δ-MnO2 is being actively developed as an ideal cathode material for aqueous zinc-ion batteries (AZIBs) with its wide interlayer spacing well-suited for ion storage. However, its poor electronic conductivity, structural collapse, and sluggish reaction kinetics are the primary factors restricting its electrochemical performance. To address this, doping engineering has been demonstrated as a highly effective approach for tailoring the crystal framework, electrical conductivity, and intrinsic electronic properties of Mn-based oxides. In this work, dual-site Nd-doped δ-MnO2 nanospheres are proposed as a high-performance cathode for AZIBs, aiming to microscopically reconstruct the electronic structure and interlayer architecture of δ-MnO2. Synthesized via a one-step hydrothermal method, the large-sized Nd3+ exhibits a unique dual-site occupancy mechanism: it not only intercalates into the interlayers to expand the (001) interplanar spacing, but also successfully substitutes a portion of the host Mn sites. Significantly, this aliovalent substitution induces a high concentration of highly stable oxygen vacancies (up to 38.95%) within the lattice, thereby achieving superior electrical conductivity and enhanced reaction kinetics. Consequently, benefiting from high-speed ion transport pathways and abundant low-energy-barrier active sites, the Nd-MnO2-2 cathode delivers a remarkable specific capacity of 267.8 mAh g−1 at 0.1 A g−1, and retains 90.34% of its capacity after 1500 cycles at a 2 A g−1.

The photocatalytic generation of hydrogen peroxide (H2 O2 ) from water and oxygen presents a sustainable alternative to the conventional anthraquinone process. However, its efficiency is hindered by rapid charge recombination and slow oxygen reduction reaction (ORR) kinetics. In this study, we have developed a 2D/2D In2 S3 /g-C3 N4 (ISCN) S-scheme heterojunction using an in-situ solvothermal growth method to overcome these challenges. The optimized ISCN-2 heterojunction achieves an H2 O2 production rate of 1.644 mmol g-1 h-1 , which is 3.5 times and 1.4 times greater than that of pristine In2 S3 and g-C3 N4 , respectively. Femtosecond transient absorption spectroscopy reveals a tri-exponential decay kinetic model, with a novel lifetime component ( tau 3 = 103.7 ps) indicating interfacial charge transfer facilitated by the built-in electric field. In-situ X-ray photoelectron spectroscopy and density functional theory calculations offer additional support for this S-scheme transfer mechanism. Moreover, in-situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) demonstrates that the 2D/2D architecture enhances O2 chemisorption and accelerates the sequential two-step, single-electron ORR pathway (& lowast;O2 - -> & lowast;OOH -> H2 O2 ). This study clarifies the charge dynamics of S-schemes at the molecular level and offers a practical approach for creating effective photocatalysts for converting solar energy into chemical energy. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.

