Raman spectroscopy has become a widely applied and powerful analytical tool across numerous scientific disciplines due to its ability to provide detailed molecular information in a non-destructive manner. In environmental sciences, it is particularly employed for the detection and analysis of hazardous chemicals as well as for the detection and identification of micro- and nanoplastics. However, the latter remains time-consuming and labor-intensive, as extensive sample preparation is typically required prior to Raman measurements. Moreover, Raman measurements are generally performed under static conditions, which further limits throughput and applicability. In this work, we present a flow Raman spectroscopy platform combined with a dedicated evaluation procedure, specifically addressing the detection of microplastics in water samples. The approach enables semi-continuous measurements with only minimal sample preparation, thereby significantly reducing the analytical effort. We showcase the accurate quantification of particle ratios and concentrations in mixed suspensions, the ability to distinguish between polymer types, and the detection of particles with diameters as small as 2 μm. Furthermore, under optimized flow conditions, the system achieves measurement rates of up to 1500 particles per minute and a detection efficiency exceeding 85%. Lastly, we successfully employ the system to detect, identify, and image microplastics in a water sample from a wastewater treatment plant. Our results underscore the potential of flow Raman spectroscopy as a powerful tool for more efficient monitoring of microplastics in environmental applications.
This review presents recent advances in integrated vibrational and X-ray spectroscopic approaches for the characterization of cultural heritage materials. Vibrational techniques, including Raman, Fourier transform infrared (FT-IR), and optical photothermal infrared (O-PTIR) spectroscopy, together with X-ray fluorescence, diffraction, and absorption methods, provide complementary molecular, elemental, and structural information for the analysis of pigments, binders, and degradation products. Emphasis is placed on analytical capabilities, limitations, and the synergistic use of these techniques within multimodal workflows.Vibrational spectroscopy plays a central role in molecular identification and monitoring chemical transformations, whereas X-ray methods provide insight into elemental composition, crystallography, and pigment alteration mechanisms. Their integration establishes a robust framework for materials identification, stratigraphic interpretation, and investigation of degradation pathways in historical objects.Representative case studies discussed in this review illustrate how integrated vibrational and X-ray spectroscopic strategies address key analytical challenges in cultural heritage research, including the characterization of lead-based materials in works attributed to Leonardo da Vinci, compositional layering in paintings by Pablo Picasso, and cadmium yellow degradation in masterpieces by Edvard Munch and Vincent van Gogh. O-PTIR is highlighted as an emerging tool for submicron analysis, while combined Raman and X-Ray spectoscopic methods resolve environmentally driven pigment transformations.This review outlines current advances, practical challenges, and future directions in spectroscopy-driven research on complex materials.
While the exceptional emission properties of two-dimensional Ruddlesden-Popper perovskites are fundamentally governed by exciton-phonon interactions, tracking these dynamic lattice couplings remains elusive due to the state-filling limitations of visible-spectrum spectroscopy. Herein, we utilize broadband mid-infrared transient absorption spectroscopy to directly probe the intraexcitonic transitions and dynamic lattice reorganizations across a dimensionality series (n = 1, 2, and 4) of phenethylammonium (PEA)-based perovskites. We observe a profound, dimensionality-driven tuning of the microscopic coupling mechanism. In the extreme confinement limit (n = 1), intense structural distortion washes out discrete transitions, yielding a broad continuum indicative of rapid self-trapping. Relaxing this confinement (n = 2) unveils beautifully resolved Franck-Condon progressions driven by ≈10 meV inorganic Pb-I optical phonons. By fitting this progression, we extract a Huang-Rhys factor of S ≈ 0.57, quantitatively confirming an intermediate coupling regime that prevents deep self-trapping. Further relaxation (n = 4) shifts the dominant interaction to higher-energy (≈33 meV) organic cation modes. This work demonstrates that structural dimensionality provides a direct lever to engineer the specific exciton-phonon bath, offering the fundamental physical blueprint required to optimize emission lineshapes for high-efficiency perovskite optoelectronics.
