Steering hydrogen evolution in CO2 electroreduction through tailoring various co-catalysts
Bibliographic record
Abstract
Electrochemical CO 2 reduction reaction (CO 2 RR) is a sustainable approach to producing carbon-neutral fuels when combined with renewable energies. Besides the emphatic consideration of developing efficient catalysts, a suitable conductive carbon agent served as co-catalyst with a low activity toward the competitive hydrogen evolution reaction (HER), is also highly needed. However, there have been limited studies focused on the investigation of co-catalyst during CO 2 RR, especially on their HER behavior. We herein explored the HER of various co-catalyst, i.e., acetylene black (AB), carbon black (CB) and graphite flake (GF) as well as carbon nanotube (CNT), and their composites with sub-25 nm Ag nanowires (NWs) as catalysts. GF and CB exhibit a higher activity toward CO 2 RR and HER, respectively. In contrast, CB/Ag NWs achieve the highest Faraday efficiency and partial current density for CO 2 RR, whereas CNT/Ag NWs prefer HER. The differences in HER suggest a critical influence of co-catalyst and this study points to a better guidance on the selection of co-catalyst for CO 2 RR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".