Tailoring the Coordination Environment of Single‐Atom Catalysts for Enhanced Electrochemical CO <sub>2</sub> ‐to‐CO Conversion Efficiency
Bibliographic record
Abstract
ABSTRACT Exploring the influence of the coordination environment of single‐atom catalysts (SACs) on the electrochemical CO 2 reduction reaction is vital for assessing the reaction mechanism and structure‐performance relationship. However, it is challenging to engineer the coordination configuration of isolated active metal atoms precisely. Herein, we strategically manipulate the coordination number of the Co–N x configuration by simply changing the order of adding the metal precursor toward improved CO 2 electrolysis performance. Compared with the symmetric Co–N 4 coordination, the asymmetric Co–N 3 coordination leads to reinforced Co–N interaction and downshifted 3d orbital energy toward the Fermi level of the active Co sites, promoting the activation of CO 2 molecules and the formation of critical intermediate *COOH. The as‐designed Co–N 3 SAC displays excellent Faradaic efficiency (FE) of 98.4% for CO 2 ‐to‐CO conversion at a low potential of −0.80 V, together with decent FE over a wide potential range (−0.50 V to −1.10 V) and high durability. This study presents an ideal platform to manipulate the coordination number of atomically dispersed metal catalysts and provides a fundamental understanding of coordination configuration‐performance correlation for CO 2 electroreduction.
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.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".