Search Results (473)

Municipal sewage sludge was used to prepare sewage sludge pyrolysis char (SS-PC). The effects of pyrolysis temperature on the morphology and structure of SS-PC were investigated, and the performance of SS-PC-800, prepared at 800 °C, for promoting gas production from corn stover pyrolysis was evaluated in a fixed-bed reactor. The results suggested that adding SS-PC-800 promoted the pyrolysis of corn stover and reduced the activation energy required for thermal decomposition. A further comparison of five metal oxides indicated that CaO had the most pronounced effect on H₂ formation under the tested conditions. A synergistic effect was observed when reactive CaO was introduced into SS-PC. At a pyrolysis temperature of 800 °C, when the mass ratio of CaO to SS-PC-800 was 2:3 and the mass ratio of mixed catalyst to corn stover was 1:5, the H₂ yield was 26.5% higher than that obtained from corn stover pyrolysis alone. In this study, SS-PC was employed as a catalytic material, and the synergistic interaction between its catalytic components and CaO effectively enhanced H₂ production during biomass pyrolysis. These findings can provide a useful reference for the resource utilization of municipal sludge and the development of catalysts for biomass thermochemical conversion. Full article

Early fault detection in microsatellite thermal control systems (TCS) faces fundamental challenges: high-dimensional redundant telemetry channels, overlapping multi-scale periodicities that obscure anomaly signatures, and severely limited daily data downlink (1–2 passes per day) that restricts the temporal window for diagnosis. Existing data-driven approaches either rely on supervised learning, requiring labeled fault data that are scarce in practice, or employ univariate analysis that fails to capture inter-sensor spatial correlations. To address these limitations, this paper introduces a hybrid framework integrating information-theoretic feature selection and spatiotemporal deep learning. The Generalized Maximum Information Coefficient (GMIC) quantifies nonlinear dependencies between temperature channels for key channel selection, reducing dimensionality by 82% while preserving diagnostic information. A dual-level Seasonal Trend Decomposition (STL) method disentangles orbital-periodic dynamics from diurnal cycles, effectively isolating distinct thermal characteristics at multiple timescales. Each decomposed component is modeled using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) networks to capture spatiotemporal dependencies for accurate temperature prediction. An adaptive threshold-based weighted error fusion mechanism enables early fault detection within a single day of telemetry data. Experimental validation on real satellite telemetry data demonstrates that the proposed framework achieves high-precision fault detection across multiple fault types using a minimal set of temperature channels, significantly outperforming existing benchmarks in both prediction accuracy and detection reliability. Full article

Photoacoustic spectroscopy (PAS) is a highly sensitive and non-destructive technique widely used for trace gas detection; however, the simultaneous quantification of methane (CH₄), ethane (C₂H₆), and ethylene (C₂H₄) remains challenging due to severe spectral cross-interference and non-linear responses across broad concentration ranges. In this work, we propose a high-precision, end-to-end detection framework based on a Deep Kernel Extreme Learning Machine (DKELM) optimized using a Mutation–Chaotic Particle Swarm Optimization (MCPSO) algorithm. To enhance diagnostic information in the photoacoustic signals, a multi-scale wavelet transform based on a db4 wavelet basis with 5-layer decomposition and a Heursure soft threshold strategy is first employed for denoising and enhancing absorption features. To address the hyperparameter sensitivity and local-optimum trapping inherent in deep models, the MCPSO algorithm integrates hybrid chaotic initialization, adaptive mutation probability control, Cauchy-based perturbation, temperature-controlled mutation amplitude, and elite-guided population updating. The proposed MCPSO-DKELM model is evaluated on an expanded dataset of 470 mixed-gas spectra and benchmarked against other frameworks, including the previously reported SVM-CPSO-KELM architecture. The experimental results demonstrate that MCPSO-DKELM achieves stable, segmentation-free quantification across the full dynamic range, with an average detection error below 3.5% and the maximum relative error constrained to under 15%, which represents a substantial improvement over existing approaches. Thus, the combination of deep kernel feature extraction and mutation–chaotic global optimization provides a robust and reliable solution for simultaneous multi-component hydrocarbon gas analysis in complex industrial environments. Full article

