Designing Tailored Gas Diffusion Layers with Pore Size Gradients via Electrospinning for Polymer Electrolyte Membrane Fuel Cells
Bibliographic record
Abstract
We present electrospinning as a versatile technique to design and fabricate tailored polymer electrolyte membrane (PEM) fuel cell gas diffusion layers (GDLs) with a pore-size gradient (increasing from catalyst layer to flow field) to enhance the high current density performance and water management behavior of a PEM fuel cell. The novel graded electrospun GDL exhibits highly robust performance over a range of inlet gas relative humidities (RH). At relatively dry (50% RH) inlet conditions that exacerbate ohmic losses, the graded GDL lowers ohmic resistance and improves high current density performance compared to a uniform GDL with larger pores and fiber diameters. Specifically, the graded GDL facilitates a beneficial degree of liquid water retention at the catalyst layer/GDL interface due to the high capillary pressure inherent in its microstructure, thereby improving membrane hydration. Additionally, enhanced graphitization and connectivity of the graded electrospun fibers improves heat dissipation from the catalyst layer interface compared to the GDL with larger fiber diameters, thereby reducing membrane dehydration. When the inlet RH is raised to fully humid (100% RH) conditions, the graded GDL mitigates liquid water accumulation and lowers mass transport resistance. Specifically, the pore size gradient directs the removal of liquid water from the GDL, resulting in superior performance at high current densities.
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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) | 0.000 | 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 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".