Steering CO<sub>2</sub> Electroreduction Pathway via Tuning Microenvironment of Cobalt Center in Molecular Catalysts
Bibliographic record
Abstract
Owing to its chemical stability and molecular-level structural tunability, the molecular electrocatalyst cobalt phthalocyanine (CoPc) demonstrates significant potential for the electrochemical reduction of CO 2 (CO 2 RR). However, the specific catalytic reaction process of CO 2 RR and the dynamic structural evolution mechanisms of CoPc remain a contentious subject. Elucidating the reaction pathways of CO 2 electroreduction to CO and tracking structural evolution pose substantial challenges. In this study, we first used density functional theory (DFT) calculations to reveal the sequential proton–electron transfer (SPET) mechanisms for CO 2 RR on CoPc. Moreover, in situ X-ray absorption spectroscopy (XAS) elucidated a detailed deactivation mechanism, providing insights into the transition from single atomic sites (SAs) to nitrogen-coordinated nanoclusters (NCs) during CO 2 reduction. Based on these insights, we modified the pendant groups by introducing electron-withdrawing fluorine groups to change the reaction pathways of [*-COOH] 2– to the concerted proton–electron transfer (CPET) process, thereby effectively promoting the CO 2 electroreduction to CO. The presence of electron-withdrawing fluorine groups triggers central electron delocalization within CoPc, effectively mitigating the demetalation effect and enhancing the electron donation ability of Co active sites. As a result, we observed a markedly enhanced CO 2 RR performance, exhibiting high stability, activity, and FE CO compared to unmodified CoPc. This study contributes to the broader understanding of designing efficient molecular electrocatalysts for CO 2 RR.
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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 | 0.000 | 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".