(Invited) Leveraging Imaging Techniques to Accelerate the Advancement of Polymer Electrolyte Membrane Water Electrolyzers
Bibliographic record
Abstract
Polymer electrolyte membrane water electrolyzers (PEMWEs) are critically needed to meaningfully incorporate clean renewable energy into our energy, transportation, and manufacturing sectors, for without this energy conversion mechanism, the intermittency of renewable energy will always pose a prohibitive cost barrier to practical use. However, in addition to overcoming the intermittency issue of renewable energy, energy storage also has the power to transform current energy infrastructure, particularly when plagued by older systems that lack modularity and flexibility. For example, electricity grids are often constrained to move electricity in and out of a jurisdiction with careful predictions based on past use and future needs. This comes at a cost when electricity must be sold at a loss to prevent a catastrophic surplus of electricity in the grid. While the potential for PEMWEs is tremendous, we must reduce their capital and operating costs in order to reach widespread adoption. In recent years, important work in the field has contributed to advancing PEMWEs in a variety of areas from new materials to flow field design; however, experimental validation, particularly spatially resolved, is necessary to take a fully informed approach to tailoring these materials and architectures. In this talk, I will discuss how we use X-ray and neutron imaging, both ex situ and operando, to provide high spatial and temporal resolution experimental performance and materials characterization for PEMWEs to inform our design process for new materials fabrication, new architectures, and operating parameters. We obtained nano-scale, 3D imaging of PEMWE catalyst layers to inform the stochastic simulations used to uncover the influence of pore size and ionomer distributions on electrical and ionic conductivities. We also used operando X-ray imaging to elucidate the influence of catalyst layer and porous transport layer interfacial conditions on liquid and gas transport behaviour. By applying neutron imaging, we elucidated the impact of temperature on the distribution heterogeneity of liquid water reactant at the catalyst layer. I will also discuss how we are using soft X-rays to perform synchrotron-based scanning transmission X-ray microscopy with absorption spectroscopy to uncover heterogeneity across performance and material characteristics for the PEMWE. Through these highly resolved techniques (in terms of time and space), we aim to advance our predictive models for designing next generation materials, interfaces, and flow configurations for high performance water electrolyzers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.020 |
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".