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Record W2589186649 · doi:10.1002/pat.4020

Kinetics and mechanism of the thermal degradation for the synthesis of poly(norbornene sulfone)s by two different polymerization methods

2017· article· en· W2589186649 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.

Bibliographic record

VenuePolymers for Advanced Technologies · 2017
Typearticle
Languageen
FieldMaterials Science
TopicThermal and Kinetic Analysis
Canadian institutionsUniversity of New Brunswick
FundersNational Natural Science Foundation of China
KeywordsSulfoneNorbornenePolymer chemistryPolymerizationMaterials scienceKineticsOlefin fiberCopolymerPolymerComposite material

Abstract

fetched live from OpenAlex

The recent development of sulfur dioxide (SO 2 ) polymerization has seen a renaissance in its chemistry, especially poly(olefin sulfone)s from the copolymerization of SO 2 , and unsaturated hydrocarbons can be found to have many applications, including transient electronic packaging, drug delivery, and electron beam‐resistant materials. In this work, a type of functional poly(norbornene sulfone) was synthesized via two different polymerization methods. Aiming to understanding the effects of different polymerization methods on poly(olefin sulfone)s and gain further understanding on the kinetics of poly(olefin sulfone)s's thermal instability, we investigated their detailed thermal degradation behaviors using thermogravimetry and analyzed the resultant kinetics in accordance with three kinetic models. The results supported the conclusion that although the poly(norbornene sulfone)s obtained have different activation energy, the thermal degradation kinetics are the same and also obey the D n type. Copyright © 2017 John Wiley & Sons, Ltd.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.386
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.016
GPT teacher head0.290
Teacher spread0.273 · 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