Dense Pt Nanowire Electrocatalyst for Improved Fuel Cell Performance Using a Graphitic Carbon Nitride‐Decorated Hierarchical Nanocarbon Support
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 mg Pt cm −2 ).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it