Search Results (6,413)

Laser-induced graphene (LIG) has great potential for multiple applications because of its large specific surface area, facile fabrication process, and tunable properties. However, abundant lattice defects severely degrade its conductivity. Herein, from an innovative perspective of precursor design, the poly-phenylamines (P-PAs) with improved solubility and strong light absorption were synthesized, which act as an intercalated polymer for graphene oxide (GO) nanosheets. On this basis, the composite precursors show remarkably enhanced photothermal conversion capability and a compact stacked structure. These bring a 60% reduction in I_D/I_G in LIG after laser irradiation. To explain the above phenomenon, an isolation effect induced by the compact stacking precursor is proposed based on experimental results. Furthermore, the cooperative effect between P-PAs and Fe²⁺ is introduced, and a fluffy LIG aerogel with the lowest I_D/I_G ratio of 0.17 is prepared, which is barely achievable in conventional LIGs. When the obtained graphene aerogel is compounded with PDMS, the as-prepared thermal-conductive composite gel reaches a thermal conductivity of 1.05 W·m⁻¹·K⁻¹ and an ultralow interfacial thermal resistance of 37.2 mm²·K·W⁻¹ under a low graphene loading of 3.3 wt%. This intercalation strategy in GO precursor supplies a new route for preparing high-quality LIGs and thermal-conductive gels, which show great application prospects in thermal management devices. Full article

The oxygen reduction reaction (ORR) remains a major bottleneck in terms of kinetics and durability in fuel cells and zinc–air batteries (ZABs). Metal–organic frameworks (MOFs) are versatile precursors for platinum-group metal (PGM)-free ORR electrocatalysts because their metal distribution, ligand chemistry, guest confinement, morphology, and porosity can be controlled before pyrolysis. This review examines how these precursor characteristics and subsequent thermal conversion govern metal migration; heteroatom retention; carbon ordering; pore evolution; and, ultimately, the nuclearity, coordination environment, and accessibility of the resulting active sites. Recent advances in conventional and asymmetric M–N_x single-atom sites, dual- and multi-atom sites, and single-atom–cluster or nanophase interfaces are critically evaluated, with particular attention to the evidence supporting structural assignments, activity, selectivity, and durability. Half-cell performance is further related to practical fuel-cell and ZAB operation by considering catalyst loading, ionomer or electrolyte contact, gas and water transport, and catalyst-layer degradation. Further progress will require simultaneous optimization of active-site structure, accessible-site density, hierarchical porosity, carbon stability, and electrode architecture, together with standardized testing protocols for reliable translation from rotating disk electrode measurements to working cathodes. Full article

Large-scale public events generate concentrated passenger demand during egress periods, often overwhelming urban transit systems. This paper proposes a multi-modal evacuation framework that coordinates in-service buses temporarily diverted from existing lines and dedicated shuttle vehicles pre-positioned at depots. The problem is formulated as a two-layer stochastic optimization under travel time uncertainty: the upper layer determines pre-event shuttle fleet sizing, while the lower layer makes real-time dispatching decisions for both modes. We propose an Uncertainty-Aware Reinforcement Learning framework with Categorical DQN (UARL-CD) that learns a robust dispatching policy through a reward function aligned with the lower-level objective, explicitly accounting for travel time uncertainty via distributional value representation and stochastic training, with an action masking mechanism enforcing operational constraints. Simulation experiments based on a realistic stadium evacuation scenario demonstrate that the proposed framework significantly outperforms deterministic optimization and rule-based strategies, achieving a 31.6% reduction in evacuation completion time and a 48.4% reduction in average passenger waiting time compared to shuttles alone, while maintaining robustness to travel time uncertainty with only 4.0% performance degradation and online decisions executed within the 2-min decision interval. Full article

