School of Chemistry and Chemical Engineering, Guangzhou University, Guangzhou 510006, Guangdong, P. R. China.
Phys Chem Chem Phys. 2023 Feb 15;25(7):5827-5835. doi: 10.1039/d2cp04635k.
The electrocatalytic nitrogen reduction reaction (eNRR) has been attracting intensive scientific attention as a potential alternative to the industrial Haber-Bosch process for ammonia production. Although many materials have been investigated, optimal catalysts for the reaction remain to be found. In this work, we performed the theoretical screening of 3d-5d transition metal doped anatase TiO for the eNRR. The most favorable doping site of each transition metal on the (101) surface was located. We found that the doping of transition metals promotes the formation of oxygen vacancies which are beneficial for the reaction. The scaling relations between the energies of the key intermediates were investigated. Using a machine learning algorithm (SVM), we identified two adsorption modes for the end-on adsorbed *HNN, which exhibited different scaling relations with *NH. From a two-step process, we screened out several candidates, among which Au and Ta were proposed to be the most efficient dopants. Electronic structure analysis reveals that they can efficiently lower the energy of the intermediates. These results should be helpful for the design of more efficient TiO-based catalysts for the eNRR.
电催化氮气还原反应 (eNRR) 作为工业哈伯-博世氨生产工艺的潜在替代方法，引起了科学界的广泛关注。尽管已经研究了许多材料，但仍需要找到该反应的最佳催化剂。在这项工作中，我们对 3d-5d 过渡金属掺杂锐钛矿 TiO2 进行了理论筛选，以用于 eNRR。确定了每种过渡金属在 (101) 表面上的最有利掺杂位置。我们发现，过渡金属的掺杂促进了氧空位的形成，这有利于反应的进行。研究了关键中间体能量之间的标度关系。使用机器学习算法 (SVM)，我们确定了两种端接吸附 *HNN 的吸附模式，它们与 *NH 表现出不同的标度关系。通过两步过程，我们筛选出了几个候选物，其中 Au 和 Ta 被认为是最有效的掺杂剂。电子结构分析表明，它们可以有效地降低中间体的能量。这些结果对于设计更高效的基于 TiO2 的 eNRR 催化剂应该是有帮助的。
