Impact of Solvent on Catalyst Layer for Anion Exchange Membrane Fuel Cell
Bibliographic record
Abstract
Anion exchange membrane fuel cell (AEMFC) is attractive energy conversion device that is an alternative to proton exchange membrane fuel cell (PEMFC) which required expensive materials, including platinum-based catalysts and perfluorosulfonic acid (PFSA) ionomers. Operating under the alkaline environment, enables the use of various types of low cost platinum-free-catalysts and the inexpensive metal stack hardware, allowing the system to be configured at a much lower cost. Over the past several years, the development of anion exchange membrane (AEM) and ionomer (AEI) with high OH - conductivity and kinetically fast water diffusivity has resulted in significantly improved power performance of AEMFC. However, the problem of water imbalance during AEMFC operation becomes a hindrance to achieving more higher performance. In the anode, one water per electron is generated by hydrogen oxidation reaction (HOR) and water is consumed by the oxygen reduction reaction (ORR) as a reactant in the cathode. Also, since the OH - migration from the cathode to the anode causes electro-osmotic drag and water molecules are transport to same direction, the difference of water content in both electrodes becomes larger. This water imbalance causes flooding in the anode and making it difficult to diffuse H 2 gas to the Pt surface, moreover, water dry-out in the cathode inhibit the OH - conduction of AEIs. These series of processes, finally, reduce the Pt utilization due to the depletion of reactants. Considering the previous studies, despite using AEM and AEI with sufficient OH - conductivity, high-loading of platinum (0.4 mgPt cm -2 or more) was used to achieve power performance. Compared to practical PEMFC (0.2 mgPt cm -2 or less), the amount of platinum is too large, and this low Pt utilization problem is fatal drawback to AEMFC commercialization, which is aimed at lower prices. To improve the Pt utilization of AEMFC, the redox-active site, called the triple-phase-boundary, where the contact region of catalyst, H 2 or O 2 reactant gas, and OH - conducting ionomer, should be maximized in catalyst layer. In manufacturing a catalyst layer having a plenty of triple-phase-boundary, inducing a homogeneous ionomer distribution is one of the most important components. As a similar perspective, the research on the ionomer distribution has recently been conducted intensively at PEMFC field. The most representative way to control the ionomer distribution is tuning the interaction between the ionomer, solvent, and catalyst. In our previous study, the size of the ionomer aggregates was controlled by changing interaction between the solvent and ionomer using different solvents. As a result, the more homogeneous ionomer distribution in the cathode catalyst layer lead to the better performance due to the facile proton migration, however, nanoscale ionomer aggregates can induce the pore clogging with a severe mass transport resistance. However, AEMFC has not yet reported any papers related to ionomer distribution, even only a few papers studied about pore structure of the catalyst layer. Thus, in-depth study of the catalyst layer needs to be required and constructing optimized catalyst layer that containing homogeneous ionomer distribution with high porosity is absolutely necessary for efficient use of the catalyst. Herein, we presented a rational design of the catalyst layer by controlling the ionomer distribution and pore structure via solvent selection of the catalyst slurry. The analytical methods introduced in this study can be universally used for any AEIs and catalysts, and a strategy for preparing a catalyst layer with high Pt utilization considering the size of AEI and catalyst will greatly contribute to the cost reduction of AEMFC for commercialization. As the solvent of this study, dimethyl sulfoxide (DMSO), methanol (MeOH), and isopropanol (IPA) were selected, which have highly different dielectric constant and commonly used for making catalyst layer. Additionally, water was selected as a cosolvent to improve the dispersity of the catalyst slurry. By changing a solvent into higher dielectric constant, homogeneous ionomer distribution was constructed in catalyst layer, achieving high electrochemical surface area (ECSA) (72.1 m 2 gPt -1 ) in the cathode. The selection of solvent for inducing a low absolute zeta-potential on the catalyst surface enables the formation of highly porous catalyst layer, which can result in improved a power performance in anode. For the first time, we observed nanomorphology of AEI dispersed on the carbon surface by scanning transmission electron microscopy (STEM). The structure and performance of the catalyst layer different by solvents were analyzed using electron microscope and electrochemical methods. Finally, we discuss the optimized catalyst layer design for AEMFC to provide a guideline for future direction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".