Study of Electrolyzer Materials at High Tritium Concentrations
Bibliographic record
Abstract
Electrolysis cells are required to drive the combined electrolysis and catalytic exchange process used in heavy water upgrading and water detritiation.Past projects have used very robust alkaline electrolyte technology for the electrolysis cells, though recently there has been a move toward proton exchange membrane (PEM) technology. In PEM electrolysis a solid polymer electrolyte (SPE) acts as the proton conductor, separator of product gases, and insulator between electrodes.The long-term effects of highly tritiated water on these SPE materials are not fully understood. At Canadian Nuclear Laboratories (CNL), an exposure study has been undertaken wherein various commercial and proprietary SPE materials were exposed to very highly tritiated water (~1000 Ci/kg, 37 TBq/kg). Exposures were done at a typical cell operating temperature (60°C) for periods that might be expected for commercial operations.Following exposure, some samples lost sufficient integrity that they could not undergo post-exposure testing. In order to test the remaining materials’ electrolytic performance and physical properties in a nonactive laboratory, a process of decontamination that would result in no further membrane degradation needed to be developed. The successful reduction in tritium content of the samples following decontamination was verified using chemical digestion and combustion analysis. All types of commercial membranes were found to lose significant ion exchange capacity, to show reduced water absorption, and to show reduced strain before failure. Tensile testing showed almost complete degradation even at low doses. In this paper, commercial membrane data are compared with data from CNL’s tritium-compatible membranes.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".