Temperature-Induced Assembly of Monodisperse, Covalently Cross-Linked, and Degradable Poly(<i>N</i>-isopropylacrylamide) Microgels Based on Oligomeric Precursors
Bibliographic record
Abstract
A simple, rapid, solvent-free, and scalable thermally driven self-assembly approach is described to produce monodisperse, covalently cross-linked, and degradable poly(N-isopropylacrylamide) (PNIPAM) microgels based on mixing hydrazide (PNIPAM-Hzd) and aldehyde (PNIPAM-Ald) functionalized PNIPAM precursors. Preheating of a seed PNIPAM-Hzd solution above its phase transition temperature produces nanoaggregates that are subsequently stabilized and cross-linked by the addition of PNIPAM-Ald. The ratio of PNIPAM-Hzd:PNIPAM-Ald used to prepare the microgels, the time between PNIPAM-Ald addition and cooling, the temperature to which the PNIPAM-Hzd polymer solution is preheated, and the concentration of PNIPAM-Hzd in the initial seed solution can all be used to control the size of the resulting microgels. The microgels exhibit similar thermal phase transition behavior to conventional precipitation-based microgels but are fully degradable into oligomeric precursor polymers. The microgels can also be lyophilized and redispersed without any change in colloidal stability or particle size and exhibit no significant cytotoxicity in vitro. We anticipate that microgels fabricated using this approach may facilitate translation of the attractive properties of such microgels in vivo without the concerns regarding microgel clearance that exist with other PNIPAM-based microgels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".