Search Results (1,294)

Accurate and robust battery health prognostics are critical for reliable battery management in electronic devices and electric vehicles. Previous studies have demonstrated that combining electrochemical impedance spectroscopy (EIS) with machine learning enables accurate health-state forecasting in LiCoO₂ coin cells. However, the applicability of this EIS-AI paradigm across diverse chemistries and industrial-grade battery formats remains unvalidated, limiting its practical deployment in energy storage systems. Here, we develop an EIS–AI battery prognostic framework and validate its performance on LiNi_1/3Mn_1/3Co_1/3O₂ (NMC111) cylindrical cells and LiNi_0.8Mn_0.1Co_0.1O₂ (NMC811) pouch cells. A hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) architecture is developed to estimate state of health (SoH) and predict remaining useful life (RUL) from EIS spectra. Trained on an in-house dataset comprising over 13,000 impedance spectra from 22 cells (8 NMC111 and 14 NMC811), the model achieves robust performance, with average coefficients of determination (R²) exceeding 0.92 for SoH estimation and 0.90 for RUL prediction across various batteries and cycling protocols. Salient feature analysis further reveals chemistry- and protocol-dependent frequency regimes associated with degradation. These results demonstrate that impedance spectra constitute physically informative descriptors for data-driven battery prognostics and provide a scalable and interpretable pathway for deploying EIS-AI frameworks in real-world battery management systems (BMSs). Full article

This study presents a highly efficient and sustainable biocatalytic platform for bisphenol A (BPA) bioremediation through the covalent immobilization of laccase onto hierarchically functionalized cotton fibers. The immobilization strategy involved selective periodate oxidation of cellulose, grafting a hexamethylenediamine (HMDA) spacer arm, and glutaraldehyde activation, ensuring stable covalent attachment. Characterization via FTIR, SEM, and BET confirmed successful surface modification and high enzyme loading, achieving an immobilization yield of 90.5%. The immobilized laccase (CT-DA-HMD-Lac) exhibited significantly enhanced performance compared to the free enzyme, with a two-fold increase in maximum reaction velocity (V_max) and a 75% improvement in catalytic efficiency of action (V_max/K_m). Furthermore, the biocatalyst demonstrated superior robustness, maintaining high activity across broader pH and temperature ranges, and retaining 75% of its initial activity after 15 consecutive reusability cycles. Storage stability was also markedly improved, with 83% activity retention after 60 days. Practical application in BPA degradation showed 85% removal efficiency within 300 min, a 2.4-fold increase in the degradation rate constant over the free enzyme. These results highlight functionalized cotton as a promising, cost-effective, and scalable support for advanced enzymatic wastewater treatment and the remediation of persistent endocrine-disrupting chemicals. Full article

Carbon quantum dots (CQDs) and stimuli-responsive hydrogels are advanced functional materials whose hybridization yields CQD-enhanced stimuli-sensitive hydrogels, opening new interdisciplinary avenues for smart material applications. This review systematically summarizes the latest advances in these composites, focusing on synthetic strategies, structure–property modulation mechanisms, and practical applications. Distinct from existing reviews that either investigate CQDs or hydrogels independently or discuss their composites in a single research field, this work features core novelties in integration strategy, application scope and critical analysis: it systematically compares the advantages, limitations and applicable scenarios of three typical CQD–hydrogel integration approaches (physical entrapment, in situ synthesis, covalent conjugation), comprehensively covers the multi-field application progress of the composites and conducts in-depth cross-field analysis of their common scientific issues and technical bottlenecks. By incorporating CQDs, the composites achieve remarkable performance optimizations: 40% improved mechanical toughness, sub-ppm-level heavy metal-sensing sensitivity, and over 80% organic dye photocatalytic degradation efficiency, addressing pure hydrogels’ inherent limitations of insufficient strength and single functionality. These enhancements enable sophisticated applications in biomedical field (real-time biosensing, controlled drug delivery), environmental remediation (pollutant detection/degradation), energy storage, and flexible electronics. The synergistic interplay between CQDs and hydrogels facilitates precise single/multi-stimulus responsiveness (pH, temperature, light), a pivotal advance for precision medicine and intelligent environmental monitoring. Despite promising progress, the large-scale practical application of CQD–hydrogel composites still faces prominent challenges: the difficulty in scalable fabrication with the uniform dispersion of CQDs in hydrogel matrices, poor long-term stability of most composites under physiological cyclic stress (service life < 6 months in practical tests), and low accuracy in discriminating multi-stimuli in complex real-world matrices. Future research should prioritize biomass-based eco-friendly CQD synthesis, machine learning-aided multimodal responsive systems, and 3D bioprinting for scalable manufacturing. Full article

