Journal of Fuel Chemistry and Technology•2026•DOI: 10.1016/S1872-5813(25)60625-6
The electrocatalytic reduction of nitric oxide to ammonia (NORR) is a key green energy conversion technology. Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield (YNH3) and Faradaic efficiency (FNH3). Conventional experimental screening methods are resource- and time-intensive. Here, machine learning combined with SHAP feature analysis was employed to establish a stacked ensemble model integrating multiple algorithms, enabling systematic investigation of key descriptors governing NORR performance based on an experimental dataset. Evaluation of eight model algorithms revealed that the Stacked-SVR model achieved an R² of 0.9223 and RMSE of 0.0608 for predicting YNH3 on the test set, while the Stacked-RF model achieved an R² of 0.9042 and RMSE of 0.0900 for predicting FNH3. The stacked ensemble model integrates strengths of individual algorithms, demonstrating strong prediction performance while avoiding overfitting. SHAP analysis revealed that Cu content in catalyst composition has the most significant impact on catalytic performance. Moreover, the combination of wet chemical reduction synthesis, carbon fiber (CF) conductive substrate, and HCl electrolyte is more favorable for enhancing catalytic activity. Additionally, moderately lowering working potential, controlling electrolyte volume at low-to-medium levels, reducing catalyst loading, and increasing electrolyte concentration synergistically enhance both YNH3 and FNH3.
SCIENCE CHINA Materials•2025•DOI: 10.1007/s40843-025-3374-x
Industrial nitric acid production relies on the Haber-Bosch process for ammonia synthesis followed by the Ostwald process for oxidation, consuming vast fossil energy and emitting substantial greenhouse gases. Direct photocatalytic conversion of dinitrogen (N2), oxygen (O2), and water (H2O) into nitric acid (HNO3) under ambient conditions offers a sustainable alternative. This review critically examines recent advances in photocatalytic nitrogen oxidation (NOF), focusing on catalyst design, mechanistic insights into N2 activation, and performance metrics. The NOF pathway requires only 4 electrons versus a minimum of 6 for nitrogen reduction (NRF), lowering reaction energy barriers and potentially enhancing efficiency. Key challenges include the extreme inertness of the N≡N triple bond (bond dissociation energy 940.95 kJ mol−1), competing oxygen evolution reaction (OER), and the need for precise control over product selectivity. We analyze state-of-the-art photocatalysts, including tungsten oxide, bismuth oxychloride, and titania-based systems, and discuss strategies such as vacancy engineering, heterojunction construction, and co-catalyst loading. Performance benchmarks from recent studies reveal nitrate yields ranging from micromolar to millimolar levels, with apparent quantum efficiencies (AQE) often below 1% under visible light. The review identifies critical gaps in mechanistic understanding, particularly regarding the role of oxygen vacancies and reactive oxygen species, and proposes standardized testing protocols to enable meaningful comparison. We conclude that while NOF holds promise for decentralized fertilizer production, substantial improvements in catalyst stability, selectivity, and solar-to-chemical conversion efficiency are required for practical implementation.