Dense Pt Nanowire Electrocatalyst for Improved Fuel Cell Performance Using a Graphitic Carbon Nitride‐Decorated Hierarchical Nanocarbon Support
Bibliographic record
Abstract
Abstract An innovative strategy is presented to engineer supported‐Pt nanowire (NW) electrocatalysts with a high Pt content for the cathode of hydrogen fuel cells. This involves deposition of graphitic carbon nitride (g‐CN) onto 3D multimodal porous carbon (MPC) (denoted as g‐CN@MPC) and using the g‐CN@MPC as an electrocatalyst support. The protective coating of g‐CN on the MPC provides good stability for the electrocatalyst support against electrochemical oxidation, and also enhances oxygen adsorption and provides additional active sites for the oxygen reduction reaction. Compared with commercial carbon black Vulcan XC‐72R (denoted as VC) support material, the larger hydrophobic surface area of the g‐CN@MPC enables the supported high‐content Pt NWs to disperse uniformly on the support. In addition, the unique 3D interconnected pore networks facilitate improved mass transport within the g‐CN@MPC support material. As a result, the g‐CN@MPC‐supported high‐content Pt catalysts show improved performance with respect to their counterparts, namely, MPC, VC, and g‐CN@VC‐supported Pt NW catalysts and the conventional Pt nanoparticle (NP) catalyst (i.e., Pt(20 wt%)NPs/VC (Johnson Matthey)) used as the benchmark. More importantly, the g‐CN‐tailored high‐content Pt NW (≈60 wt%) electrocatalyst demonstrates high PEM fuel cell power/performance at a very low cathode catalyst loading (≈0.1 mgPt cm−2).
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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 | 0.000 | 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".