Kinetics of Polymerization of a Liquid with Nanosize Structural Heterogeneities
Bibliographic record
Abstract
We report the effects of chemically reacting, nanometer-size structural heterogeneity on a polymerization process. Heterogeneity is introduced by adding 2 nm size molecules of polyhedral oligomeric silsesquioxane with multiepoxide groups (POSS) while maintaining stoichiometry of a polymerizing triamine-diepoxide mixture. Calorimetric studies show that POSS addition first increases the polymerization rate and then decreases it progressively more. In the presence of nanometer-scale structural heterogeneity, diffusion-controlled kinetics begins sooner in time. The enthalpy of polymerization decreases with the amount of POSS heterogeneity according to the mixture rule; the glass-liquid transition endotherm of the partially polymerized state becomes broader, and the enthalpy of post polymerization decreases. The POSS-alone mixture polymerizes relatively slower, and the glass-liquid transition exotherm of the polymerized state is indistinguishably broad. Both are attributed to the distribution of diffusion rates or dispersive kinetics, and the development of dynamic heterogeneity more rapidly for the POSS-only mixture than for others. Increase in the polymerization rate on initial addition of nanometer-size POSS and then decrease on further addition is explained in terms of decoupling of diffusion from viscous flow, that is, when the diffusion rate decreases less rapidly with the polymerization time than the viscosity increases.
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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".