Strategic synthesis and characterization of starch-based interpenetrating polymer networks for enhanced thermal stability and photophysical properties
Bibliographic record
Abstract
We report the synthesis of starch-based interpenetrating polymer networks (IPNs) via grafting starch with 2-methylidenebutanedioic acid (MBDA) and prop-2-enamide (PEA), with in situ incorporation of 2-(acrylamidomethyl) malonic acid (AMA). Five polymers (P1–P5) were prepared by varying MBDA:PEA ratios and characterized by GPC, FTIR, NMR, TGA, DSC, XRD, SEM, UV–vis, fluorescence, and DLS. Among them, P4 (MBDA:PEA = 1:5) exhibited the highest thermal stability and distinct emission behavior. Enhanced properties were attributed to strong hydrogen bonding, dipolar interactions, and supramolecular ordering introduced by starch grafting and amidic functionalities. P4 displayed broad UV absorption (270–300 nm) and excitation-dependent emissions (420–440 nm), supported by conformational rigidification and clustering-induced effects. These findings demonstrate that strategic starch grafting can yield thermally stable, photophysically active biopolymers with potential in membranes, tissue engineering, and dye/metal ion removal. • First synthesis of starch-grafted IPNs (P1–P5) with enhanced thermal & photophysics • P4 (MBDA:PEA = 1:5) shows highest thermal stability via TGA, DSC & FTIR, due to H-bonding. • P4 exhibits strong fluorescence due to rigidification & interactions (UV–vis, fluorescence). • GPC, NMR, FTIR, XRD, SEM, DLS & EDX confirm starch grafting, crosslinking, & stability. • Eco-friendly starch-based materials enable membranes, drug delivery & remediation.
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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) | 0.000 | 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 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".