Chitosan Nanocomposite Mesoporous Membranes: Mechanical and Barrier Properties as a Function of Temperature
Bibliographic record
Abstract
This work investigates the influence of the type and wt.% of nanofillers on the tensile strength behaviour and barrier properties of fabricated chitosan (CS) mesoporous membranes and their nanocomposites using graphene (G) and fullerene (F) nanofillers. Non cross-linked chitosan (NCLCS) as well as cross-linked chitosan (CLCS) solutions with sodium tripolyphosphate (TPP) were both mixed with G and F nanofillers with different wt.%. NCLCS membranes displayed yield tensile strength of 24MPa while the CLCS membranes displayed a much lower yield tensile strength of 2.87MPa. The addition of G and F nanofiller enhanced the yield tensile strength of the CS membranes up to 45MPa. However, the increase in % elongation for CLCS membranes was 75% higher than that for NCLCS ones. Furthermore, the results revealed that there was a significant effect of the operating temperature on the membrane pore size, which decreased the tensile strength and the barrier of the produced membranes. The enhancement of the tensile properties of polymer nanocomposites membranes (PNC) membranes is crucial to avoid film fracture or delamination. Moreover, the addition of nanofillers improve the barrier properties of PNC membranes, thus the membranes are used in packaging and current filtration techniques. In this work, experimental as well as statistical analysis of the fabricated CS membranes and their yield tensile strength and % elongation data are presented. This study presents the effect of the filler type, the filler content and the cross-linking of CS membranes on tensile strength behaviour both experimentally as well as theoretically.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".