Single-molecular heterojunctions have demonstrated significant application potential in the fields of photocatalysis due to prominent photoelectric properties, while the charge transfer behavior still needs to be further discussed. In this work, a fresh single-molecular Van der Waals heterojunction is fabricated through self-assembly of dibromo(1,10-phenanthroline-kappa N1,kappa N10)nickel (NiphenBr) molecules on the surface PCN nanosheets, which dramatically boosts the performance of selective photocatalytic CO2 reduction to CO. This unique assembled architecture effectively regulates electronic band structure and promotes the interfacial transfer and separation of photogenerated carriers owing to the pi-pi coupling effect between NiphenBr and PCN. Meanwhile, the single-molecular dispersed NiphenBr molecules also prevent their aggregation on PCN under the strong pi-pi interaction, and further provide abundant single-atom active sites for CO2 reduction reaction. Therefore, the average rate of photocatalytic reduction of CO2 to CO for the optimal NiphenBr/PCN-1 sample reaches 5.46 and 2.73 times that of PCN and NiphenBr, respectively. This work opens a new avenue for the single-molecular heterojunction in the application of photocatalytic reactions. (c) 2025 Institute of Process Engineering, Chinese Academy of Sciences. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Although stainless steel heat transfer tubes offer cost and corrosion resistance advantages over copper, their lower thermal conductivity limits two-phase flow heat transfer performance. Enhanced tubes can mitigate this limitation and improve energy efficiency. However, detailed visualizations of flow boiling inside stainless steel tubes with reticulate-thread and micro-structured surfaces are scarce. This work presents the first systematic experimental investigation combining flow visualization and flow boiling measurements in stainless steel tubes featuring reticulate-thread structures with micro-scale surface modifications. Four types of stainless steel enhanced tubes were studied: large reticulate-thread (SS-R1), large reticulate-thread/laser-textured (SS-R1-L), small reticulate-thread (SS-R2), and small reticulate-thread/sandblasted (SS-R2-S) tube. Comparative experiments with a micro-finned copper (Cu-MF) tube were conducted using R32 under mass fluxes of 100-300 kg/ (m2 & sdot;s) and vapor qualities of 0.2-0.8. Flow patterns and boiling heat transfer performance were analyzed to elucidate surface enhancement mechanisms. Laser texturing and sandblasting significantly enhanced surface wettability and nucleation site density, improving heat transfer. Reticulate-thread structures promoted flow disturbance and guided liquid-vapor interaction, accelerating transition toward annular flow. Under slug flow, enhanced tubes exhibited longer liquid waves and more persistent impingement compared with smooth tubes. Overall, the SS-R1-L tube performed best at low mass fluxes, while SS-R2-S tube was superior at high mass fluxes, indicating strong potential as substitutes for copper micro-finned tubes. This study provides new insights into two-phase flow in stainless steel enhanced tubes and informs the design of cost-effective, corrosion-resistant heat exchangers.

The H2O2 photosynthesis via the oxygen reduction reaction is a sustainable technology, yet it is severely impeded by sluggish exciton dissociation and limited active sites in bulk graphitic carbon nitride. Herein, a synergistic defect-engineering strategy involving acid-assisted exfoliation and molten-salt reconstruction is developed to synthesize cyano-rich porous carbon nitride (Ex-Rc-CN). This protocol integrates morphological optimization with electronic modulation: the exfoliation step creates an ultrathin porous architecture with high specific surface area, while the subsequent reconstruction grafts abundant cyano (–C≡N) groups into the framework to significantly extend visible-light absorption and facilitate charge separation. Tracking ultrafast carrier dynamics by Femtosecond transient absorption spectroscopy reveal that electron-withdrawing –C≡N groups act as deep electron traps. Concurrently, Kelvin probe force microscopy provides direct spatial evidence for the induction of a strong built-in electric field through intuitive surface potential mapping. Theoretical calculations demonstrate that these dual effects synergistically promote the ultrafast spatial dissociation of photogenerated electron-hole pairs. Consequently, the Ex-Rc-CN exhibits a high H2O2 production rate of 5,690 μmol g−1 h−1. To evaluate practical applicability, the H2O2 photosynthesis performance of Ex-Rc-CN was investigated in natural and simulated seawater, demonstrating its robust adaptability to varying water qualities. Mechanistically, the enhanced O2 activation and in-situ fourier transform infrared results unravel a dual-channel pathway: the cyano-mediated reduction of O2 coupled with a singlet oxygen (1O2)-induced selective oxidation of isopropanol. This work presents a novel paradigm for orchestrating structural and electronic synergies to achieve efficient solar-to-chemical conversion.

During high-speed flight, severe aerodynamic heating is encountered by the vehicle, and the accurate identification and prediction of surface heat flux are regarded as essential requirements for the design and optimization of thermal protection systems. Real time feedback on the spatiotemporal evolution of active thermal protection systems such as transpiration cooling is difficult to obtain by conventional approaches, and complex internal processes including microporous permeation and gas liquid phase change introduce significant thermal lag effects. These effects further increase the difficulty of real time estimation and prediction and reduce the responsiveness of coolant dynamics in active thermal protection systems. In this study, a heat flux identification and rapid prediction method for transpiration cooling systems is proposed based on a Channel Attention Temporal U-Net model, in which spatial features are extracted by convolutional networks, temporal dependencies are modeled by convolutional long-Short-Term-Memory units, and channel features are adaptively calibrated through squeeze and excitation modules. Through this integrated structure, the accuracy of spatiotemporal heat flux modeling is enhanced and both the current heat flux and its future evolution can be predicted with high fidelity. It is shown that under typical transpiration cooling conditions a root mean square error of 0.0189 MW/m2 is achieved by the proposed model, corresponding to an 84.7% reduction relative to the baseline U-Net model, and the prediction error within a 10-30 s forecast window is maintained within 2.5%. The influence of thermal lag on system performance is thereby alleviated, and improvements in accuracy, robustness, and computational efficiency are demonstrated, providing theoretical support and technical foundations for real time dynamic thermal management in high-speed vehicle active thermal protection systems.

