Modulated Ionomer Distribution for High Performance PEMFC
Bibliographic record
Abstract
PEMFC technology is almost ready for large-scale commercialization, yet the technology sector perceives that the current devices are not sufficiently durable, have limited operational flexibility, and are not cost competitive. In particular, the automotive industry is demanding a membrane electrode assembly (MEA) that endows fuel cells with a cost-to-power ratio of $30/kW and high operation temperatures above 90 °C. From the catalyst layer design perspective, ionomer has a vital function in meeting these needs. The ionomers in the catalyst layers function as proton conductors to expand the electrochemically active region, binding materials to impart mechanical stability, and hydrophilic agents to retain moisture and prevent membrane dehydration. In all optimization processes for fuel cell development, the ionomer content is a vital design parameter. If the amount of ionomer is insufficient to form a three-dimensional network, the protons cannot access every part of the catalyst layer. Therefore, only parts of the catalyst can be utilized as active sites for electrochemical reactions. In contrast, if a MEA contains too much ionomer, the electronic conduction paths and gas transport channels (pores) in the catalyst layers will be blocked by either ionomer material or flooded water inside the more hydrophilic pores, particularly at a high current density. In the same way that a platinum loading and its dispersion are key factors in the design of catalysts, the ionomer content and its distribution are essential for providing a good proton transport rate in the catalyst layer without increasing the mass transport resistance but still obtaining high efficiency in the electrochemical conversion. From this perspective, the ionomer distribution of the catalytic layer must be controlled in order to achieve high performance. However, the method of controlling the distributions of the ionomer, catalyst, and water is evolving slowly. The researches on the ionomer distribution have raised questions regarding how to expand the ionomer coverage and reduce the proton transport resistance, while not increasing the oxygen transport resistance, and how to modulate the ionomer distribution from molecular-scale to macro-scale throughout the catalyst layer for better performance. In this work, we propose a new approach to modulate the ionomer distribution through the introduction of poly(ethylene glycol) (PEG) to the cathode catalyst layer and leaching of the PEG phase using the water generated during the fuel cell operation. The PEG, which induces nano-phase separation with ionomer, expands the ionomer phase in the catalyst layer through dilution, which results in a higher ionomer surface coverage on a Pt/C matrix. Furthermore, the ionomer phase in the catalyst layers becomes more connected due to the expansion. Then, the water soluble PEG is removed during the break-in, which retrieves the reaction sites and mass transport channels that are blocked by the PEG. Overall, the key concept in the strategy is to increase the ionomer coverage at fixed ionomer content. We prepared a series of catalyst layers with varying PEG to ionomer ratio from 0 to 0.4, and investigated morphology, electrochemical properties, and power performances of the catalyst layers. In the PEG/ionomer range of 0 to 0.3, both electrochemical active surface area and double layer capacitance increased with the PEG/ionomer ratio and they decreased with a further increase of the ratio up to 0.4. The proton transport resistance thorugh the catalyst layers was decreased with the PEG addition; these were 0.37, 0.29, 0.16, and 0.18 Ohmcm2for PEG/ionomer ratios of 0, 0.2, 0.3, and 0.4, respectively. These results clearly deomstrate the modulation of ionomer distribution is realized with this approach. At an intermediate PEG content of 30%, a maximum power performance was observed; the power performance at 0.6V was increased by 1.7-fold as a result of 1.3-fold increase in the electrochemial active area and two-fold increase in the proton transport rate in the catalyst layer.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".