Search Results (567)

Microbial fuel cells (MFCs) utilizing livestock wastewater represent a critical path toward sustainable energy and net-zero emissions. To maximize this potential, this study investigates a novel circuit configuration, integrating twin MFCs with dual supercapacitors in a closed-loop system, to enhance charge storage and electricity generation. By utilizing molasses-seawater anolytes, the study establishes a performance benchmark for optimizing energy recovery in future livestock wastewater treatment applications. The self-adjusting potential difference between interconnected MFCs is verified, and supercapacitors significantly improve energy harvesting by reducing load impedance and balancing capacitor plate charges. Voltage gain across supercapacitors exceeds that of single MFC charging, demonstrating the benefits of series integration. Experimental results reveal that catholyte properties—electrical conductivity, salinity, pH, and dissolved oxygen—strongly influence MFC performance. Optimal conditions for a neutralized anolyte (pH 7.12) include dissolved oxygen levels of 5.37–5.68 mg/L and conductivity of 24.3 mS/cm. Under these conditions, supercapacitors charged with sterile diluted seawater catholyte store up to 40% more energy than individual MFCs, attributed to increased output current. While the charge balance mechanism of supercapacitors contributes to storage efficiency, its impact is less pronounced than that of conductivity and oxygen solubility. The interplay between electrochemical activation and charge balancing enhances overall electricity harvesting. These findings provide valuable insights into optimizing MFC-supercapacitor systems for renewable energy applications, particularly in livestock wastewater treatment. Full article

An accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safety and reliability of energy storage systems and electric vehicles. However, existing methods face challenges: physics-based models are computationally complex, traditional data-driven methods rely heavily on manual feature engineering, and single models lack the ability to capture both local and global degradation patterns. To address these issues, this paper proposes a novel hybrid LSTM–Transformer model for LIB SOH estimation using actual measurement data. The model integrates Long Short-Term Memory (LSTM) networks to capture local temporal dependencies with the Trans-former architecture to model global degradation trends through self-attention mechanisms. Experimental validation was conducted using eight 18650 Nickel Cobalt Manganese (NCM) LIBs subjected to 750 charge–discharge cycles under room temperature conditions. Sixteen statistical features were extracted from voltage and current data during constant current–constant voltage (CC-CV) phases, with feature selection based on the Pearson correlation coefficient and maximum information coefficient analysis. The proposed LSTM–Transformer model demonstrated superior performance compared to the standalone LSTM and Transformer models, achieving a mean absolute error (MAE) as low as 0.001775, root mean square error (RMSE) of 0.002147, and mean absolute percentage error (MAPE) of 0.196% for individual batteries. Core features including cumulative charge (CC Q), charging time, and voltage slope during the constant current phase showed a strong correlation with the SOH (absolute PCC > 0.8). The hybrid model exhibited excellent generalization across different battery cells with consistent error distributions and nearly overlapping prediction curves with actual SOH trajectories. The symmetrical LSTM–Transformer hybrid architecture provides an accurate, robust, and generalizable solution for LIB SOH assessment, effectively overcoming the limitations of traditional methods while offering potential for real-time battery management system applications. This approach enables health feature learning without manual feature engineering, representing an advancement in data-driven battery health monitoring. Full article

As the core technology of the new energy revolution, lithium-ion batteries have broad development prospects and significant strategic importance. With continuous improvements in energy density, enhanced safety, and breakthroughs in fast-charging technology, lithium-ion batteries will play a more substantial role in fields such as new energy vehicles and energy storage. Nevertheless, the development of the lithium-ion battery industry still faces safety issues related to thermal runaway risks. The intense exothermic reactions during thermal runaway can release flammable gases, potentially leading to uncontrolled combustion or explosions, thereby posing major safety threats. This paper reviews the analysis of gas composition and patterns during lithium-ion battery thermal runaway under different conditions, as well as research on gas explosion characteristics. It introduces advanced methods for gas detection and suppression during thermal runaway and summarizes studies on the chemical kinetic mechanisms and predictive models of gas generation during thermal runaway. These studies provide a scientific basis for improving the reliability of renewable energy storage systems and formulating and refining battery safety standards. Full article

