Multimodal Large Language Models (MLLMs) have achieved remarkable progress across a range of vision-language tasks and demonstrate strong potential for traffic accident understanding. However, existing MLLMs in this domain primarily focus on coarse-grained image-level or video-level comprehension and often struggle to handle fine-grained visual details or localized scene components, limiting their applicability in complex accident scenarios. To address these limitations, we propose SafePLUG, a novel framework that empowers MLLMs with both pixel-level understanding and temporal grounding for comprehensive traffic accident analysis. SafePLUG supports both arbitrary-shaped visual prompts for region-aware question answering and pixel-level segmentation based on language instructions, while also enabling the recognition of temporally anchored events in traffic accident scenarios. To advance the development of MLLMs for traffic accident understanding, we curate a new dataset, SafePLUG-Bench, which contains diverse multimodal question–answer pairs with detailed pixel-level annotations and temporal event boundaries across a wide range of accident scenarios. Experimental results show that SafePLUG achieves strong performance on multiple tasks, including region-based question answering, pixel-level segmentation, temporal event localization, and accident event understanding. These capabilities lay a foundation for a fine-grained understanding of complex traffic scenes, with the potential to improve driving safety and enhance situational awareness in smart transportation systems.

Solid-state batteries (SSBs) are widely regarded as promising candidates for next-generation energy storage systems due to their inherent safety and high energy density, making them ideally suited for transportation applications such as electric vehicles. While extensive efforts have been devoted to developing solid-state electrolytes (SSEs) with high ionic conductivity and broad electrochemical stability windows, the cycling life and power density of SSBs still fall short of commercial requirements. These limitations are primarily attributed to electrochemical and mechanical failures at the interfaces during battery operation. The overall performance of SSBs is not solely determined by the properties of the SSEs themselves, such as conductivity and stability, but is critically influenced by the interfacial compatibility between the SSEs and the electrodes (both cathode and anode), as well as the interparticle interfaces within the electrolyte. Therefore, advancing interfacial engineering and enhancing ion transport across these interfaces are essential for the continued development and practical deployment of SSBs technologies.

Manganese-based NASICON materials are promising cathodes for sodium-ion batteries owing to their open three-dimensional framework and high Na⁺ mobility. However, their cycling performance is hindered by Jahn–Teller distortions and anti-site defects that destabilize the lattice. Recent studies show that rational electronic structure and defect engineering can effectively address these challenges. High-valent cation doping, such as Ti⁴⁺ or Zr⁴⁺, reduces lattice strain and stabilizes Mn redox reactions, while controlled synthesis suppresses Mn–Na anti-site defects and enhances Na⁺ transport. Furthermore, partial anion substitution with fluorophosphate or silicate units strengthens Mn–O bonding and increases operating voltage. Integrating these strategies enables improved structural stability and long-term reversibility, offering a feasible pathway toward durable, high-energy sodium-ion batteries for large-scale energy storage.

Significant advancements have been made in metal-organic frameworks (MOFs) for proton conductivity in proton exchange membrane fuel cells; however, a comprehensive and rational design road map for these materials remains under explored, particularly regarding the quantitative contributions of internal structural factors (proton donor-acceptor types and spatial distances) and external environmental factors (temperature and humidity) to hydrogen-bond formation. In this study, the hydrogen-bonded network formation potential (Ф_H) is proposed based on these factors to establish structure-property relationship of MOFs via artificial neural network (ANN) model, by which a linear function relationship has been fitted to facilitate the rational design of MOFs, providing a simple assessment of proton conductivity performance in reverse. As a proof of concept, UiO-66-NH₂-SO₃H with varying group ratios was fabricated, and UiO-66-NH₂-SO₃H-1:5 exhibited favorable proton conductivity (0.08 S∙cm^–1) due to the favorable matching of Brönsted acid-base pairs, which facilitates the extensive formation of hydrogen-bonded networks. The proposed hydrogen-bonded network formation potential is expected to advance the design and synthesis of MOF-based proton-conductive materials, serving as a valuable index for the proton exchange membrane fuel cell research community.

