Design and optimization of superabsorbent hydrogels based on acrylic acid/<scp>2‐acrylamido‐2‐methylpropane</scp> sulfonic acid copolymers
Bibliographic record
Abstract
Abstract Although acrylic acid‐based superabsorbent hydrogels (SAHs) are widely used in hygiene and personal care applications, the low degrees of true ionization in such SAHs under practical application places a limit on the degree of superabsorbency that can be achieved. Herein, the preparation and optimization of SAHs based on copolymers of acrylic acid (AA) with 2‐acrylamido‐2‐methylpropane sulfonic acid (AMPS), a strong acid comonomer that remains ionized at all relevant physiological pH values, is described. AA‐AMPS recipes optimized using a central composite design response surface method demonstrate significantly enhanced absorbance under load (AUL) values (>15–20% increase) and comparable centrifuge retention capacity (CRC) values compared to the corresponding AA‐only controls while maintaining strong mechanics even in the swollen state (as much as twice the modulus of the commercial AA‐based superabsorbent). The simplicity of this recipe, coupled with the reproducible achievement of the improved superabsorbency properties using a small adaptation of the commercial SAH synthesis protocol, makes AA‐AMPS superabsorbent hydrogels potential candidates for use in commercial hygiene products.
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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".