Potential-Controlled Pd Segregation on Au–Pd Alloy Nanoparticles
Bibliographic record
Abstract
The pledge to curb CO 2 emissions with the help of clean energy is an ambitious undertaking, one that is tasked with the development of durable electrocatalysts for fuel cells and electrolyzers. Recent development of gold–palladium (Au–Pd) alloy nanoparticles outlines a contemporary strategy in combining the synergistic effects of a more electrochemically stable gold (Au) metal with catalytically active palladium (Pd). Unfortunately, not all compositions are optimal for an intended application, which requires further fine-tuning─a task that seems difficult with the prevalent wet-chemical synthetic methods. Here, we demonstrate a new approach for fine-tuning surface composition in Au–Pd nanoparticles via a procedure involving the selective diffusion of Pd atoms from the core to the surface induced by the adsorption of oxygen atoms at the surface of Au–Pd NPs. Going forward, a similar technique can be more generally applied toward development of ‘self-healing’ electrocatalysts that can alter properties suiting different external requirements with a simple switching of voltage, or a controlled pretreatment.
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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 | 0.000 | 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 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".