Nanostructured Cobalt‐Based Electrocatalysts for CO<sub>2</sub>Reduction: Recent Progress, Challenges, and Perspectives
Bibliographic record
Abstract
Abstract CO2reduction reaction (CO2RR) provides a promising strategy for sustainable carbon fixation by converting CO2into value‐added fuels and chemicals. In recent years, considerable efforts are focused on the development of transition‐metal (TM)‐based catalysts for the selectively electrochemical CO2reduction reaction (ECO2RR). Co‐based catalysts emerge as one of the most promising electrocatalysts with high Faradaic efficiency, current density, and low overpotential, exhibiting excellent catalytic performance toward ECO2RR for CO and HCOOH productions that are economically viable. The intrinsic contribution of Co and the synergistic effects in Co‐hybrid catalysts play essential roles for future commercial productions by ECO2RR. This review summarizes the rational design of Co‐based catalysts for ECO2RR, including molecular, single‐metal‐site, and oxide‐derived catalysts, along with the nanostructure engineering techniques to highlight the distribution of the ECO2RR products by Co‐based catalysts. The density functional theory (DFT) simulations and advanced in situ characterizations contribute to interpreting the synergies between Co and other materials for the enhanced product selectivity and catalytic activity. Challenges and outlook concerning the catalyst design and reaction mechanism, including the upgrading of reaction systems of Co‐based catalysts for ECO2RR, are also discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".