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Addressing the thermal management challenges of prismatic aluminum shell lithium battery modules in electric vehicles under high-rate charge–discharge conditions, this study proposes a multi-objective optimization design method for integrated biomimetic liquid cooling plates. By integrating various highly efficient heat transfer structures from nature, including fractal-tree-like networks, leaf vein branching systems, and spider web radial distribution, a novel biomimetic liquid cooling plate topology was constructed. A multi-physics coupled numerical model considering electrochemical heat generation, thermal conduction, convective heat transfer, and thermal stress deformation was established. The NSGA-II algorithm was employed to globally optimize 12 design variables including channel geometric parameters, operating conditions, and structural dimensions, achieving collaborative optimization objectives of maximum temperature minimization, temperature uniformity maximization, pressure drop minimization, and structural lightweighting. The weight coefficients for the four optimization objectives were determined through the Analytic Hierarchy Process (AHP) with verified consistency (CR = 0.02 < 0.10), ensuring rational priority allocation aligned with automotive safety standards. The optimization results demonstrated that compared to the initial design, the optimal solution reduced the maximum temperature under 3C discharge conditions by 9.9% to 34.7 °C, decreased the temperature difference by 31.3% to 3.3 °C, lowered the pressure drop by 24.6% to 2150 Pa, reduced structural mass by 4.0%, and decreased maximum stress by 16.7%. Quantitative comparison with single biomimetic structures under identical boundary conditions showed that the integrated design achieved a 3.3% lower maximum temperature and 25.7% better flow uniformity than the best-performing single structure, demonstrating the synergistic advantages of multi-biomimetic integration. These synergistic performance improvements can be attributed to the hierarchical multi-scale architecture where fractal networks provide macro-scale flow distribution, leaf vein branches ensure meso-scale coverage, and spider web radials achieve micro-scale thermal matching. Long-term cycling tests conducted at 1C/1C rate with 25 ± 1 °C ambient temperature showed that the optimized design maintained a capacity retention rate of 92.3% after 1000 charge–discharge cycles, demonstrating excellent durability. The complex biomimetic channel structure can be fabricated using selective laser melting technology with minimum feature sizes below 0.3 mm, indicating promising manufacturing feasibility. The research findings provide theoretical guidance and technical support for the engineering design of high-performance battery thermal management systems. Full article

High-ash coal slime water, characterized by its stable colloidal suspension of fine kaolinite particles, poses a significant challenge in the coal preparation industry because it is hard to achieve efficient solid–liquid separation. While traditional coagulants and flocculants often suffer from limited bridging capabilities and distinct pH sensitivity, novel molecular architectures offer potential solutions. In this study, a star-shaped inorganic–organic hybrid flocculant (Al-PAM) was synthesized via in situ polymerization. Its flocculation performance and interfacial adsorption mechanism on the specifically targeted aluminol basal plane of kaolinite were systematically investigated and compared with Polyaluminum Chloride (PAC), Non-ionic Polyacrylamide (NPAM), and their combination (PAC + NPAM). Settling tests revealed that Al-PAM exhibited superior performance at a significantly lower dosage (10 mg∙L⁻¹) compared to the PAC + NPAM binary reagent system. It achieved a rapid initial settling velocity and reduced the supernatant turbidity to 48.45 NTU, while maintaining a near-neutral pH favorable for water recycling. Furthermore, Quartz Crystal Microbalance with Dissipation (QCM-D) monitoring confirmed that Al-PAM forms a thick, viscoelastic, and irreversible adsorption layer on the Al₂O₃ substrate. The dissipation shifts (ΔD) revealed that the star-shaped architecture promotes distinct bridging and electrostatic adsorption, overcoming the limitation of linear polymers. This work elucidates the specific contribution of the alumina-surface interaction with flocculants and proposes an efficient strategy for treating refractory coal slime water. Full article

