An In‐Depth Theoretical Exploration of Influences of Non‐Metal‐Elements Doping on the ORR Performance of Co−gN<sub>4</sub>
Bibliographic record
Abstract
Abstract Single atom catalysts (SACs) show a great attraction towards the oxygen reduction reaction (ORR) owing to its advantage in overcoming the cost issue of fuel cell because of its high utilization, low cost and great CO tolerance. However, theoretical investigation on the influences of different doping on the ORR catalytic activity of Co SACs has not been systematically performed. In this regard, the influences of non‐metal‐elements doping (B, N, Si, P, S) on the ORR catalytic activity of Co−gN4 is well explored based on density functional theory (DFT). The rate‐limiting step for the ORR on Co−gN4 is the formation of OOH. When the doping content of B increases, the adsorption on Co site becomes weaker which limits the occurrence of 4e− ORR process. N doping shows a weak promotion on the ORR process on Co site. The Si site next to N can be poisoned by OH. The P site next to N will be poisoned by O at high potential and OH at low potential. The S site next on N would be poisoned by O. It is revealed that the ORR process on Co site can be promoted when the carbon next to N is replaced by Si/P/S by promoting the rate‐limiting step.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".