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Record W2906977501 · doi:10.1021/acsomega.8b02826

Photoinduced Degradation of Polymer Films Using Polyglyoxylate–Polyester Blends and Copolymers

2018· article· en· W2906977501 on OpenAlexafffund
Spencer M. Heuchan, Jarret P. MacDonald, Lukas Bauman, Bo Fan, Hugh A. L. Henry, Elizabeth R. Gillies

Bibliographic record

VenueACS Omega · 2018
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDepolymerizationPolyesterMaterials sciencePolymerCopolymerPolycaprolactoneDegradation (telecommunications)CoatingGlass transitionChemical engineeringAmorphous solidThermal stabilityPolymer chemistryComposite materialOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Polymeric coatings are commonly employed to alter surface properties. While some coatings are designed to remain stable over a prolonged period, in applications such as pharmaceuticals or fertilizers, the coating is designed to erode and reveal or release the underlying material. Self-immolative polymers (SIPs) undergo depolymerization following the cleavage of stimuli-responsive end-caps from their termini, enabling controlled depolymerization in the solid state and in solution. Poly(ethyl glyoxylate) (PEtG) is a promising SIP because of its depolymerization to benign products, but its amorphous structure and low glass-transition temperature make it unsuitable alone for coating applications. This study explored the blending of PEtG with polyesters including polycaprolactone (PCL), poly(l-lactic acid), and poly(R-3-hydroxybutyrate). Block copolymers of PEtG with PCL were also synthesized and studied. It was found that the phase separation behavior and consequently the thermal and mechanical properties of the materials could be tuned according to the composition of the blend, while the stimuli-responsive degradation of PEtG was retained in the blends. This work therefore provides a framework for the application of PEtG-based coatings in applications ranging from pharmaceuticals to agricultural products.

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 score0.008
Threshold uncertainty score0.639

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 head0.035
GPT teacher head0.255
Teacher spread0.219 · 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

Citations12
Published2018
Admission routes2
Has abstractyes

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