Engineering the Low Coordinated Pt Single Atom to Achieve the Superior Electrocatalytic Performance toward Oxygen Reduction
Bibliographic record
Abstract
Configuring metal single-atom catalysts (SACs) with high electrocatalytic activity and stability is one efficient strategy in achieving the cost-competitive catalyst for fuel cells' applications. Herein, the atomic layer deposition (ALD) strategy for synthesis of Pt SACs on the metal-organic framework (MOF)-derived N-doped carbon (NC) is proposed. Through adjusting the ALD exposure time of the Pt precursor, the size-controlled Pt catalysts, from Pt single atoms to subclusters and nanoparticles, are prepared on MOF-NC support. X-ray absorption fine structure spectra determine the increased electron vacancy in Pt SACs and indicate the Pt-N coordination in the as-prepared Pt SACs. Benefiting from the low-coordination environment and anchoring interaction between Pt atoms and nitrogen-doping sites from MOF-NC support, the Pt SACs deliver an enhanced activity and stability with 6.5 times higher mass activity than that of Pt nanoparticle catalysts in boosting the oxygen reduction reaction (ORR). Density functional theory calculations indicate that Pt single atoms prefer to be anchored by the pyridinic N-doped carbon sites. Importantly, it is revealed that the electronic structure of Pt SAs can be adjusted by adsorption of hydroxyl and oxygen, which greatly lowers free energy change for the rate-determining step and enhances the activity of Pt SACs toward the ORR.
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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) | 0.001 | 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".