and SRichards [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] models\u0026mdash;were applied to fit the methane production data. By comparing the correlation coefficients (R\u003csup\u003e2\u003c/sup\u003e) (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the Modified Gompertz model, demonstrating the highest applicability, was selected (Eq. 2) to simulate biomethane yield and production rates from different coal seams degraded by different microbial sources (Figs. \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). The Modified Gompertz model is formulated as follows:\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n \u003cp\u003ewhere: \u003cem\u003ef\u003c/em\u003e (t) is the cumulative methane yield (\u0026micro;mol/g); \u003cem\u003eA\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e is the maximum methane potential (\u0026micro;mol/g), typically characterizing the substrate\u0026rsquo;s hydrolysability; \u003cem\u003e\u0026lambda;\u003c/em\u003e is the lag phase (d), evaluating microbial adaptability to the substrate\u0026mdash;a lower \u003cem\u003e\u0026lambda;\u003c/em\u003e indicates stronger adaptability; \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e is the maximum methane production rate (\u0026micro;mol/g\u0026middot;d), representing the slope of the fitted curve and characterizing the substrate degradation rate; \u003cem\u003et\u003c/em\u003e is the anaerobic fermentation time (d); and \u003cem\u003eT\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e is the time at which \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e occurs (d) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFitting coefficient R\u003csup\u003e2\u003c/sup\u003e of different hydrocarbon generation kinetics models\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eModified Gompertz\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eB-WGT-1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eY-WGT-1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eB-WGT-2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eY-WGT-2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eB-WGT-3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eY-WGT-3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eB-BD-1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eY-BD-1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003eB-BD-2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003eY-BD-2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c10\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c11\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLogistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDoseResp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSRichards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\n \u003cp\u003e0.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAnalysis revealed that WGT and BD samples exhibited similar simulated curves within their respective groups. Notably, degradation by the exogenous consortium demonstrated significant advantages in both \u0026ldquo;quantity\u0026rdquo; (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026uarr;) and \u0026ldquo;rate\u0026rdquo; (\u003cem\u003e\u0026lambda;\u003c/em\u003e\u0026darr;, \u003cem\u003eT\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e\u0026darr;), achieving comprehensive enhancement of the bioconversion process. The increase in \u003cem\u003eA\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (\u0026Delta;\u003cem\u003eA\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e) was greater for WGT coals (5.07\u0026ndash;7.88) compared to BD coals (1.91\u0026ndash;2.41). This likely relates to the inherently more biodegradable structure of WGT coals. Their low \u003cem\u003eA\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e under indigenous degradation is primarily due to the functional limitations of the indigenous microbiome. Once high-efficiency exogenous consortia were introduced, this \u0026ldquo;high-quality\u0026rdquo; substrate was rapidly utilized, releasing its substantial potential, with a maximum increase of 147.76% observed for WGT-2. Furthermore, the exogenous consortium exhibited higher methanogenic efficiency than the indigenous one. This is because, during methane production using laboratory-acclimated mixed microbes, the microorganisms can directly utilize organic matter from the nutrient solution. Additionally, long-term acclimation reduces their adaptation time to the coal substrate [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], a finding consistent with the microbial sequencing results. The methane production rates (\u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e) for all five coals degraded by either indigenous or exogenous microbes followed a trend of initial increase followed by decrease. The Y-X samples exhibited a shorter lag phase (\u003cem\u003e\u0026lambda;\u003c/em\u003e) compared to the B-X samples, and the methane production peak for B-X samples was consequently delayed (\u003cem\u003eT\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e: B-X\u0026thinsp;\u0026gt;\u0026thinsp;Y-X). This indicates that exogenous microorganisms likely possess more efficient metabolic pathways or a more robust enzymatic system for decomposing organic matter in coal, thus requiring less time for initiation and optimization. Their superior adaptability to the coal environment allows them to adjust their metabolic activity more rapidly to maximize methanogenic efficiency.