Multi‐Component Kinetic Modeling for Controlling Local Compositions in Thermosensitive Polymers
Bibliographic record
Abstract
Abstract Summary: An explicit terminal copolymerization kinetic model accounting for the copolymerization of up to four different comonomers is developed and applied to model the conversion profiles and local compositional gradients in functionalized PNIPAM‐based polymer and hydrogel systems. The kinetics of the functional comonomer(s) have a large influence on both the mole fraction and chain distribution of functional groups in polymers. Strategies are developed to synthesize polymers with uniform compositions by applying semi‐batch techniques or via copolymerization of multiple monomers with the same target functionality but with divergent reactivities relative to NIPAM. Synthetic protocols are also designed to maximize the compositional uniformity and randomness of ampholytic polymers. Instantaneous mole fractions of monomers in polymers as a function of the overall monomer conversion for the copolymerizations of NIPAM, MBA, and two functional monomers: MMA and acrylamide. magnified image Instantaneous mole fractions of monomers in polymers as a function of the overall monomer conversion for the copolymerizations of NIPAM, MBA, and two functional monomers: MMA and acrylamide.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".