Specific Metal Nanostructures toward Electrochemical CO<sub>2</sub> Reduction: Recent Advances and Perspectives
Bibliographic record
Abstract
Abstract Electrochemically converting CO2 (CO2 reduction reaction(CO2RR)) to value‐added fuels is an advanced technology to effectively alleviate global warming and the energy crisis. However, thermodynamically high energy barriers, sluggish reaction kinetics, and inadequate CO2 conversion rate as well as poor selectivity of target products and rapid materials degradation severely limit its further large‐scale application, which highlights the importance of high‐performance electrocatalysts. Metal nanomaterials, due to their intrinsically high but still insufficient reactivity, selectivity, and stability, have been brought to the forefront and have lead to many reviews from various points of view. However, reviews which comprehensively unravel the importance and excellence of specific metal nanostructures and their associated properties for CO2RR are quite limited. To bridge this gap, various specific monometal and bimetal nanostructures are summarized, with an emphasis on the deep understanding of crystal orientation, surface structure, surface crystallography, surface modification, and many associated effects benefiting from the constructed specific metal nanostructures as well as the intrinsic relationships of specific metal nanostructure‐property‐CO2RR activities. Finally, the challenges and the perspectives to advance CO2RR are proposed to pay particularly more attention to architecture evolution during CO2RR with in situ/operando techniques, high‐throughput theoretical computations, and facile synthetic strategies with high yield and production for scale‐up applications.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".