To clarify how injection-induced cooling and reservoir properties jointly control rock damage during hydraulic fracturing of deep shale reservoirs, this study develops a coupled thermo–hydro–mechanical phase-field model incorporating fracture pressurization, matrix seepage, heat transfer, thermoelastic stress redistribution, and tensile damage evolution. The hydraulic fracture component is verified against the classical KGD analytical benchmark, and the thermal damage component is benchmarked against a ceramic quenching experiment. The phase-field formulation is constructed using tensile-compressive strain-energy decomposition so that only the tensile part of the elastic energy contributes to damage evolution, while the compressive stiffness is retained. The results show that low-temperature fluid injections produce a steep but spatially limited cooling zone near the fracture wall. The constrained contraction of the cooled rock generates additional thermoelastic tensile stress, strengthens fracture-tip stress localization, and accelerates phase-field damage accumulation. In the baseline case, thermal cooling increases the peak tensile stress near the fracture tip along profile c from 10.2 MPa in the hydraulic-only case to 22.5 MPa at t = 2 h, while the phase-field damage value increases from 0.03 to 0.77. Five-case sensitivity analyses show that, as α_T increases from 0.5 × 10⁻⁵ to 1.5 × 10⁻⁵ 1/°C, the fracture-tip tensile stress at t = 2 h increases from approximately 18.6 MPa to 25.7 MPa, and the damage value increases from approximately 0.80 to 0.96. As permeability increases from 0.0001 mD to 0.01 mD, the pore pressure at 2 m from the fracture wall increases from approximately 50.4 MPa to 71.2 MPa, and the tensile stress along profile c increases from approximately 16.4 MPa to 21.8 MPa. These results demonstrate that coupled thermal and hydraulic effects govern fracture initiation, localization, and propagation tendency during thermally assisted hydraulic fracturing in deep shale reservoirs. Full article

The cement industry is a major contributor to global carbon emissions. Therefore, reducing emissions while utilizing industrial wastes is critical for its sustainable development. Red mud, a solid waste byproduct of alumina smelting with main components like SiO₂, Al₂O₃, and CaO, can partially replace limestone as a raw material in cement production. TG-DSC thermal analysis clarified red mud’s three-stage weight loss characteristic during calcination (total weight loss rate of 22.11%), and orthogonal experiments identified calcination temperature as the core factor for its CaO content, with the optimal calcination pretreatment process confirmed (0.075–0.09 mm particle size, 1373 K, 1 h residence time, CaO content up to 21.1%). Based on the results, this study uses ANSYS Fluent 2021 R1 to simulate a TTF-type precalciner, establishing a validated multi-physical field model (all relative errors < 5%) to explore red mud blending ratios of 0%, 2.5%, 5%, 7.5% and 10%. Unlike previous experimental studies, this work uses a CFD model to quantify how red mud blending ratios affect the coupled thermo-chemical environment in a TTF precalciner, revealing a mechanism-driven trade-off among decomposition rate, CO₂, and NO_x that experiments alone cannot capture. Results show red mud slightly alters the internal temperature field and reduces the raw meal decomposition rate. The decomposition rate remains within the industrial acceptable range of 85–95% when the red mud blending ratio is no more than 5%, while further increasing the blending ratio to 7.5% and 10% causes the decomposition rate to drop below 85%. Therefore, a blending ratio of 5% is recommended, which balances waste utilization, decomposition rate, and emission reduction, providing solid technical support for red mud’s large-scale use in cement production. Full article

Tailings storage facility (TSF) failures have caused severe casualties and economic losses. This study used Enhanced Small Baseline Subset InSAR (E-SBAS-InSAR) and 88 Sentinel-1A images to retrieve the 2022–2024 surface deformation time series of the Shiguilong TSF, located in the Fe–Cu polymetallic metallogenic belt of the middle–lower Yangtze River. The reliability of the results was assessed through consistency comparisons with Small Baseline Subset InSAR (SBAS-InSAR) and Persistent Scatterer InSAR (PS-InSAR). A time-series decomposition model was applied to extract seasonal deformation components and analyze their lagged responses to temperature and intense rainfall events. The results show that: (1) E-SBAS-InSAR achieved a monitoring-point density nearly 7 times higher than SBAS-InSAR, enabling dense and long-term deformation characterization; (2) subsidence at Shiguilong continued to increase, with cumulative subsidence reaching −76.8 mm and a maximum annual mean subsidence rate of −22.78 mm/yr; (3) deformation was mainly controlled by long-term consolidation of loose tailings and creep of dam–tailings materials, while seasonal factors induced stage-dependent fluctuations; (4) seasonal deformation showed lagged responses of 6 days to temperature variations and 2 days to intense rainfall events, with rainfall exerting a more pronounced influence. This work is significant for TSFs monitoring under complex surface conditions. Full article

