Ionomer Fibers-Decorated Polymer Electrolyte Membrane for Low Pt-Loaded Fuel Cells
Bibliographic record
Abstract
Polymer electrolyte membrane fuel cell (PEMFC) technology faces considerable demands of cost competitiveness for mass commercialization of fuel cell electric vehicle. A significant challenge to the cost reduction is the reduction of Pt loading for membrane electrode assembly (MEA). Besides a lowered catalytic activity, low Pt-loaded catalyst layer (CL) suffers from large mass transports resistance, resulting in low power performance at high current densities. Such larger mass transport resistance at a lowered Pt loading can be attributed to a larger oxygen transport resistance from the ionomer film covering catalysts and more significant water flooding due to a lower amount of the pore volume in cathode CL. For conventional CL structure, gaseous oxygen and liquid water share the same network of meso-pores (< 50 nm) inside a flat CL for their transport, therefore, the water condensation in the pores inevitably causes a blocking of oxygen transport. In order to address this issue, an ionomer fiber-induced macro-porous CL is presented. It is fabricated via electrospinning of ionomer fibers onto the membrane, followed by spray-coating catalyst ink on the ionomer fiber-decorated membrane. The ionomer fiber deposition on the membrane induce a roughening of the membrane surface, which allows the formation of a CL, morphology of which dictates that of the ionomer fibers, and of micron-scale pores between the CL and gas diffusion layer. The new CL structure dramatically improves power performances at high current densities owing to an effective oxygen and water transport through the micron-scale mass transport pathway. From polarization curve and oxygen transport resistance analysis, and stability under constant current operation for various feeds, the efficacy of the unique CL morphology in enhancing power performances is demonstrated and understood. Also, the relationship between the macro-pore in CL and mass transport resistance is investigated with systematically varying the pore size and porosity.
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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".