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Record W4378218223 · doi:10.1002/ange.202304708

A Cleavable Crosslinking Strategy for Commodity Polymer Functionalization and Generation of Reprocessable Thermosets

2023· article· en· W4378218223 on OpenAlexafffund
Liting Bi, Benjamin Godwin, Miranda J. Baran, Rashid Nazir, Jeremy E. Wulff

Bibliographic record

VenueAngewandte Chemie · 2023
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversity of Victoria
FundersMitacsCanada Research ChairsUniversity of Victoria
KeywordsThermosetting polymerPolymerCovalent bondThermoplasticReagentPolymer scienceCommodity chemicalsChemical modificationMaterials scienceThermal stabilitySurface modificationPolymer chemistryChemistryChemical engineeringOrganic chemistryCombinatorial chemistryCatalysis

Abstract

fetched live from OpenAlex

Abstract Covalently crosslinked polymeric materials, known as thermosets, possess enhanced mechanical strength and thermal stability relative to the corresponding uncrosslinked thermoplastics. However, the presence of covalent inter‐chain crosslinks that makes thermosets so attractive is precisely what makes them so difficult to reprocess and recycle. Here, we demonstrate the introduction of chemically cleavable groups into a bis ‐diazirine crosslinker. Application of this cleavable crosslinker reagent to commercial low‐functionality polyolefins (or to a small‐molecule model) results in the rapid, efficient introduction of molecular crosslinks that can be uncoupled by specific chemical inputs. These proof‐of‐concept findings provide one potential strategy for circularization of the thermoplastic/thermoset plastics economy, and may allow crosslinked polyolefins to be manufactured, used, reprocessed, and re‐used without losing value. As an added benefit, the method allows the ready introduction of functionality into non‐functionalized commodity polymers.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.066
GPT teacher head0.288
Teacher spread0.222 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

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