Synthesis and characterization of positive volume phase transition hydrogel membrane prepared using a cellulose substrate
Bibliographic record
Abstract
Thermo-responsive hydrogels display swell-collapse behavior in response to changing temperature. While the hydrogels themselves have many applications such as environmental and chemical separations, the hydrogel can be incorporated within a membrane substrate to improve its mechanical properties and expand the opportunities for development and implementation. The present work describes synthesis of positive volume transition temperature responsive poly(acrylamide-co-acrylic acid) hydrogels on cellulose paper substrates. Effects of factors including co-monomer ratios, degree of crosslinking and mass loading are assessed for Series A random copolymer hydrogels in terms of permeability response. Examination of the membranes is then extended to investigate inter-penetrating networks (IPN) between acrylamide and acrylic acid homo-polymer hydrogels. Series B membranes explore the impact of nonpolar ligands on temperature response through addition of butyl methacrylate co-monomer. For both random copolymer and IPN hydrogel membranes, mass loading dominates permeability performance, though certain criteria must be met. Performance of the Series A membranes was found to be ideal when monomer ratio was 1:1 and there was a lower degree of crosslinking. Addition of butyl methacrylate did not appear to have a consistent impact on the membrane response, appearing to be tied to the degree of crosslinking for random copolymer hydrogel coatings.
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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) | 0.001 | 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".