Electrolyte migration through electrochemical membranes: Potential source of error in batch electrochemical cells
Bibliographic record
Abstract
Abstract Electrochemical membranes (ECMs) and porous electrodes have gained much attention in a broad range of applications including water and wastewater treatment, energy production and storage, and carbon dioxide capture. Lab scale batch experiments (electrochemical stirred cells) are the baseline for developing ECMs and porous electrodes. We observed electrochemical dissolution of metal fasteners (alligator clips), used to hold porous conductive and non‐conductive membranes in batch electrochemical cells, despite being kept outside the electrolyte. The electrolyte migrated through the porous membranes by the action of capillary forces, forming a closed electrochemical circuit with the metal fasteners. This unexpected leaching can lead to misleading results for electrochemical experiments on porous electrodes and ECMs. In this study, we compared (1) porous membranes versus non‐porous electrodes, (2) hydrophilic versus hydrophobic membranes, and (3) conductive versus non‐conductive membranes in their ability to cause capillary wetting‐induced corrosion of metal fasteners. We proposed a simple solution for the problem: separating the metal fasteners from the porous membrane electrode with a non‐porous conductive graphite foil, which keeps the electrochemical circuit open. We have validated this solution and propose it as a standard method for experiments using porous electrodes and electrically conductive 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 | 0.026 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".