Lithium titanate oxide (Li₄Ti₅O₁₂, LTO) anode-based batteries are widely recognized for their excellent safety characteristics and long cycle life. However, constant-voltage overcharging (CVOC), arising from cell-to-cell variations and delays in the response of a battery management system (BMS), can accelerate capacity degradation and increase the risk of thermal runaway. In this work, we systematically investigated the effects of CVOC at different voltages on the capacity retention and thermal safety of LTO-based batteries. By subjecting cells to overcharging cycling at elevated voltages, we elucidate the aging mechanisms and degradation pathways. Noninvasive diagnostics combined with postmortem analyses are employed to correlate the electrochemical behavior with the electrode morphology and composition under CVOC conditions. A critical voltage of 3.5 V is identified, beyond which severe degradation occurs. At 4.0 V, continuous CVOC induces the growth of a thick organic-rich solid-electrolyte interphase (SEI) and byproducts, leading to only 60% capacity retention compared with nearly 100% retention under 3.0 V CVOC or conventional constant-current/constant-voltage charging. Furthermore, the self-heating onset temperature decreases by 44.8 °C, indicating a significant reduction in thermal stability associated with high-voltage overcharge. These findings are corroborated by detailed postmortem characterization. Overall, this study demonstrates that the CVOC critically impacts both the electrochemical performance and thermal safety of LTO-based batteries, offering important insights for the design of high-stability, highly safe power sources for electric transportation systems.

In traffic scenarios, the dynamic characteristics and random behaviors of vehicles are the main reasons for the frequent occurrence of collision accidents. Traditional early warning systems restrict traffic safety due to low detection accuracy and poor tracking effect. Research has proposed a vehicle safety distance early warning system based on deep learning to enhance traffic safety. Innovations include: adopting the self-calibrated illumination (SCI) algorithm to overcome light interference; the YOLOv11 algorithm is improved by introducing a secondary innovative cross-domain feature attention (CDFA) mechanism, reconstructing the feature pyramid, and integrating knowledge distillation to balance detection accuracy and real-time performance. The DeepSORT algorithm is improved by applying group convolution to reduce the number of parameters and replacing Intersection over Union (IoU) with MPDIoU to enhance tracking accuracy. The distance between vehicles is calculated by using the monocular vision ranging method. The detection, tracking, and ranging modules are integrated into a vehicle safety distance early warning system. Experimental evaluation demonstrates a marked improvement in the system's performance. On the public dataset, the detection model exhibits a gain of 3.89% in mAP@0.5 and 2.76% in mAP@0.5:0.95, while the tracking model achieves a 0.9% increase in multiple object tracking accuracy (MOTA). Furthermore, real-world vehicle validation confirms that the synergistic operation of the detection and tracking modules effectively mitigates the miss rate, thereby substantiating a tangible enhancement in overall system safety.

Flexible sensors have attracted wide attention like never before in the fast-growing flexible electronics era, because they are easily adaptable to curved or soft surfaces based on their inherent flexibility and simultaneously detect multiple external stimuli. In the field of intelligent driving, they utilize novel conductive materials, including conductive polymers, carbon-based nanomaterials, and liquid metals, to construct multifunctional flexible sensor networks, thereby accomplishing seamless integration with curved vehicle surfaces and comprehensive status monitoring. The advantages are that achieving dynamic deformation adaptability through flexible materials and manufacturing, enhancing system redundancy/robustness/real-time performance through a sensor network deployment, and improving perception accuracy through multimodal fusion. Therefore, flexible sensors exhibit great potential in intelligent cockpit interaction, chassis obstacle detection, and vehicle health diagnostics. However, its large-scale commercialization still faces challenges in automotive-grade integration, weather resistance, data security, and power supply. Furthermore, flexible sensors are expected to integrate with AI models, lightweight architectures, and self-healing smart materials in the future, thereby advancing autonomous driving development, facilitating vehicle-road-cloud coordination, and revolutionizing mobility paradigms.