Heterojunction construction is a key strategy for enhancing the photocatalytic efficiency of semiconductors. In this study, a composite of sulfur-doped CoAl layered double hydroxide and Fe-doped C₃N₅(S-CoAl-LDH/Fe-C₃N₅) was developed via a hydrothermal method for the synergistic oxidation-reduction degradation of the organic pollutant tetracycline (TC) and the detoxification of heavy-metal ions (Cr(VI)) in wastewater. After optimization, the CAF-4 heterojunction (the composite with 20 wt% Fe-C₃N₅ loading) exhibited TC degradation rates 5.51 and 3.97 times higher than those of pristine Fe-C₃N₅ and S-CoAl-LDH, respectively; under simulated sunlight, the Cr(VI) reduction rates were 11.75 and 4.22 times higher, respectively. The as-prepared catalyst demonstrated good stability across a wide pH range, in the presence of various cations and anions, and in different water matrices. Under coexisting pollutant conditions, the composite still achieved removal efficiencies of 82.1% for Cr(VI) and 64.7% for TC. After five cycling runs, the adsorption-photocatalytic efficiency of the composite for the removal of Cr(VI) and TC composite pollutants remained above 80%. Overall, CAF-4 shows great promise for application in the adsorption-photocatalytic treatment of wastewater containing combined Cr(VI) and TC pollution. Full article

Raffinose-family oligosaccharides (RFOs) such as raffinose, stachyose and verbascose are abundant in legumes and are associated with gastrointestinal discomfort, particularly in individuals sensitive to FODMAPs. This study aimed to identify and characterize lactic acid bacteria capable of degrading RFOs in order to improve the tolerability of legume-based food products. Lactic acid bacteria were isolated from chicken feces and screened for α-galactosidase activity and raffinose utilization. Selected strains were further evaluated in a liquid medium containing raffinose as the single carbon source and in yellow pea flour fermentations to assess their RFO-degrading capacity. A total of 23 of 138 strains, including Limosilactobacillus reuteri (13), Ligilactobacillus salivarius (4), Leuconostoc mesenteroides (4), Lactiplantibacillus plantarum (1) and Weissella paramesenteroides (1), were identified as α-galactosidase-positive and capable of raffinose metabolism. In liquid culture, six L. reuteri showed particularly high efficiency, with complete raffinose depletion within 8 h. In pea flour fermentations, RFO reductions of up to 100% were achieved. However, RFO metabolism by L. reuteri was associated with mannitol formation, a sugar alcohol classified as a FODMAP. Overall, the results demonstrate that targeted fermentation with selected lactic acid bacteria enables effective RFO reduction in pea flour, highlighting their potential for the development of legume-based meat alternatives. Full article

This study reports on a new one-pot copolymerization process by simultaneous oxidative disulfide and β-thioester formation by reacting bifunctional monomers, 3,6-dioxa-1,8-octane-dithiol (DODT) with diacrylates, poly(ethylene glycol) diacrylate (PEGDA) and 1,6-hexanediol diacrylate (HDODA), in the presence of N,N,N′,N″,N″-pentamethyldiethylenetriamine (PMDETA), as the same base catalyst for both reactions, in air at room temperature with short reaction times. The resulting random copolymers consist of disulfide linkages between DODTs and β-thioester units formed by thiol-ene Michael addition click reaction. With a stoichiometric DODT/diacrylate feed ratio, diacrylate-telechelic copolymers are obtained in a single-step polymerization reaction. The T_gs of the P(DODT-co-PEGDA) copolymers are nearly constant at around −53 °C, while it decreases with increasing HDODA content in the P(DODT-co-HDODA) copolymers. Reductive degradation with thiols, such as 2-mercaptoethanol and dithiothreitol, led to chain scission via the disulfide–thiol exchange reaction. Treatment with NaOH solution resulted in further degradation by hydrolysis of the β-thioester units. These results indicate that these novel copolymers are fully degradable under mild conditions. This new process, applying simultaneous thiol oxidation and thiol-ene reactions, enables to prepare a large variety of sulfur-containing end-functional degradable copolymers useful for a broad range of advanced application possibilities. Full article

Multivariate time series forecasting requires modeling both hierarchical temporal dynamics and complex inter-variable dependencies, a dual requirement that often degrades predictive performance and incurs high computational costs in standard Transformer architectures. Unlike current channel-independent models that ignore vital cross-variable synergies, or dense-attention frameworks that suffer from quadratic computational noise, our approach extracts structurally sparse dependencies. To address these specific limitations, this study introduces the FCA-Transformer. The proposed framework integrates a Feature Pyramid Network (FPN) to isolate macroscopic trends from high-frequency localized fluctuations via hierarchical downsampling. Concurrently, a structured Transformer-based Cross-Attention (TCA) mechanism employs Dimensional Segmentation with Weighting (DSW) and a Two-Stage Attention (TSA) layer to map topological variable interactions, effectively extracting robust cross-variable pathways and mitigating distributional noise. Extensive empirical evaluations across three real-world multivariate benchmarks (ETTh1, Electricity, and Exchange Rate) demonstrate that the FCA-Transformer achieves an average reduction of up to 4.39% in MSE and 5.11% in MAE compared to leading baselines. These findings indicate that the proposed architecture successfully reconciles multi-scale feature extraction with lightweight dependency modeling, enhancing structural generalization and providing a scalable framework for real-time temporal analysis in complex industrial environments. Full article

