Bibliographic record
Abstract
While the beneficial physical properties of silicone polymers are exploited in many sustainable applications, the high energy requirement for their synthesis compromises to a degree their sustainability. We report a strategy to mitigate this issue by filling the silicone with inexpensive and renewable starch. Elastomeric materials with covalently grafted starch, utilizing anhydride-modified silicones, permits loading of up to about 75% starch while maintaining many of the properties of the silicone. Alternatively, 50 wt.% starch-filled silicone foams can be prepared simply by mixing powdered starch with a mixture of HSi-functional silicone fluids in the presence of B(C6F5)3. The physical properties of the resulting foams are determined by the quantity of SiH, which controls the final density of the foams (ranging from 0.258–0.875 g mL−1), their Young’s modulus, and their degree of elasticity; both rigid and flexible foams were prepared. Materials with a high natural and renewable material content better adhere to green chemistry principle 7, should enhance the ease of degradation at end of life, and augment the sustainability of these silicone composites.
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 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) | 0.002 | 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".