New perspectives on poly(lauryllactam) <scp>PA12</scp>: Optimization of process parameters for <scp>AROP</scp> of ω‐lauryllactam
Bibliographic record
Abstract
Abstract This study focuses on how catalyst concentration, activator concentration, and initial polymerization temperature impact in‐situ anionic ring‐opening polymerization (AROP) of ω‐lauryllactam. Catalyst and activator roles are fulfilled by NaH and toluene‐2,4‐diisocyanate (TDI), respectively, with varying catalyst/activator ratios to assess the influence of a bifunctional activator on the polymerization process. The materials produced undergo a thorough analysis, with a specific emphasis on solidification time. The examination extends to scrutinizing how different concentrations of catalyst/activator and polymerization temperatures affect crucial physical and chemical parameters. The study identifies NaH‐6 mol%/TDI‐3 mol% as the optimal formulation for solidification among the three explored temperatures. Notably, at 180 and 200°C, PA12 exhibits enhanced monomer conversion when a catalyst/activator ratio of 1.7 or 2 is applied. These findings underscore the significant impact of the catalyst/activator ratio and their individual concentrations on the polymer's final properties. This influence extends to factors such as crystallinity, polymer chain regularity, and dynamic mechanical properties. Additionally, the experimental conditions utilized for anionic polymerization are observed to shape the characteristics of PA12.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".