The Clausius–Mossotti (CM) factor underpins the theoretical description of dielectrophoresis (DEP) and is widely used in micro- and nano-scale systems for frequency-dependent particle and cell manipulation. It has further been proposed as an “electrophysiology Rosetta Stone” capable of linking DEP spectra to intrinsic cellular electrical properties. In this paper, the mathematical foundations and interpretive limits of this proposal are critically examined. By analyzing contrast factors derived from Laplace’s equation across multiple physical domains, it is shown that the CM functional form is a universal consequence of geometry, material contrast, and boundary conditions in linear Laplacian fields, rather than a feature unique to biological systems. Key modelling assumptions relevant to DEP are reassessed. Deviations from spherical symmetry lead naturally to tensorial contrast factors through geometry-dependent depolarisation coefficients. Complex, frequency-dependent CM factors and associated relaxation times are shown to inevitably arise from the coexistence of dissipative and storage mechanisms under time-varying forcing, independent of particle composition. Membrane surface charge influences DEP response through modified interfacial boundary conditions and effective transport parameters, rather than by introducing an independent driving mechanism. These results indicate that DEP spectra primarily reflect boundary-controlled field–particle coupling. From an inverse-problem perspective, this places fundamental constraints on parameter identifiability in DEP-based characterization. The CM factor remains a powerful and general modelling tool for micromachines and microfluidic systems, but its interpretive scope must be understood within the limits imposed by Laplacian field theory. Full article

Nanoparticle-mediated photothermal conversion exploits the unique light-to-heat transduction properties of engineered nanomaterials to address challenges in energy, water, and healthcare. This review first examines fundamental mechanisms—localized surface plasmon resonance (LSPR) in plasmonic metals and broadband interband transitions in semiconductors—demonstrating how tailored nanoparticle compositions can achieve >96% absorption across 250–2500 nm and photothermal efficiencies exceeding 98% under one-sun illumination (1000 W·m⁻², AM 1.5G). Next, we highlight advances in solar steam generation and desalination: floating photothermal receivers on carbonized wood or hydrogels reach >95% efficiency in solar-to-vapor conversion and >2 kg·m⁻²·h⁻¹ evaporation rates; three-dimensional architectures recapture diffuse flux and ambient heat; and full-spectrum nanofluids (LaB₆, Au colloids) extend photothermal harvesting into portable, scalable designs. We then survey photothermal-enhanced thermal energy storage: metal-oxide–paraffin composites, core–shell phase-change material (PCM) nanocapsules, and MXene– polyethylene glycol—PEG—aerogels deliver >85% solar charging efficiencies, reduce supercooling, and improve thermal conductivity. In biomedicine, gold nanoshells, nanorods, and transition-metal dichalcogenide (TMDC) nanosheets enable deep-tissue photothermal therapy (PTT) with imaging guidance, achieving >94% tumor ablation in preclinical and pilot clinical studies. Multifunctional constructs combine PTT with chemotherapy, immunotherapy, or gene regulation, yielding synergistic tumor eradication and durable immune responses. Finally, we explore emerging opto-thermal nanobiosystems—light-triggered gene silencing in microalgae and poly(N-isopropylacrylamide) (PNIPAM)–gold nanoparticle (AuNP) membranes for microfluidic photothermal filtration and control—demonstrating how nanoscale heating enables remote, reversible biological and fluidic functions. We conclude by discussing challenges in scalable nanoparticle synthesis, stability, and integration, and outline future directions: multicomponent high-entropy alloys, modular photothermal–PCM devices, and opto-thermal control in synthetic biology. These interdisciplinary innovations promise sustainable solutions for global energy, water, and healthcare demands. Full article

Rechargeable lithium-, sodium-, and potassium-ion batteries are utilized as essential energy storage devices for portable electronics, electric vehicles, and large-scale energy storage systems. In these systems, anode materials play a vital role in determining energy density, cycling stability, and safety of various batteries. However, the complex electrochemical reactions and dynamic changes that occur in anode materials during charge–discharge cycles generate major challenges for performance optimization and understanding failure mechanisms. In situ characterization techniques, capable of real-time tracking of microstructures, composition, and interface dynamics under operating conditions, provide critical insights that bridge macroscopic performance and microscopic mechanisms of anodes. This review systematically summarizes the applications of such techniques in studying anodes for lithium-, sodium-, and potassium-ion batteries, with a focus on their contributions across different anode types. It also indicates current challenges and future directions of these techniques, aiming to offer valuable references for relevant applications and the design of high-performance anodes. Full article