Accurate identification of heat flux in transpiration cooling thermal protection systems (TPS) is critical for precise temperature control and optimization of cooling efficiency during active control processes. However, identifying surface heat flux in such systems poses more significant challenges than conventional inverse heat conduction problems (IHCP), primarily due to the complex interactions among high-Mach-number flows, compressible outflow, and microscale percolation effects in a porous medium. In this paper, the inverse heat infiltration coupling problem (IHICPP), which focuses on identifying surface heat flux in porous transpiration cooling TPS, is analyzed in detail. To transform HICPP into an optimization problem, a local thermal non-equilibrium model is employed to simulate the direct problem. The objective function is formulated using the least-squares method. An improved simulated annealing algorithm (ISAA) is proposed, which leverages gradient iteration within the simulated annealing framework to enhance identification accuracy and computational efficiency. The ISAA demonstrates merit performance across various surface heat flux waveforms and TPS thicknesses, achieving stable and reliable results. This study serves as a valuable reference for active control and optimization of TPS, offering a robust methodology for IHICPP in high-Mach-number aircraft.

Triply periodic minimal surface (TPMS) structures provide a large heat-transfer area and interconnected flow passages, but their behavior in LHS devices with coupled convection and phase change remains poorly characterized. In this work, an additively manufactured Primitive TPMS LHS device was fabricated and tested during charging and discharging, and a numerical model was used to investigate the phase-change process and evaluate structural parameters. The experimental results show that natural convection in the molten PCM produces inter-cell circulation and vertical thermal stratification. Under the standard charging condition, the layer-averaged PCM temperature difference between the upper and lower layers reached 14.2 °C. The latent heat remains the dominant contribution, accounting for 60.7–70.5% of the stored heat during charging and 64.0–71.8% of the released heat during discharging, while sensible heat in the PCM and TPMS skeleton cannot be neglected. Increasing the HTF inlet temperature mainly enhances the charging/discharging rate by increasing the thermal driving force, whereas increasing the HTF flow rate from 200 to 1100 L/h shortens the complete melting and solidification times by 37.5% and 19.9%, with only minor changes in the total stored/released heat. Numerical analysis shows that increasing the PCM-to-HTF volume ratio from 0.5 to 2.5 raises the heat storage density from 85.1 to 185.2 MJ/m3, but reduces the energy storage-to-pumping work ratio (EPR) from 5.18 to 0.04. Reducing the cell size from 20 to 2 mm shortens the melting time from 167.0 to 5.0 s and increases EPR from 1.81 to 5.31. These results indicate that TPMS-based LHS design should balance PCM inventory, charging rate, and pumping work rather than maximizing storage volume alone.

Carbon fiber aerogels are widely adopted as thermal protection materials in the 1200°C temperature range. Their ultra-low density and high thermal resistance make them particularly promising for applications in aerospace thermal protection. However, the thermal properties of these materials are significantly influenced by their complex, multiphase mesostructure. Traditional experimental methods, which rely on trial and error, often struggle to provide efficient and accurate predictions of performance or to effectively regulate structural parameters. As a result, this limits the efficiency of material design and the potential for broader applications.To overcome these challenges, this paper proposes an integrated computational framework that combines morphological feature extraction with a three-dimensional residual convolutional neural network (3D-ResCNN) surrogate model. The goal of this framework is to achieve rapid and precise predictions of the thermal properties of carbon fiber aerogel composites and the inverse design of their microstructures. Additionally, this study introduces a CNN surrogate-assisted Pareto optimization strategy. By using the trained neural network as a surrogate model, the proposed strategy enables an inverse search for optimal microstructural configurations based on specified performance criteria. During the optimization process, the carbon fiber skeleton is preserved, while the aerogel-air distribution is regulated to achieve different combinations of thermal insulation performance and lightweight characteristics. This process creates a closed-loop solution that connects macroscopic performance requirements to microstructure design.Unlike traditional methods, which suffer from low efficiency in modeling the relationship between microstructure and performance, this method offers a feasible technical pathway for rapid performance evaluation and novel inverse design of carbon fiber aerogel materials. The developed fusion framework not only enhances understanding of heat transfer mechanisms in multiphase composite materials but also establishes a theoretical foundation for the structural customization and performance optimization of high-performance thermal protection materials.

