Evaluation of physicochemical, thermomechanical, and structural properties of chickpea flour composite films reinforced with crystalline nanocellulose
Bibliographic record
Abstract
ABSTRACT Chickpea flour–crystalline nano‐cellulose (CNC) composite films were prepared using chickpea flour as the film forming material, glycerol as the plasticizer and 2.5 to 10% CNC as reinforcement supplement. Mechanical properties of the composite films increased with an increase in the CNC content up to 5%. By inclusion of CNC up to 10%, the storage modulus of chickpea flour matrix increased by an order of magnitude from 7.3 to 86.4 MPa. The storage modulus of all samples, on the other hand, decreased as temperature increased. The T g values measured by dynamic mechanical analysis (DMA) and differential scanning calorimetry (DSC) were shifted toward higher temperatures by the addition of CNC. The creep resistance of chickpea flour films increased again up to 5% added CNC, and strain sweep tests confirmed the extension of linear viscoelastic region. X‐ ray diffraction (XRD) confirmed a positive correlation between the degree of crystallinity of chickpea flour composties and CNC content. FTIR spectroscopy confirmed the molecular interaction between chickpea flour, glycerol and CNC. SEM results revealed agglomeration in the structure of the film with the addition of CNC beyond 5% and diminishing of surface homogeneity, while decreasing the pore size of chickpea flour matrix. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137 , 48389.
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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 | 0.002 | 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 | 0.001 |
| 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".