We demonstrate the measurement of the magnetization dynamics at femtosecond time scales using a split helix resistive magnet reaching magnetic field strengths up to 25 T. The high frequency resolution achieved by ultrafast lasers coupled with the high magnetic fields that can achieve magnetization oscillations in the hundreds of gigahertz makes this a very valuable experimental tool. We demonstrate the technique by using time-resolved magneto-optic Kerr effect spectroscopy to measure the spin dynamics of the ferrimagnet (FeCo)1-xGdx at magnetic fields up to 25 T for different magnetic field orientations. Three orientations were used to investigate the magnetization dynamics, perpendicular to the sample plane, parallel to the sample plane, and 20° off the sample plane. The polar time-resolved magneto-optic Kerr effect data show different dynamics depending on the alignment of the external magnetic field with the rare earth or transition metal magnetization. In time-resolved magneto-optic Kerr effect measurements with magnetic fields at 20° off the sample plane, we obtain oscillations due to the precession of the net magnetization. The oscillation frequency increases linearly with the external magnetic field, reaching 500 GHz at 20 T. When the magnetic field is applied parallel to the sample plane, the observed magnetization dynamics undergoes a 180° phase shift when the magnetic field direction is reversed. The different magnetic field orientations with respect to the sample lead to very different magnetization dynamics, which probe different aspects of the magnetic properties of the alloy, emphasizing the importance of the versatility of the present instrumentation.
Programable electron-beam scanning offers new opportunities to improve dose efficiency and suppress scan-induced artifacts in scanning transmission electron microscopy. Here, we systematically benchmark the impact of non-raster trajectories, including spiral and multi-pass sequential patterns, on electron energy-loss spectroscopy (EELS) and ptychography. Using DyScO3 as a model perovskite, we compare spatial resolution, spectral fidelity, and artifact suppression across scan modes. Ptychographic phase reconstructions consistently achieve atomic resolution and remain robust to large jumps in probe position. In contrast, atomic-resolution EELS maps show pronounced sensitivity to probe motion, with sequential and spiral scans introducing non-uniform elemental contrast. Finally, spiral scanning applied under cryogenic conditions in BTO thin films improves dose uniformity and mitigates drift-related distortions. These results establish practical guidelines for the implementation of non-raster scan strategies in 4D-STEM and highlight the inherent resilience of ptychography to trajectory-induced artifacts.
Laser-induced breakdown spectroscopy (LIBS) provides rapid multi-element analysis for steel classification, but its application to fine-grained steel identification remains limited by spectral overlap, matrix effects, feature instability, and the need for scalable model updating when new steel subclasses are introduced. In this study, a hybrid framework integrating dual Lorentzian decomposition and incremental learning was developed to improve the reliability and adaptability of LIBS-based steel classification. Emission lines were first screened using the NIST database, followed by dual Lorentzian decomposition to quantify adjacent-line interference and identify reliable spectral features. Correlation analysis and coefficient-of-variation filtering were further applied to improve feature stability. Experiments were conducted on 40 steel samples covering 28 subclasses, with 12 samples used as held-out test samples. From 1776 candidate emission lines, 53 optimized features were retained. To evaluate the effect of spectral screening, different feature sets were compared using the same DER++ with BiC framework. When the final screened feature set was used as raw-intensity input, the integrated framework achieved a held-out test accuracy of 95.83%, while the average incremental accuracy reached 98.85%. Among the compared static classifiers, SVM achieved the highest held-out test accuracy of 96.11%, whereas DER++ with BiC achieved a competitive held-out test accuracy of 95.83% with the shortest inference time and class-incremental updating capability. In incremental evaluation, DER++ with BiC maintained an average incremental accuracy of 98.85%, a low average forgetting rate of 0.73%, and the highest held-out subclass-mapping accuracy among the compared incremental learning methods. This work combines physically interpretable spectral-line reliability screening with a class-incremental learning framework for evolving steel spectral libraries. The proposed DER++ with BiC framework is not positioned as the highest static-accuracy classifier, but as a scalable model-updating strategy that balances competitive held-out test accuracy, fast inference, compact model size, and low forgetting. The results demonstrate that integrating spectral quality control with incremental learning can improve the robustness, stability, and adaptability of LIBS-based steel classification.
