Qiuwan Shen

In the current situation of carbon reduction in the shipping industry, hydrogen as a future energy source is the key to hydrogen-powered ships. Onboard methanol steam reforming (MSR) hydrogen production technology is of great significance in solving the hydrogen safety issues of hydrogen-powered ships. The key to this technology is to obtain MSR catalysts with lower CO selectivity and higher hydrogen production performance for use as hydrogen sources in high-temperature proton-exchange membrane fuel cells (HT-PEMFCs). A new Cu_0.75Mg_0.25Ga_0.7Fe_1.3O₄ spinel catalyst was synthesized and subsequently dispersed at a nominal loading of 15 wt% onto three distinct oxide supports: CeO₂, ZrO₂, and Cr₂O₃. The synthesized samples were characterized, and their catalytic behavior in MSR was evaluated in a fixed-bed reactor. The results establish that the loading procedure leaves the bulk spinel structure intact. Among the series, the CeO₂-supported specimen exhibits a higher degree of active-phase dispersion, a loosely packed and highly porous architecture, and more extensive interfacial contact between the spinel crystallites and the support. Relative to its ZrO₂- and Cr₂O₃-supported counterparts, Cu_0.75Mg_0.25Ga_0.7Fe_1.3O₄@CeO₂ delivers markedly superior overall catalytic performance. Under conditions of 320 °C, a water-to-methanol molar ratio of 3:1, and a liquid hourly space velocity (LHSV) of 15 h⁻¹, complete methanol conversion (100%) is achieved, together with a hydrogen production rate of 8.71 mmol·min⁻¹·gcat⁻¹ and CO selectivity approaching zero. The outstanding catalytic behavior may be attributed to the interface synergy between the spinel active center and CeO₂ support, particularly the synergistic effect of oxygen vacancies and Ce⁴⁺/Ce³⁺ redox shuttle, which facilitates substrate replacement while inhibiting CO production. This catalyst architecture provides a promising material platform for on-line methanol reforming integrated with fuel cell power systems on hydrogen-powered ships.

This study establishes a three‐dimensional, nonisothermal, two‐phase flow anion exchange membrane electrolyzer cell (AEMEC) numerical model that includes a microporous layer (MPL). The effects of flow field design, operating parameters, and key component structural parameters on the performance of electrolytic cells were studied. Results indicate that the serpentine flow channel, with its “piston flow” effect, can achieve more uniform species distribution and temperature field compared to parallel flow channels. It exhibits superior electrochemical performance at high operating voltage with a current density increase of approximately 7.8% at 2.3 V. Parameter analysis shows that increasing operating voltage and inlet water temperature can significantly improve gas production rate. For MPLs, there exists an optimal range of MPL parameters (porosity ε  = 0.5–0.6, thickness d = 25–35 µm) that can achieve the best balance between mass transfer resistance and conductivity. In addition, reducing the thickness of the AEM can effectively reduce the Ohmic loss. This study provides theoretical basis and data support for the design and optimization of key components of high‐performance AEMEC.

Perovskite materials have emerged as promising candidates for gas sensing owing to their tunable structures, adjustable compositions, rich defect chemistry, and efficient charge transport properties. These characteristics enable the effective regulation of active sites, oxygen vacancies, heterointerfaces, and band alignment, thereby enhancing gas adsorption and sensing performance. This review summarizes recent advances in perovskite-based gas sensors, focusing on synthesis and fabrication strategies, structural engineering, sensing mechanisms, theoretical simulations, and intelligent sensing applications. The effects of doping, defect engineering, morphology control, and heterojunction construction on sensitivity, selectivity, response and recovery behavior, humidity tolerance, and stability are discussed. In addition, the roles of first-principles calculations and artificial intelligence in elucidating sensing mechanisms, identifying gases, predicting concentrations, and suppressing interference are highlighted. Finally, the remaining challenges and future perspectives are discussed to guide the development of stable, low-power, selective, and intelligent perovskite-based sensing systems.

