Bipolar Membrane Electrode Assemblies for Water Electrolysis – Goals and Challenges
Bibliographic record
Abstract
Hydrogen production from water electrolysis is considered a promising large-scale solution for energy storage of intermittent, renewable sources. Besides mature alkaline electrolysis employing caustic KOH solutions as a liquid electrolyte, modern technologies focus on the use of ion exchange membranes as a solid electrolyte. While the widespread application of state of the art proton exchange membrane (PEM) water electrolyzers is impaired by the requirement of costly and rare iridium-based catalyst for the oxygen evolution reaction (OER), anion exchange membranes (AEM) are recently reaching higher technological readiness levels. Besides the progress in AEM water electrolysis, also the combination of a PEM for the cathode and an AEM for the anode, referred to as bipolar membrane water electrolysis is gaining more scientific interest. As depicted below, the bipolar interface (preferably decorated with a water dissociation (WD) catalyst [1]) enhances the water dissociation rate into protons and hydroxide, which feed the respective electrode reactions. We have established various modular AEM- and PEM-based building blocks, which allow us to thoroughly investigate zero-gap bipolar membrane electrode assemblies for water electrolysis in various configurations. [2] As the AEM is expected to contribute the most to ohmic losses, we have employed additive manufacturing techniques to decrease the AEM layer thickness stepwise. Moreover, we have evaluated performance determining parameters such as the WD interlayer, operating conditions and water management in the membrane electrode assembly. All these studies allow us a critical assessment of the potential and the drawbacks of this novel MEA system for water electrolysis. Acknowledgements This work was performed in collaboration with the National Research Council of Canada in the Materials for Clean Fuels Challenge Program. References [1] S. Z. Oener, M. Foster, S. W. Boettcher, Science (2020). [2] B. Mayerhöfer, D. McLaughlin, T. Böhm, M. Hegelheimer, D. Seeberger and S. Thiele, ACS Appl. Energy Mater. (2020). Figure 1
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".