Abstract
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/SnOx 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/SnOx 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.
Abstract
Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.
Scanning electrochemical microscopy screening of CO2 electroreduction activities and product selectivities of catalyst arrays