Electrocatalysis is a promising approach to convert waste nitrate to ammonia and help close the nitrogen cycle. This renewably powered ammonia production process sources hydrogen from water (as opposed to methane in the thermal Haber–Bosch process) but requires a delicate balance between a catalyst’s activity for the hydrogen evolution reaction (HER) and the nitrate reduction reaction (NO₃RR), influencing the Faradaic efficiency (FE) and selectivity to ammonia/ammonium over other nitrogen-containing products. We measure ammonium FEs ranging from 3.6 ± 6.6% (on Ag) to 93.7 ± 0.9% (on Co) across a range of transition metals (TMs; Ti, Fe, Co, Ni, Ni_0.68Cu_0.32, Cu, and Ag) in buffered neutral media. To better understand these competing reaction kinetics, we develop a microkinetic model that captures the voltage-dependent nitrate rate order and illustrates its origin as competitive adsorption between nitrate and hydrogen adatoms (H*). NO₃RR FE can be described via competition for electrons with the HER, decreasing sharply for TMs with a high work function and a correspondingly high HER activity (e.g., Ni). Ammonium selectivity nominally increases as the TM d-band center energy (E_d) approaches and overcomes the Fermi level (E_F), but is exceptionally high for Co compared to materials with similar E_d. Density functional theory (DFT) calculations indicate Co maximizes ammonium selectivity via (1) strong nitrite binding enabling subsequent reduction and (2) promotion of nitric oxide dissociation, leading to selective reduction of the nitrogen adatom (N*) to ammonium.

中文翻译：

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电子结构在硝酸盐还原成铵中的作用：一个周期性的旅程

电催化是将废硝酸盐转化为氨并帮助关闭氮循环的有前途的方法。这种可再生动力的氨生产工艺从水中获取氢气（与热哈伯-博世工艺中的甲烷相反），但需要在析氢反应 (HER) 和硝酸盐还原反应 (NO ₃ RR)的催化剂活性之间取得微妙的平衡，影响法拉第效率 (FE) 和对氨/铵的选择性，而不是其他含氮产品。我们在一系列过渡金属（TM；Ti、Fe、Co、Ni、Ni _0.68 Cu _0.32、Cu 和 Ag）在缓冲的中性介质中。为了更好地理解这些竞争反应动力学，我们开发了一个微动力学模型，该模型捕获了电压依赖性硝酸盐速率顺序，并将其起源说明为硝酸盐和氢吸附原子 (H*) 之间的竞争吸附。NO ₃ RR FE 可以通过与 HER 竞争电子来描述，对于具有高功函数和相应高 HER 活性（例如，Ni）的 TM 急剧下降。随着 TM d 带中心能量 ( E _d ) 接近并克服费米能级 ( E _{F )，铵选择性名义上增加，但与具有相似}E _d的材料相比，Co 的选择性异常高. 密度泛函理论 (DFT) 计算表明，Co 通过 (1) 强亚硝酸盐结合实现随后的还原和 (2) 促进一氧化氮解离，导致氮吸附原子 (N*) 选择性还原为铵，从而最大限度地提高铵的选择性。