Synthesis and Structure of Organic-Inorganic Hybrid Semi-interpenetrating Polymer Network Gels
Bibliographic record
Abstract
Semi-interpenetrating polymer network (semi-IPN) gels have been synthesized using a hydrosilylation reaction of 1,3,5,7-tetramethylcyclotetrasiloxane (TMCTS) as a joint molecule, and a,w-nonconjugated dienes, 1,5-hexadiene (HD) or 1,9-decadiene (DD) as linker molecules in the presence of polystyrene (PS) as a liner polymers in toluene or cyclohexane. Network structure, mesh size and mesh size distribution, of the resulting semi-IPN gels was quantitatively characterized by means of a scanning microscopic light scattering (SMILS). The relaxation peaks derived from three kinds of structures were detected in the semi-IPN gels prepared in toluene by the SMILS analysis. One was derived from the mesh formed by TMCTS/a,w-nonconjugated dienes about 1-2 nm. Others were derived from transition networks about 20-150 nm and large clustered liner polymer chains about 700-2300 nm. Effect of concentration and molecular weight of the liner polymer on the network structure of the semi-IPN gels in toluene was investigated. The relaxation peaks derived from transition networks or random coils formed by aggregated PS chains were detected in the semi-IPN gels containing high concentration or high molecular weight PS. The semi-IPN gels containing PS were also prepared in cyclohexane as a poor solvent for PS at 40ºC, which was a higher temperature than the upper critical solution temperature (UCST = 34ºC) of PS in cyclohexane. The network structure of the semi-IPN gels was traced by SMILS on the cooling process. In the semi-IPN gel with the short linker molecule of HD, the relaxation peak derived from clustered PS chains was detected over the UCST. By contrast, the relaxation peak derived from transition network was observed in the semi-IPN gel with the long linker molecule of DD.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".