Lithium-ion batteries have become crucial energy carriers in multiple core fields owing to their excellent comprehensive performance. Nevertheless, as battery energy and power densities continue to rise and operating conditions grow increasingly complex, thermal safety issues have become increasingly prominent. Immersion liquid cooling technology has attracted widespread attention in academic and engineering fields for its outstanding heat transfer and temperature uniformity performance. As a core component of this technology, the selection of liquid coolants is of vital importance. Various coolants investigated in existing studies generally suffer from limitations to varying degrees. Against this backdrop, intrinsically safe fluorocarbon C₇H₄F₁₂O (3F-135) serves as an ideal liquid cooling medium for lithium-ion batteries, thanks to its high thermal stability, superior electrical insulation and environmental friendliness (zero ODP, extremely low GWP). However, its decomposition mechanism and reaction pathways under extreme thermal runaway conditions of batteries remain unclear. In this study, a tube furnace was adopted to simulate high-temperature environments induced by thermal runaway, and gas chromatography–mass spectrometry (GC-MS) was employed to analyze decomposition products and decomposition ratios of 3F-135. Subsequently, density functional theory (DFT) calculations were utilized to construct the pyrolysis reaction network of 3F-135. Ultimately, the dominant pyrolysis pathways in different temperature ranges were clarified, providing theoretical support for the application and selection of intrinsically safe liquid coolants. Full article

To meet the requirements of precise temperature regulation in solar greenhouses, traditional machine learning algorithms often suffer from poor adaptability, high energy consumption, and difficulties in integrating agronomic expertise. This study developed an intelligent greenhouse temperature regulation framework based on Model Predictive Control (MPC). The core components of the framework include: (1) an expert-experience-based simulator using a Sparrow Search Algorithm-optimized Random Forest (SSA-RF) model to digitize the temperature management strategies of high-yield farmers into dynamic reference trajectories and (2) a hybrid prediction model (CNN-BiLSTM-Attention) combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Permutation Entropy (CEEMDAN-PE) denoising with a Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention mechanism to achieve high-precision multi-step temperature forecasting. Validation in a cucumber solar greenhouse demonstrated that the SSA-RF model achieved an R² of 0.976 on the test set, showing a significant improvement over the traditional RF model. Compared to the conventional LSTM model, the hybrid prediction model reduced the RMSE to 0.642 and 0.947 for 15 min and 30 min predictions, respectively, with a maximum R² of 0.994 and excellent generalization capabilities. Finally, these two components were theoretically integrated into an MPC-oriented decision framework. The framework describes how expert reference trajectories, multi-step predictions, actuator constraints, and control increments can be combined in a receding-horizon optimization problem. Since online actuator control data were not available, the MPC module was formulated as a theoretical decision framework rather than a fully validated closed-loop controller. This study provides a modelling basis and technical path for future real-time greenhouse temperature control. Full article

This study presents an AI-assisted long-distance fiber Bragg grating (FBG)-based sensing approach for simultaneous temperature and vibration measurement using a single bare FBG sensor. To address the strong coupling between temperature- and vibration-induced effects in the wavelength time series, a signal processing framework based on adaptive variational mode decomposition (AVMD) is developed. With power-spectral-density-guided parameter selection, the mixed wavelength signal is separated into a low-frequency temperature-related component and a high-frequency vibration-related component, enabling stable temperature–vibration decoupling within a single-sensor architecture. Experiments conducted with a 10 km fiber link between the sensor and the interrogator demonstrate that the proposed method can stably track the dominant vibration frequency under various temperature and vibration conditions, while the reconstructed low-frequency component remains consistent with the thermal evolution trend even in the presence of vibration. Random vibration tests and low-frequency vibration resolution analysis further confirm the stability and practicality of the proposed approach under long-distance fiber transmission conditions. In addition, an AI-assisted condition-monitoring scheme is demonstrated using a one-dimensional convolutional autoencoder trained solely with normal wavelength time-series data. Rather than relying on raw reconstruction error alone, the diagnostic layer derives a latent transition score from encoder bottleneck features through temporal pooling, L2 normalization, cosine-distance evaluation, smoothing, and baseline removal. Deviations from steady operating conditions can thereby be preliminarily indicated, highlighting the potential for integrating physics-driven signal processing with data-driven artificial intelligence in long-distance fiber sensing systems. Full article

