Modeling the Effect of Material Properties on Liquid-Alkaline Water Electrolysis
Bibliographic record
Abstract
Liquid-alkaline water electrolyzers (LAWEs) use electricity to drive the conversion of water to H 2 and O 2 gas. These devices benefit from the use of low-cost nickel electrodes and metal-oxide separators, but suffer from lower current densities and higher cell voltages than proton-exchange-membrane water electrolyzers. Identifying the inefficiencies that result in this poor performance is key to mitigating losses and optimizing LAWEs. Here, we report an experimentally-validated 1-D continuum model of a LAWE that elucidates the gradients within the cell, simulates H 2 crossover, and projects the energy improvements made possible by modulating the properties of the electrodes and separator. The model captures the Nernstian polarization losses and the distribution of gas- and liquid-phases within the electrodes, enabling quantification of energy losses associated with kinetic, ohmic, and bubble-induced (mass-transport) resistances. Simulations demonstrate that LAWE can achieve energy intensities of 50 kWh kg −1 of H 2 at 1 A cm −2 using improved electrode and separator properties.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".