Characterization and swelling behavior of hydrogels from <i>N</i>-isopropylacrylamide, <i>N,N</i>′-diethylacrylamide, acrylic acid, and its use for drug release
Bibliographic record
Abstract
Here, a series of semi-interpenetrating polymer network (semi-IPN) hydrogels were synthesized and investigated by combining three materials such as N-isopropylacrylamide (NIPAM), N,N′-diethylacrylamide (DEA), and acrylic acid (AAc). The linear copolymer p(NIPAM-co-AAc) was introduced into DEA solution in the presence of N,N′-methylenebisacrylamide to synthesize p(NIPAM-co-AAc)/pDEA semi-IPN hydrogels using free radical polymerization. Scanning electron microscope images demonstrated the porous morphology of hydrogels with pore sizes in the range of 220–969 µm. The physicochemical properties of polymers were evaluated by various characterization techniques, including gel permeation chromatography, dynamic light scattering, differential scanning calorimetry, rheological measurement, mechanical properties, and swelling behaviors. The results indicated that the introduction of linear copolymers into conventional hydrogels has significantly improved the properties of the hydrogels. Besides, the use of NIPAM has significantly improved the thermal sensitivity of the polymers in this work. Finally, a selected semi-IPN hydrogel was evaluated for its potential for the drug release of paracetamol. The sorption of paracetamol was an exothermic process that obeyed the Freundlich isotherms, with the highest sorption of 119.57 mg/g. The paracetamol release followed the Korsmeyer–Peppas model and was consistent with the Fickian diffusion mechanism.
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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".