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Analysis of constraining factors for biomethane production efficiency\u003c/h2\u003e\n \u003cp\u003eThe physicochemical properties of coal, controlled by depositional environment and coalification processes, are key factors influencing coal bioconversion and consequently constrain the efficiency of biomethane generation [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. When evaluating samples, it is insufficient to consider only single or a few specific characteristics; instead, a comprehensive, systematic, and scientific assessment is essential [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Principal Component Analysis (PCA) serves as a convenient tool for this purpose, utilizing dimensionality reduction to transform multiple indicators into a few comprehensive components while retaining a substantial portion of the information from the original variables. PCA was performed based on five sets of gas production data obtained from the degradation of five different coals (WGT-1, WGT-2, WGT-3, BD-1, and BD-2) by the exogenous microbial consortium. This analysis integrated various physicochemical parameters of the raw coals\u0026mdash;including elemental content (C, H, O, N, S), proximate analysis (moisture, ash, volatile matter, fixed carbon), and functional group indices (DOC, AR, \u003cem\u003ef\u003c/em\u003ea, A\u003csub\u003eal\u003c/sub\u003e/A\u003csub\u003ear\u003c/sub\u003e, CH\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e3\u003c/sub\u003e, C\u0026thinsp;=\u0026thinsp;O/C\u0026thinsp;=\u0026thinsp;C) \u0026mdash;as well as corresponding kinetic parameters (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, \u003cem\u003e\u0026lambda;\u003c/em\u003e, \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e). The results identified three principal components with a cumulative variance contribution rate of 91.52%, integrating most of the physicochemical information. The contribution rates of the first three principal components were 44.64%, 25.57%, and 21.25%, respectively (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e\n \u003cp\u003ePC1 exhibited the highest contribution, representing the most influential factors in the coal matrix. All WGT samples had positive scores on PC1 (0.27\u0026ndash;1.10), indicating homogeneity in the properties represented by this component. In contrast, BD samples had negative scores (-1.14 to -0.95) (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), demonstrating that PC1 is the primary factor distinguishing gas production potential between WGT and BD coals. Here, positive scores can be interpreted as indicating the \u0026ldquo;easily degradable\u0026rdquo; end of the spectrum, while negative scores represent the \u0026ldquo;difficult to degrade\u0026rdquo; end. Based on loading analysis, the high positive loadings (promoting degradation) on PC1 were H\u003csub\u003edaf\u003c/sub\u003e (0.316), H/C (0.308), and A\u003csub\u003eal\u003c/sub\u003e/A\u003csub\u003ear\u003c/sub\u003e (0.314) (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). A high H/C ratio signifies abundant aliphatic hydrogen, which serves as a primary hydrogen source for methanogenesis. A high Aal/Aar indicates a relatively rich aliphatic structure [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The high negative loadings (inhibiting degradation) on PC1 were AR (-0.314), \u003cem\u003ef\u003c/em\u003ea (-0.299), and A\u003csub\u003ead\u003c/sub\u003e (-0.206) (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Highly aromatic molecular frameworks are exceptionally stable and difficult for microbial enzyme systems to cleave or oxidize [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Higher ash content reduces the proportion of bioavailable organic carbon in coal, leading to insufficient carbon supply for microorganisms and consequently restricting their growth and metabolism [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Concurrently, the lag phase (\u003cem\u003e\u0026lambda;\u003c/em\u003e) showed a strong negative correlation with PC1 (-0.198), meaning that coals with higher PC1 scores also initiated biodegradation more rapidly (smaller \u003cem\u003e\u0026lambda;\u003c/em\u003e). WGT samples had higher H/C and richer aliphatic structures, corresponding to higher maximum methane production potential and shorter lag time; in contrast, BD samples had higher aromaticity and poorer biodegradability, manifesting as a longer lag time.\u003c/p\u003e\n \u003cp\u003eIn the PC2 dimension, the high positive loadings included CH\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e3\u003c/sub\u003e (0.402), O\u003csub\u003edaf\u003c/sub\u003e (0.286), and O/C (0.308), while \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e (-0.429) and DOC (-0.251) exhibited negative loadings (Fig. \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Sample scores showed that WGT-3 (1.360) was much higher than the other samples, WGT-2 (-0.39) and WGT-1 (-0.80) were negative, and BD-1 (-0.90) and BD-2 (0.74) were located in the negative and positive regions, respectively. Considering the loading directions, coals with high CH\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e3\u003c/sub\u003e and high O/C (represented by WGT-3) corresponded to higher \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e, while coals dominated by aromatic structures (represented by BD-1) exhibited the lowest \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e. Therefore, structures with longer aliphatic chains and lower degrees of aromatic condensation may be more readily degraded via pathways such as \u003cem\u003e\u0026beta;\u003c/em\u003e-oxidation and exhibit relatively open physical structures [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], which also confirms the understanding that aliphatic side chains are the priority attack sites for extracellular enzymes of hydrolytic fermentative bacteria. In the PC3 dimension, the positive loadings included C\u0026thinsp;=\u0026thinsp;O/C\u0026thinsp;=\u0026thinsp;C (0.372), Q\u003csub\u003egr,d\u003c/sub\u003e (0.334), and FC (0.299), while the negative loadings included O/C (-0.306) and V\u003csub\u003edaf\u003c/sub\u003e (-0.280). Meanwhile, \u003cem\u003eA\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e exhibited a positive loading (0.182) on PC3, indicating that the positive end of PC3 contributes positively to gas production potential.