The ionomer coating structure on the catalyst surface critically determines the performance and durability of proton exchange membrane fuel cell (PEMFC) electrodes. Conventional fabrication often yields non‑uniform overlayers, increasing oxygen transport resistance, blocking active sites, and accelerating interfacial degradation. Advancing from random deposition to controlled coating structure is therefore a key objective. Ionomer adsorption is governed by its intrinsic properties, support characteristics, and catalyst morphology. In liquid environments, adsorption involves backbone hydrophobicity and side‑chain chemical bonding, with the latter dominating the final configuration. Solvent composition, particularly water content, modulates dispersion, and adsorption behavior. Optimization strategies focus on two main approaches: suppressing detrimental adsorption (e.g., by tailoring support porosity or using additives like ionic liquids) and constructing favorable mass‑transport pathways (e.g., by designing ionomers with cyclic/porous structures or engineering porous overlayers). Rational design of ionomer properties, interface regulation, and integration of these strategies can synergistically enhance proton conduction, mass transport, and stability, laying a foundation for high‑performance, durable fuel cell electrodes.

Steam electrolysers are a key technology for high-efficiency hydrogen production, offering superior thermodynamic performance compared with liquid-water electrolysis when operated at elevated temperatures. Despite these advantages, their wider deployment remains constrained by manufacturing complexity, component cost, and stringent processing requirements for ceramic membranes, electrodes, and interconnect-related components. This mini-review provides a comprehensive assessment of fabrication strategies relevant to steam-electrolyser manufacturing, encompassing compression-based forming, colloidal processing, fibre- and ink-based deposition techniques, as well as physical vapour deposition. Particular attention is given to how electrode architecture, cell geometry, and interfacial compatibility influence viable processing routes and manufacturability, rather than device operation at the single-cell level. Emphasis is placed on solid-state ceramic membranes and electrodes based on gadolinium-doped ceria (GDC) and yttria-stabilised zirconia (YSZ), which remain central to current and emerging steam-electrolyser technologies. Because most scalable fabrication routes ultimately depend on controlled densification and microstructural optimisation of polycrystalline ceramics, the review critically examines low-temperature sintering mechanisms, including grain-boundary-activated densification, transient liquid-phase formation, and nano-powder engineering. Recent advances in single- and multi-element sintering aids are discussed, with co-doping strategies shown to reduce densification temperatures by several hundred degrees while preserving ionic conductivity and long-term stability. A quantitative techno-environmental analysis further demonstrates that reducing electrolyte sintering temperatures from 1,400℃ to 650℃–950℃ can lower energy consumption by 30%–55%, reduce fabrication costs, and significantly decrease CO₂ emissions associated with manufacturing. By integrating fabrication methodologies with low-temperature processing chemistry in a steam-electrolysis–relevant context, this review highlights the key materials–process interactions governing manufacturability and identifies future research directions for scalable, durable, and economically competitive steam-electrolyser technologies.

Benefiting from the synergistic integration of aerial agility and terrestrial endurance, aerial-ground amphibious platforms can effectively traverse unstructured environments, demonstrating considerable potential for emergency response and reconnaissance applications. However, most existing systems adopt a decoupled configuration, utilizing independent propulsion units for ground and aerial modes. Although this approach enables dual-domain operation, the duplicated actuators and transmission chains inevitably incur structural redundancy and additional mass, which reduce overall system integration and payload efficiency. To address these limitations, this paper proposes a novel aerial-ground amphibious platform based on an integrated wheel-propeller structure. In the proposed design, a single electric motor actuates both the wheel and ducted propeller through a shared powertrain, where an electromagnetic clutch selectively engages the transmission path to switch between terrestrial locomotion and aerial propulsion. Building upon this mechanism, an amphibious platform configuration incorporating eight-wheel-propeller units is developed. By establishing a dynamic simulation model of the platform, the dynamic response characteristics during the drive-to-fly transition were investigated. The results demonstrate the feasibility of the operational principle underpinning the platform. The research provides a feasible technical approach for lightweight and highly integrated aerial-ground platforms, laying the foundation for future experimental implementation.