Accurate prediction of the state of health (SOH) of lithium-ion batteries is essential for ensuring the safety and reliability of electric vehicles and energy storage systems. While machine learning (ML)-based models have demonstrated strong predictive performance, their limited interpretability remains a major challenge for deployment in safety-critical applications. Although explainable boosting machines (EBMs) provide an interpretable alternative through their additive structure, existing studies still rely on manual analysis of model outputs, which restricts scalability and reproducibility. To address this limitation, this study proposes a structured interpretation framework that integrates EBMs with multimodal large language models (MLLMs). The proposed framework employs EBMs to generate SOH predictions along with global feature importance and variable-level score-density visualizations. These outputs are subsequently processed by an MLLM to perform automated interpretation at both global and variable levels, followed by aggregation, cross-validation, and generation of a unified interpretation report. Experiments were conducted on a lithium-ion battery degradation dataset and the EBM achieved competitive predictive performance compared to baseline ML models. In addition, the quality of the generated interpretations was evaluated using both an MLLM-as-a-Judge and a user study. The evaluation results show that the generated interpretations consistently achieved high scores, with average ratings exceeding 4.5 out of 5 across key criteria such as interpretation accuracy and faithfulness, as assessed by both independent MLLMs and domain experts. These results demonstrate that the proposed framework enables reliable and scalable interpretation of battery SOH prediction models, providing a practical solution for explainable artificial intelligence in battery health management. Full article

The integration of electric mobility and energy systems has emerged as a key research domain in the transition toward sustainable energy and decarbonized transport, yet the literature is lacking systematic quantitative overviews of its scientific development. This study addresses this gap by conducting a bibliometric analysis of research activities across five domains central to electric vehicle–energy system integration: central energy management systems; renewable energy, hydrogen production, and large-scale storage; industrial applications; smart energy communities, virtual power plants, and vehicle-to-X; and urban high-power charging parks with local storage. Using publication data from Web of Science and Scopus, performance analysis and science mapping techniques were applied to examine publication dynamics, thematic structures, and intellectual linkages. Results indicate strong growth and consolidation around smart grids and decentralized flexibility solutions, particularly within energy management, renewable integration, and community-based energy systems, while industrial applications and high-power charging infrastructures remain comparatively underrepresented. The findings suggest a maturing interdisciplinary field characterized by expanding connections between mobility and energy research, alongside emerging opportunities related to industrial integration, charging infrastructure, and vehicle-to-grid deployment. The study provides a structured, multi-domain perspective on the convergence of electric mobility and energy systems, enabling a differentiated understanding of research dynamics. The study provides a structured, multi-domain perspective on the convergence of electric mobility and energy systems. The findings highlight priority areas for future research, particularly industrial integration and scalable charging infrastructure, and offer insights for policymakers and industry stakeholders. Full article

This paper presents an integrated and scalable methodology for assessing the feasibility of Renewable Energy Communities (RECs) in rural and agricultural settings, particularly in areas with limited technical and consumption data. By incorporating Geographic Information System (GIS) data, photovoltaic potential estimation, and energy consumption profiling, the study provides a decision-support framework suitable for various municipalities. A case study conducted in Caorso, a municipality in northern Italy, showcases the framework’s capability to model energy exchanges and estimate self-sufficiency levels for a predominantly rural area. The results highlight seasonal variations in energy production and consumption, identifying opportunities for improvement through energy storage and enhanced energy-sharing strategies. Overall, the proposed approach supports municipalities in the pre-feasibility assessment of RECs by enabling the evaluation of local renewable potential and minimum rooftop utilization thresholds under limited data availability. Full article

