Predicting the Volume Phase Transition Temperature of Multi-Responsive Poly(<i>N</i>-isopropylacrylamide)-Based Microgels Using a Cluster-Based Partial Least Squares Modeling Approach
Bibliographic record
Abstract
Despite the various potential applications of dual pH/temperature-responsive microgels, the multiple (and often interacting) physical and chemical factors that influence the volume phase transition temperature (VPTT) in such microgels make it challenging to directly design a microgel with a particular targeted swelling response. Herein, we address this challenge by designing and implementing a data-driven model that can predict a microgel swelling profile and subsequently VPTT based only on the microgel recipe. A clustering-based adaptation of partial least squares modeling is developed and subsequently applied to a data library of pH 4 (fully protonated) and pH 10 (fully ionized) swelling responses of 32 pH/temperature-responsive poly(N-isopropylacrylamide) microgels functionalized with various carboxylic acid-functionalized comonomers. We demonstrate that the best-performing clustering and data arrangement strategies can predict the VPTT of the microgels within 1.0 °C at pH 4 and 2.4 °C at pH 10, an accuracy similar to the uncertainty estimates from the experimental transition temperature data (0.6 °C at pH 4 and 2.2 °C at pH 10). Such an approach thus paves the way for faster customization of a microgel swelling profile as needed for a target application.
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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 | 0.001 | 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 | 0.001 | 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) | 0.000 | 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".