Chemical looping steam methane reforming (CL‐SMR) is a promising syngas and hydrogen production technology that could achieve efficient methane conversion through the recycling of oxygen carrier. Perovskite‐type oxygen carrier LaFeO ₃ has high oxygen transport capability due to the abundant oxygen vacancies in structure, but the reaction capacity is relatively low. In this study, La _{1− x} Sr _x FeO ₃ /CeO ₂ perovskite with different Sr doping ratios ( x = 0, 0.2, 0.4, 0.6, 0.8) was prepared by the sol–gel method as the oxygen carrier of CL‐SMR. The characterization results indicated that the doping of Sr enhances the specific surface area and pore structure of the prepared oxygen carrier. Among them, La _0.4 Sr _0.6 FeO ₃ /CeO ₂ exhibited the best redox performance with the CH ₄ conversion rate of 98%, the CO selectivity of 95%, and the stable H ₂ /CO ratio of 2.1–2.2 at 850 °C. After 10 cycles, the CH ₄ conversion rate and CO selectivity were still kept above 86% and 93%. This study lays an experimental foundation for La _{1− x} Sr _x FeO ₃ /CeO ₂ as the oxygen carriers in CL‐SMR.

Skeletal kinetic mechanisms are essential for reducing the computational cost of ammonia combustion simulations while retaining the key chemical features governing ignition, flame propagation, and NO formation. This study extends the DRG-CSP-ANN reduction and optimization framework to ammonia combustion over a broader multi-condition parameter space, aiming to develop a compact skeletal mechanism applicable to different pressures, equivalence ratios, and temperatures. Sixteen detailed ammonia combustion mechanisms were first assessed against experimental data covering ignition delay time, laminar flame speed, and NOx species concentrations over wide ranges of pressure, temperature, equivalence ratio, and oxidizer composition. Based on the overall error evaluation, the detailed mechanism with the most balanced predictive performance was selected as the parent mechanism. The parent mechanism was then reduced using the Directed Relation Graph and Computational Singular Perturbation methods, yielding an initial skeletal mechanism, RA-Ori, with 20 species and 76 reactions. To compensate for the accuracy loss caused by mechanism reduction, an Artificial Neural Network surrogate was constructed to optimize the pre-exponential factors of selected sensitive reactions within their evaluated uncertainty ranges, leading to the final mechanism, RA-ANN. The validation results show that RA-ANN reasonably reproduces ignition delay times, laminar flame speeds, and NO concentrations under different ammonia combustion conditions. Quantitatively, RA-ANN reduces the overall error from 0.335 for RA-Ori to 0.206, corresponding to a 38.4% reduction, while maintaining the same compact size. Its overall error is close to that of the parent detailed mechanism and lower than that of several existing skeletal mechanisms considered in this work. These results demonstrate that the proposed DRG-CSP-ANN strategy can construct a compact ammonia skeletal mechanism that achieves a favorable balance between computational efficiency, predictive accuracy, and applicability over representative multi-condition ammonia combustion regimes.

With the accelerated transformation of green shipping and the advancement of ship electrification, lithium-ion batteries have become the core solution for ship propulsion due to their advantages of high energy density and zero emission. Efficient thermal management serves as a key technical support to ensure the safe and stable operation of batteries, extend their service life, and mitigate the risk of thermal runaway. Lithium-ion batteries accumulate heat during discharge, and pure phase change material (PCM) cooling systems are limited by low thermal conductivity, leading to excessive battery temperature rise and poor temperature uniformity. To address this problem, RT42 (a paraffin-based PCM with a melting temperature range of 311.15–316.15 K) was selected as the PCM in this study. The battery thermal management system (BTMS) coupling RT42 with a three-dimensional fin network structure was designed. Numerical simulations were conducted via ANSYS Fluent, and the enthalpy-porosity method was adopted to simulate the PCM phase change process. The effects of fin distribution, spacing and layer number on BTMS performance were systematically investigated and compared. Results show that the heat transfer process in the PCM can be significantly improved due to the three-dimensional fin network, and the battery maximum temperature can be reduced by 7.53 K compared with the pure PCM system. This study provides theoretical support for the design and optimization of high-efficiency BTMS.