Activity Descriptor Identification for Oxygen Reduction on Platinum-Based Bimetallic Nanoparticles: <i>In Situ</i> Observation of the Linear Composition–Strain–Activity Relationship
Bibliographic record
Abstract
Despite recent progress in developing active and durable oxygen reduction catalysts with reduced Pt content, lack of elegant bottom-up synthesis procedures with knowledge over the control of atomic arrangement and morphology of the Pt-alloy catalysts still hinders fuel cell commercialization. To follow a less empirical synthesis path for improved Pt-based catalysts, it is essential to correlate catalytic performance to properties that can be easily controlled and measured experimentally. Herein, using Pt-Co alloy nanoparticles (NPs) with varying atomic composition as an example, we show that the atomic distribution of Pt-based bimetallic NPs under operating conditions is strongly dependent on the initial atomic ratio by employing microscopic and in situ spectroscopic techniques. The PtxCo/C NPs with high Co content possess a Co concentration gradient such that Co is concentrated in the core and gradually depletes in the near-surface region, whereas the PtxCo/C NPs with low Co content possess a relatively uniform distribution of Co with low Co population in the near-surface region. Despite their different atomic structure, the oxygen reduction reaction (ORR) activity of PtxCo/C and Pt/C NPs is linearly related to the bulk average Pt-Pt bond length (RPt-Pt). The RPt-Pt is further shown to contract linearly with the increase in Co/Pt composition. These linear correlations together demonstrate that (i) the improved ORR activity of PtxCo/C NPs over pure Pt NPs originates predominantly from the compressive strain and (ii) the RPt-Pt is a valid strain descriptor that bridges the activity and atomic composition of Pt-based bimetallic NPs.
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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 | 0.001 | 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".