Chemically Folded Polyelectrolytes with Superior Alkaline Stability
Bibliographic record
Abstract
In recent years, the development of anion-exchange membranes (AEMs) for anion-exchange membrane fuel cell (AEMFC) applications has been rapidly growing due to their numerous advantages over mainstream proton-exchange membrane fuel cells. However, a major challenge in the development of practical AEMs is the low chemical stability of the AEM quaternary ammonium (QA) functional groups in the strongly alkaline and the relatively dry environment produced during operation of the AEMFC. Herein, we investigate the effect of polymer chain folding on the chemical stability of the QA groups. While these polymers have virtually the same chemical composition, their molecular architectures are quite different, significantly affecting the kinetics of nucleophilic attacks on the QAs embedded inside the folded chains. The stability tests reveal a remarkable improvement in the stability of the folded chains compared to the linear (unfolded) control, resulting in polyelectrolytes that are two orders of magnitude more stable. We provide here a simple method for the preparation of chemically stable AEMs with different QA groups and polymer backbones. These folded architectures present a very promising family of polyelectrolyte membranes for AEMFCs and other electrochemical applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".