As countries accelerate efforts to meet carbon neutrality targets, carbon capture and storage (CCS) is gaining renewed attention as a critical mitigation strategy. This review provides a structured synthesis of global CCS policy evolution, highlighting how incentive mechanisms have shifted from generic subsidies toward hybrid approaches integrating carbon pricing, green finance, and legal risk-sharing. Building on this, the paper introduces a policy classification framework that categorizes instruments into market-based tools, command-and-control regulations, and institutional support, offering analytical coherence for future cross-country comparisons. Beyond the industrial and power sectors, the review foregrounds an often-overlooked CCS application: addressing residual emissions in hard-to-abate sectors such as transportation. We emphasize the strategic role of CO₂ transport infrastructure–including pipelines, shipping routes, and port retrofitting–and introduce a dedicated section analyzing its cost-leveraging effects, infrastructure integration models, and life-cycle trade-offs. By embedding a life-cycle assessment (LCA) perspective, we demonstrate how different transport modes shape both economic and carbon efficiency. Drawing on case studies from the U.S., EU, and China, we distill key governance lessons and identify the enabling conditions for scaling up CCS deployment. The paper also highlights governance gaps related to cross-border infrastructure coordination, carbon market integration, and just siting. Overall, this review contributes to literature on CCS governance and infrastructure transitions by offering a replicable analytical structure, expanding the scope beyond capture technology, and linking fragmented insights into an integrated policy roadmap for large-scale CCS commercialization.

Two-dimensional (2D) magnetic semiconductors have emerged as promising materials for next-generation nanoelectronic and spintronic devices, owing to their unique coupling of electronic and magnetic properties. CrSBr, a quasi-2D magnetic semiconductor, offers a versatile platform for exploring coupled electronic and magnetic phenomena in low-dimensional systems. Here, we systematically investigate the surface work function of CrSBr nanosheets using scanning Kelvin probe microscopy (SKPM), revealing a nonlinear dependence on thickness and a thermally tunable, reversible behavior. Furthermore, a CrSBr-based 2D field-effect transistor (FET) demonstrates that such thermal modulation of the work function effectively alters the Schottky barrier height, directly impacting charge injection. Density functional theory (DFT) calculations reveal the layer- and magnetism-dependent nature of the work function in few-layer CrSBr. This work highlights the importance of work function engineering in CrSBr and provides a foundation for its application in magnetoelectric coupling, spintronic devices, and van der Waals electronics.

Aqueous zinc-ion batteries (AZIBs) have attracted increasing interest due to their intrinsic safety and low cost, yet their energy density remains limited by the lack of suitable cathode materials. Covalent organic frameworks (COFs), with tunable porosity, structural diversity, and redox-site designability, have emerged as promising AZIBs cathodes. The integration of multiple redox-active sites, such as carbonyl (C=O) and imine (C=N) groups, enables stepwise multi-electron transfer, offering a pathway to higher capacity and voltage output. This review systematically categorizes COFs into n-type, p-type, and bipolar systems based on redox-active moieties and analyzes their charge storage behaviors through cyclic voltammetry (CV). We focus on how the spatial arrangement and electronic nature of C=O/C=N sites influence one-, two-, or three-step electron transfer, as well as Zn²⁺ and H⁺ co-insertion mechanisms. Electrochemical performances, redox kinetics, molecular structure, and electrolyte adaptability are also discussed in detail. By elucidating the structure-mechanism relationships of redox-active COFs, this review highlights molecular design principles that enhance redox-site utilization and energy density. The insights provided herein aim to guide the development of next-generation, high-performance organic cathodes for multi-electron AZIBs.