Universal aspects of hydrogel gelation kinetics, percolation and viscoelasticity from PA-hydrogel rheology
Bibliographic record
Abstract
Polyacrylamide (PA) hydrogels have been studied extensively, but fundamental aspects of their gelation kinetics, percolation dynamics, and viscoelasticity are still not well understood. This paper focuses on the rheology of PA hydrogels having unusually low monomer concentrations (ca ≈ 3 w% equivalent to 0.42 mol l−1). These furnish loss tangents that span 4 orders of magnitude when varying the crosslinker concentration. An optimum crosslinker concentration (cbis/ca ≈ 2.5 mol. % equivalent to 5.3 w%) is identified, below which the storage modulus G′ increases almost linearly, and the loss modulus G″ acquires a local maximum. Above the optimum crosslinker concentration, G′ and G″ both plateau, accompanied by a notable decrease in the maximum strain (increase in brittleness) before breaking. The dynamic shear moduli reveal universal dynamics at the gel point, as indicated by (i) scaling exponents (y = 3.1 ± 0.1, z = 2.1 ± 0.1 and Δ = 0.70 ± 0.02) that are consistent with the de Gennes [“On a relation between percolation theory and the elasticity of gels,” J. Phys. Lett. 37, L1–L2 (1976)] electrical network analogy, and (ii) a critical relaxation exponent that is close to the Rouse limit Δ = 2/3 from the scaling theory of Martin. A close correspondence of the exponents with that of Adam and Delsanti [Macromolecules 18, 2285–2290 (1985)] for the radical copolymerization of a different material supports the long-standing hypothesis that dynamics at the gel point are universal for a prescribed gelation 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 | 0.001 |
| 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 | 0.000 |
| 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".