Accurate estimation of the state of health (SOH) of lithium-ion batteries is crucial for the safe operation of electric vehicles and energy storage systems. However, most existing methods rely on complete charging curves or manual feature engineering, making them difficult to adapt to practical scenarios where only limited charging segments are available. To fully exploit degradation information from limited charging data, this paper proposes a dual-modal mixture of Kolmogorov–Arnold network (DM-MoKAN) for lithium-ion battery SOH estimation using only early-stage constant-current charging voltage data. The proposed method incorporates three synergistic modules: an image branch, a sequence branch, and a dual-modal fusion regression module. The image branch converts one-dimensional voltage sequences into two-dimensional Gramian Angular Difference Field (GADF) images and extracts spatial degradation features through a lightweight network integrating Ghost convolution and efficient channel attention (ECA). The sequence branch employs a patch-based Transformer encoder to directly model local patterns and long-range dependencies in the raw voltage sequence. The dual-modal fusion module concatenates features from both branches and feeds them into a MoKAN regression head composed of multiple KAN experts and a gating network for adaptive nonlinear mapping to SOH. Experimental results demonstrate that DM-MoKAN outperforms various baseline methods on both Oxford and NASA datasets, achieving average RMSE/MAE of 0.28%/0.19% and 0.89%/0.71%, respectively. Ablation experiments further verify the effective contributions of the dual-modal fusion strategy, ECA attention mechanism, and MoKAN regression head to estimation performance improvement. Full article

In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we developed a stage-wise state-of-health (SOH) prediction approach that combined offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-rate. Multi-dimensional health features (HFs) were extracted, and an accelerated aging probability $p_{AA}$ was defined. Based on the correlation statistics between HFs, $k_{HF}$, the SOH, and $p_{AA}$, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, and short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method is innovative in the following ways: (1) The stage-wise multi-model strategy significantly improves the SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging. (3) The analysis of feature importance from the model outputs allows the indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs. If certain features cannot be obtained or are of poor quality, the prediction process does not fail. Full article

Defective graphene has emerged as a promising strategy to enhance electrochemical performance of pristine graphene (p-Gr) as anodes in lithium-ion batteries (LIBs). Herein, we perform a comprehensive first-principles study based on density functional theory (DFT) to systematically investigate the Li adsorption, charge transfer, and diffusion behaviors of p-Gr and defective graphene (d-Gr) with single vacancy (SV Gr) and double vacancy (DV5-8-5 Gr) defects, aiming to clarify the mechanism by which defects modulate Li storage performance. Structural optimization reveals that SV Gr undergoes notable out-of-plane distortion after Li adsorption, while DV5-8-5 Gr retains planar geometry but exhibits more significant C-C bond length variations compared to p-Gr. Binding energy results confirm that defects enhance Li adsorption stability, with DV5-8-5 Gr showing the strongest Li–graphene interaction, followed by SV Gr and p-Gr. Bader charge analysis and charge density difference plots further validate that defects enhance charge transfer from Li ions to graphene. Using the nudged elastic band (NEB) method, we find that defects reduce Li diffusion barriers: DV5-8-5 Gr exhibits a lower barrier than p-Gr. Our findings demonstrate that DV5-8-5 Gr exhibits the most favorable Li storage performance, providing a robust theoretical basis for designing high-performance graphene anodes for next-generation LIBs. Full article

