Electrochemical Water Splitting at Bipolar Interfaces of Ion Exchange Membranes and Soils
Bibliographic record
Abstract
Under the influence of a direct current, the dissociation rate of water in contact with bipolar ion exchange membranes (BPMs) is 10 6 to 10 7 times faster than in free solution. This phenomenon, termed accelerated water splitting , is well known in industry where BPMs are designed for electrosynthesis of acids and bases. In this work, it is hypothesized that (i) accelerated water splitting can also take place at the bipolar interface between ion exchange membranes (IEMs) and the ion exchange surfaces of soils, and (ii) electroosmosis plays a key role. If the IEM has an electrostatic charge opposite in sign to the predominant charge on the soil colloidal particles, the interface is, in effect, bipolar. If an external electric field is then applied, conditions can give rise to accelerated water splitting. Laboratory experiments performed on various mixtures of Ottawa sand, bentonite, talc, and anion exchange resin indicate that accelerated water splitting occurs when the free pore solution in the low permeable soil moves away from the bipolar interfaces due to electroosmosis, thus causing an unsaturated zone at these interfaces. Accelerated water splitting then initiates at these interfaces since there are not enough counterions in contact with the IEMs to maintain an ionic current. Very little cation exchange capacity (CEC), anion exchange capacity (AEC), or clay is needed for water splitting to occur at a bipolar IEM and soil interface.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".