Applying Copolymerization Kinetics to Understand and Optimize Swelling Responses in Superabsorbent Hydrogels
Bibliographic record
Abstract
While acrylic acid (AA)-based superabsorbent hydrogels (SAHs) have been widely used in multiple applications, the effects of counterion condensation and the polyelectrolyte effect at suppressing the effective degree of ionization in such systems limit their superabsorbency. Herein we describe the use, and investigate the mechanism, of sulfate comonomers containing different types of polymerizable functional groups for increasing the superabsorbency of acrylic acid-based SAHs. Specifically, acrylamido-2-methylpropanesulfonic acid (AMPS), 3-sulfopropyl acrylate (SPAK), and 3-sulfopropyl methacrylate (SPMK) sulfated monomers featuring similar distances between the polymer backbone and the sulfate group but different polymerizable groups were copolymerized with acrylic acid to fabricate SAHs. Measurements of the effective homopolymerization rate constants and copolymerization ratios associated with each copolymerization enabled the prediction of the relative chain distributions of monomers in each copolymer, as quantified by the blockiness parameter (i.e., the instantaneous or average number of consecutive monomers of each type polymerized). While all sulfated comonomers significantly enhanced swelling relative to an acrylic acid control, copolymers in which longer AA blocks are produced (AA-AMPS) resulted in lower swelling than copolymers in which shorter AA blocks are produced (AA-SPAK, AA-SPMK), a result attributed to the suppressed polyelectrolyte effect in copolymers with shorter AA blocks. The formation of limited length blocks of the sulfated monomer SPMK showed additional benefits for enhancing swelling, consistent with enhanced direct charge–charge repulsion between fixed charges in such copolymer systems. We anticipate this linkage between copolymerization kinetics and swelling properties offers the potential to enable improved rational design of superabsorbent hydrogels with higher sorbency.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".