\u003c/p\u003e\n \u003cp\u003eTherefore, PCA analysis reduced the multidimensional coal quality parameters affecting microbial gas production to three independent principal components and revealed a hierarchical control model. Within this model: PC1 is the \u0026ldquo;bioavailability\u0026rdquo; component. Its high scores are significantly correlated with high H/C, high aliphatic structures (A\u003csub\u003eal\u003c/sub\u003e/A\u003csub\u003ear\u003c/sub\u003e), as well as low ash content (A\u003csub\u003ead\u003c/sub\u003e) and low aromaticity (AR, \u003cem\u003ef\u003c/em\u003ea). It is the key dimension distinguishing WGT coals (easily degradable) from BD coals (difficult to degrade) and controlling their maximum gas production potential (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e) and initiation speed (\u003cem\u003e\u0026lambda;\u003c/em\u003e). PC2 is the \u0026ldquo;gas production rate regulation\u0026rdquo; component. Its high scores are significantly correlated with high aliphatic chain length (CH\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e3\u003c/sub\u003e) and high O/C. This component determines the peak intensity (\u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e) of the gas production process, with coals characterized by high aromatic structures (e.g., BD-1) scoring the lowest on this dimension,exhibiting a restricted rate. PC3 fine-tunes the degradation process at a more detailed chemical structure level, involving influences such as a high proportion of reactive oxygen-containing functional groups (C\u0026thinsp;=\u0026thinsp;O/C\u0026thinsp;=\u0026thinsp;C), high calorific value (Q\u003csub\u003egr,d\u003c/sub\u003e), and low volatile matter (V\u003csub\u003edaf\u003c/sub\u003e), providing an auxiliary positive contribution to gas production potential (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e). This hierarchical model clarifies that the biogenic gas production performance of coal is not controlled by a single parameter but is governed by the hierarchical characteristics of its chemical structure: the foundational potential is established by its \u0026ldquo;bioavailability\u0026rdquo; (PC1), the gas production rate is subsequently regulated by \u0026ldquo;gas production rate regulation\u0026rdquo; (PC2), and finally fine-tuned by PC3. Together, these three components constitute the core evaluation system for assessing the microbial gasification potential of coal, among which high H/C, rich aliphatic chains, high CH\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e3\u003c/sub\u003e, and moderate O/C are the key indicators for medium-rank coal selection.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 The pathways and coordination mechanisms of biomethane generation\u003c/h2\u003e\n \u003cp\u003eExtensive research has confirmed the crucial role of microorganisms in converting coal into biomethane [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], a process that requires the synergistic efforts of various organisms, including hydrolytic and fermentative bacteria, acetogenic bacteria, and methanogenic archaea [\u003cspan additionalcitationids=\"CR51 CR52\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The bioconversion of coal primarily involves two major phases: the acidification phase and the methanogenic phase [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The acidification phase encompasses key pathways such as glycolysis, saturated hydrocarbon degradation, fatty acid synthesis and consumption, and aromatic hydrocarbon degradation [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The subsequent methanogenic phase converts the methane precursors generated during acidification into biomethane via hydrogenotrophic, acetoclastic, and methylotrophic pathways [\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Based on 16S rRNA gene sequencing results, the PICRUSt software was used to predict the metabolic functions of the microbial communities. PICRUSt predictions for metabolic functions have an accuracy of approximately 84%\u0026ndash;95%, providing a good reflection of functional gene composition and enabling the exploration of differences in methane generation pathways from coal degraded by different microbial sources.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the changes in the abundance of key functional genes related to the generation of methanogenic precursors during the acidification phase of coal bioconversion. Glycolysis, as the core pathway for microbial energy metabolism, shows gene abundances that directly reflect the initial capacity of the microbial consortium to utilize soluble organic matter and carbohydrates from coal. Analysis of genes for key glycolytic steps (from glucose to pyruvate) indicates the occurrence of glycolysis at critical time points during bioconversion. The exogenous consortium HN1 exhibited significantly higher abundances for most of these genes (e.g., Hexokinase EC 2.7.1.2, 6-Phosphofructokinase EC 2.7.1.11, Pyruvate kinase EC 2.7.1.40, etc.) compared to all indigenous consortia. This provides a genetic basis for the observation in gas production experiments that coal degradation by the exogenous consortium yields higher gas volumes than by indigenous consortia. The initiation of saturated hydrocarbon degradation primarily follows two