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.

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.

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.

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.