In this work, we present a novel online acoustic sulfur hexafluoride (SF₆) monitoring system utilizing a miniaturized lithium niobate tuning fork (LNTF) sensor. The proposed system demonstrates enhanced stability and a broadband vibration–frequency response. The LNTF exhibits a fundamental resonance frequency of 32,901 Hz, and its quality factor (Q-factor) decreases from 19,700 to 18,300 as the SF₆ concentration increases at atmospheric pressure. Verification experiments at room temperature reveal a quantifiable correlation between the SF₆/N₂ mixture concentration ratio and the sensor’s mechanical impedance. Specifically, an impedance shift of 100 Ω corresponds to a concentration change of 0.0145 g/L. In air, with a signal integration time of 80 s, the measured noise voltage and current are 0.13 µV and 0.18 pA, respectively. These results underscore the potential of the LNTF as a compact, high-stability sensing platform for greenhouse gas monitoring in electrical infrastructure and industrial environments. Full article

China’s rapid electric vehicle (EV) market expansion—from 331,000 units in 2015 to over 9.5 million in 2023—is generating an unprecedented wave of retired lithium-ion batteries projected to exceed 94 TWh cumulatively by 2060, presenting critical challenges for sustainable resource management. While grid decarbonization can reduce use-phase emissions, the substantial embodied carbon in battery production (55–130 kg CO₂-eq/kWh) remains a critical challenge for achieving carbon neutrality. This study presents an integrated dynamic material flow analysis (MFA) and prospective life cycle assessment (LCA) framework—calibrated against the latest peer-reviewed literature—to quantify the carbon mitigation potential of battery recycling and second-life applications from 2020 to 2060. We evaluate four end-of-life management scenarios: baseline linear economy, enhanced recycling, second-life dominant, and synergistic optimization. Our results reveal that the synergistic scenario achieves the highest cumulative avoided emissions of 3844 Mt CO₂-eq, representing a 12.1-fold improvement over the baseline. Monte Carlo uncertainty analysis (n = 10,000) confirms robust scenario differentiation, with 100% probability that synergistic optimization outperforms enhanced recycling alone. Material security analysis shows that recycled supply can meet 100% of lithium, cobalt, nickel, and copper demand by 2060 under optimal management. These findings provide quantitative evidence for chemistry-differentiated battery management policies aligned with China’s dual carbon goals and the transition toward a sustainable circular economy. Full article

Lithium–sulfur (Li–S) batteries have emerged as a promising next-generation energy storage solution as the capacity demands on lithium-ion systems begin to exceed practical limits. In a global push for renewable energy and sustainable practices, Li–S technology offers several compelling advantages. Both lithium and sulfur are relatively inexpensive (especially compared to the transition metals used in lithium-ion cells), and Li–S batteries are easier and less costly to recycle. Moreover, Li–S chemistry carries a theoretical energy density about five times greater than that of current lithium-ion batteries, making it attractive for high-energy-density applications. Because of these advantages, research interest in Li–S batteries remains high despite significant challenges that still limit their performance and lifespan. However, despite these advantages, several fundamental challenges limit the practical deployment of Li–S batteries, including the polysulfide shuttle effect, large volume expansion of sulfur during cycling, low intrinsic electrical conductivity of sulfur and its discharge products, and instability of the lithium metal anode caused by dendrite formation. This paper explains the working principles of Li–S batteries, analyzes the key challenges and recent achievements in their development, and surveys various mechanical engineering applications for which Li–S batteries are being explored, as well as prospects for their future commercialization and sustainability. Full article

Lithium-ion batteries are widely used across diverse applications due to their high energy density, long cycle life, and fast charging capabilities. As battery-powered systems become increasingly critical, accurate estimation of the Remaining Useful Life (RUL) is essential for ensuring reliability, safety, and effective maintenance planning. This work investigates Evidential Deep Learning (EDL) for data-driven RUL estimation and introduces a novel risk-aware loss function designed to enhance both predictive accuracy and uncertainty quantification in the End-of-Life (EoL) region, where precise and trustworthy predictions are most needed. Using a publicly available dataset of lithium iron phosphate (LFP) cells, we benchmark the proposed approach against a baseline Conv–LSTM model, Monte Carlo (MC) Dropout, and Deep Ensembles. The results show that integrating the risk-aware loss into the EDL framework substantially improves the calibration of predictive uncertainty while achieving state-of-the-art accuracy near EoL. Unlike MC Dropout and Deep Ensembles, which exhibit increasing or unstable uncertainty as degradation accelerates, the proposed EDL model demonstrates a consistent reduction in uncertainty and significantly higher reliability in late-stage predictions. The findings indicate that the risk-aware evidential framework offers a reliable and computationally efficient solution for battery RUL estimation, enabling more informed decision-making in both safety-critical and consumer-oriented applications. Full article

