Scanning Electrochemical Microscopy (SECM) for Accelerated Screening of CO<sub>2 </sub>Electroreduction to Formate Catalyst
Bibliographic record
Abstract
CO2 electroreduction to formate (CO2RF) is the key reaction in many renewable energy storage schemes, sequestering CO2 into a formate solution at ambient condition1. However, CO2RF catalysts are still afflicted by poor selectivity and low activity2,3. Compounding the issue, CO2RF catalyst screening is still dominated by slow, one catalyst sample at a time experimental method. Theoretical catalyst screening is also affected, with increasingly faster computational models waiting for sluggish empirical validation4. Our objective is to develop scanning electrochemical microscopy (SECM) as a high-throughput CO2RF catalyst screening tool using multi-catalyst arrays. Our SECM method was tested on arrays of Sn/SnOx catalysts (x = 1,2), evaluating the effectiveness of pre-electroreduction treatment on final activity. Theoretically, SnO/SnO2 mixtures are the most promising catalysts for CO2RF , but in practice, they are thermodynamically unstable at potentials at which aqueous CO2 reduction takes place5,6. Sn/SnOx catalyst pre-electroreduction at −1.25 VAg/AgCl yielded an agglomerated surface with increased SnOx composition and CO2RF activity. Conversely, pre-electroreduction at −3 VAg/AgCl deposited a film of nanoparticles (≈70μm) on the surface but decreased CO2RF activity and SnOx composition. In a single SECM experiment, the CO2RF activity of unreduced Sn/SnOx surface and of the two reduced surfaces was determined, demonstrating the technique’s potential for accelerating CO2RF catalyst screening. We will also discuss the advantages and challenges of using SECM for high-throughput CO2 reduction catalyst study. S. Fukuzumi, Joule, 1, 689–738 (2017) . C. E. Moore and E. L. Gyenge, ChemSusChem, 10, 3512–3519 (2017). B. Khezri, A. C. Fisher, and M. Pumera, J. Mater. Chem. A, 5, 8230–8246 (2017) . M. Zhong et al., Nature, 581, 178–183 (2020). A. Dutta, A. Kuzume, M. Rahaman, S. Vesztergom, and P. Broekmann, ACS Catal., 5, 7498–7502 (2015). S. Wang, J. Wang, and H. Xin, Green Energy Environ., 2, 168–171 (2017). Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".