Battery prognosis is a critical component of battery management systems, enabling the prediction of end of life (EoL) and remaining useful life (RUL). However, obtaining sufficiently large labelled datasets for data-driven prognosis is challenging because battery ageing experiments are time-consuming and expensive. To address this data scarcity, we propose a conditional generative adversarial network (GAN) framework for targeted synthetic battery-data generation, in which degradation regime is explicitly used as conditioning information. The framework generates samples from three degradation regions—early, pre-knee, and post-knee—and is investigated using two representations of the same underlying battery data: direct time series and Gramian Angular Fields (GAFs). The generated data are evaluated using representation-specific quantitative metrics together with qualitative distributional analyses. As an additional validation of synthetic-data utility, GAN-generated samples are incorporated as unlabelled data in a Mean Teacher semi-supervised EoL prediction framework. Across 10 matched random-seed runs, augmentation reduces the mean EoL prediction error for both representations. For the time-series workflow, MAE and RMSE decrease by 6.37% and 3.66%, respectively, while the GAF-based workflow shows larger reductions of 15.93% and 16.20%. The improvements in both metrics are statistically significant for the GAF-based workflow, and after augmentation no statistically significant difference is detected between the aggregate EoL prediction errors of the GAF and time-series-based models. These findings demonstrate the potential of targeted conditional GANs for battery-data augmentation and highlight GAF-based generation as a promising complementary approach to conventional time-series-based augmentation for battery prognosis. Full article

Soil microbial functional potential is crucial to maintaining forest productivity and ecosystem functions. However, how microbially mediated soil nutrient cycling responds to environmental changes, particularly those caused by variations in elevation, remains poorly understood. Using the natural temperature gradient in a temperate mountain forest, this study investigated the differences in functional microbial groups and functional genes involved in soil carbon, nitrogen and phosphorus cycling along the elevation gradient, and analyzed the associations between environmental factors and these differences. The results showed that the low-elevation gradient (LE) had significantly higher abundances of genes involved in carbon degradation (pfkC, pgi1, and LSC1) but significantly lower abundances of those involved in carbon fixation (K18602, K18603, and K18604). Compared with the high-elevation gradient (HE), the LE had a significantly higher abundance of the nitrogen-cycle gene involved in organic degradation and synthesis (nao), but significantly lower abundances of denitrification (norB) and dissimilatory nitrate reduction genes (narG, narI, and napC). The abundances of the key genes involved in phosphorus metabolism (aphA and purO) were significantly higher at HE than at LE, whereas the abundance of the key gene associated with phosphorus transport (phnT) was significantly lower. The composition of the microbial community at the phylum level involved in carbon, nitrogen and phosphorus cycling at different elevations was similar, but the relative abundance of Thermoproteota and Nitrospirota increased significantly at HE. The annual average temperature, pH and carbon acquisition enzymes (β-glucosidase and β-D-cellobiosidase) were significantly associated with microbial community composition and functional genes related to carbon, nitrogen and phosphorus cycles. Additionally, genes involved in the carbon, nitrogen and phosphorus cycles were closely related through synergy and antagonism, especially the metabolic pathways encoded by purO, phnT and nrfA. These results provide metagenomic insights into the response patterns of microbial functional potential associated with soil carbon, nitrogen, and phosphorus cycling along an elevational gradient in a warm-temperate forest. Full article