To enhance the energy and travel efficiency for electric bus routes, a cloud-supported efficient eco-driving control strategy based on a vehicle-cloud collaborative layered architecture is proposed in this paper. At the cloud layer, an efficient velocity planning model balancing energy consumption and travel time is constructed based on route map information and solved using the Dynamic Programming (DP) algorithm. At the vehicle layer, a safety-prioritized arbitration strategy is designed to switch decisions between the cloud-planned optimal velocity and the adaptive cruise control (ACC) car-following velocity to ensure driving safety. An experimental platform based on the vehicle-road-cloud collaborative architecture was constructed, and validation was conducted under real-world intercity road conditions. Experimental results demonstrate that the system exhibits favorable communication, real-time performance, and reliability. Compared with the ACC strategy, the proposed strategy increased average velocity by approximately 4.21% and reduced energy consumption by about 1.12% while ensuring operational punctuality.

Large Language Models (LLMs) are propelling Intelligent Transportation Systems (ITS) toward an agentic paradigm, enabling intent understanding, constraint-aware reasoning, and decision-making. This paper focuses on the Group Meeting Location Recommendation (GMLR) problem, which aims to jointly optimize group preferences and mobility costs under real-world transportation network constraints. However, existing methods often overlook real-world geographic context, fail to jointly coordinate group preferences and spatial feasibility, and rely heavily on static, structured data to model group preferences, limiting their applicability to complex and dynamic group scenarios. We propose MEET-LLM, an agentic, LLM-driven, closed-loop framework for GMLR. MEET-LLM comprises three modules: (1) a Preference Profiling Module that constructs structured semantic representations of users and merchants by using LLMs to extract multilevel preferences from natural language reviews, overcoming the limitations of static aggregation; (2) a Spatial Optimization Module that introduces a mobility cost (MC) metric to evaluate group-level travel convenience and account for user-specific travel constraints; and (3) a Chain-of-Thought (CoT) Reasoning Module that coordinates group preferences and mobility costs through multi-turn LLM reasoning to generate interpretable, executable recommendations with map-based navigation guidance. MEET-LLM jointly coordinates group preferences and spatial feasibility, bridging natural language reasoning with real-world mobility execution. It enables dynamic, structured, and action-ready recommendations in real urban environments, highlighting the potential of LLMs as the cognitive core for agentic mobility coordination in ITS.

Triboelectric nanogenerators (TENGs) are constrained by the inherent material limitations and systemic performance bottlenecks. Conventional polydimethylsiloxane (PDMS) triboelectric layers offer limited control over surface electronegativity, interfacial polarization, and charge retention, hindering stable self-powered operation. To address these challenges, we report a flexible triboelectric system based on a fluorinated metal-organic framework (MOF), UiO-66-4F, which integrates energy harvesting and storage via coordinated design across materials, devices, and circuits. The triboelectric nanogenerator (UF-TENG), fabricated from a UiO-66-4F@PDMS composite layer with 10 wt% filler, generates an open-circuit voltage of 180 V and a short-circuit current of 11 μA, representing 3- and 1.83-fold enhancements over pristine PDMS. The UF-TENG also exhibits stable output over 10,000 cycles under 20% tensile strain. The flexible supercapacitor incorporating 20 wt% UiO-66-4F/LiCl exhibits lower high-frequency impedance and near-ideal low-frequency phase angle, and has typical double-layer capacitance characteristics for efficient charge management. The output-voltage fluctuation of the integrated device is 60% lower than that of a separate TENG, maintaining a stable output for 4,500 s, effectively improving power storage and output stability. This work demonstrates that functional group engineering in MOFs not only enhances triboelectric charge generation but also facilitates efficient charge management, offering a compact, durable, and integrable power solution for wearable electronics.

Developing low-carbon transportation is crucial for ecological conservation and energy security. This paper addresses eco-driving challenges in connected vehicles by proposing a model-guided deep reinforcement learning framework to enhance the practicality and reliability of eco-driving strategies. Integrating model-guided and data-driven approaches, the method first designs a speed guidance algorithm based on the Intelligent Driver Model (IDM), which flexibly adjusts driving conservatism through parameter tuning. Subsequently, a reinforcement learning framework based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) is proposed. The reference speed generated by the IDM is embedded into the state space as prior knowledge to guide the agent toward rapid convergence during training. This strategy significantly optimizes vehicle energy consumption while strictly ensuring driving safety and having minimal impact on travel efficiency. Experimental results demonstrate that compared to baseline methods and traditional deep reinforcement learning strategies, the proposed approach achieves superior performance in energy savings, driving smoothness, and traffic continuity. It achieves an average energy savings rate of 7.96% with only a 0.44% increase in travel time, validating its effectiveness and practicality for eco-driving tasks.