Low Pt Thin Cathode Layer Catalyst Layer by Reactive Spray Deposition Technology
Bibliographic record
Abstract
National Research Council Canada's Institute for Fuel Cell Innovation, NRC-IFCI, has been developing the Reactive Spray Deposition Technology (RSDT) process to optimize composite electrode layer formation and develop novel electrocatalysts and catalyst layers. The RSDT process provides the means necessary to develop the next generation of thin, low platinum or alloy catalyst layers for PEM MEA's. In order to best manage water distribution, mass transport and conductivity, the structure should be a gradient with controlled porosity and controlled distribution of both platinum and ionomer across the catalyst layer. The RSDT process allows good control of the platinum particle size as they are created directly from metal vapors, which prevents agglomeration in the catalyst layer. Additionally, it has the flexibility to build a gradient layer structure across a very thin film catalyst layer (<1 um). In our design, a platinum sub-layer (100 nm) was deposited directly on a Nafion® 117 membrane as a columnar structure. After the sub-layer, the platinum loading was reduced in a co-deposited layer of Pt-Nafion (ionomer)-carbon with the lowest loading closest to the GDL. The combined loading of both layers was <0.05mg/cm2 Pt with this approach. The manufactured catalyst layer has a performance of 0.65V at 1 A/cm2 with 0.05mg/cm2 Pt loading using pure oxygen .
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".