ZIF-derived Co–N–C ORR catalyst with high performance in proton exchange membrane fuel cells
Bibliographic record
Abstract
Metal and nitrogen-doped carbon (M-N-C) materials have been considered as the most promising non-precious metal oxygen reduction (ORR) catalysts to replace expensive Pt catalysts. Due to high Fenton catalytic activity of Fe element and the resulting instability, Co-based N–C (Co–N–C) catalysts without Fenton catalytic activity should be a worthier ORR catalyst being explored. Although the high ORR activity of Co–N–C catalyst has been demonstrated in aqueous half-cell tests, their performance under PEMFC working condition is still far away from that of state-of-the-art Fe–N–C catalysts. In this study, a high-performance Co–N–C catalyst was synthesized by one-step pyrolyzing Co-doped ZIF-8 (zeolitic imidazolate framework-8) particles in-situ grown on the high-surface-area KJ600 carbon black with high electronic conductivity. The resulting Co–N–C catalyst exhibited high intrinsic ORR activity, fast mass transfer rate and high electronic conductivity, and thus yielded a remarkable peak power density of 0.92 W cm-2 in H2–O2 PEMFC, which is comparable to state-of-the-art Fe–N–C catalyst. This strategy is helpful to synthesize highly active M-N-C ORR catalysts with improved mass transfer and electric conductivity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".