Upgrading the State‐of‐the‐Art Electrocatalysts for Proton Exchange Membrane Fuel Cell Applications
Bibliographic record
Abstract
Abstract Carbon‐supported Pt‐based electrocatalysts have been widely investigated for diverse electrochemical energy storage and conversion applications. Vulcan XC72R (VC) supported Pt(20 wt%) nanoparticles (NPs) (denoted as Pt(20 wt%)NPs/VC produced by Johnson Matthey (JM)) is the most commonly‐used catalyst and is thus widely considered to be the state‐of‐the‐art electrocatalyst. Although Pt(20 wt%)NPs/VC(JM) has demonstrated very good electrocatalytic performance in these applications, further improvement in its electrocatalytic activity and electrochemical stability is still highly desired. In this study, an innovative strategy, involving graphitic carbon nitride (g‐CN) coating of the electrocatalyst, is shown to improve the performance. Although other researchers have attempted modifying the support and components during the process of making an electrocatalyst, the authors report, for the first time, the nitriding of an existing catalyst, that is, post‐modification of the electrocatalyst. Due to the unique features of g‐CN coating, which include high chemical stability, good oxygen adsorption and improved ionomer distribution in the catalyst layer, the g‐CN‐coated Pt(20wt%)NPs/VC(JM) electrocatalyst (g‐CN content: 0.61 wt%) has demonstrated both improved polarization performance and electrochemical stability. This simple but very effective strategy is believed to open a new avenue for improvement of electrocatalytic performance for a range of diverse commercially‐available electrocatalysts used in proton exchange membrane fuel cells.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".