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Record W7135846324

New opportunities in sulfur-curing by CTS

2024· article· en· W7135846324 on OpenAlexaff
Gina Butuc, Jose Swart, Marcel Simons, Jan van Velde, Kees van Leerdam, Brenda D. Rossenaar, Auke Talma, Anke Blume

Bibliographic record

VenueUniversity of Twente Research Information · 2024
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsAkzoNobel (Canada)
Fundersnot available
KeywordsSulfurReactivity (psychology)Chemical reactionChemical modificationRaman spectroscopyNatural rubber
DOInot available

Abstract

fetched live from OpenAlex

Sulfur donors are used commonly in rubber crosslinking, usually in conjunction with elemental sulfur. In the crosslinking process, sulfur donors release in-situ free sulfur. This study analyses the difference between two sulfur donors, bis(triethoxysilylpropyl)-tetrasulfide (TESPT) and cyclic tetrasulfide (CTS), both containing at least one tetrasulfidic group in the molecule. Chemical model studies are deployed to assess the chemical reactivity of the two molecules. The difference in reactivity of the two sulfur donors was observed in chemical model studies in reaction with 2,3-dimethyl-2-butene, as followed by Raman Spectroscopy, GC-MS and NMR analysis of the products of reaction. These studies represent a fundamental analysis and comparison between the two tetrasulfidic compounds and the findings of these experiments can be extrapolated to crosslinking processes in rubber.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.069
GPT teacher head0.281
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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