Cascade Anchoring Strategy for Fabricating High-Loading Pt Single Atoms as Bifunctional Catalysts for Electrocatalytic Hydrogen Evolution and Oxygen Reduction Reactions
Bibliographic record
Abstract
Carbon supports containing single-atomically dispersed metal-N x (denoted as M SAC –N x C y, x, y: coordination number) have attracted increasing attention due to their superb performance in heterogeneous catalysis. However, large-scale controllable preparation of single-atom catalysts (SACs) with high concentration of supported metal-N x is still a big challenge because of the metal atom agglomeration during synthesis at high density and temperatures. Herein, we report a stepwise anchoring strategy from a 1,10- o -phenanthroline Pt chelate to an N x -doped carbon (N x C y ) with isolated Pt single-atom catalysts (Pt SAC –N x C y ) containing Pt loadings up to 5.31 wt % measured via energy-dispersive X-ray spectroscopy (EDS). The results show that 1,10- o -phenanthroline Pt chelate predominantly contributes to the formation of chelate single metal sites that bind tightly to platinum ions to prevent metal atoms from aggregating, resulting in high metal loading. The high-loading Pt SAC –N x C y exhibits a low hydrogen evolution (HER) overpotential of 24 mV at 0.010 A cm –2 current density with a relatively small Tafel gradient of 60.25 mV dec –1 and excellent stable performance. In addition, the Pt SAC –N x C y catalyst shows excellent oxygen reduction reaction (ORR) catalytic activity with good stability, represented by the fast ORR kinetics under high-potential conditions. Theoretical calculations show that Pt SAC –NC 3 ( x = 1, y = 3) offers a lower H 2 O activation energy barrier than Pt nanoparticles. The adsorption free energy of a H atom on a Pt single-atom site is lower than that on a Pt cluster, which is easier for H 2 desorption. This study provides a potentially powerful cascade anchoring strategy in the design of other stable M SAC –N x C y catalysts with high-density metal-N x sites for the HER and 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) | 0.001 | 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".