Morphological and Property Analyses of Multicomponent Block Copolymer Nanocomposites Gels
Bibliographic record
Abstract
Thermoplastic elastomer gels (TPEGs), molecular networks composed of a microphase-separated multiblock copolymer swollen to a large extent by a low-volatility midblock-selective solvent, are ubiquitous in a wide range of contemporary technologies, including home and office products, athletic equipment and telecommunications devices. In this work, we investigate the effect of several network-forming nanoscale modifiers — two different silica nanoparticles, 3 different nanoclays, a multiwalled carbon nanotube and a semicrystalline homopolymer — on the property development of a TPEG prepared from a microphase-ordered poly(styrene-b-(ethylene-co-butylene)-b-styrene) (SEBS) triblock copolymer imbibed with an EB-compatible aliphatic mineral oil. Dynamic rheological measurements of the resultant nanocomposite TPEGs (NCTPEGs) confirm that addition of these modifiers tends to increase the linear viscoelastic threshold, the dynamic elastic modulus (G') and the flow onset temperature (where G' plummets) of the parent TPEG. Variable-temperature stress-relaxation studies indicate that these NCTPEGs undergo substantial relaxation irrespective of added modifier at temperatures above ~60°C. Complementary x-ray diffraction analysis reveals that the nanoclay particles used to generate three series of the NCTPEGs examined here are swollen with copolymer and/or solvent and are therefore intercalated.
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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 | 0.000 |
| 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".