modes: terminal oxidation of alkanes and alkyl side chains (EC 1.14.15.3) [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e] and hydrolysis by lipases (EC: 3.1.1.3) [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Both reactions produce fatty acids, which are rapidly degraded via \u003cem\u003e\u0026beta;\u003c/em\u003e-oxidation, ultimately yielding methane and carbon dioxide [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Among the indigenous consortia, gene abundances related to these pathways were markedly higher for samples from the BD area than for those from the WGT area, with BD1 showing particularly high abundances. Polycyclic or monocyclic aromatic hydrocarbons (e.g., toluene, ethylbenzene) undergo a series of \u003cem\u003e\u0026beta;\u003c/em\u003e-oxidation-like reactions where side chains are modified and shortened, ultimately converging in their conversion to benzoyl-CoA. Here, benzoyl-CoA reductase (EC: 1.3.8.4) acts as a key activation enzyme [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. This leads to the formation of a linear fatty acid derivative, which then enters the conventional \u003cem\u003e\u0026beta;\u003c/em\u003e-oxidation pathway and is completely degraded to acetyl-CoA, providing precursors for methane generation [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The exogenous consortium HN1 demonstrated substantially higher abundances of these core anaerobic aromatic degradation genes, which is likely key to its efficient gas production across all coal samples. In contrast, the aromatic degradation capacity of indigenous consortia was generally limited and exhibited regional variation. In summary, comparison of the functional gene profiles of the original microbial consortia clearly indicates that the exogenous consortium HN1 possesses the most comprehensive genetic arsenal for substrate degradation, which aligns with its superior gas production performance. Conversely, the inherent advantages of BD1 in glycolysis and hydrocarbon degradation potential accurately predicted its status as the best-performing indigenous consortium.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the gene coding for metabolic enzymes related to methanogenic pathways in the six microbial consortia. As shown in Fig. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the methanogenic phase can be categorized into three types based on substrate: hydrogenotrophic, acetoclastic, and methylotrophic. Among these three methanogenic pathways, enzyme abundances related to the hydrogenotrophic pathway were the highest, while gene abundances for the acetoclastic pathway were the lowest. Enzymes associated with the methylotrophic pathway were found in substantial quantities, intermediate between the other two. Within the indigenous microbial systems, enzyme counts related to the hydrogenotrophic pathway were generally higher for BD area samples than for WGT samples. The opposite trend was observed for the methylotrophic pathway, where WGT samples exhibited higher abundances. Concurrently, WGT samples also showed an advantage in the acetoclastic pathway, evidenced by higher abundances of genes such as [EC: 6.2.1.1], [EC: 2.7.2.1], [EC: 2.3.1.8], and [EC: 1.5.98.2]. This pattern in functional gene abundance is entirely consistent with the dominant genera identified through microbial sequencing: hydrogenotrophic \u003cem\u003eMethanobacterium\u003c/em\u003e and \u003cem\u003eMethanoregula\u003c/em\u003e were dominant in the BD area, while the WGT area hosted hydrogenotrophic \u003cem\u003eMethanomicrobia\u003c/em\u003e acetoclastic \u003cem\u003eMethanothrix\u003c/em\u003e, and the metabolically versatile \u003cem\u003eMethanosarcina\u003c/em\u003e. This indicates that the simultaneous presence of all three methanogenic pathways does not necessarily guarantee higher gas production; sometimes, a single dominant pathway can also yield high output. Furthermore, we observed that the exogenous microbial system generally harbored a greater diversity and higher relative abundance of these functional genes, belonging to well-acclimated consortia with complete metabolic capabilities. This is a primary reason why the same coal, when degraded, yielded better gas production with the exogenous consortium than with the indigenous ones.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAbundance of biological enzymes related to methanogenic metabolism\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMethanogenic metabolic pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eEnzyme number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e\n \u003cp\u003eEnzyme abundance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eWGT-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eWGT-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eWGT-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eBD-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003eBD-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003eHN-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\n \u003cp\u003eHydrogenotrophic methanogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.1.1.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2,5-diamino-6-(ribosylamino)-4(3H)-pyrimidinone 5\u0026rsquo;-phosphate reductase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2303.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2515.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2613.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2647.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e3401.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e2906.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.3.1.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFormylmethanofuran\u0026ndash;tetrahydromethanopterin N-formyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1874.