Phosphorus-Driven Electron Delocalization on Edge-Type FeN<sub>4</sub> Active Sites for Oxygen Reduction in Acid Medium
Bibliographic record
Abstract
Precise tuning of the chemical environment of neighboring atomic FeN 4 sites is extremely important for optimizing Fe–N–C catalysts to produce the fast oxygen reduction reaction (ORR) kinetics both in acidic and alkaline media, but it is actually very challenging. Heteroatoms could affect the metal charge of the active center through long-range electron delocalization; however, there are a few studies on it. Herein, density functional theory (DFT) calculations demonstrate that the addition of long-range P into edge-type FeN 4 can drive the electron delocalization and decrease the band gap of the FeN 4 center, leading to a substantial decrease in the free energy barrier to direct four-electron ORR kinetics compared to P-free edge-type FeN 4, indicating superior intrinsic ORR activity. Experimentally, by incorporating P in edge-rich FeN 4 supported on N,P-doped carbon (Fe–N–C–P/N,P–C), the created Fe–N–C catalyst presents the greatly increased acidic ORR activity, with a half-wave potential ( E 1/2 ) of 0.80 V (vs a reversible hydrogen electrode), which approaches that of commercial Pt/C and also has a high half-wave potential of 0.87 V, beyond Pt/C for alkaline ORR. In addition, it shows higher proton exchange membrane fuel cell and Zn-air battery performances than the pristine Fe–C–N catalyst (Fe–N–C/N–C). This work will guide the rational design of highly active metal atomic scale catalysts with optimized chemical surroundings in terms of P incorporation as a chemically tunable method.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".