Towards High Conductivity in Anion‐Exchange Membranes for Alkaline Fuel Cells
Bibliographic record
Abstract
Quaternized poly(2,6-dimethylphenylene oxide) materials (PPOs) containing clicked 1,2,3-triazoles were first prepared through Cu(I) -catalyzed "click chemistry" to improve the anion transport in anion-exchange membranes (AEMs). Clicked 1,2,3-triazoles incorporated into AEMs provided more sites to form efficient and continuous hydrogen-bond networks between the water/hydroxide and the triazole for anion transport. Higher water uptake was observed for these triazole membranes. Thus, the membranes showed an impressive enhancement of the hydroxide diffusion coefficient and, therefore, the anion conductivities. The recorded hydroxide conductivity was 27.8-62 mS cm(-1) at 20 °C in water, which was several times higher than that of a typical PPO-based AEM (TMA-20) derived from trimethylamine (5 mS cm(-1) ). Even at reduced relative humidity, the clicked membrane showed superior conductivity to a trimethylamine-based membrane. Moreover, similar alkaline stabilities at 80 °C in 1 M NaOH were observed for the clicked and non-clicked membranes. The performance of a H2 /O2 single cell assembled with a clicked AEM was much improved compared to that of a non-clicked TMA-20 membrane. The peak power density achieved for an alkaline fuel cell with the synthesized membrane 1a(20) was 188.7 mW cm(-2) at 50 °C. These results indicated that clicked AEM could be a viable strategy for improving the performance of alkaline fuel cells.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".