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1348.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1301.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2716.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1711.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e2758.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3.5.4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eMethenyltetrahydromethanopterin cyclohydrolase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1562.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1441.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1297.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1800.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1707.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1648.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.5.98.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eMethylenetetrahydromethanopterin dehydrogenase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1520.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1265.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1297.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1793.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1704.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1753.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.1.2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eGlycine hydroxymethyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1784.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1569.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1326.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1794.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1758.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1522.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.1.1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eTetrahydromethanopterin S-methyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e16135.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e11781.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e12968.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e14914.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e13649.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e15078.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003eMethylotrophic methanogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.1.1.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e[Trimethylamine-corrinoid protein] Co-methyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1659.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2263.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3914.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e190.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e46.9258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e2626.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.1.1.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e[Dimethylamine-corrinoid protein] Co-methyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1784.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1614.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1326.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e190.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e79.5912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1522.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.1.1.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e[Methylamine-corrinoid protein] Co-methyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1077.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e2180.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e2593.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e127.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e84.2503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e2689.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003eAceticlastic methanogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e6.2.1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAcetate-CoA ligase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e4742.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e3716.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1490.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e5787.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e6856.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e4583.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.7.2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eAcetate kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e17.3314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e522.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1289.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e105.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e50.8433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e552.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.3.1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePhosphate acetyltransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e17.2725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e499.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1289.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e101.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e69.8788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e552.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.5.98.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5,10-methylenetetrahydromethanopterin reductase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1648.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e1266.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e1297.