Homopolymer and copolymers of 4‐benzyloxycarbonylphenyl acrylate with glycidyl methacrylate: Synthesis, characterization, reactivity ratios, and application as adhesive for leather
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract 4‐Benzyloxycarbonylphenyl acrylate (BCPA) was prepared by reacting benzyl‐4‐hydroxy benzoate dissolved in methyl ethyl ketone with acryloyl chloride in the presence of triethylamine. The homopolymer and copolymers of BCPA with glycidyl methacrylate (GMA) having different compositions were synthesized in methyl ethyl ketone using benzoyl peroxide as a free‐radical initiator at 70 ± 1°C. All the polymers were characterized by FTIR, 1 H‐NMR, and 13 C‐NMR spectroscopic techniques. The solubility of the polymers was tested in various polar and nonpolar solvents. The molecular weights ( M w and M n ) and polydispersity indices of the polymers were determined using gel permeation chromatography. The glass‐transition temperatures of the copolymers increased with increasing GMA content in the copolymer. Thermogravimetric analysis of the polymers performed in air showed that the thermal stability of the copolymer increased with increasing BCPA content. Copolymer compositions were determined using 1 H‐NMR analysis. The monomer reactivity ratios were determined by the application of conventional linearization methods such as Fineman–Ross ( r 1 = 0.5237; r 2 = 1.9646), Kelen–Tüdös ( r 1 = 0.4996; r 2 = 1.8741), and extended Kelen–Tüdös ( r 1 = 0.4652; r 2 = 1.9046) as well as a nonlinear error‐in‐variables model (EVM) method, using the computer program RREVM ( r 1 = 0.4644; r 2 = 1.8324). The peel strength of the leather adhesives prepared from the copolymers was also determined. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 91: 3604–3612, 2004
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it