Electroabsorption (E-A) and electrophotoluminescence (E-PL) spectra of fluorescein (FL) doped in poly(vinyl alcohol) (PVA) film were measured in the visible region at various concentrations of FL in PVA. By analyzing the E-A spectra, the changes in the electric dipole moment and polarizability following excitation into the S1 state are determined for each concentration. The analysis of the E-A spectra indicates the formation of H-aggregates of FL in PVA at high concentrations. The electric-field-induced changes in the radiative decay rate constant (kr) of FL in PVA, estimated from the field-induced change in the total absorption intensity, indicate that kr decreases in the presence of an electric field and that the magnitude of the field-induced change in kr decreases with increasing FL concentration. In contrast to the kr of the FL monomer, the kr of H-aggregates is considered to increase with the application of an electric field. The fluorescence lifetime of FL in the PVA film decreases rapidly with increasing FL concentration, indicating a monotonic decrease in the fluorescence quantum yield with increasing FL concentration due to the rapid increase of the rate constant of the nonradiative decay process (knr). The nonradiative process from the emitting state of FL is probably assigned to the energy dissipation to nonemissive H-aggregates, i.e., energy transfer from the emitting state of the FL monomer to the H-aggregate. The applied electric field quenches the fluorescence of FL in PVA, indicating the field-induced increase in knr. Further, the magnitude of the field-induced quenching increases rapidly with increasing FL concentration, indicating that the field-induced increase of knr increases significantly with increasing FL concentration in PVA.
Q355C steel is a critical structural material widely used in offshore wind power facilities, including steel piles and tower cylinders, corrosion caused by marine salt spray poses a serious threat to long term structural integrity. In this study, laser-induced breakdown spectroscopy (LIBS) was employed to assess the corroded characteristics of Q355C steel. A field-deployable LIBS system based on two-dimensional scanning was developed for surface analysis. A quantitative calibration curve was established between the surface salt density of uncorroded Q355C steel and the sodium spectral intensity, achieving a high coefficient of determination (R2=0.94). Subsequently, LIBS measurements were performed on simulated corrosion specimens, revealing variations in elemental distribution within the corrosion zone with depth. Since surface corrosion products primarily consist of iron oxide whose thickness varies with corrosion severity, the ratio of manganese to iron spectral intensities (IMn/IFe) was identified as a potential indicator for distinguishing corrosion severity. By exploiting the spectral differences between genuine corrosion areas and pseudo-corrosion areas (areas covered by flow rust), accurate differentiation between the two was achieved. The accuracy of the LIBS binarization method was approximately 24% higher than that of traditional visual methods.
Lithium-ion batteries are widely used in electric vehicles and portable electronic devices. Accurate estimation of the State of Health (SOH) is essential to guarantee their safe and reliable operation. Electrochemical Impedance Spectroscopy (EIS) can characterize the internal electrochemical aging properties of batteries. However, traditional EIS-based methods only adopt single impedance parameters, which fail to fully describe the coupled aging behaviors and thus suffer from unsatisfactory prediction accuracy. To address this issue, this paper proposes a lithium-ion battery SOH prediction method combining multi-feature combinations of EIS and optimized Back Propagation (BP) neural network. Firstly, we analyze the cyclic aging experimental data of the same type of batteries under different operating conditions. Spearman's Rank Correlation Coefficient (SRCC) is employed to select valid features highly correlated with capacity degradation, and two sets of EIS multi-feature combinations are established for comparative analysis. Secondly, three optimization algorithms, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Ant Colony Optimization (ACO), are used to optimize the initial parameters of the BP neural network, which overcomes the drawback that the conventional BP model is prone to falling into local optima. The experimental results reveal that under the operating conditions of 35C01 and 35C02 with insufficient samples and prominent data noise, the BP neural networks optimized by ACO and GA achieve superior prediction performance on the test set, with lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). For 45C01 and 45C02 with sufficient samples and low data noise, ACO-BP and GA-BP maintain stable prediction performance. The proposed method realizes the organic integration of electrochemical mechanism analysis and data-driven modeling, and effectively improves the prediction robustness and generalization ability. It provides a high-precision and practical technical solution for the online SOH monitoring of lithium-ion batteries.
