Proposition of a New Power Dissipation Model and Its Analytic Formulation for Electric-Field-Driven Water Dissociation in the Cationic/ Anionic Bipolar Polymer Membranes Junctions
Bibliographic record
Abstract
Bipolar Polymer Membranes (BPMs) enable the creation of large, stable pH gradients by drivingwater dissociation (WD) at the cation/anion junction under reverse bias, a process central to electrodialysis, CO₂ capture, and emerging acid–alkaline water electrolysis. Yet, despite decades of study, the mechanism by which intense interfacial electric fields accelerate WD remains debated and is often modeled with ad hoc assumptions. Here, we outline key limitations of existing models of field-enhanced WD in BPMs and we present a power-dissipation model and its formalism that address them. In this new framework, we emphasize that minority ions from water autoprotolysis act as carriers that continuously dissipate field-supplied power in the hydrated nanometric junction. This dissipative input raises the local probability of heterolytic O–H bond cleavage and leads analytically to the dissociation rate’s quadratic dependence on the field. Without adjustable parameters, the model reproduces the required orders of magnitude for the enhancement ratio kd(E)/kd(0), where kd(E) is the field-enhanced water-dissociation rate constant and kd(0) its zero-field value, across typical BPM fields and yields a quadratic current–voltage junction law. A proof-of-principle measurement on a commercial Fumasep® FBM confirms the quadratic current–voltage trend, supporting a power dissipation field-driven WD and providing a concise, falsifiable baseline for future studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".