Comprehensive investigation of the solvent-ionomer interactions on the fabrication of the catalyst layer for proton exchange membrane fuel cells
Bibliographic record
Abstract
A comprehensive understanding of the interactions between ionomers and solvents is crucial for optimizing catalyst ink formulations in industrial coating processes, such as slot-die coating. This study systematically examines interactions between solvents and ionomers and their effect on the rheology and evolved catalyst layer microstructure of the pseudo-catalyst ink. Using pseudo-catalyst inks omits expensive platinum group metals while maintaining similar rheological properties and structuration behaviors that can be translated to actual catalyst inks. Various ionomer types were evaluated in water/1-propanol solvent systems, including Aquivion D72, Aquivion D79 (short side-chain ionomers with shorter perfluoroether segments connecting the sulfonic acid group to the backbone), Nafion D2020, and Nafion D2021 (long side-chain ionomers with longer perfluoroether segments in their molecular structure), using rheological and Fluorine-19 Nuclear Magnetic Resonance measurements. The findings revealed that ionomers with long and short side-chain lengths show distinctly different inter-aggregation behavior in the same solvent system, greatly affecting their viscosity. Dispersions containing the short side chain (SSC) ionomer exhibited higher viscosities, which were more apparent in high n-propyl alcohol (NPA) solvent compositions, suggesting stronger NPA-ionomer interactions. In contrast, the long-side chain (LSC) ionomer dispersions showed increased relative viscosity with increasing proportion of water in the solvent composition. In addition, the impact of solvent-modulated ionomer structures on crack formation was investigated. The results demonstrated that SSC-based inks in NPA-rich dispersions produced crack-free layers but showed cracking in water-rich systems. In contrast, LSC-based inks displayed the opposite behavior. This study highlights the importance of the ionomer's dispersion-phase morphologies in catalyst layer fabrication. • SSC ionomers aggregate more in NPA; LSC ionomers aggregate in water • SSC dispersions show higher viscosity in NPA; LSC is higher in water systems • SSC pseudo-CLs are crack-free in NPA-rich but crack in water-rich; LSC shows the opposite
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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".