Functional Group Distributions in Carboxylic Acid Containing Poly(<i>N</i>-isopropylacrylamide) Microgels
Bibliographic record
Abstract
Control of the functional group distribution is of fundamental importance in the design of functional polymer particles, particularly in biological applications. Surface-functionalized particles are useful for bioconjugation and medical diagnostics, while internally functionalized particles may have applications in drug delivery. We have prepared a series oftemperature-sensitive poly(N-isopropylacrylamide) (PNIPAM)-based microgels containing carboxylic acid functional groups via copolymerization with methacrylic acid and acrylamide, which was selectively hydrolyzed under optimized conditions to generate the carboxylic acid functionality. The resulting microgels were analyzed using conductometric and potentiometric titration, dynamic light scattering, and electrophoresis. Acrylamide-containing microgels hydrolyzed below the volume phase transition temperature (VPTT) show broad particle size versus temperature profiles, relatively low electrophoretic mobilities at basic pH, and time-dependent base titration profiles, suggesting the presence of internal functional groups whose titration is diffusion-controlled. Methacrylic acid containing microgels show sharper particle size versus temperature profiles, higher electrophoretic mobilities at basic pH, and time-independent base titration profiles, suggesting the presence of a "core-shell" structure with primarily surface functionalization. Similar results were obtained when acrylamide-containing microgels were hydrolyzed at temperatures above the VPTT. Thus, through selection of comonomer and hydrolysis conditions, we have developed strategies to control and characterize the number and distribution ofcarboxylic acid functional groups in PNIPAM-based microgels.
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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) | 0.000 | 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".