Electrocatalysts for the CO2 Electrochemical Reduction Reaction
Bibliographic record
Abstract
The ever-rising level of atmospheric carbon dioxide and limited fossil fuel reserves have driven intensive studies on electrochemical conversion of CO2 into value-added chemicals and fuels. However, several bottlenecks have hindered the wide adoption of this technology, especially the unsatisfying performance of catalysts, which have led to the great effort on searching and developing high-performance catalytic materials. In this study, Pd-Au based nanomaterials with a unique core-shell and grain boundary-rich structure are developed. Compared with Pd nanoparticles, these materials have a significantly improved CO selectivity. A maximum CO faradaic efficiency of 94.3% (at -0.6 V), and an extremely low overpotential of 90 mV for CO formation with a faradaic efficiency of 8.5% can be achieved. Combined in situ infrared spectroscopic studies and density function theory calculations reveal that surface CO could be more facilely generated at much lower overpotentials.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".