Improvement in compression performance of the polysulfide sealant by thiol‐acrylate reaction
Bibliographic record
Abstract
Abstract Trimethylolpropanetriacrylate (TMPTA) was added to the polysulfide‐manganese dioxide (PSF‐MnO2) liquid mixtures as a crosslinker to improve their crosslinking capability. The samples were cured at room temperature for different times and the crosslinking degree was characterized by extraction and swelling tests. Mechanical properties of the cured samples including tensile, compression (stress relaxation, permanent set, and cyclic compression), and dynamic mechanical behaviors were investigated. The results indicated that the TMPTA crosslinker significantly increased the crosslinking degree and the homogeneity of the formed PSF networks. As a result, the tensile and compression stress and relaxation performances of the cured PSF rubber were dramatically improved. This result was also consistent with the results from the swelling, cyclic compression, and dynamic mechanical measurements. Interestingly, the tensile strength of the TMPTA cured samples did not show apparent change when the curing time was longer than 14 days, whereas their compression stress and relaxation performance were growing remarkably from 14 to 60 days. The improved performances were attributed to the high efficiency of thiol‐acrylate Michael addition reaction for the crosslinking. It promoted the curing rate, resulting in good compression properties in a much shorter curing time.POLYM. ENG. SCI., 2011. © 2011 Society of Plastics Engineers
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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) | 0.001 | 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".