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Record W3020753835 · doi:10.1002/marc.202000161

Energy‐Dissipating Polymeric Silicone Surfactants

2020· article· en· W3020753835 on OpenAlexaff
Kyle Faiczak, Michael A. Brook, Andrea Feinle

Bibliographic record

VenueMacromolecular Rapid Communications · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSiliconeViscoelasticityMaterials sciencePolymerSilicone oilComposite materialViscosityRheometryPolymer sciencePolymer chemistry

Abstract

fetched live from OpenAlex

Materials that are able to withstand impact loadings by dissipating energy are crucial for a broad range of different applications, including personal protective applications. Shear-thickening fluids (STFs) are often used for this purpose, but their preparation is still limited, in part, to high production costs. It is demonstrated that polymeric surfactants comprised of linear telechelic sugar-modified silicones-with neither additives nor particles-generate transient polymer networks (TPNs) that represent a promising alternative to STFs. The reported polymers have distinct viscoelastic properties and can turn from a liquid into a rubbery network when force is applied. Saccharide-modified silicones with short chains (degree of polymerization (DP) ≈ 34, 68) are solids, but become energy-absorbing viscoelastic fluids when diluted in low-viscosity silicone oils; longer silicones (DP ≈ 338, 675) with low saccharide contents are viscoelastic fluids at room temperature. Excellent damping properties are found for the reported silicone surfactants, even those containing only 0.1% saccharides. The degree of energy absorption can be tailored simply by controlling the sugar/silicone ratio.

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 score0.164
Threshold uncertainty score0.828

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.030
GPT teacher head0.254
Teacher spread0.224 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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