Local ionic transport enables selective PGM-free bipolar membrane electrode assembly
Bibliographic record
Abstract
Bipolar membranes in electrochemical CO2 conversion cells enable different reaction environments in the CO2-reduction and O2-evolution compartments. Under ideal conditions, water-splitting in the bipolar membrane allows for platinum-group-metal-free anode materials and high CO2 utilizations. In practice, however, even minor unwanted ion crossover limits stability to short time periods. Here we report the vital role of managing ionic species to improve CO2 conversion efficiency while preventing acidification of the anodic compartment. Through transport modelling, we identify that an anion-exchange ionomer in the catalyst layer improves local bicarbonate availability and increasing the proton transference number in the bipolar membranes increases CO2 regeneration and limits K+ concentration in the cathode region. Through experiments, we show that a uniform local distribution of bicarbonate ions increases the accessibility of reverted CO2 to the catalyst surface, improving Faradaic efficiency and limiting current densities by twofold. Using these insights, we demonstrate a fully platinum-group-metal-free bipolar membrane electrode assembly CO2 conversion system exhibiting <1% CO2/cation crossover rates and 80-90% CO2-to-CO utilization efficiency over 150 h operation at 100 mA cm−2 without anolyte replenishment. Here, authors report that local ionic transport across cathode catalyst layers is vital in improving CO production from CO2. This work demonstrates the potential of a CO2 electrolyzer constructed from materials free from platinum group metals.
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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 | 0.000 | 0.000 |
| 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".