Lin Changzheng, Li Weijia, Chen Hao, Feng Jiangtao, Zhu Mengyuan, Shi Jinwen, Li Mingtao, Hou Bo, Wang Zhenyu, Chen Xin, Liu Jia, Yan Wei
Department of Environmental Science & Engineering, School of Energy and Power Engineering, Xi'an Jiaotong University, 28 Xianning West Road, Xi'an, 710049, China.
International Research Center for Renewable Energy (IRCRE), State Key Laboratory of Multiphase Flow in Power Engineering, School of Energy and Power Engineering, Xi'an Jiaotong University, 28 Xianning West Road, Xi'an, 710049, China.
Adv Sci (Weinh). 2025 Jul;12(26):e2502262. doi: 10.1002/advs.202502262. Epub 2025 Apr 15.
Ammonia is essential across industry, agriculture, and as a future carbon-free energy carrier. Electrocatalytic nitrate reduction (NitRR) offers a sustainable path for removing nitrate contaminants from wastewater and groundwater while using abundant nitrate ions as nitrogen sources under eco-friendly conditions. However, the NitRR pathway, which involves sequential reactions, poses challenges in synchronizing the rate of nitrate-to-nitrite conversion with the subsequent reduction of nitrite to ammonia, particularly as the initial reduction step is rate-limiting. This study presents a CoNi layered double hydroxide (LDH) approach to finely control hydrogen radical (*H) supply, paired with Cu/CuO redox coupling, to achieve optimal rate matching. CoNi LDH is engineered with various anion intercalations (NO , Cl, SO , MoO , WO ) to regulate *H capacity. By integrating Cu/CuO and CoNi LDH, tandem kinetic descriptors, including a volcano curve, are employed to predict rate constants, facilitating ideal kinetic matching for efficient ammonia synthesis. The optimized MoO-CoNi LDH/CuO NW/CF electrode demonstrated exceptional performance, achieving a 99.78% Faraday efficiency, a yield of 1.12 mmol cm h at -0.2 V vs. RHE, and robust 14-h stability. The model descriptors effectively elucidated the kinetic pathway, linking reaction rates and factors impacting ammonia production.
氨在工业、农业以及作为未来无碳能源载体方面都至关重要。电催化硝酸盐还原(NitRR)为从废水和地下水中去除硝酸盐污染物提供了一条可持续途径,同时在环保条件下将丰富的硝酸根离子用作氮源。然而,NitRR途径涉及一系列连续反应,在使硝酸盐向亚硝酸盐的转化率与随后亚硝酸盐还原为氨的速率同步方面存在挑战,特别是因为初始还原步骤是限速步骤。本研究提出了一种钴镍层状双氢氧化物(LDH)方法,通过精细控制氢自由基(H)供应,并与铜/氧化铜氧化还原偶联相结合,以实现最佳的速率匹配。通过各种阴离子插层(NO 、Cl、SO 、MoO 、WO )对钴镍LDH进行工程设计,以调节H容量。通过整合铜/氧化铜和钴镍LDH,采用包括火山曲线在内的串联动力学描述符来预测速率常数,有助于实现高效氨合成的理想动力学匹配。优化后的MoO - 钴镍LDH/氧化铜纳米线/碳纤维电极表现出卓越性能,在相对于可逆氢电极(RHE)为 -0.2 V时,法拉第效率达到99.78%,产率为1.12 mmol cm h,并且具有14小时的稳健稳定性。该模型描述符有效地阐明了动力学途径,将反应速率与影响氨生成的因素联系起来。