Although virtual asynchronous machine (VAM) control has been proposed for virtual energy storage systems (VESSs), research into its secondary control applications is still limited. Thus, a distributed secondary frequency restoration control strategy based on VAMs is presented for VESSs. First, the VAM control is introduced, and a detailed electro-thermal coupling model of the VESS is developed. This model includes indoor-outdoor temperature differences, heat transfer through building envelope (walls, windows, and roof), solar radiations, ventilation losses, and electric boiler dynamics. It effectively captures the coupling between indoor temperature regulation and grid power balancing. Next, a distributed secondary frequency restoration control strategy based on VAM is proposed. It addresses parameter heterogeneity within a nonlinear multi-agent framework among VESSs. The nonlinear dynamics are converted into a linear reference model, which simplifies controller design and stability analysis. Using only local and neighboring information, the proposed strategy restores frequency and ensures active power sharing. Furthermore, the proposed strategy coordinates thermal power regulation to maintain indoor temperature balancing across VESSs within seasonal thermal comfort ranges. This improves thermal comfort without compromising dynamic response. Finally, the stability of the proposed strategy is verified using Lyapunov method, and simulation results from an islanded microgrid (MG) test system under parameter variations, communication imperfections, and winter/summer operating scenarios validate the effectiveness and robustness of the proposed strategy.

Safe and efficient operation of high-energy-density battery systems relies on thermal management. But current methods of thermal management are not always up to snuff when it comes to thermal response and flame resistance. By manipulating the microstructure of low-grade graphene through gas evaporation and directional freeze-drying, we were able to create a thermally switchable flame-retardant material that can undergo fast transitions. In 20 s at around 140 degrees C, the material goes from being conductive (1.23 W m-1 K-1) to being insulating (0.11 W m-1 K-1), thus combining the roles of efficient heat transfer and thermal insulation. By acting as a separator in nickel-manganese lithium-ion batteries, this material improves the stability of the modules and decreases the dangers of explosions caused by thermal runaway by drastically reducing heat diffusion. Additionally, a thermosensitive flame-retardant composite with many functions based on graphene was created to assist with responsive heat management. In order to facilitate proactive risk assessment, infrared imaging and real-time temperature data were used to detect early-stage thermal abnormalities using the machine learning system. A new, economical, and scalable method for controlling electric vehicle and energy storage battery safety has been developed through the combination of intelligent detection and thermal switching. This method promotes improvements in performance and inherent safety.

Electric vehicles (EV) nowadays have rapidly expanded, introducing flexibility resources meanwhile instability for the power system. To address the instability issue, a properly configured photovoltaic (PV)-battery energy storage system (BESS)-EV system with vehicle to grid (V2G) mode is proposed in recent years. However, accurate capacity allocation of PV-BESS-EV system requires comprehensive operational conditions, including V2G scenarios, internal power flows and device states, which generally leads to computational inefficiency due to higher-order nonlinear modeling. In this paper, to enhance the accuracy and efficiency of capacity allocation, a model-based reinforcement learning Agent is proposed, by which economic benefits and emergency service for V2G mode are improved significantly. First, a PV-BESS-EV system model with V2G scenarios is established to reveal the internal power flows and device states. Second, a Bayesian Neural Network (BNN) dynamic model is developed to rapidly predict operational results. Third, depending on these predictions, an allocation Agent via Deep Deterministic Policy Gradient (DDPG) algorithm is designed to efficiently search for optimal capacity configurations. The proposed method is evaluated using UK case studies across five representative cities. The results show that hour-level operation can be simulated by the proposed model. The operational results can be accurately predicted by the proposed BNN model with a Mean Absolute Percentage Error < 11.41%. The optimal configurations are obtained with a relative Root Mean Square Error < 0.46%, while the allocation time is reduced by approximately 70% by the proposed Agent. The suggested configurations can reduce annual electricity costs by 40.93% and enhance outage supply capability by replacing the BESS with V2G. Under these configurations, since the reduced average state of charge effectively mitigates battery calendar aging, the additional EV battery degradation is limited to 1.03%.

