Recent progress in understanding the dispersion stability of catalyst ink for proton exchange membrane fuel cell and water electrolyzer
Bibliographic record
Abstract
A cell with a superior performance and durable catalyst layer can be achieved by optimizing the catalyst ink formulation. A typical commercial catalyst layer for both proton exchange membrane fuel cells and water electrolyzers is prepared via dispersion-based catalyst inks. Generally, ink dispersion composed of supported catalyst, ionomer, and solvent. The ink formulation governs physical properties such as homogeneity, the interaction between ink components, ability to store the ink (shelf-life) for an extended period and reproducibility. This is pivotal in optimizing the catalyst ink formulation for high performance, extended durability, and reproducibility, particularly in large-scale industrial manufacturing in polymer electrolyte membrane (PEM) fuel cells and electrolyzers. Despite the substantial progress on synthesis of different catalysts and ionomers, there is a limited understanding of the colloidal stability of the catalyst ink. The novelty of this review is to provide a guide to understand the importance of catalyst ink dispersion stability for manufacturing MEAs at scale-up level. This is addressed through targeting fundamental interaction behavior and physical properties of the ink materials. Further, this review covers the effect of particle surface, effect of solvent composition, and the effect of ionomer concentration that ensures a homogenized catalyst ink. In addition, this review covers the influence of different dispersion methodologies and the relevant characterization techniques for better understanding the dispersion stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".