Applying the principles of green chemistry to achieve a more sustainable polymer life cycle
Bibliographic record
Abstract
This mini-review highlights how many of the principles of green chemistry can be used to make polymers more sustainable. The use of renewable feedstocks has grown enormously in the recent years including the use of bio-derived monomers and modifications of natural polymers such as carbohydrates. Polymers are also being designed to allow entry into the circular economy especially where triggered depolymerization (e.g. catalytic recycling to monomer) can occur, which can allow easy separation from other plastics in a mixed waste stream. Computational studies and reaction monitoring are useful in identifying and understanding reactivity trends for polymer synthesis and degradation. Solvent-free reactions, including mechanochemistry, can be employed to reduce process mass intensity and environmental impacts. Use of standard polymer degradation conditions (e.g. ISO standards) and life-cycle assessments, in particular hot spot analyses, should be encouraged in order to accelerate progress in this important field. • Many principles of green chemistry can be applied to polymer chemistry. • Beginning and end of life cycles need to be considered for sustainable polymers. • Closing the loop will allow new low carbon footprint plastics economies to emerge. • Enormous scope to develop new sustainable materials, catalysts, and polymer systems. • Standards and life-cycle assessments are needed so comparisons can be made and necessary changes can be accelerated.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".