Bloodstream infections are associated with considerable morbidity and mortality, necessitating timely and accurate antimicrobial susceptibility testing (AST) to guide appropriate therapy. Current diagnostic methods primarily rely on culture-based AST, which is time-consuming, or on genotypic approaches that lack phenotypic relevance. We present the RamanBioAssay (RBA) platform, a novel diagnostic tool integrating dielectrophoretic on-chip bacterial enrichment with label-free Raman spectroscopy, to enable rapid phenotypic AST and simultaneous bacterial identification (ID). The RBA platform delivers AST results within 3.5 h from a positive blood culture, substantially reducing the diagnostic turnaround time compared to standard culture-based techniques. The RBA platform demonstrated high concordance with conventional AST using quality control strains: 94.4% and 100% for E. coli treated with ciprofloxacin and S. aureus treated with oxacillin in medium controls, 91.7% and 97.2% in artificial blood cultures, respectively. For proof-of-concept evaluation, six patient blood cultures were analyzed, yielding concordance rates of 91.7% and 83.3% for E. coli treated with ciprofloxacin and S. aureus treated with oxacillin, respectively (1/6 samples showed a S/R mismatch, 1/6 samples was nonconclusive). The mean diagnostic turnaround time for clinical samples was 3 h and 6 min (±24 min). Additionally, the family level classification of E. coli and S. aureus, shown exemplary in medium controls, was achieved with 97.2% accuracy. These findings highlight the RBA platform as a promising tool for rapid phenotypic AST combined with bacterial ID, providing comprehensive and clinically actionable results significantly faster than conventional methods.
Optical lattice clocks achieve fractional frequency uncertainties of 10⁻¹⁸, yet stability is constrained by the dead time between cooling, preparation, interrogation, and detection. This sampling aliases local-oscillator noise into the clock signal (the Dick effect) and prevents continuous accumulation of oscillator phase information. We demonstrate spatially defined Rabi spectroscopy of ultracold ⁸⁸Sr atoms continuously transported in a moving optical lattice. A longitudinal excitation geometry preserves Lamb-Dicke confinement and suppresses Doppler broadening. Clock excitation is enabled only within a localised region by a transverse magnetic mixing field, defining the atom-laser interaction in space rather than in time and decoupling interrogation from preparation and detection. Transporting atoms at 16 mm s⁻¹ through a 12-mm interaction region yields a 1.2-Hz-wide spectrum close to the transit-time Fourier limit while maintaining uninterrupted atom delivery. This approach provides a practical route toward dead-time-free optical clock interrogation of continuously delivered atomic ensembles.
In this work, in order to improve the low solubility and poor oral bioavailability of puerarin, we first applied melt sonocrystallization (MSC) technology to systematically investigate the effects on its solubility and crystallographic properties. We applied MSC to treat puerarin, evaluated its solubilization effect using UV spectroscopy, and characterized it by dissolution rate, optical microscope, powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), Thermogravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FTIR). Following MSC treatment, puerarin exhibited higher solubility and faster dissolution, the product displayed reduced particle size and a tendency to agglomerate. The results of PXRD, DSC, FTIR and TGA showed that the crystal form of MSC treated puerarin transformed into puerarin monohydrate, with a decrease in crystallinity and partial amorphization. The reduction in particle size and amorphization may be the driving forces of the increase in solubility and dissolution. In summary, MSC can significantly improve the solubility and dissolution of puerarin, providing new ideas for enhancing its bioavailability and improving its oral formulations.
Silver (Ag)-decorated Bi2WO6 (BW) nanoflakes were developed as a visible-light-responsive plasmonic semiconductor photocatalyst for enhanced hydrogen evolution and efficient photocatalytic degradation of Congo red under natural sunlight irradiation. The Ag-Bi2WO6 (ABW) nanocomposites were characterized by UV-visible spectroscopy, XRD, XPS, TRPL analysis, BET analysis, Raman spectroscopy, FESEM and HRTEM. XRD showed the formation of the orthorhombic crystal phase of Bi2WO6 and the face-centered cubic structure of Ag. UV-visible spectroscopy showed the extended absorption of ABW composites in the visible range, whereas BW showed an absorption peak at 320 nm. Raman analysis confirmed the successful decoration of Ag onto BW, altering its vibrational mode and structural bonding. FESEM confirmed the nanoflake morphology with a densely packed and self-assembled plate/disc structure of BW and spherical Ag nanoparticles (diameter ∼6 nm) well-deposited on it. The high-resolution TEM results showed lattice fringes with interplanar spacings of ∼0.22 nm and 0.27 nm, corresponding to the (111) and (113) planes of Ag and BW, respectively. The ABW nanocomposite showed excellent photocatalytic activity, with 94% CR dye (25 ppm) degradation within 6 minutes under natural sunlight, along with a pseudo-first-order rate constant of 0.43 min-1. The obtained cumulative hydrogen production rate using BW and the ABW nanocomposites is 8231 µmol g-1 and 13 054 µmol g-1, respectively, within 4 hours under a mercury vapor lamp (400 W).
