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Record W4405962984 · doi:10.1016/j.cogsc.2024.100996

Applying the principles of green chemistry to achieve a more sustainable polymer life cycle

2024· article· en· W4405962984 on OpenAlexaff
Francesca M. Kerton

Bibliographic record

VenueCurrent Opinion in Green and Sustainable Chemistry · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicChemistry and Chemical Engineering
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsGreen chemistryChemistryBiochemical engineeringNanotechnologyEnvironmental economicsNatural resource economicsEngineeringMaterials scienceEconomicsOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.254
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2024
Admission routes1
Has abstractyes

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