A high throughput optical method for studying compositional effects in electrocatalysts for CO2 reduction
Bibliographic record
Abstract
Abstract In the problem of electrochemical CO 2 reduction, the discovery of earth-abundant, efficient, and selective catalysts is essential to enabling technology that can contribute to a carbon-neutral energy cycle. In this study, we adapt an optical high throughput screening method to study multi-metallic catalysts for CO 2 electroreduction. We demonstrate the utility of the method by constructing catalytic activity maps of different alloyed elements and use X-ray scattering analysis by the atomic pair distribution function (PDF) method to gain insight into the structures of the most active compositions. Among combinations of four elements (Au, Ag, Cu, Zn), Au 6 Ag 2 Cu 2 and Au 4 Zn 3 Cu 3 were identified as the most active compositions in their respective ternaries. These ternary electrocatalysts were more active than any binary combination, and a ca. 5-fold increase in current density at potentials of −0.4 to −0.8 V vs. RHE was obtained for the best ternary catalysts relative to Au prepared by the same method. Tafel plots of electrochemical data for CO 2 reduction and hydrogen evolution indicate that the ternary catalysts, despite their higher surface area, are poorer catalysts for the hydrogen evolution reaction than pure Au. This results in high Faradaic efficiency for CO 2 reduction to CO.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".