Controlling Nanoparticle Interconnectivity in Thin-Film Platinum Catalyst Layers
Bibliographic record
Abstract
The optimization of conventional hydrogen fuel cell catalyst layers suffers from a poor understanding of their composite nanostructure during both initial preparation and its evolution during use. We demonstrate how highly active, ultralow loading platinum (Pt) catalyst layers can be fabricated in a single, solution-processable step using electroless deposition. Growing Pt nanoparticles directly in the surface of a polyelectrolyte Nafion membrane yields a mechanically robust film with tunable optical reflectance and electronic conductivity. Small changes in the polymer hydration and Pt film thickness critically modulate nanoparticle interconnectivity near the percolation threshold. Conductive atomic force microscopy (AFM) and electron microscopy reveal how the film’s dynamic nanoscale morphology allows control over bulk electrochemical and optical properties. Well-defined composition and structure make these layers an experimentally accessible model system for studying thin-film electrocatalyst architectures.
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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 | 0.001 | 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".