Aerogels derived from microbial exopolysaccharides are useful in the food, pharmaceutical, and environmental sectors, but their application in anticancer therapy is constrained by inadequate characterization, especially regarding effects on normal cells. This study used ethanol precipitation and trichloroacetic acid deproteinization to extract crude exopolysaccharide from the fermentation broth of Bacillus amyloliquefaciens SQ-2. The pure fraction, EPS-3791, was obtained using Sephadex G-100 gel filtration chromatography and DEAE cellulose ion exchange. The weight–average molecular weight of EPS-3791 was 64.4 kDa. Monosaccharide analysis indicated fructan as the dominant component, which was consistent with the results of methylation analysis and NMR spectroscopy, confirming that EPS-3791 is a fructan mainly linked by →1)–Fruf–(2→bonds. UV scanning indicated high purity. FTIR analysis revealed functional groups including hydroxyl, carbonyl, and C–O–C groups. EPS-3791 exhibited a porous three-dimensional network morphology by SEM, with a decomposition temperature of 191.61 °C by TGA. Additionally, aerogels were prepared by freeze drying. EPS-3791 aerogels demonstrated minimal cytotoxicity to normal L929 cells while inhibiting the growth of human lung cancer A549, breast cancer MCF–7, and cervical cancer HeLa cells in a dose-dependent manner. Scratch wound healing experiments revealed that EPS-3791 aerogels hindered HeLa cell migration while promoting L929 wound closure. These findings identify EPS-3791 as a fructan-type exopolysaccharide aerogel with specific anticancer properties. Full article

Marine by-products represent a promising source of bioactive peptides. This study aimed to isolate and characterize a low-molecular-weight peptide fraction with antioxidant activity from Argentine shortfin squid carcass by-products, and to evaluate in vitro its cytocompatibility and protective effects against corticosterone (CORT)-induced oxidative injury in rat adrenal pheochromocytoma (PC12) cells and human astrocyte (hACs) cells. Argentine squid antioxidant peptide (PASN) was obtained by size-exclusion chromatography and fractionation-based screening. PASN exhibited the strongest overall free-radical-scavenging activity and consisted predominantly of components below 1 kDa (211.73–1013.48 Da). Spectroscopic analyses indicated that enzymatic hydrolysis transformed its structure from a rigid triple-helix conformation to a more flexible conformation dominated by β-turns (50.78%) and random coils (17.38%). In addition, thermogravimetric analysis confirmed its excellent thermal stability, with an onset decomposition temperature as high as 244.81 °C, supporting its potential applicability in high-temperature food-processing matrices. In vitro assays demonstrated that PASN exhibited high biocompatibility and promoted proliferation of both PC12 cells and hACs, while significantly improving cell viability under CORT challenge. PASN also reduced lactate dehydrogenase (LDH) leakage (hACs: 38.31%; PC12: 31.17%) in both cell models and restored total superoxide dismutase (T-SOD) activity (hACs: 69.46%, PC12: 66.40%). Immunofluorescence further revealed that PASN rescued the expression of brain-derived neurotrophic factor (BDNF) (hACs: 35.23%, PC12: 12.50%) and glutamate decarboxylase (GAD1/2) (hACs: 102.66%, PC12: 31.31%), key markers associated with synaptic plasticity and GABAergic sleep regulation. Collectively, PASN is a thermally stable squid-derived peptide fraction that exerts antioxidant and cytoprotective effects in neural cell models in vitro and represents a promising sustainable candidate for nutraceutical development. Full article

Stratospheric Aerosol Injection (SAI) has been investigated as a climate intervention strategy to offset global warming, and regional impacts studies rely on simulations from the Geoengineering Large Ensemble (GLENS). The probabilistic behavior of the GLENS ensemble has not been systematically characterized for Southeast Asia. Because GLENS is a counterfactual experiment combining the Representative Concentration Pathway 8.5 (RCP8.5) forcing with active SAI, comparison with observations cannot validate the SAI response itself. In the early protocol years, the SAI forcing is small, so the early window provides a diagnostic of statistical consistency between the ensemble and the observed climate and of ensemble spread reliability. We compare the 21-member GLENS ensemble for 2020–2025 with ERA5 for daily precipitation and mean and maximum temperature using empirical coverage of the 95% prediction interval, rank histograms with the Jolliffe–Primo decomposition, the Continuous Ranked Probability Score, and the Brier Score for rainfall occurrence. Coverage is well below nominal for all variables, and rank histograms show pronounced U-shapes dominated by the dispersion error component, indicating systematic underdispersion. Because the underlying mechanisms are properties of the ensemble system rather than of the SAI forcing, this underdispersion is expected to persist in the future record, motivating statistical post-processing of GLENS before its use in SAI impact assessments. Full article