Understanding the internal pore properties of crystalline covalent organic frameworks (COFs) is crucial for chemically fine-tuning these porous materials and optimizing them for applications. Typical characterization based on adsorption isotherms, such as N2-adsorption can in some cases be limited due to the cryogenic temperatures that are used, limiting molecular motion and thereby giving a static picture. We synthesized alkyl and triethylene glycol (TEG) functionalized COFs, which, despite high crystallinity, showed low BET values owing to the large side chains leading to pore blocking. We applied continuous-wave electron paramagnetic resonance (CW EPR) spectroscopy to shed light on their pore properties. Using different EPR-active radicals as spin probes, the adsorption affinity, the type and strength of interactions, the local concentrations of the guest radicals, and the polarity difference between alkyl- and TEG-containing pores could be sensed and quantified. We could identify the spectral signatures of multiple non-covalent interactions from the adsorbed radical species, including hydrogen bonds, dipolar, dispersion, and π-π-interactions. This study demonstrates the potential of CW EPR spectroscopy to characterize COF pore environments and pore-guest interactions with radicals in suspensions. It opens the door to a new complementary methodology for pore characterization.
Absorbance spectroelectrochemistry is a powerful in situ technique utilized to investigate redox changes and charge carrier properties in electronic materials, such as mixed ionic-electronic conducting polymers. In this method, the absorption spectrum of the material is obtained as a function of applied potential, and the results are used to understand its electrochemical behavior. The standard approach uses chronoamperometry, in which a constant potential is applied, and an absorption spectrum is measured at electrochemical equilibrium for a series of potentials selected across the electrochemical window of the sample. However, this method is limited by slow, labor-intensive data acquisition and lack of time-resolved absorbance information. Herein, we describe an automated absorbance spectroelectrochemical method that couples a high-speed, broadband optical absorbance instrument with normal pulse voltammetry to capture full absorbance spectroelectrochemistry spectra in a single measurement. The instrument measures absorption spectra over a 400 - 2500 nm window at a maximum spectral acquisition rate of 200 Hz (5 ms response time). We demonstrated the capability of this spectroscopy technique to automatically obtain a full set of potential-dependent absorbance spectra and absorbance transients by applying it to study the electrochemical charging behavior of a polythiophene electrode, which was time-resolved at 1 spectrum/s.
Zanthoxylum bungeanum Maxim. (Z. bungeanum) is widely applied in the processing of stewed beef with spices (SBS) to improve sensory quality, yet the processing-induced interactions between its flavonol glycosides and myofibrillar protein (MP) and their role in taste modulation remain poorly understood. In particular, the structural basis and binding behavior underlying protein-flavonol interactions during stewing, as well as their contribution to taste regulation. In this study, metabolomics combined with random forest modeling was employed to screen key taste-active compounds in SBS. The structure-affinity relationship and the mechanism underlying the interaction between flavonol glycosides and MP were investigated using fluorescence spectroscopy, Fourier transform infrared spectroscopy, and molecular docking. The top 15 key taste compounds were identified by a random forest model as significant contributors to flavor enhancement. A subsequent investigation focused on isoquercetin and astragalin due to their notably high variable importance scores and acknowledged chemical relevance. E-tongue results suggest that MP exhibits strong bitterness-masking activity. The interaction of flavonol glycosides (isoquercetin and astragalin) with MP in higher pH environments caused fluorescence quenching. Molecular docking results indicate that variations in the molecular geometries of isoquercetin and astragalin contribute to their distinct binding interactions with bitter taste receptor TAS2R14. These findings provide mechanistic insight into processing-induced protein-flavonol interactions and offer theoretical guidance for optimizing spice-assisted meat processing strategies and taste quality regulation. © 2026 Society of Chemical Industry.
