FTIR Study Measuring the Monomer Reactivity Ratios for Ethylene−Vinyl Acetate Polymerization in Supercritical CO<sub>2</sub>
Bibliographic record
Abstract
An understanding of the monomer reactivity ratios is extremely useful for tailoring copolymers with desired compositions for a diversity of end-use applications. In situ ATR-FTIR was employed to monitor the initial formation of copolymers of ethylene and vinyl acetate (VAc) during polymerization in the “green” solvent supercritical carbon dioxide (scCO 2 ). To obtain the copolymer composition, a calibration curve of the absorbance ratio 1737/2929 cm −1 versus vinyl acetate content of poly(ethylene- co -vinyl acetate) (PEVA) was established, covering the range of 5−98 wt %. The reactivity ratios for copolymerization of ethylene and VAc in scCO 2 were determined using both the Kelen−Tudos and the nonlinear least-squares (NLLS) methods. Off-line 1 H NMR analysis was also utilized for obtaining the copolymer composition, by which the determined reactivity ratios were compared with the ones obtained using in situ FTIR. The effect of reaction temperature and pressure on reactivity ratios was examined at 50 and 72 °C and at 13.8 and 27.6 MPa, with the lower temperature and higher pressure conditions increasing the reactivity ratios slightly. In situ FTIR was found to be able to accurately measure the reactivity ratios, and both the Kelen−Tudos and NNLS methods gave similar results. The obtained reactivity ratios will promote the application of the green solvent scCO 2 in the production of the widely commercialized copolymer PEVA.
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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 | 0.001 |
| 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".