Preparation of Multicomponent Biocomposites and Characterization of Their Physicochemical and Mechanical Properties
Bibliographic record
Abstract
This work focused on a mutual comparison and characterization of the physicochemical properties of three-component polymer composites. Binary polyaniline–chitosan (PANI–CHT) composites were synthesized by in situ polymerization of PANI onto CHT. Ternary composites were prepared by blending with a third component, polyvinyl alcohol (PVA). Composites with variable PANI:CHT (25:75, 50:50 and 75:25) weight ratios were prepared whilst fixing the composition of PVA. The structure and physicochemical properties of the composites were evaluated using thermal analysis (thermogravimetric analysis (TGA), differential scanning calorimetry (DSC)) and spectroscopic methods (infrared (IR), nuclear magnetic resonance (NMR)). The equilibrium and dynamic adsorption properties of composites were evaluated by solvent swelling in water, water vapour adsorption and dye adsorption isotherms. The electrical conductivity was estimated using current–voltage curves. The mechanical properties of the samples were evaluated using dynamic mechanical analysis (DMA) and correlated with the structural parameters of the composites. The adsorption and swelling properties paralleled the change in the electrical and mechanical properties of the materials. In most cases, samples with higher content of chitosan exhibit higher adsorption and mechanical properties, and lower conductivity. Acid-doped samples showed much higher adsorption, swelling, and electrical conductivity than their undoped analogues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".