Preparation of composite anion exchange membranes based on in‐situ copolymerization of N‐vinyl formamide and divinylbenzene in porous PTFE
Bibliographic record
Abstract
Abstract A series of composite anion exchange membranes was synthesized via in‐situ copolymerization of various ratios N‐vinyl formamide (NVF) and divinylbenzene (DVB), supported by porous polytetrafluoroethylene (PTFE) polymer matrix, and followed by alkaline hydrolysis, and quaternization of the composite membranes with epoxypropyltrimethylammonium chloride (EPTMAC). FTIR and SEM analyses revealed that the composite membranes were successfully prepared. Moreover, the hydrophilic property of the composite membrane improved by introduction of the quaternized poly(NVF‐co‐DVB) copolymer. Water uptake, swelling ratio, and conductivity showed upward trends by increase of NVF amount. The copolymer with 95% of NVF showed the highest elongation at break (102%, room temperature) and conductivity (5.15 × 10−2 S/cm, 80°C). After immersion of the PNDB95%‐N membrane in 5 mol/L NaOH solution for 96 h at room temperature, the conductivity (60°C) of the membrane decreased to 3.99 × 10−2 S/cm. Moreover, the membrane registered weight loss under 4.5%, caused by degradation of the quaternary ammonium groups in NaOH solution. All in all, in 3 mol/L methanol solutions, the composite membranes showed permeability ranging from 7.6% to 19.7%, if compared to the Nafion®‐115 membrane, showing good alcohol resistance.
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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 | 0.001 |
| 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".