Mediterranean cork oak forests provide essential ecosystem services but face increasing threats from climate change, ecosystem simplification, and oak decline. Ensuring their long-term sustainability requires governance approaches that integrate regional planning frameworks with international certification standards. This study presents a pioneering case of public cork oak forest management in Alà dei Sardi, Sardinia (Italy), where municipal forest planning was aligned with national and regional regulations and further enhanced through Forest Stewardship Council^® (FSC^®) certification. The FSC system offers internationally recognized standards and the Ecosystem Services Procedure (FSC-PRO-30-006 v2-1) to verify responsible forest management and quantify key ecosystem benefits. The Alà dei Sardi forest is the first publicly owned municipal cork oak forest to achieve FSC Forest Management certification, with demonstrated positive impacts of its management activities on biodiversity conservation, carbon sequestration and storage, water protection, soil conservation, and recreational services. The certification process integrated management planning, stakeholder engagement, monitoring, and adaptive interventions, showing that public institutions can combine legal frameworks with voluntary standards to enhance ecological performance, accountability, and socio-economic value. This case illustrates a potentially scalable and replicable model for sustainable forest governance, linking territorial planning with market-based mechanisms, and provides a practical example of governance for resilient and multifunctional forest systems. Full article

With the rapid development of new energy vehicles and energy storage systems, accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is of great importance for predictive maintenance and operational safety. However, battery degradation during cycling usually exhibits multi-scale characteristics, including long-term degradation trends, stage-wise drifts, and stochastic disturbances, which makes existing methods still face significant challenges in multi-step forecasting and cross-domain generalization. To address this issue, this paper proposes a time–frequency fusion model for multi-step RUL prediction, termed TF-RULNet (Time-Frequency RUL Network). The model takes cycle-level feature sequences as input and consists of three components: a multi-scale temporal convolution encoder (MSTC) for parallel extraction of degradation cues at different temporal scales; a multi-head spectral interaction module (MHSI), which performs 1D-FFT along the temporal dimension for each head and further applies adaptive band-wise mask refinement to capture local spectral structures and hierarchical band patterns with a computational complexity of $O(LlogL)$; and a cross-gated fusion module (CGF), which generates gating signals from the summary of one domain to modulate the features of the other domain, thereby enabling dynamic balancing and complementary enhancement of time–frequency information. Experiments are conducted on the NASA dataset (B005/B007) for in-domain evaluation, and further cross-dataset tests from NASA to the Maryland dataset (CS-35/CS-37) are carried out to verify the robustness of the proposed model under distribution shifts. The results show that, compared with the strongest baseline PatchTST, TF-RULNet reduces RMSE and MAE by more than 38.23% and 50.51%, respectively, in cross-dataset generalization, while achieving an additional RMSE reduction of about 24% in in-domain prediction. In summary, TF-RULNet can effectively characterize the multi-scale time–frequency degradation patterns of batteries and improve cross-domain generalization, providing a high-accuracy and scalable modeling solution for practical battery health management and life prognostics. Full article

The explosive growth of temporal graph data has led to significant training overheads for Dynamic Graph Neural Networks (DGNNs), a bottleneck primarily driven by massive data movement between host processors and storage arrays across conventional PCIe I/O buses. While near-data processing with Computational Storage Devices (CSDs) can alleviate this bottleneck, a single CSD is inherently incapable of meeting the terabyte-scale capacity requirements and complex sequence modeling demands of modern large-scale DGNNs. Horizontal scaling with multi-CSD clusters over standard PCIe topologies presents a viable, cost-effective solution, yet our in-depth profiling identifies two critical architectural bottlenecks in naive multi-CSD architectures: host-bounced memory copies significantly compromise inter-device communication efficiency, and sparse graph sampling frequently exceeds the capacity of the tightly constrained local DRAM of CSDs, resulting in excessive flash I/O and performance degradation. To address these interconnected bottlenecks, we propose M-DGNN, a hardware–software co-designed architecture optimized for standard PCIe interconnects. First, M-DGNN orchestrates direct peer-to-peer (P2P) DMA dataflows for inter-CSD hidden state exchange, completely bypassing host operating system intervention and reducing the context-switching overhead. Second, we design a host-assisted caching strategy with a Host-Pinned Memory Extension (HPME) mechanism, which leverages host-pinned memory as an asynchronous DMA extension pool to shield resource-constrained CSDs from high-latency flash I/O during structural subgraph sampling. Extensive experimental evaluations across seven large-scale dynamic graph datasets demonstrate that M-DGNN delivers up to a 6.2× end-to-end speedup over the state-of-the-art DGNN systems. This work establishes an efficient, scalable near-data computing paradigm for large-scale DGNN training. Full article

