Strategies to Improve the Sustainability of Silicone Polymers
Bibliographic record
Abstract
Silicones underpin an enormous range of simple and advanced technologies. Often, only small quantities of silicone are used to enable a technology such that, on a "per use" basis, one might suppose the environmental impact is low. However, silicone preparation processes have a very high carbon footprint, and billions of kg are produced each year. To provide context to the consideration of new strategies to improve silicone sustainability, we first outline traditional silicone chemistry and then describe strategies to improve the degree to which silicones are green, sustainable and circular. One strategy involves dilution of the silicone oil or elastomer by tethering organic entities, particularly natural products, that may provide new properties including facilitated degradation in nature at end-of-life. A greater focus is given to strategies that permit extensive reuse and repurposing of oils and elastomers (e.g., with thermoplastic elastomers), before the silicone undergoes recycling. Each reuse, repurposing or recycling step reduces the net carbon footprint. These mostly involve straightforward, high-yielding organic chemical processes that work efficiently in a silicone milieu. Silicones will eventually end up in the environment, where linear oils are known to rapidly degrade, particularly when compared to organic polymers. Alternative strategies that permit triggered or biological degradation of oils and, more importantly elastomers, are described, including enzymatic degradation and composting.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".