Ultralow Loading and High-Performing Pt Catalyst for a Polymer Electrolyte Membrane Fuel Cell Anode Achieved by Atomic Layer Deposition
Bibliographic record
Abstract
Decreasing Pt loading in the anode layer below ∼0.025 mg·cm –2 is found to reduce the hydrogen oxidation reaction rate during polymer electrolyte membrane fuel cells (PEMFCs) normal operation, when using conventional Pt/C catalysts and electrode coating methods. To achieve extremely low Pt loading in the anode catalyst layer while maintaining high PEMFC performance and durability, a series of membrane electrode assemblies (MEAs) with low Pt loading in the anode layer are successfully prepared using an atomic layer deposition (ALD) technique. When the ALD cycle number is controlled, the Pt nanoparticles (NPs) with different sizes and loadings are directly deposited on the carbon layer to form the anode catalyst layer. The ALDPt NPs with uniform particle sizes are highly distributed on the carbon surface, which promotes the ALDPt with high electrochemical active surface area and enables enhanced performance of ALDPt-MEAs. Particularly, the 50ALDPt-MEA with the anode Pt prepared by 50ALD cycles shows excellent H 2 –air PEMFC activity and durability. Importantly, the 20ALDPt-MEA with an ultralow anode Pt loading of 0.01 mg·cm –2 displays a significantly high surface area of 155 m 2 ·g –1 Pt, approximately 3 times higher than the 50.3 m 2 ·g –1 Pt for commercial Pt catalyst. The 20ALDPt anode also shows better stability than that of the commercial Pt/C during the anode potential cycling test. The ultralow Pt loading, uniform Pt distribution, high MEA performance, and durability achieved indicate that the ALD technique has great potential in developing high-performing electrocatalysts for PEMFC.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".