Coal gasification fine ash (CGFA) is a carbon–mineral composite solid waste whose valorization is severely hindered by poor interfacial compatibility with organic media due to its highly polar surface. Here, we report a surface alkylation strategy using haloalkanes with variable chain lengths to systematically tune the surface chemistry and thermo-oxidative behavior of CGFA. Comprehensive spectroscopic characterizations (XPS, FTIR, and ¹³C NMR) confirm successful grafting of alkyl chains, which increases aliphatic C-H content from 24.8% to 43.9% while reducing polar carboxyl groups from 7.9% to 1.6%, with the mineral framework remaining intact. Thermogravimetric analysis reveals that alkylation lowers the onset decomposition temperature from 358 °C to 295 °C and enhances the maximum mass-loss rate. Kinetic analysis shows that grafted alkyl chains act as low-energy initiation sites, reducing the initial activation energy to 95 kJ/mol, while the later-stage oxidation becomes diffusion-limited. Notably, long straight-chain alkylation achieves the best performance, whereas branched chains are less effective due to steric hindrance and pore blockage. This work establishes a clear chain-length-dependent structure–thermal response relationship, positioning alkylated CGFA as a designable precursor for functional carbon materials, intelligent char-forming agents, and tunable components for energy or responsive material systems. Full article

This study investigates the hydrothermal modification of commercial titanium dioxide (TiO₂) in the presence of a natural licorice root extract (Glycyrrhiza glabra L.), serving as a stabilizing and growth-modulating agent. The experimental framework combines hydrothermal treatment in a Teflon-lined autoclave with subsequent thermal calcination to elucidate the structural, morphological, and chemical evolution of the material. The plant-based extract significantly influences particle assembly during synthesis, fostering the formation of an initial organic–inorganic hybrid system that results in enhanced morphological homogeneity compared to pristine TiO₂. Thermal analyses (TGA and DSC) demonstrated the progressive decomposition of the organic components with increasing temperature, yielding a thermally stable, predominantly inorganic material at 600 °C. Scanning Electron Microscopy (SEM) observations confirmed a more uniform particle distribution in the modified samples. X-ray diffraction (XRD) patterns corroborated that the primary crystalline phase of TiO₂ remains intact across all conditions, with structural variations limited to peak definition and long-range organization. Furthermore, FTIR spectroscopy supported the preservation of characteristic TiO₂ vibrational features while indicating a gradual depletion of weakly bound surface species following thermal treatment. In conclusion, these findings demonstrate that natural extracts can effectively function as growth-modulating agents, steering material organization without altering its intrinsic chemical properties. This approach aligns with the principles of Green Chemistry and the circular economy, highlighting the potential of renewable plant-based resources as functional additives for the sustainable processing of inorganic materials. Rather than seeking to outperform commercial benchmarks, this work establishes a viable and low-environmental-impact strategy for morphological and structural modulation. Full article

Fly ash (FA) from Zhundong coal combustion features high alkali/calcium content and a low Si/Al ratio, limiting its potential for conventional utilization. To enable its high-value application, six size-fractionated samples (FA1–FA6) were characterized via laser particle sizing, SEM-EDS, XRF, XRD, FT-IR, and TGA, to elucidate particle-size-dependent physicochemical and thermal properties. The results show that the size distribution centered at 48–150 μm (~71%). With decreasing size, the morphology shifted from irregular aggregates to smooth vitreous spheres. The chemical composition exhibits significant elemental segregation; the SiO₂ content decreases with decreasing particle size, while active components such as CaO, MgO, and Fe₂O₃ are significantly enriched in fine particles. The thermal conversion behavior is regulated by particle size: The combustion reaction under an air atmosphere conforms to the second-order kinetic model, with the activation energy decreasing from 192.73 kJ·mol⁻¹ for coarse particles (>150 μm) to 63.53 kJ·mol⁻¹ for fine particles (<43 μm); under a nitrogen atmosphere, the weight loss originates from the removal of structural water and the decomposition of carbonates, and fine particles exhibit a higher pyrolysis activation energy (504.15 kJ·mol⁻¹) in the high-temperature stage (850–940 °C) due to being rich in high-crystallinity carbonates. The results of this study elucidate the structure–activity relationship of “particle size-composition-activity” for Zhundong coal fly ash and propose a graded utilization scheme where coarse fractions are suitable for low-grade building fillers, while fine fractions can be used as feedstocks for coal pyrolysis catalysts and functional adsorbents, providing a theoretical basis for its targeted resource utilization based on particle size fractionation. Full article