Quaternized Graphene Oxide Nanocomposites as Fast Hydroxide Conductors
Bibliographic record
Abstract
Nanocomposites play a key role in performance improvements of hydroxide conductors employed in a wide range of alkaline-electrochemical systems such as fuel cells and metal-air batteries. Graphene oxide (GO) nanosheets are considered to be outstanding nanofillers for polymeric nanocomposites on account of their excellent physicochemical strength and electrochemical properties. In this work, a fast hydroxide conductor was developed on the basis of a chemically modified GO nanocomposite membrane. The high surface area of GO was functionalized with highly stable hydroxide-conductive groups using a dimethyloctadecyl [3-(trimethoxysilyl)propyl]ammonium chloride (DMAOP) precursor, named QAFGO, and then composed with porous polybenzimidazole PBI (pPBI) as a well-suited polymeric backbone. The nanocomposite exhibited outstanding hydroxide conductivity of 0.085 S cm(-1), high physicochemical strength, and electrochemical stability for 21 days. An alkaline fuel cell (AFC) setup was fabricated to determine the functionality of QAFGO/pPBI nanocomposite in an alkaline-based system. The high AFC performance with peak power density of 86.68 mW cm(-2) demonstrated that QAFGO/pPBI nanocomposite membrane has promising potential to be employed as a reliable hydroxide conductor for electrochemical systems working in alkaline conditions.
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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 | 0.000 |
| 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".