This paper reviews the research progress of supercapacitors (SCs), including the influence of electrode materials on energy storage mechanism and performance, and life prediction. Supercapacitors show application potential in many fields due to their high-power density, fast charge–discharge capability, long cycle life, and environmental protection characteristics. In this paper, the energy storage mechanism of the double-layer capacitor, pseudocapacitor, and asymmetric supercapacitor are discussed. New electrode materials, such as carbon-based materials, metal oxides, and conductive polymers, are reviewed based on the performance optimization measures that are involved in the microstructure design of electrode materials, and integrate the rule prediction of supercapacitors into comprehensive learning. When designing and using supercapacitors, we should not only pay attention to their life but also pay attention to their remaining service life in real time. The paper also mentions the progress of life prediction technology, which is of great significance to improve the reliability and maintenance efficiency of energy storage equipment, and ensure the long-term stable operation of energy storage systems. Future research directions include increasing energy density, extending life, adapting to extreme environments, developing flexible and wearable devices, intelligent management, and reducing costs. Full article

The Agricultural Park Integrated Energy System (APIES) is a key platform for integrating distributed renewable energy (DRE) with agricultural production. However, its economic operation and the stability of crop growth environments are severely challenged by bidirectional uncertainties from external meteorology. These include the inherent variability of wind-solar generation and critical agricultural loads, such as supplementary lighting and temperature control, a challenge that existing models with static environmental parameters fail to address. To solve this, a bi-level optimization scheduling model for APIES considering meteorological uncertainty is proposed. The upper layer minimizes operation costs by quantifying uncertainties via triangular fuzzy chance constraints, with core constraints on DRE output, energy storage charging-discharging, and load shifting, solved by YALMIP-Gurobi linear programming. The lower layer maximizes crop growth environment satisfaction using a dynamic weight adaptive mechanism and NSGA-II multi-objective algorithm. The two layers iterate alternately for coordination. Using a small agricultural park in Xinjiang, China, as a case study, the results indicate that the proposed two-layer optimal scheduling model reduces costs by 10.8% compared to the traditional single-layer optimization model, and improves environmental satisfaction by 4.3% compared to the fixed-weight two-layer optimization model. Full article

To further enhance economic efficiency and optimize energy conservation and emission reduction performance, an optimized energy management strategy (EMS) tailored for the hybrid power system of rubber-tyred gantry cranes is proposed. Wavelet packet decomposition (WPD) was employed as the signal processing approach, and this method was further integrated with EMS for hybrid power systems. Through a three-layer progressive architecture comprising WPD frequency–domain decoupling, fuzzy logic real-time adjustment, and PSO offline global optimization, a cooperative optimization mechanism has been established in this study between the frequency-domain characteristics of signals, the physical properties of energy storage components, and the real-time and long-term states of the system. Firstly, the modeling and simulation of the power system were conducted. Subsequently, an EMS based on WPD and limit protection was developed: the load power curve was decomposed into different frequency bands, and power allocation was implemented via the WPD algorithm. Meanwhile, the operating states of lithium batteries and supercapacitors were adjusted in combination with state of charge limits. Simulation results show that this strategy can achieved reasonable allocation of load power, effectively suppressed power fluctuations of the auxiliary power unit system, and enhanced the stability and economy of the hybrid power system. Afterward, a fuzzy controller was designed to re-allocate the power of the hybrid energy storage system (HESS), with energy efficiency and battery durability set as optimization indicators. Furthermore, particle swarm optimization algorithms were adopted to optimize the EMS. The simulation results indicate that the optimized EMS enabled more reasonable power allocation of the HESS, accompanied by better economic performance and control effects. The proposed EMS demonstrated unique system-level advantages in enhancing energy efficiency, extending battery lifespan, and reducing the whole-life cycle cost. Full article

Polyampholytes combine cationic and anionic groups in one macromolecular platform and are emerging as versatile components for energy storage and conversion. This review synthesizes how their charge balance, hydration, and architecture can be engineered to address ionic transport, interfacial stability, and safety across batteries, supercapacitors, solar cells, and fuel cells. We classify annealed, quenched, and zwitterionic systems, outline molecular design strategies that tune charge ratio, distribution, and crosslinking, and compare device roles as gel or solid electrolytes, eutectogels, ionogels, binders, separator coatings, and interlayers. Comparative tables summarize ionic conductivity, cation transference number, electrochemical window, mechanical robustness, and temperature tolerance. Across Li and Zn batteries, polyampholytes promote ion dissociation, homogenize interfacial fields, suppress dendrites, and stabilize interphases. In supercapacitors, antifreeze hydrogels and poly(ionic liquid) networks maintain conductivity and elasticity under strain and at subzero temperature. In solar cells, zwitterionic interlayers improve work function alignment and charge extraction, while ordered networks in fuel cell membranes enable selective ion transport with reduced crossover. Design rules emerge that couple charge neutrality with controlled hydration and dynamic crosslinking to balance conductivity and mechanics. Key gaps include brittleness, ion pairing with multivalent salts, and scale-up. Opportunities include soft segment copolymerization, ionic liquid and DES plasticization, side-chain engineering, and operando studies to guide translation. Full article