LiNi0.5Co0.2Mn0.3O2 (NCM523) is a mainstream lithium-ion battery cathode material with high specific capacity, which is widely applied in energy storage and power batteries. Nevertheless, it suffers from transition-metal dissolution, structural phase transition, and interfacial side reactions during long cycling, causing rapid capacity fading and inferior stability, which restricts its large-scale application. Traditionally, the Al2O3 coating layer can effectively isolate the cathode material from direct contact with the electrolyte, alleviate electrolyte decomposition, and suppress transition-metal dissolution as well as interfacial side reactions. However, the underlying charge-transfer-driven bonding mechanism between the coating layer and cathode substrate remains insufficiently understood. Herein, a sol-gel method was used to prepare Al2O3-coated NCM523 with an optimal 1.0 wt % coating content. Electrochemical tests show that 1.0 Al-NCM delivers an initial discharge capacity of 177.1 mAh g-1 at 0.2 C, retains 80.3% capacity after 200 cycles at 0.5 C (8.4% higher than bare NCM523), and exhibits 138.2 mAh g-1 at 5 C. Beyond the conventional physical protection effect of the Al2O3 coating layer, X-ray photoelectron spectroscopy, synchrotron X-ray absorption spectroscopy, and density functional theory calculations reveal a charge-transfer-driven interfacial bonding mechanism between the Al2O3 coating layer and NCM523 substrate, featuring the formation of strong Al-O-TM bonds. The charge transfer across the interface promotes Al-O-TM bonding and regulates the surface electronic structure, thereby enhancing the structural reversibility and cycling stability of the layered cathode. These findings provide new insights into the active role of the Al2O3 interfacial layer in regulating surface chemistry and enhancing the structural stability of layered oxide cathodes.
Halide double perovskite (DP) nanocrystals (NCs) have emerged as promising lead-free alternatives for optoelectronic applications, yet their optical performance remains limited by surface and structural defects. In this work, we report the synthesis of Cs2Ag0.6Na0.4In0.8Bi0.2Cl6 colloidal NCs and systematically tune their photophysical properties via halide exchange by substituting Cl- with Br- using an antisolvent precipitation method. Photoluminescence (PL) spectroscopy, time-resolved PL, and femtosecond transient absorption spectroscopy reveal that moderate Br- incorporation optimizes the radiative recombination dynamics by significantly reducing trap states and enhancing self-trapped exciton (STE) emissions. Such effects are further supported by increased PL quantum yield up to 48%, prolonged STE lifetimes, and reduced nonradiative decay rates. X-ray diffraction and transmission electron microscopy further confirm that Br- substitution also influences lattice strain and distortion, affecting the emission behavior. Our findings reveal that halide composition tuning is an effective strategy for controlling trap-mediated dynamics and improving emission efficiency in DP NCs, offering valuable insights for developing broadband, stable white-light-emitting materials.
Biomass-based catalyst research is advantageous for the biodiesel industry's sustainability due to the catalysts' readily available raw materials, nontoxic properties, and biodegradability. In this study, sulfonated carbon catalysts were synthesized from lavender biomass residue, obtained after essential oil extraction, through hydrothermal carbonization (HTC) followed by in situ sulfonation, and applied in the acetalization of glycerol with acetone to produce solketal, a bio-based fuel additive. To the best of our knowledge, this type of residue has not yet been explored for this specific application. Characterization by XRD and Fourier-transform infrared spectroscopy (FT-IR) confirmed the amorphous carbon structure and successful incorporation of ─SO3H groups. The formation of solketal was confirmed by nuclear magnetic resonance (NMR) and gas chromatography-mass spectrometry (GC-MS). The catalyst exhibited excellent performance under mild conditions, achieving 97% glycerol conversion and 98% selectivity toward solketal. Optimal conditions (40°C, 30 min, 7.5 wt% catalyst loading, and a glycerol-to-acetone molar ratio of 1:20) afforded high glycerol conversion and selectivity toward solketal. The catalyst exhibited remarkable stability, maintaining >96% conversion and >97% selectivity after seven reuse cycles, confirming the potential of lavender waste as a sustainable precursor for catalyst development. These results highlight the potential of lavender agro-residues as a sustainable carbon source for the production of efficient heterogeneous catalysts in renewable fuel applications.
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, 16 S 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 biomethane generation process, increasing the maximum methane potential (A0) by approximately 80% on average, shortening the lag phase (λ) by about 55% on average, and significantly enhancing the maximum methane production rate (µm). Coal chemical structure was identified as the primary factor controlling gas‑production variability, with high H/C, and a high aliphatic structures (Aal/Aar, CH2/CH3), and moderate O/C serving as the most critical predictors, demonstrating excellent bioavailability. Coal physicochemical structure, microbial functional potential, and methanogenic pathway distribution govern biomethane output. The physicochemical structure of coal sets the upper limit of potential; the predicted functional gene abundance of the initial microbial community serves as an indicator of substrate degradation potential; 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.