Ordered Porous Electrodes by Design: Toward Enhancing the Effective Utilization of Platinum in Electrocatalysis
Bibliographic record
Abstract
Platinum‐nanoparticle‐functionalized, ordered, porous support electrodes are prepared and characterized as a potential new class of oxygen reduction reaction (ORR) electrocatalysts. This study aims to develop electrode materials that enhance the effective utilization of Pt in electrocatalytic reactions through improved mass transport properties, high Pt mass specific surface area, and increased Pt electrochemical stability. The electrodes are prepared using modular sacrificial templates, producing a uniform distribution of Pt nanoparticles inside ordered porous Au electrodes. This method can be further fine‐tuned to optimize the architecture for a range of characteristics, such as varying nanoparticle properties, pore size, or support material. The Pt‐coated Au, ordered, porous electrodes exhibit several improved characteristics, such as enhanced Pt effective utilization for ORR electrocatalysis. This includes a nearly twofold increase in Pt mass specific surface area over other ultrathin designs, superior mass transport properties in comparison to traditional catalyst layers of C black supported Pt nanoparticles mixed with ionomer, good methanol tolerance and exceptional stability toward Pt chemical and/or electrochemical dissolution through interfacial interactions with Au. The methods to prepare Pt‐coated ordered porous electrodes can be extended to other architectures for enhanced catalyst utilization and improved performance of Pt in electrochemical processes.
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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 | 0.001 |
| 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 | 0.001 |
| 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".