Thermal protection for supersonic vehicles is essential due to the intense aerodynamic heating encountered during flight. Active cooling methods have been employed to address these thermal challenges, but the specific mechanisms by which coolant mass injection affects wall heat flux under high enthalpy conditions remain insufficiently understood. This study utilizes laminar numerical simulations to investigate the impact of various injected coolants on wall heat flux. The computational framework models high-altitude supersonic flight regimes characterized by low freestream Reynolds numbers, specifically focusing on micro-porous transpiration systems operating at low injection mass fluxes. The results indicate that coolants with lower molecular weight achieve higher injection velocities and foster stronger synergy between the velocity and temperature fields, thereby enhancing cooling efficiency. Under the investigated high-enthalpy conditions, the cooling efficiency among different coolants exhibits an approximately log-linear trend with molecular weight. Notably, hydrogen emerges as a promising coolant due to its capacity to generate relatively low reaction heat, effectively reducing wall heat flux. In contrast, methane produces significant reaction heat due to methyl oxidation, which is observed to correlate with an unfavorable local wall heat flux enhancement; however, potential numerical sensitivities at the injection boundary warrant further targeted investigation This study can provide certain support and reference for the optimal design of active thermal protection systems for supersonic vehicles.

Developing bifunctional oxygen electrocatalysts that combine high activity with robust stability remains a central challenge in electrocatalysis, particularly for rechargeable Zn-air batteries (RZABs). Herein, we report a unique electrocatalyst, MoP@3DNPC, consisting of layer-by-layer stacked MoP nanoparticles embedded in threedimensional (3D) porous carbon nanosheets via an inorganic-organic hybrid route. The distinctive architecture of MoP@3DNPC, featuring carbon-coated MoP nanoparticles, facilitates direct mass transport and provides a highdensity active surface area for reactants. Furthermore, the accelerated electron transfer kinetics between MoP and the N, P-doped carbon framework contribute to its exceptional bifunctional oxygen reduction reaction (ORR)/oxygen evolution reaction (OER) performances. As-fabricated MoP@3DNPC demonstrates impressive half-wave potential (0.82 V) and limiting current density (5.78 mA cm- 2) for ORR, as well as a relatively low potential of 1.66 V at 10 mA cm- 2 toward OER. As a cathode in RZABs, it produces a peak power density of 170.15 mW cm- 2 and maintains excellent cycling stability over 480 cycles. This work reveals an efficient and innovative in-situ phosphating strategy for carbon-based transition metal phosphide electrocatalysts.

Packed-bed thermal energy storage (TES) offers a promising solution for large-scale, high-temperature sensible-heat applications. However, the pre-selection of storage materials remains a persistent challenge, owing to the wide diversity of candidate solids and the computational intensity of detailed simulations. In this study, a simplified, physics-informed framework was developed for engineering-oriented screening of sensible-heat storage materials in packed-bed TES systems. An application-driven performance metric—qv,ave, the average volumetric heat-transfer rate per unit temperature difference—is introduced. The analysis revealed that enhancing the thermal conductivity (λs) of the storage material improves qv,ave, but only up to a distinct critical threshold. Beyond this critical λs value, further thermal conductivity enhancement yields diminishing returns with negligible improvement in qv,ave. This crucial observation underpins the development of a practical engineering matching diagram, which delineates the optimal (or necessary) λs ranges for storage materials compatible with different heat transfer fluids. Additionally, to further enhance the heat transfer performance of the sensible heat storage sphere while accounting for the pressure drop and practical constraints, a three-step engineering selection framework was proposed. This framework integrates the application requirement definition, candidate material screening (leveraging the matching diagram), and particle size design, thereby facilitating rapid and efficient material selection for packed-bed TES engineering applications.