The increasing volume, temporal density, and diversity of satellite Earth observation (EO) data have fundamentally transformed quantitative vegetation remote sensing. Dense multi-sensor time series and computationally intensive modelling have rendered traditional download-and-process workflows increasingly impractical. Cloud-native computing—where data access, storage, and computation are co-located and analyses are executed in data-proximate environments—has therefore emerged as a key paradigm for scalable and reproducible vegetation EO analysis. This review provides a science-oriented synthesis of cloud-native EO for quantitative vegetation research. We examine architectural principles, data models, and compute patterns that shape how vegetation analyses are implemented, scaled, and scientifically interpreted. Particular attention is given to machine learning as a system component, including model lifecycle management, domain shift, and evaluation integrity in distributed environments. We analyse how cloud-native data abstractions influence algorithmic assumptions, validation design, and long-term product consistency, highlighting trade-offs between analytical complexity, computational cost, latency, and scientific robustness. We provide a forward-looking perspective on emerging imaging spectroscopy missions and the growing system-level requirements for reproducible, scalable, and uncertainty-aware vegetation analytics at continental-to-global scales. We also outline how cloud-native EO infrastructures are driving new scientific paradigms based on continuous monitoring, systematic reprocessing, and AI-driven modelling. Full article

Urban districts are increasingly exposed to overlapping heat stress and stormwater loads driven by warming trends, more intense rainfall, and continued growth of impervious surfaces. Pavements occupy a large share of the public right-of-way, so their material and structural design offers a scalable pathway for urban climate adaptation. Yet the literature on porous asphalt remains fragmented, with hydrological performance often assessed using infiltration or permeability metrics in isolation, while thermal studies frequently report surface cooling without consistently tracking the governing water budget or its persistence. To reconcile these disconnected strands, this review synthesizes a conceptual hydro-thermal balance framework in which runoff mitigation and heat moderation are treated as a coupled problem controlled by storage, drainage pathways, and evaporative demand. Within this framing, cooling is primarily water-limited: permeability enables wetting and redistribution, but the magnitude and duration of temperature reduction depend on how much water is retained near the surface and how long it remains available for evaporation, rather than on permeability alone. The review integrates the current understanding of mixture structure and pore connectivity, permeability–storage behavior, moisture availability and evaporation, and the operational factors that govern performance persistence. Laboratory and field evaluation approaches are summarized alongside modeling methods used to interpret coupled hydro-thermal responses under different climates. Practical constraints—including clogging, maintenance requirements, and durability risks under repeated moisture–temperature cycling—are discussed as mechanisms that can progressively suppress both infiltration and water availability, undermining long-term function without performance-based specifications and life-cycle planning. Finally, design and policy implications are outlined for integrating porous asphalt into coordinated heat-and-stormwater strategies, and research priorities are identified to advance standardization, long-term monitoring, and coupled hydro-thermal–mechanical assessment. Full article

Mangroves are widely recognized as climate-relevant ecosystems, yet the extent to which soils are incorporated into climate mitigation research remains unclear. This study conducted a hierarchical bibliometric analysis (Scopus, 1950–2025) across five progressively restrictive search levels, moving from general mangrove research (Level 1) to studies incorporating climate change (Level 2), mitigation (Level 3), and soil-related processes (Levels 4 and 5). Results show that although 30,084 articles addressed mangrove broadly, only 25 articles (0.08%) explicitly linked mangrove soils to climate change mitigation, with the majority published after the emergence of the blue carbon concept in 2009. Keyword evolution and network analyses indicate a shift from descriptive ecological themes (e.g., distribution and vegetation dynamics) toward carbon-related and soil-associated processes (e.g., blue carbon, carbon sequestration, soil organic carbon), particularly after the late 2000s, accompanied by gradual diversification into Environmental Science, Earth and Planetary Sciences, and chemistry-related domains associated with soil processes and mitigation mechanisms. Despite these conceptual advances, keyword analysis shows that mitigation-related studies (Levels 3 and 5) remain largely focused on terms such as “mangroves” (336 occurrences), “carbon sequestration” (187), “organic carbon” (82), and “carbon storage” (62), with limited representation of mechanistic soil processes (e.g., redox-processes, soil greenhouse gas fluxes, carbon–iron–sulfur coupled dynamic) in climate mitigation frameworks. Expanding this integration represents a key scientific frontier for improving the robustness and scalability of mangrove-based climate mitigation strategies. Full article

