Highly selective custom‐made chitosan based membranes with reduced fuel permeability for direct methanol fuel cells
Bibliographic record
Abstract
Abstract Custom‐made nanocomposite proton exchange membranes (PEMs) are fabricated using the blends of sulfonated chitosan (S‐Chitosan) and sulfonated graphene oxide (SGO) nanosheets for direct methanol fuel cells (DMFCs). Sulfonation of chitosan and GO are carried out by 1,3‐propane sultone and sulfanilic acid, respectively. Scanning electron microscope (SEM) with energy dispersive X‐ray investigation revealed that the thick, folded and wrinkled sheet‐like morphology of SGO and the existence of elemental sulfur. SEM and atomic force microscopy images showed the uniform dispersion of hydrophilic SGO nanosheets. Besides the S‐Chitosan/SGO membranes showed higher water uptake, swelling ratio and ion exchange capacity due to the enhancement in hydrophilicity. The modified PEMs displayed improvement in proton conductivity since the ion‐exchangeable sulfonic acid groups facilitate the proton conduction and effectively resist the methanol permeability by forming a strong hydrogen bond network with chitosan and thus diminish the void volume. Particularly, S‐Chiotsan‐1 membrane showed superior proton conductivity of 4.86 × 10−3 Scm−1 at (25°C), selectivity of 1.89 × 105 Scm−3 s and lesser methanol permeability of 2.57 × 10−8 cm2s−1. Overall results suggest that the S‐Chitosan/SGO membranes found to be a suitable alternate for Nafion® in DMFCs.
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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".