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1792.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e1705.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e1521.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eCommon pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e1.8.98.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCoB-CoM heterodisulfide reductase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e9905.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e8518.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e11458.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e7553.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e6846.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e13039.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e2.8.4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCoenzyme-B sulfoethylthiotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e5726.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e4565.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e3905.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e7004.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c8\"\u003e\n \u003cp\u003e5124.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c9\"\u003e\n \u003cp\u003e6880.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBuilding upon the comprehensive analysis and discussion, this study proposes a synergistic mechanism of \u0026ldquo;physicochemical structure\u0026ndash;microbial function\u0026ndash;metabolic pathway\u0026rdquo; driven by indigenous and exogenous microorganisms. First, the physicochemical structure of coal acts as the primary screening net, determining the scale and accessibility of substrates available for microbial utilization. This constitutes the potential control tier. Second, the inoculated microbial community (indigenous or exogenous) serves as the \u0026ldquo;bioreactor\u0026rdquo;, whose completeness and abundance of functional genes dictate the rate and extent of substrate degradation. The introduction of highly efficient exogenous consortia can effectively overcome the functional limitations inherent to the indigenous flora. This represents the efficiency modulation tier. Finally, the acidification products drive specific methanogenic metabolic pathways, whose flux matching and conversion efficiency accomplish the final energy transformation from chemical energy to biomethane. This is the terminal conversion tier. The ultimate output of the entire process (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, \u003cem\u003e\u0026lambda;\u003c/em\u003e, \u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e) is the result of the dynamic coupling, stepwise transmission, and amplification across these three tiers. This mechanism clarifies why the same microbial consortium performs differently on various coal samples and provides a theoretical basis for optimizing coalbed bioconversion processes.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study examined the kinetics, controlling factors, and synergistic mechanisms of biomethane production from regionally distinct coals degraded by indigenous and exogenous microorganisms. The main conclusions are as follows:\u003c/p\u003e\n\u003cp\u003e(1) Both indigenous and exogenous consortia exhibited similar gas-generation trends across three consistent stages: adaptation and rapid production (0–14 d), stable and slow production (14–35 d), and plateau/cessation (35–49 d). Bioaugmentation with an efficient exogenous consortium (HN1) systematically enhanced the methane‑producing process, increasing the maximum methane potential (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e↑), shortening the lag phase (\u003cem\u003eλ\u003c/em\u003e↓), and advancing the gas‑production peak (\u003cem\u003eT\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e↓). This improvement was most pronounced for easily degradable coals with high aliphatic and low aromatic contents (e.g., WGT-2), where methane yield increased by up to 147.76%.\u003c/p\u003e\n\u003cp\u003e(2) The chemical structure of medium-rank coals, particularly H/C, A\u003csub\u003eal\u003c/sub\u003e/A\u003csub\u003ear\u003c/sub\u003e, and CH\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e3\u003c/sub\u003e, serves as the primary and quantifiable key factor controlling their biogenic gas potential. Principal Component Analysis (PCA) revealed a hierarchical model governing coal biogenic gas production efficiency: the primary tier is “Bioavailability”, which is mainly dominated by high H/C, high aliphatic structures (A\u003csub\u003eal\u003c/sub\u003e/A\u003csub\u003ear\u003c/sub\u003e), as well as low ash content (A\u003csub\u003ead\u003c/sub\u003e) and low aromaticity (AR, \u003cem\u003ef\u003c/em\u003ea). Its scores distinguish easily degradable (WGT) from recalcitrant (BD) coals and determine the upper limits of gas potential (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e) and initiation speed (\u003cem\u003eλ\u003c/em\u003e). The secondary tier achieves regulation of the maximum methane production rate (\u003cem\u003eμ\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e) via CH\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e3\u003c/sub\u003e and O/C. The tertiary tier involves fine-tuning through fine chemical structural parameters such as oxygen-containing functional groups and calorific value.