The Deep Understanding into the Promoted Carbon Dioxide Electroreduction of ZIF‐8‐Derived Single‐Atom Catalysts by the Simple Grinding Process
Bibliographic record
Abstract
Zeolitic imidazolate framework (ZIF‐8)‐derived single‐atom catalysts (SACs) are widely studied in many catalytic reactions such as hydrogen evolution reaction (HER), oxygen reduction reaction (ORR), and CO2 reduction reactions (CO2RR). Grinding procedures involved in the synthesis of ZIF‐8‐derived SACs could affect the catalytic performance but are less evaluated in the literature. Herein, a series of ZIF‐8‐derived cobalt SACs (C–Co–ZIFs) with different grinding processes to investigate the impact of the grinding degrees on the performance of the electrochemical reduction reaction of CO2 (ECO2RR) is presented. The moderate grinding process affords a boost in CO Faradaic efficiency (FE, around 15% higher than that of the original C–Co–ZIF) and the highest current densities among all the samples. The variations in the electronic structure of the Co active sites in the ground catalysts are confirmed by X‐Ray absorption spectroscopy (XAS) and X‐Ray emission spectroscopy (XES) for improved catalytic performance. The increased micropores in the moderately ground catalyst provide more exposed active sites while the increased meso‐ and macropores promote the mass transfer, benefiting the ECO2RR performance. It suggests that the impact of grinding processes on the synthesis of ZIF‐8‐derived SACs should be considered for the evaluation of the catalytic performance.
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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) | 0.000 | 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 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".