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Record W4414846911 · doi:10.1021/jacs.5c12988

Tuning the Degradability Profiles of Polyesters with Indium-Catalyzed Incorporation of Poly(ε-thiocaprolactone) Blocks

2025· article· en· W4414846911 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of the American Chemical Society · 2025
Typearticle
Languageen
FieldMaterials Science
TopicSynthesis and properties of polymers
Canadian institutionsUniversity of British Columbia
FundersDivision of ChemistryNatural Sciences and Engineering Research Council of Canada
KeywordsCationic polymerizationPolymerizationCopolymerMacromoleculePolymerDegradation (telecommunications)PolyesterHydrolysis

Abstract

fetched live from OpenAlex

There are no examples of high molecular weight (>100 000 g/mol) poly(ε-thiocaprolactone) (PtCL), and the mechanism of ε-thiocaprolactone (tCL) polymerization with organometallic complexes has not been investigated. In this work, we demonstrate the synthesis of the highest reported molecular weight PtCL ( M n = 109 000 g/mol) by using a cationic indium thiolate catalyst formed in situ via the addition of benzyl mercaptan. The mechanism of the polymerization is thoroughly investigated and the reaction coordinate is elucidated via computational calculations; the polymerization propagates through a coordination–insertion mechanism. If the PtCL is not isolated during the polymerization, the resulting indium-PtCL macromolecule can be used as an initiator to form copolymers with poly(lactide) (PLA) and poly(ε-caprolactone) (PCL). Incorporating 10% PtCL in these polymers alters their degradation behavior: the hydrolytic degradation period of PLA is reduced by nearly half, while PCL becomes more resistant to hydrolysis.

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.

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)0.001
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 score0.006
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.233
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