Composite Anion Exchange Membranes Based on Functionalized Graphene Oxide and Poly(Terphenylene Piperidinium) for Application in Water Electrolysis and Fuel Cells
Bibliographic record
Abstract
Composite anion exchange membranes (AEMs) based on poly(terphenylene piperidinium) (PTPiQA) and impregnated with varying loadings of quaternized graphene oxide (QGO) as filler were developed, and their properties as anion exchange membranes for use in water electrolysis (AEMWEs) and fuel cells (AEMFCs) were explored. This study investigates the trade-off between mechanical robustness, ionic conductivity, and alkaline stability in QGO-reinforced twisted polymer backbones. QGO synthesized by functionalization with ethylenediamine (EDA), followed by quaternization with glycidyl trimethylammonium chloride (GTMAC), was used as a filler for PTPiQA, and the properties of the resulting composites PTPiQA-QGO-X investigated as a function of QGO loading for X between 0.1 and 0.7 wt%. Among all compositions, PTPiQA-QGO-0.3% exhibited the highest OH− conductivity of 71.56 mS cm−1 at room temperature, attributed to enhanced ionic connectivity and water uptake. However, this increase in conductivity was accompanied by a slight decrease in ion exchange capacity (IEC) retention (91.8%) during an alkaline stability test in 1 M KOH at 60 °C for 336 h due to localized cation degradation. Mechanical testing revealed that PTPiQA-QGO-0.3% offered optimal dry and wet tensile strength (dry TS of 42.77 MPa and wet TS of 30.20 MPa), whereas higher QGO loadings yielded low mechanical strength. These findings highlight that 0.3 wt% QGO balances ion transport efficiency and mechanical strength, while higher loadings improve alkaline durability, compromising mechanical durability and guiding the rational design of AEMs for AEMWEs and AEMFCs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".