Zwitterionic Ionenes toward Processable, Low Water-Containing Anion Exchange Membranes: Synthesis, Characterization, and Application in Electrochemical CO<sub>2</sub> Reduction
Bibliographic record
Abstract
Anion exchange membranes (AEMs) have gained significant attention in the past two decades for their potential use in electrochemical energy conversion devices. Achieving a balance between facile anion transport and chemical/mechanical stability remains a challenge due to the correlated nature of key membrane properties, such as water uptake, ion exchange capacity, dimensional swelling, and susceptibility to nucleophilic hydroxide ion attack. We present the preparation and characterization of novel zwitterionic functionalized AEMs based on hexamethyl- p -terphenyl poly(benzimidazolium) (HMT-PMBI). By introducing sulfobetaine side chains to induce ionic cross-linking within the polymeric membrane, properties such as transport, stability, and hydration behavior were modulated. The zwitterionic functionalization of HMT-PMBI reduced excessive swelling while maintaining high conductivity and alkaline stability as well as mechanical robustness, demonstrating the potential of this approach for improving membrane properties for facilitating their implementation in electrochemical devices. The utility of these membranes in CO 2 electrolyzer cells is demonstrated, where they are found to limit carbonate crossover and increase K 2 CO 3 retention at the cathode.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".