Member 2 of the Xujiahe Formation in the Anyue area of the Sichuan Basin exhibits significant resource potential for tight sandstone gas. However, its characteristic of “extensive gas presence with localized enrichment” leads to substantial variations in single-well productivity, challenges in target zone optimization, and unclear enrichment mechanisms, which hinder efficient exploration and development. This study proposes a hierarchical classification scheme of “two-level, six-type” source–reservoir structures based on the developmental characteristics of fault–fracture systems and vertical source–reservoir configurations. The gas-bearing heterogeneity is quantitatively characterized using parameters such as effective gas layer thickness, charge intensity, and effective gas layer probability, thereby revealing the differential enrichment mechanisms of tight sandstone gas controlled by source–reservoir structures. Our key findings include the following: (1) Member 2 of the Xujiahe Formation develops six subtypes of source–reservoir structures grouped into two levels, with gas-bearing capacities ranked as follows: source–reservoir separation type > source–reservoir adjacent type I > source–reservoir adjacent type II. Among these, the source–reservoir separation type (Level I) and fault–fracture conduit type (Level II) represent the most favorable structures for gas enrichment. (2) Tight sandstone gas enrichment is governed by a tripartite synergistic mechanism: hydrocarbon supply from source rocks, vertical cross-layer migration dominated by fault–fracture systems, and reservoir storage capacity determined by fracture density and reservoir thickness. (3) Three enrichment models are established: (i) a strong enrichment model characterized by “multi-layer source rocks beneath the reservoir, cross-layer migration, and thick fractured reservoirs”; (ii) a moderate enrichment model defined by “single-layer source rocks, localized migration, and medium-thick fractured reservoirs”; and (iii) a weak enrichment model featuring “single-layer hydrocarbon supply, pore-throat migration, and thin tight reservoirs.” This research provides a theoretical basis for optimizing exploration targets in Member 2 of the Xujiahe Formation in the Anyue area and offers insights applicable to analogous continental tight gas reservoirs. Full article

Neuromorphic computing, an emerging computational paradigm, aims to overcome the bottlenecks of the traditional von Neumann architecture. Two-dimensional materials serve as ideal platforms for constructing artificial synaptic devices, yet existing devices based on these materials face challenges such as insufficient stability. Indium selenide (InSe), a two-dimensional semiconductor with unique properties, demonstrates significant potential in the field of neuromorphic devices, though its application research remains in the initial stage. This study presents an artificial synaptic device based on the InSe/Charge Trapping Layer (CTL)/h-BN heterojunction. By applying oxygen plasma treatment to h-BN to form a controllable charge-trapping layer, efficient regulation of carriers in the InSe channel is achieved. The device successfully emulates fundamental synaptic behaviors including paired-pulse facilitation and long-term potentiation/inhibition, exhibiting excellent reproducibility and stability. Through investigating the influence of electrical pulse parameters on synaptic weights, a structure–activity relationship between device performance and structural parameters is established. Experimental results show that the device features outstanding linearity and symmetry, realizing the simulation of key synaptic behaviors such as dynamic conversion between short-term and long-term plasticity. It possesses a high dynamic range ratio of 7.12 and robust multi-level conductance tuning capability, with stability verified through 64 pulse cycle tests. This research provides experimental evidence for understanding interfacial charge storage mechanisms, paves the way for developing high-performance neuromorphic computing devices, and holds broad application prospects in brain-inspired computing and artificial intelligence hardware. Full article