As automated vehicles move from controlled environments to unpredictable real-world roads, scenario-based testing has become the cornerstone of safety validation. In recent years, substantial progress has been made in scenario representation standards and generation methodologies. However, integrating scenario generation, standards-aligned packaging, validation, curation, and structured querying into a reproducible end-to-end lifecycle remains challenging in practice. This work presents a reproducible reference architecture for Scenario Databases (SCDBs) that treats scenario collections as lifecycle-governed data systems rather than static repositories. The proposed architecture unifies the scenario lifecycle within a single workflow. It integrates scenario generation and ingestion, validation and curation, immutable storage, semantic and value-based querying, and reproducible export. Scenario semantics are represented using ASAM OpenX formats (OpenDRIVE and OpenSCENARIO), together with ASAM OpenLABEL metadata, enabling standards-aligned interoperability. Querying is performed over categorical and value-carrying metadata without requiring inspection of raw scenario artifacts at query time. The reference implementation is deployed using Infrastructure-as-Code, supporting reproducibility and low operational overhead. Execution-based metric enrichment is supported as an optional extension, enabling scenarios to be augmented with execution-derived measurements and trace metadata. The contribution is not a centralized database, but a reference architecture and deployment blueprint that supports interoperable and federated scenario ecosystems. By framing SCDBs as reproducible lifecycle systems, this work supports scalable scenario reuse and more transparent safety validation workflows. Full article

Sodium-ion batteries (SIBs) have emerged as one of the most promising alternatives to lithium-ion systems, driven by the abundance and low cost of sodium resources as well as the urgent demand for sustainable large-scale energy storage. In recent years, remarkable advances have been achieved in electrode materials, electrolytes, and interfacial engineering, which have significantly improved the electrochemical performance of SIBs. Hard carbons and alloy-type anodes have shown encouraging progress in balancing capacity and stability, while layered oxides, polyanionic compounds, and Prussian blue analogues are leading candidates for cathodes due to their structural diversity and tunable redox properties. Concurrently, the development of advanced liquid and solid electrolytes, together with strategies to control the solid–electrolyte interphase (SEI) and cathode–electrolyte interphase (CEI), is enhancing safety and long-term cycling. Despite these achievements, critical challenges remain, including limited energy density, volumetric expansion in alloying anodes, interfacial instability, and scalability issues. This review provides a comprehensive overview of the fundamental principles, recent material innovations, and failure mechanisms of SIBs, and highlights the current status of industrial progress led by companies such as Faradion, HiNa Battery, CATL, and Tiamat. Finally, future perspectives are discussed, emphasizing the role of sodium-ion technology in grid-scale storage, renewable energy integration, and sustainable battery recycling. By bridging academic advances and industrial development, this article outlines the roadmap toward the commercialization of sodium-ion batteries. Full article

The rational design of electrode materials with tailored composition and architecture is crucial for advancing high-capability electrochemical energy storage systems. This study reports that gadolinium-modified NiFe₂O₄ nanosheet electrodes were effectively synthesized on nickel foam via a hydrothermal approach followed by thermal treatment. A series of compositions (NiFe, NiFe–Gd1, NiFe–Gd2, and NiFe–Gd3) were prepared to systematically examine the effect of Gd incorporation on structural features and electrochemical properties. X-ray diffraction (XRD) analysis confirmed the formation of the cubic spinel NiFe₂O₄ phase without detectable secondary phases, indicating that the crystal structure remains intact after Gd introduction. X-ray photoelectron spectroscopy (XPS) further verified the presence of Ni²⁺, Fe³⁺, and Gd³⁺ species within the lattice environment. Morphological analysis using field-emission scanning electron microscopy (FESEM) revealed a nanosheet-based architecture, where the optimized NiFe–Gd2 electrode exhibited a porous and interconnected nanosheet framework with abundant exposed edges. This structural configuration improves electrolyte penetration and facilitates efficient ion transport during charge storage processes. Electrochemical measurements demonstrated that the NiFe–Gd2 electrode delivers an areal capacitance of 5235 mF cm⁻² at 10 mA cm⁻², along with improved reaction kinetics and low internal resistance. An asymmetric supercapacitor assembled using NiFe–Gd2 as the positive electrode and activated carbon as the negative electrode operated stably within a 0–1.5 V potential window, achieving an energy density of 0.136 mWh cm⁻² and a power density of 3.14 mW cm⁻², while retaining 86.55% of its initial capacitance after 7000 cycles. These results highlight the potential of rare-earth engineering as a viable strategy for designing advanced spinel ferrite electrodes and pave the way for the development of high-performance, durable, and scalable supercapacitor systems for practical energy storage applications. Full article