Accurate and efficient prediction of aerodynamic heating over complex configurations along practical flight trajectories is a crucial prerequisite for the refined design of thermal protection systems in high-Mach-number vehicles. However, conventional computational fluid dynamics (CFD) simulations often result in extremely high computational costs and low efficiency when applied to complex vehicle configurations across the entire mission profile or full lifecycle. To enhance the performance of full-configuration coupled field prediction for complex geometries under diverse service conditions, this paper proposes a synergistic multi-scale neural operator architecture, called SynoPhys, an end-to-end neural partial differential equation (PDE) solver built upon a Transformer architecture with adaptive fusion and multi-scale clustering. By integrating multi-scale geometric encoding with an adaptive fusion mechanism, this framework effectively captures both global and local flow-field characteristics. It learns the nonlinear mapping from flight parameters to the full-field heat flux, eliminating the need for manual meshing or intermediate preprocessing. The proposed model has been evaluated on four representative configurations: blunt-cone, double-cone, double-ellipsoid, and lifting-body. It demonstrates high-fidelity generalization, achieving mean relative errors of 1.13

Photocatalytic synthesis of hydrogen peroxide (H2O2) from water and oxygen is a promising yet challenging green route, primarily limited by severe charge recombination and the inefficient activation of inert O2 molecules. To address these dual bottlenecks, this work constructs an organic/inorganic step-scheme (S-scheme) heterojunction by intimately coupling C3N4 with Bi2O3. This unique architecture, as deciphered by in-situ XPS and femtosecond transient absorption spectroscopy, drives efficient S-scheme charge transfer. This process not only achieves spatial separation of powerful photogenerated carriers but also retains their high redox potential. Crucially, density functional theory calculations reveal that the interfacial electronic coupling induces a significant electron redistribution, which dramatically enhances the adsorption and activation of O2 molecules—a finding corroborated by oxygen temperature-programmed desorption. Consequently, the optimized C3N4/Bi2O3 photocatalyst delivers a remarkably high H2O2 production rate of 4.03 g-1 h-1 under simulated sunlight. The in-situ generated H2O2 further translates into superior disinfection efficacy, achieving 99.9% inactivation of E. coli within 60 min. This work elucidates the charge dynamics at organic/inorganic S-scheme interfaces and showcases a viable pathway for designing efficient photocatalysts for coupled solar fuel production and environmental applications.

To address the trade-off between electrode permeability and specific surface area in vanadium redox flow battery, this paper proposes a novel electrode structure termed the directional mesh composite fiber electrode (DMFE). This electrode structure employs a directional fiber electrode (DFE) as the transport layer and a mesh fiber electrode (MFE) as the catalytic layer, effectively alleviating concentration gradients along both the through-plane and in-plane directions within the electrode while maintaining satisfactory electrochemical performance. DMFE is continuously fabricated via electrospinning, and this integrated preparation process effectively eliminates the contact resistance between the two electrode layers. The feasible ranges of porosity and fiber diameter for each layer are first determined experimentally, after which a genetic algorithm is employed to identify the optimal structural matching scheme, thereby exploiting the structural advantages of the gradient electrode. Experimental results show that MFE fiber diameter can be achieved in the range of 0.20 to 1.02 μm, whereas DFE fiber diameter is fixed at 3.89 μm. The corresponding porosity ranges are 0.80 to 0.90 for MFE and 0.85 to 0.95 for DFE. According to the genetic algorithm optimization, the optimal matching scheme for DMFE consists of a transport layer with a fiber diameter of 3.89 μm and a porosity of 0.85, and a catalytic layer with a fiber diameter of 1.01 μm and a porosity of 0.90. At a current density of 100 mA·cm−2, the energy efficiency of DMFE assembled cell reaches 75.84%, which is 11.25% higher than that of the cell using DFE alone.