Nanostructured Cobalt‐Based Electrocatalysts for CO<sub>2</sub>Reduction: Recent Progress, Challenges, and Perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract CO 2 reduction reaction (CO 2 RR) provides a promising strategy for sustainable carbon fixation by converting CO 2 into 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 CO 2 reduction reaction (ECO 2 RR). 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 ECO 2 RR 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 ECO 2 RR. This review summarizes the rational design of Co‐based catalysts for ECO 2 RR, including molecular, single‐metal‐site, and oxide‐derived catalysts, along with the nanostructure engineering techniques to highlight the distribution of the ECO 2 RR 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 ECO 2 RR, are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.000 | 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 it