Coordination Environment in Single‐Atom Catalysts for High‐Performance Electrocatalytic CO<sub>2</sub> Reduction
Bibliographic record
Abstract
The electrochemical reduction of carbon dioxide (EC CO2RR) is a promising technology to achieve a carbon‐neutral society. EC CO2RR can directly convert the greenhouse gas emitted from stacks into valuable fuels and chemical feedstocks for various industrial applications. Numerous metal‐based electrocatalysts have been researched to reduce overpotential and enhance the product selectivity of CO2RR. Recently, single‐atom catalysts (SACs) are attracting intensive attention due to their low‐cost, extremely high activities per loading amounts, and extensive stability of catalytic active sites due to the strong chemical interaction with coordination atoms. The coordination environments of SACs affect the electronic structure of active sites and change the energetics of the CO2RR pathways. Herein, the principles of EC CO2RR, including reaction mechanisms, figures of merits, and electrolysis systems, are first discussed. Then, the recent progress in the synthesis and characterization of SACs on various supports is accessed. Most importantly, the coordination environments of single‐metal atoms and their influence on CO2RR catalytic ability, product selectivity, and active site stabilization are focused. This review provides a milestone along the design of SACs from the perspective of optimizing atomic configuration surrounding the active sites for EC CO2RR.
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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 | 0.000 | 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".