The electroreduction of CO₂ is one of the most investigated reactions and involves testing a large number and variety of catalysts. The majority of experimental electrocatalysis studies use conventional one-sample-at-a-time methods without providing spatially resolved catalytic activity information. Herein, we present the application of scanning electrochemical microscopy (SECM) for simultaneous screening of different catalysts forming an array. We demonstrate the potential of this method for electrocatalytic assessment of an array consisting of three Sn/SnO_x catalysts for CO₂ reduction to formate (CO2RF). Simultaneous SECM scans with fast scan (1 V s⁻¹) cyclic voltammetry detection of products (HCOO⁻, CO and H₂) at the Pt ultramicroelectrode tip were performed. We were able to consistently distinguish the electrocatalytic activities of the three compositionally and morphologically different Sn/SnO_x catalysts. Further development of this technique for larger catalyst arrays and matrices coupled with machine learning based algorithms could greatly accelerate the CO₂ electroreduction catalyst discovery.

CO ₂的电还原是研究最多的反应之一，涉及测试大量和多种催化剂。大多数实验性电催化研究使用传统的一次一个样品的方法，而不提供空间分辨的催化活性信息。在此，我们介绍了扫描电化学显微镜 (SECM) 在同时筛选形成阵列的不同催化剂方面的应用。我们展示了这种方法对由三种 Sn/SnO _x催化剂组成的用于将 CO ₂还原为甲酸盐 (CO2RF) 的阵列进行电催化评估的潜力。同步 SECM 扫描与快速扫描 (1 V s ^-1 ) 循环伏安法检测产物 (HCOO ^-, CO 和 H ₂ ) 在 Pt 超微电极尖端进行。我们能够始终如一地区分三种成分和形态不同的 Sn/SnO _x催化剂的电催化活性。进一步开发这种用于更大催化剂阵列和矩阵的技术，再加上基于机器学习的算法，可以大大加快 CO ₂电还原催化剂的发现。