This paper presents a novel fault-tolerant model predictive control (FTMPC) framework for fuel cell hybrid electric vehicles (FCHEVs) used for postal delivery applications. The main contribution of the proposed FTMPC is the adaptive adjustment of the model predictive control cost function weights based on fault severity. The proposed reformulation incorporates fault characterization across the diverse degradation mechanisms while maintaining reliable vehicle operation. The FTMPC approach dynamically adapts cost function weights and system constraints based on the fault severity index. The resulting control strategy provides fault-aware power allocation between the fuel cell and battery while accounting for the specified operating and safety constraints. To isolate the contribution of the proposed health-dependent adaptation mechanism, a controlled ablation study was performed against a structurally identical fixed-MPC controller under the same vehicle model, driving cycle, initial conditions, prediction and control horizons, solver configuration, and fault scenarios. The adaptive FTMPC achieved a 10.6956% reduction in direct hydrogen consumption relative to the fixed-MPC baseline. Because differences in terminal battery state of charge (SoC) can influence comparisons based solely on hydrogen consumption, a charge-corrected hydrogen-equivalent metric was also evaluated; using this more conservative metric, the adaptive FTMPC retained a 2.7276% improvement. The final quadratic programming implementation achieved a 100% successful optimization rate in the validation run with no fallback-controller activation, while the maximum soft-constraint slack remained on the order of 10−9. Additional sensitivity analyses were conducted to evaluate the influence of relevant vehicle and operating conditions on energy consumption and battery utilization. These results provide direct quantitative evidence of the contribution of the proposed fault-adaptive mechanism and demonstrate its numerical feasibility for FCHEV energy management, while the limitations of the present simulation-based validation are explicitly acknowledged. Full article

Degradation processes forming as a result of direct and indirect environmental interactions with soluble salts are important for the structural material of marble and in geotechnical applications. This is necessary, not only to identify the source and describe the material, but also to ensure the sustainability of artifacts and to be able to provide appropriate material for preservation and restoration. In this study, the durability of Afyon–İscehisar marbles (calcitic and dolomitic) to cyclic salt mist was investigated in a laboratory environment by considering weight, color, and ultrasonic pulse velocity values. Additionally, comprehensive experiments including mineralogical and petrographic investigations (polarizing microscope, X-ray diffractometry, scanning electron microscope (SEM/EDX), geochemical investigations, and physical and mechanical tests) were performed to evaluate the durability against the effect of salt mist. The results show that the calcite content in the calcitic marbles varied from 96% to 99%, while the dolomite content in the dolomitic marbles reached 64%–74%, in line with microscopic and semi-quantitative XRD analyses and geochemical investigations. The calcitic–dolomitic differentiation was tightly controlled by the magnesium (Mg) content. With the effect of salt mist, the samples had negligible weight changes (0.02% in the KH sample) and there was no significant material loss in any of the groups. For color analyses, total color differences (ΔE) in the KH and MN samples reached 4.39 and 4.06, respectively, and exceeded the human perception threshold. The dolomitic KS and PB samples and calcitic KP sample had increases identified for ultrasonic pulse velocity. Contrary to this, the GR (calcitic) sample was found to have a 15.15% reduction in ultrasonic pulse velocity. These findings provide critical selection, performance prediction and preservation criteria for Afyon–İscehisar marbles in both modern structural engineering applications and in archeological studies. Full article

To investigate the effects of model selection, time-varying parameters and climatic differences on the carbon emission reduction performance of rural rooftop photovoltaic (PV) systems, this study selected a typical rural residential rooftop PV system in Beijing as the research object. Hourly simulations using the System Advisor Model (SAM) and an operational-period baseline method were employed to optimize the PV array parameters and conduct a multi-factor analysis of carbon emission reductions. The results show that the first-year electricity generation was 15,902 kWh, corresponding to an annual carbon emission reduction of 12.60 tCO₂. The carbon emission reduction estimates obtained using the three irradiance models differed, with a maximum deviation of 0.51 tCO₂. When both PV module degradation and changes in the grid emission factor were considered, the cumulative carbon emission reduction decreased by 68.37 tCO₂ relative to the ideal scenario. Moreover, the faster the grid transition speed, the lower the cumulative carbon emission reduction over the system’s operational period. In addition, extending the analysis from a single region to multiple regions revealed that the annual carbon emission reductions in representative rural residential rooftop PV systems in Beijing and Wuhan differed by 2.94 tCO₂, with differences also observed in their seasonal variation patterns. The assessment of rural rooftop PV systems should comprehensively consider irradiance model selection, module power degradation, grid transition pathways, regional climatic conditions and economic feasibility. Full article