Accurate estimation of lithium-ion battery state of health and capacity is critical for intelligent battery management. This study develops a multi-variable cross-condition capacity estimation model based on incremental capacity (IC) curve features. First, the IC curve area is extracted to construct a health indicator. To capture the coupled, non-linear effects of temperature and discharge current on capacity fade, a temperature-zoned modeling framework is implemented. Specifically, first-order linear polynomials are applied for room temperature conditions to prevent overfitting, while second-order polynomials with interaction terms are utilized for high and low temperature conditions to model complex degradation behaviors. Furthermore, to mitigate estimation errors caused by individual battery inconsistency and varying initial states across different operating conditions, the capacity retention rate (CRR) and health indicator retention rate metrics are defined and integrated into the estimation framework. Validation across multiple dynamic operating conditions demonstrates that the optimized CRR-based model achieves an average root mean square error of 0.0261 Ah and a mean absolute percentage error of 2.83%. The proposed temperature-zoned approach provides a robust, data-driven methodology for cross-condition battery health monitoring. Full article

The recycling of spent lithium iron phosphate (LiFePO₄, LFP) batteries is receiving increasing attention as electric-vehicle deployment accelerates worldwide. Pyrometallurgical reduction offers a viable route for large-scale recovery of iron-rich products from spent LFP batteries; however, the resulting Fe-based alloys often retain residual copper (Cu), which deteriorates alloy quality and constrains downstream utilization and refining. In this study, a sulfidation–slag refining process was developed to selectively remove Cu from an Fe–P–Cu alloy produced by dry reduction of spent LFP batteries. FeS was employed as a sulfidizing agent to promote preferential conversion of Cu into sulfide phases, while fayalite (Fe₂SiO₄) slag was introduced to enhance phase separation between metallic and sulfide/slag phases. Thermodynamic calculations coupled with high-temperature experiments were conducted at 1400–1600 °C under various Cu:FeS ratios to identify operating conditions that maximize Cu removal while minimizing Fe loss. The results indicate that Cu is selectively transferred from the metallic phase to Cu–Fe–S sulfide phases, whereas Fe remains predominantly in the metal phase. Under the optimal condition (1400 °C, Cu:FeS = 2:1), the refined metal reached an Fe content of 90.80 wt.%, achieving an Fe recovery of 87.42% and a Cu removal efficiency of 81.13%. The proposed approach provides a practical stepwise refining strategy for upgrading Fe-rich secondary resources recovered from spent LFP batteries and facilitates subsequent impurity-control processes. Full article

The State of Health (SOH) of a battery is an important indicator for measuring the performance degradation of batteries. In view of the deficiencies of existing SOH estimation methods in feature processing and model accuracy, this paper conducts research on high-precision SOH estimation methods for lithium-ion batteries. A BiLSTM model optimized by the Sparrow Search Algorithm (SSA) is adopted for SOH estimation. The SSA-BiLSTM model is constructed, and the experiments are conducted on multiple types of battery datasets, such as NCM811 and LFP, and the cross-validation strategy is used to evaluate the model’s performance. The experimental results show that the SOH prediction system software developed based on this model has the functions of rapid estimation and three-dimensional trend visualization. The paper verifies the functions of the SOH prediction system software developed by the model, which has practical reference significance for the development and application of SOH estimation systems in energy storage scenarios. Full article

