Scanning Electrochemical Microscopy (SECM) for High-Throughput Screening of Tin Oxide Derived Catalyst Arrays for CO<sub>2</sub> Electro-Reduction to Formate
Bibliographic record
Abstract
Formate produced from the electro-reduction of CO2 has been proposed as a safe liquid alternative to hydrogen gas in renewable energy storage schemes1 . However, catalysts for electro-reduction of CO2 to formate reaction still suffer from poor stability and activity2,3. Furthermore, the CO2 electroreduction catalysis discovery and screening process is generally hampered by slow, one catalyst sample at a time, experimental procedures. Our overall objective is to develop SECM as a reliable high-throughput technique for screening of electrocatalyst arrays. Here, using SECM, we evaluated the activity of three different tin oxide derived catalysts arranged in an array. Tin(IV) dioxide, has been previously shown to be one of the most promising catalysts for formate generation, while also being thermodynamically reduced to metallic tin at the potential at which CO2RR happens in an aqueous medium4,5. We created two different tin oxide derived catalysts by subjecting mirror-polished native tin oxide to electro-reduction at a constant potentials prior to the CO2 reduction experiments. Electro-reduction of the native tin oxide surface at -3.00 V vs. Ag/AgCl yielded a tin(IV) dioxide deficient surface covered in nanospheres (≈70 um) while electro-reduction at -1.25 V vs. Ag/AgCl create a slightly porous surface exhibiting high proportion of tin(IV) dioxide. Fine tuning the SECM technique to analyze three samples (the two electroreduced and one native tin oxide surface) in an array, we demonstrate the successful evaluation of the electrocatalytic activities of these catalysts and we discuss the advantages and challenges of using SECM to screen catalysts for CO2 reduction. 1. S. Fukuzumi, Joule, 1, 689–738 (2017) https://doi.org/10.1016/j.joule.2017.07.007. 2. C. E. Moore and E. L. Gyenge, ChemSusChem, 10, 3512–3519 (2017) http://doi.wiley.com/10.1002/cssc.201700761. 3. B. Khezri, A. C. Fisher, and M. Pumera, J. Mater. Chem. A, 5, 8230–8246 (2017) http://xlink.rsc.org/?DOI=C6TA09875D. 4. A. Dutta, A. Kuzume, M. Rahaman, S. Vesztergom, and P. Broekmann, ACS Catal., 5, 7498–7502 (2015). 5. S. Geiger, O. Kasian, A. M. Mingers, K. J. J. Mayrhofer, and S. Cherevko, Sci. Rep. (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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".