Improving Oxygen Reduction Reaction Activity through Defect Engineering of Atomically Dispersed Iron Electrocatalysts for Proton Exchange Membrane Fuel Cells
Bibliographic record
Abstract
Atomically dispersed and nitrogen-coordinated iron catalysts(Fe-N-Cs) have the potential as an alternative to platinum-group metal catalysts for the oxygen reduction reaction (ORR). However, in the context of practical proton exchange membrane fuel cell (PEMFC) applications, the membrane electrode assembly (MEA) performances of Fe-N-Cs remain unsatisfactory. To address this issue, a defect engineering strategy has been developed to prepare high-performance PEMFC MEAs using atomically dispersed Fe-N-C catalysts. This strategy involves the use of a zeolitic imidazolate framework (ZIF)-derived nitrogen-doped carbon with additional CO2 activation to create atomically dispersed iron sites with a controlled number of defects. By adjusting the extent of defect formation in the carbon plane using CO2 activation, it is anticipated that changes in the oxidation state and spin state of the Fe center will modify the electronic structure of the Fe-N4 active sites. The Fe-N-C species with the optimal number of defect sites exhibit excellent ORR performance with a high half-wave potential of 0.83 V in 0.5 M H2SO4. Fine-tuning the number of defects can be optimized the ORR activity by adjusting the contribution of the Fe d-orbitals to the reaction intermediate binding energies. The resulting MEA based on the defect-engineered Fe-NC catalyst exhibits remarkable peak power densities in both H2/O2 and H2/air fuel cells, making it one of the most active atomically dispersed catalyst materials at the MEA level.
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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".