Noble metal supported hexagonal boron nitride for the oxygen reduction reaction: a DFT study
Bibliographic record
Abstract
Discovering active, stable and cost-effective catalysts for the oxygen reduction reaction (ORR) is of utmost interest for commercialization of fuel cells. Scarce and expensive noble metals such as Pt and Pd are the state-of-the-art active ORR catalysts but suffer from low stability against CO poisoning. Hexagonal boron nitride (h-BN) is a particularly attractive material due to its low cost and stability; however, it suffers from intrinsic low activity toward the ORR in the pristine form as a result of its inherently low conductivity with a large band gap of ∼5.5 electron volts. During the past few years, several strategies such as using metal supports, metal doping and atomic vacancies have been reported to significantly increase the conductivity, thereby promoting the ORR activity. Herein we use density functional theory calculations to systematically study these strategies for activating inert h-BN and further examine the stability against CO poisoning. We show that noble metals, such as Ag, Pd, and Pt, require boron (B) or nitrogen (N) vacancies to reasonably activate h-BN toward the ORR. For example, Pd supported h-BN with B-vacancies exhibits significantly high ORR activity. All three examined metal supported h-BNs are predicted to be stable against CO poisoning. These results demonstrate that supporting h-BN on noble metals is a promising strategy to increase the stability against CO poisoning while maintaining high ORR activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".