This in vitro study evaluated the fracture resistance of CAD/CAM-fabricated lithium disilicate and 3D-printed resin crowns with varying occlusal thicknesses (0.5, 1.0, and 1.5 mm) following thermomechanical aging. Sixty extracted human molars were assigned to six experimental groups (n = 10), categorized by crown material and occlusal thickness. The crowns were fabricated using CAD/CAM technology in accordance with the manufacturer’s protocol. All specimens underwent thermomechanical aging, which consisted of thermocycling between 5 and 50 °C (5500 cycles) combined with mechanical loading of 50 N at 1.6 Hz for 75,000 cycles. The fracture loads were measured using a universal testing machine, and the failure modes were assessed using scanning electron microscopy. Statistical evaluation was performed using two-way analysis of variance with Tukey’s post hoc test (α = 0.05). Both the material type and occlusal thickness had a statistically significant effect on fracture resistance (p < 0.001). Lithium disilicate crowns exhibited higher fracture loads than 3D-printed resin crowns independent of occlusal thickness. Although the fracture resistance of 3D-printed resin crowns was lower, specimens with occlusal thicknesses ≥1.0 mm exhibited fracture loads exceeding average physiological masticatory forces, suggesting that 3D-printed resin crowns may represent a clinically acceptable option for conservative posterior restorations. In contrast, crowns with an occlusal thickness of 0.5 mm demonstrated fracture resistance values below the reported functional masticatory loads. Additionally, the proportion of repairable fractures increased with increasing occlusal thickness for both materials. Overall, the findings suggest that an occlusal thickness of at least 1.0 mm may represent a reliable threshold for posterior restorations, whereas a thickness of 0.5 mm may be insufficient to withstand functional occlusal loads in molar regions. Full article

Density functional theory (DFT) was used to investigate how bisubstitution doping in graphene alters its electronic structure and interfacial stability with two model lignocellulosic binders, carboxymethylcellulose (CMC), and a representative aromatic fragment (LCmA). The properties were evaluated at the ωB97X-D/LANL2DZ level for pristine graphene and its bisubstitution-doped variants with nitrogen (graphene-2N) and sulfur (graphene-2S), integrating frontier orbitals, electrostatic potential (ESP) maps, electronic localization functions (ELF/LOL), and QTAIM topology. Doping with 2N markedly reduces the HOMO–LUMO gap from 0.16052 eV (graphene) to 0.10560 eV (−34.2%), while 2S reduces it to 0.14222 eV (−11.4%), evidencing different electronic activation mechanisms. The interaction energies show doping-controlled selectivity: In pristine graphene, adsorption strongly favors LCmA ($\mathsf{\Delta }{E}_{int}$ = −99.3 kcal·mol⁻¹) over CMC (−23.7 kcal·mol⁻¹); in graphene-2N, CMC coupling intensifies (−93.7 kcal·mol⁻¹) while maintaining a high interaction with LCmA (−74.3 kcal·mol⁻¹); and in graphene-2S, CMC remains favorable (−71.9 kcal·mol⁻¹) while LCmA falls to a practically marginal regime (−4.1 kcal·mol⁻¹). QTAIM the presence of confirms closed-layer interactions in all complexes (∇²Pc > 0, H > 0, |V|/G < 1), with |V|/G close to unity for graphene–LCmA (0.994) and less compaction when doped with 2N (0.760 for 2N–LCmA). The bisubstitution modulates the electronic heterogeneity of the basal plane and redefines the binder–surface compatibility, favoring the multipoint anchoring of polar ligands in 2N and penalizing efficient aromatic stacking in 2S. Full article

Background/Objectives: Lithium disilicate (LD) veneers are widely used for minimally invasive anterior rehabilitation because of their favorable optical and mechanical properties. Fully digital workflows have been proposed as alternatives to conventional milling. These approaches combine computer-aided design and manufacturing (CAD/CAM) with 3D-printed burn-out patterns and subsequent heat pressing of LD ingots. However, clinical documentation of multi-unit anterior cases fabricated exclusively through this additive-plus-pressing route remains scarce. This case report aims to describe a fully digital additive-plus-pressing workflow for four maxillary anterior LD veneers and to report 48-month clinical outcomes. Case Presentation: A 52-year-old female presented with esthetic concerns involving the maxillary central and lateral incisors (teeth 11, 12, 21, and 22). After clinical and radiographic evaluation, a minimally invasive veneer-based rehabilitation was planned. Preparations were performed under magnification, and immediate dentin sealing was applied. Digital impressions were obtained with an intraoral scanner, and veneers were designed using CAD software(Exocad DentalDB 3.0 Galway (Exocad GmbH, Darmstadt, Germany). Castable resin patterns were 3D-printed, invested, and heat-pressed using LD ingots, followed by finishing and glazing. Adhesive cementation was performed under rubber dam isolation after hydrofluoric acid etching and silanization of the intaglio surfaces and conditioning of the tooth substrates according to the adhesive protocol, using a dual-cure resin cement. At the 48-month follow-up, all veneers remained intact, with clinically acceptable marginal adaptation, stable color and surface gloss, and no signs of secondary caries or marginal discoloration. The patient reported sustained esthetic satisfaction and comfortable function without postoperative sensitivity. Conclusions: This single-patient report suggests that a fully digital additive-plus-pressing workflow may be clinically viable for high-demand anterior LD veneers, providing favorable medium-term esthetics and patient-centered outcomes with no technical or biological complications. The reproducible protocol described may facilitate the integration of 3D printing and heat pressing into digital veneer rehabilitation and supports further controlled clinical investigations. Full article

