Rapidly Responding pH- and Temperature-Responsive Poly (<i>N</i>-Isopropylacrylamide)-Based Microgels and Assemblies
Bibliographic record
Abstract
Abstract Rapidly responding stimuli-responsive materials can have a benefit in a myriad of applications, for example, sensing and biosensing, actuation, and in drug delivery systems. Thermo- and pH-responsive materials have been among the most widely studied, and can be triggered at physiologically relevant temperatures and pH. Here, we have used a “homologous series” of acids based on the acrylic acid (AAc) backbone and incorporated them into N-isopropylacrylamide (NIPAm)-based microgels. Specifically, the acids used were AAc, methacrylic acid (MAAc), ethylacrylic acid (EAAc), and butylacrylic acid (BAAc), which have pKa’s in the range of 4.25–7.4. The resultant microgels were characterized by optical microscopy, and their responsivity to temperature and pH studied by dynamic light scattering. The microgels were subsequently used to generate optical devices (etalons) and their pH and temperature response was also investigated. We found that the devices composed of BAAc-modified microgels exhibit unusually fast response kinetics relative to those of the rest of the devices. We also found that the speed of the response decreased as the length of the acid pendant group decreased, with AAc-modified microgel-based devices exhibiting the slowest response kinetics. Finally, we showed that the kinetics of the device’s temperature response also decreased as the length of the acid pendant group decreased, which we hypothesize is a consequence of the hydrophobicity of the acid groups, that is, increased hydrophobicity leads to faster responses. Understanding this behavior can lead to the rational design of fast responding materials for the applications mentioned above.
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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".