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Formation of Polyurethane Film Containing Silicone Polymer with Silanol Residue

2017· article· en· W2753506658 on OpenAlexvenueno aff
Shigeki Habaue, Yuto Takahashi, Hiroaki Suzuki

Bibliographic record

VenueInternational Journal of Biotechnology for Wellness Industries · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSilicone and Siloxane Chemistry
Canadian institutionsnot available
Fundersnot available
KeywordsSilanolPolyurethaneSiliconeResidue (chemistry)Materials sciencePolymerPolymer sciencePolymer chemistryChemical engineeringComposite materialChemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The silicone polymers containing a silanol residue were synthesized from silica gel, while the polyurethanes (PUs) bearing a carboxyl moiety were prepared using 2,2-bis(hydroxymethyl)propionic acid (BHMPA) as one of the diol components. The fabrication of the PU films was carried out using these two polymers, then the tensile and thermal properties were investigated. Both the amounts of the carboxyl side groups of the PU chain and the residual silanol of the silicone polymer significantly affected the mechanical property of the film, in addition, the incorporation of 3-aminopropyltrimethoxysilane (APTMS) was quite effective for enhancing the elastic modulus ( E ). As a result, the film prepared from the polyurethane containing the BHMPA unit with a combination of APTMS and the silicone polymer, having a proper amount of silanol groups, showed the highest effect ( E = 5.36 N/mm 2 ), while that observed for the film prepared without using BHMPA and a silicone polymer was 2.10 N/mm 2 . An acid-base interaction between the carboxyl moiety of the PU and amino group of APTMS occurred, while the formation of the siloxane linkage through the silylation reaction between the trimethoxysilyl group of APTMS and silanol also took place.

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)0.001
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 score0.022
Threshold uncertainty score0.434

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 communication0.0000.001
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 head0.021
GPT teacher head0.277
Teacher spread0.256 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Biotechnology for Wellness IndustriesSame topicSilicone and Siloxane ChemistryFrench-language works237,207