Blended nanocomposite solid polymer electrolytes are gaining considerable attention as next-generation materials for use in flexible lithium-ion battery systems. These materials help ensure a more uniform distribution of lithium ions at the electrode–electrolyte interface, contributing to the development of a stable interfacial layer that mitigates lithium dendrite formation. In this study, solid polymer electrolytes were synthesized using a binary polymer matrix composed of polyethylene oxide (PEO) and polymethyl methacrylate (PMMA), with lithium iodide (LiI) as the ionic salt. Zirconium dioxide (ZrO₂) nanoparticles were introduced as nanofillers in varying concentrations to investigate their influence on the physical and functional characteristics of the polymer matrix. Characterization was carried out using Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), and X-ray Diffraction (XRD). SEM images indicated that ZrO₂ nanoparticles remained well-dispersed up to 3 wt%, while higher loadings showed slight agglomeration. FTIR analysis revealed noticeable changes in absorption bands, suggesting strong interactions among polymer chains and the nanofillers. XRD data confirmed the semi-crystalline behavior of the PEO/PMMA blend system. The inclusion of ZrO₂ nanofillers enhanced the structural integrity and ionic conductivity of the polymer matrix, making them promising candidates for applications in electrochemical energy storage and advanced material interfaces. The systematic incorporation of ZrO₂ nanofillers into the PEO/PMMA matrix significantly improved the microstructural uniformity, polymer–filler interactions, and ionic transport behavior of the solid polymer electrolytes. Full article

To address the long-term operational challenges of space environment monitoring buoys under extreme Arctic conditions, this paper proposes an energy management optimization method based on deep reinforcement learning (DRL). By constructing a buoy system model that integrates renewable energy sources, a primary lithium battery power supply, and a battery energy storage unit, combined with an Arctic environmental model incorporating low-temperature efficiency degradation, a reward function was designed to minimize power supply deficits while ensuring system reliability. The Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm was employed to optimize energy scheduling strategies. Simulation results based on real Arctic data (August 2024–January 2025) demonstrate that integrating wind turbines significantly reduces reliance on primary lithium batteries. Specifically, the required lithium battery capacity was reduced by 87.5% (from 61.44 kWh to 7.685 kWh), and procurement costs were lowered by approximately $68,830 compared to non-rechargeable schemes1. This method significantly enhances the buoy’s endurance and scheduling intelligence, offering valid insights into energy management in intelligent polar observation equipment. Full article

Thermal runaway accidents in lithium batteries necessitate effective thermal management. This study proposes a liquid cooling plate with internal spiral-array fins and investigates its performance under electrochemically coupled temperature-dependent heat generation conditions. A pseudo-two-dimensional (P2D) electrochemical model simulates battery discharge at 0.5C–2C rates to obtain heat generation characteristics, which serve as inputs for a fluid–solid coupled heat transfer model. The effects of spiral fin parameters—pitch (S) and height (h)—are systematically analyzed. Three main contributions are presented: spiral fins induce secondary flow that disrupts thermal boundary layer development and enhances fluid mixing, with smaller pitch extending the flow path and increasing radial velocity; a performance evaluation criterion (PEC)-based analysis identifies the optimal parameter range that balances heat transfer enhancement and pressure drop penalty; and increasing the fin height raises the finned area proportion and swirl intensity, suppressing bypass flow and strengthening heat transfer, with effects more pronounced at higher discharge rates. Key quantitative findings show that at 2C discharge, the optimized configuration (S = 3 mm, h = 0.5 mm) achieves a comprehensive performance index of 2.19 and reduces the maximum temperature by 25.32% compared to smooth channels. This work integrates electrochemical and thermal models to provide a new approach for optimizing spiral fin microchannels tailored to lithium battery operation. Full article