Design of Bipolar Membranes to Increase CO Formation Rates in Bicarbonate Electrolysers at Low Voltage
Bibliographic record
Abstract
The electrolysis of bicarbonate solutions offers a direct route for converting alkaline CO2 capture solutions into value-added products. Alkaline CO2 capture exploits the reaction of CO2 with OH- to form aqueous HCO3-, which can be converted back into CO2 in-situ ( i-CO2) by protons sourced from a bipolar membrane (BPM). Bicarbonate electrolysis is currently too energy intensive to be economically viable, with the largest energy input coming from the voltage drop across the membrane. BPMs are usually thicker than monopolar membranes, causing high Ohmic losses and limited water transport to the water dissociation junction. This causes the membrane to dry out and require excessively high voltages to operate at high current densities. To date, little research has been directed towards designing BPMs for bicarbonate electrolysis. Our work focuses on designing BPMs with low voltage drops while keeping the amount of i-CO2 suppliedto the cathode high. In this study, we present custom-made BPMs that enable bicarbonate electrolysis at a current density of 100 mA cm-2 and a cell voltage < 3 V, with cell voltage remaining < 10 V at current densities in excess of 1 A cm-2. We also demonstrate a correlation between a thicker cation exchange layer and higher i-CO2 generation and elucidate the cause of this phenomenon.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".