Highly Active and CO-Tolerant Trimetallic NiPtPd Hollow Nanocrystals as Electrocatalysts for Methanol Electro-oxidation Reaction
Bibliographic record
Abstract
Pt-based catalysts for methanol fuel electro-oxidation typically suffer from CO intermediate poisoning. Herein, we incorporated secondary Ni element into PtPd hollow nanocrystals (HNCs) to fabricate a trimetallic NiPtPd-HNCs catalyst with superior CO tolerance and high activity for methanol electro-oxidation. The as-prepared trimetallic NiPtPd-HNCs exhibit promising specific and mass activity of 10.68 mA·cm –2 and 3.95 A·mg Pd+Pt –1, respectively, which is 4.2- and 4.5-fold higher than that of commercial Pt/C. Notably, CO-stripping tests and 3000 s chronoamperometry experiments in a rigorous CO-saturated medium show that trimetallic NiPtPd-HNCs possess higher CO tolerance compared with that of the bimetallic counterparts. Ultimately, we ascribed to the enhanced activity and CO tolerance of trimetallic NiPtPd-HNCs to (i) the preponderance of hollow interior and dendritic morphology, (ii) the considerably improved binding energy of OH ads on NiPtPd surface which is beneficial to the removal of the partial oxidation intermediates during the methanol electro-oxidation, and (iii) the modification of the electronic structure of Pt and Pd caused by Ni heteroatoms exposure to surface. The employment of Ni may be extended to the rational development of other Pt-based multimetallic nanocrystals with high CO tolerance and promising activity in the small molecules fuel electro-oxidation.
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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) | 0.000 | 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".