Calorimetric Analysis of Thermal Phase Transitions in Functionalized Microgels
Bibliographic record
Abstract
Differential scanning calorimetry (DSC) is used to investigate the thermal phase transitions of a range of N-isopropylacrylamide (NIPAM)-based, carboxylic acid-functionalized microgels with well-defined radial and chain functional group distributions. The transition enthalpies of protonated microgels can be correlated with the hydrophobicity of the functional comonomer, while the transition enthalpies for ionized microgels can be correlated with the degree of microgel deswelling achieved across the thermal phase transition. The peak widths at half-height vary inversely with the average length of NIPAM blocks in each of the microgels, as calculated using a kinetic copolymerization model. Deconvolution of the asymmetric DSC thermograms is accomplished using a two-transition model, thought to relate to core-shell-type transitions induced by the significant local heterogeneities within the functionalized microgels. The ratio between the two transition temperatures of these deconvoluted peaks is a useful quantitative probe of the radial functional group distribution. An additional, low-temperature transition is also observed in the thermogram of the vinylacetic acid-functionalized microgel, indicative of the occurrence of local chain rearrangements prior to the macroscopic phase transition in this microgel. Complementary light scattering analysis suggests that microphase separation may account for this additional transition peak.
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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".