Carbon-fiber structural batteries represent a class of multifunctional energy-storage systems that integrate electrochemical energy storage with mechanical load-bearing capability. Unlike conventional batteries, which are mainly evaluated based on cell-level energy density, structural batteries provide new opportunities for system-level weight reduction by reducing inactive structural mass, improving space utilization, and enabling distributed energy storage within integrated structures. In recent years, substantial progress has been achieved in carbon-fiber electrodes, structural electrolytes, laminated devices, electrolyte topology engineering, and fully carbon-fiber structural batteries. Nevertheless, most reported advances have been demonstrated under relatively ideal static testing conditions, while maintaining multifunctional performance under manufacturing and long-term service conditions remains a critical challenge. This review systematically examines the development of carbon-fiber structural batteries from a reliability perspective. First, the system-level motivations and technological evolution are introduced, and existing architectures are categorized according to their integration depth and degree of multifunctional coupling. Carbon-fiber electrodes are then discussed with emphasis on balancing capacity, ion transport, cycling stability, mechanical property retention, interfacial robustness, and manufacturing scalability. Furthermore, structural electrolytes are reviewed from the viewpoint of topology-enabled regulation of ion transport and load transfer, with particular focus on the intrinsic trade-off between ionic conductivity and mechanical modulus. In addition, manufacturing routes and device architectures are analyzed from the perspective of multifunctionality-degrading defects, including voids, dry regions, coating cracks, weak interfaces, and current-collector discontinuities. Finally, retained multifunctionality is used as a reliability-oriented evaluation criterion to examine the preservation of electrochemical, mechanical, interfacial, and safety functions, with particular emphasis on the carbon-fiber-specific failure chain linking interfacial and manufacturing heterogeneities to multifunctionality-degrading defects, coupled-field localization, and damage propagation. This review emphasizes that reliable carbon-fiber structural batteries require application-specific and coordinated optimization of materials, interfaces, electrolyte topology, coupled degradation behavior, and validation protocols. Full article

This study proposes an Electricity 5.0-oriented supervisory control framework for an interior permanent-magnet synchronous motor (IPMSM) drive that integrates field-oriented control, MTPA, field weakening, loss-minimization control (LMC), speed estimation, and adaptive mode selection. Four operating modes, Performance, Balanced, Eco-LMC, and Resilient, are coordinated according to dynamic, electrical, and sensing conditions. The framework is evaluated under variable-speed operation, load disturbances, motor-parameter variations, DC-link voltage reduction, sensor degradation, and combined disturbances. Performance Mode achieves the lowest speed RMSE of 44.52 rpm, while Eco-LMC reduces iron-loss energy by 26.6% and total modeled electrical losses by 10.2%, with a 2.1% reduction in consumed electrical energy. During sensor degradation, the speed observer maintains an RMSE of approximately 19–21 rpm. In the combined supervisory scenario, only eight mode transitions occur, confirming effective chattering suppression through hysteresis and a 35 ms dwell time. No sustained SVPWM saturation is observed in any scenario. The results demonstrate that adaptive coordination of performance, efficiency, and resilience can improve PMSM drive operation within an Electricity 5.0-oriented control framework. Full article

Precast concrete frame joints in coastal atmospheric environments are susceptible to mechanical performance degradation caused by chloride-induced corrosion, yet joint-scale numerical studies incorporating multi-indicator time-dependent mechanical responses remain limited. This paper presents a coupled framework that integrates chloride diffusion, corrosion, and finite-element analysis for a typical precast beam–column joint to evaluate the relative changes in seismic performance indicators across service ages of 0, 15, 30, 40, and 50 years. The numerical model was baseline-validated against uncorroded and corroded test specimens under cyclic loading. Time-dependent models accounting for chloride diffusion, rebar corrosion, and material strength degradation were implemented. The elastic modulus reduction was restricted to the damaged covering concrete rather than the intact internal concrete. The simulation results show that mechanical degradation is limited in the early service stage, whereas hysteretic pinching and deformation-related deterioration become more pronounced with increasing service age. By 50 a, the peak load-bearing capacity has decreased by 13.68%, whereas the ultimate displacement and ductility coefficients have declined by 15.10% and 37.40%, respectively. These results should be interpreted as case-specific predictions under the adopted cover thickness, chloride exposure condition, and material parameters rather than as universal deterioration thresholds. The findings indicate that service-life evaluations of precast joints in coastal atmospheric environments should not rely solely on strength indicators but should also incorporate stiffness, ductility, and energy dissipation capacity. Full article