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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".