In Situ Investigation of Swelling Dynamics of Acrylamide‐Acrylic Acid Superabsorbent Microparticles at a Single Particle Level
Bibliographic record
Abstract
Abstract Investigating the swelling behavior of superabsorbent polymer microparticles (SAP‐MPs) at a single‐particle level using traditional methods is constrained by low resolution and insufficient real‐time data, especially for particles smaller than 300 µm. To address these challenges, a novel microfluidic device capable is developed of real‐time, high‐precision single‐particle analysis. This platform hydrodynamically traps individual SAP‐MPs, enabling continuous monitoring of their swelling dynamics under controlled conditions. SAP‐MPs with varying sizes (90–270 µm), crosslinker concentrations (0.25%<Cr<2%), neutralization degrees (50%<ND<100%), and acrylic acid concentrations (10%<AA<90%) are synthesized via inverse suspension polymerization and systematically studied using the response surface method (RSM). Kinetic modeling revealed the dominance of the pseudo‐first‐order (PFO) model over the pseudo‐second‐order (PSO) model in describing diffusion‐driven swelling dynamics. The PFO model demonstrated superior predictive accuracy (R 2 >0.98) and minimal equilibrium volumetric swelling ratio deviations (ΔVSR eq <4%), confirming diffusion as the primary swelling mechanism, particularly for smaller particles. Smaller SAP‐MPs exhibited enhanced performance, with VSR eq of ≈140 m 3 /m 3 —40% higher than their larger counterparts—and swelling rates (SR) up to 10 m 3 m − 3 ·s. This study establishes microfluidics as a transformative tool for single‐particle characterization and provides insights into engineering hydrogels tailored for advanced applications in drug delivery, tissue engineering, and environmental sensing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| 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 teacher head, 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".