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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".