\u003c/p\u003e\n\u003cp\u003e(3) Coal biodegradation and methanogenesis result from a synergistic interplay among coal physicochemical structure, microbial function, and metabolic pathways. Coal structure predetermines substrate bioavailability and potential limits. The abundance and completeness of microbial functional genes (e.g., acidogenesis and aromatic degradation genes in the exogenous consortium) govern the efficiency of substrate breakdown. The resulting degradation precursors then channel carbon flux into specific methanogenic pathways (hydrogenotrophic, acetoclastic, etc.), whose coordination ultimately determines methane conversion efficiency. This mechanistic framework provides a theoretical basis for enhancing coal bioconversion.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCRediT authorship contribution statement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXueru Chen\u003c/strong\u003e: Conceptualization, Methodology, Data curation, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eYuan Bao\u003c/strong\u003e: Conceptualization, Formal analysis, Visualization, Investigation, Writing \u0026ndash; review \u0026amp; editing, Supervision, Funding acquisition. \u003cstrong\u003eXingui Wang\u003c/strong\u003e: Visualization, Writing \u0026ndash; review \u0026amp; editing. \u003cstrong\u003eYiliang Hu\u003c/strong\u003e: Methodology, Software, Validation. \u003cstrong\u003eZexi Zhu\u003c/strong\u003e: Investigation.\u003c/p\u003e\n\u003cp\u003eDeclaration of Competing Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eData availability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China [grant numbers: 42172200; 41972183].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWang S, Liu L, Zhu M, et al. 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However, the differences in biomethane generation and the constraining factors under the drive of indigenous versus exogenous microorganisms remain unclear. To address this, anaerobic fermentation experiments simulating coal‑derived biomethane generation were conducted using two distinct microbial sources: indigenous microorganisms enriched from fresh coal samples from the study area and exogenous microorganisms optimized under laboratory conditions. Five representative coal samples from the Wuguantun and Baode mining areas were used as carbon substrates. The efficiency of biomethane production was evaluated based on gas chromatography and analysis using four kinetic models. By integrating methods including coal petrographic and proximate analyses, 16S rRNA high-throughput sequencing, and Fourier transform infrared spectroscopy, principal component analysis and metabolic pathway analysis were applied to systematically elucidate the main controlling factors and synergistic mechanisms governing biomethane generation. The results indicate that although both indigenous and exogenous microorganisms follow a similar three‑stage process during coal‑degrading methanogenesis, their gas production efficiencies differ significantly. Bioaugmentation with exogenous microbial consortia systematically optimized the gas‑generation process, increasing the maximum methane potential (\u003cem\u003eA\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e) by approximately 80% on average, shortening the lag phase (\u003cem\u003eλ\u003c/em\u003e) by about 55% on average, and significantly enhancing the maximum methane production rate (\u003cem\u003e\u0026micro;\u003c/em\u003e\u003csub\u003em\u003c/sub\u003e). Coal chemical structure was identified as the primary factor controlling gas‑production variability, with high H/C, and a high aliphatic structures (A\u003csub\u003eal\u003c/sub\u003e/A\u003csub\u003ear\u003c/sub\u003e, CH\u003csub\u003e2\u003c/sub\u003e/CH\u003csub\u003e3\u003c/sub\u003e), and moderate O/C serving as the most critical predictors, demonstrating excellent bioavailability. Biomethane output is governed by a three‑level synergistic mechanism of \u0026ldquo;coal physicochemical structure\u0026ndash;microbial function\u0026ndash;metabolic pathway\u0026rdquo;: the physicochemical structure of coal sets the upper limit of potential; the functional gene abundance of the microbial community determines substrate degradation efficiency; and the distribution of downstream methanogenic pathways ultimately governs biomethane conversion efficiency. This study not only deepens the understanding of the complex biogeochemical process of coal bioconversion but also provides key scientific evidence for refining theoretical models of biomethane generation.\u003c/p\u003e","manuscriptTitle":"Kinetic profiles and efficiency disparities in biomethane production mediated by indigenous versus exogenous microbial consortia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 15:26:34","doi":"10.21203/rs.3.rs-9470237/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"126892537607771814008488874105373385605","date":"2026-04-29T21:53:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-27T14:21:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-24T12:17:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-24T11:36:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bioprocess and Biosystems Engineering","date":"2026-04-20T09:42:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bioprocess-and-biosystems-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Bioprocess and Biosystems Engineering](https://www.springer.com/journal/449)","snPcode":"449","submissionUrl":"https://submission.nature.com/new-submission/449/3","title":"Bioprocess and Biosystems Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"992015c2-e630-47b4-8bdc-c5a76ca2d55e","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"126892537607771814008488874105373385605","date":"2026-04-29T21:53:38+00:00","index":24,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T15:26:35+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 15:26:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9470237","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9470237","identity":"rs-9470237","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}