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Record W4403451802 · doi:10.1002/marc.202400408

Organic–Inorganic Hybrid Materials from Vegetable Oils

2024· review· en· W4403451802 on OpenAlexafffund
Eline Laurent, Milan Marić

Bibliographic record

VenueMacromolecular Rapid Communications · 2024
Typereview
Languageen
FieldMaterials Science
TopicSilicone and Siloxane Chemistry
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRaw materialHybrid materialMaterials scienceSiloxaneFiller (materials)CompatibilizationSilaneReactive materialPolymerizationRenewable resourcePolymer scienceNanotechnologyChemical engineeringPolymerOrganic chemistryRenewable energyComposite materialChemistryCopolymerPolymer blendEngineering

Abstract

fetched live from OpenAlex

The production of materials based on fossil resources is yielding more sustainable and ecologically beneficial methods. Vegetable oils (VO) are one example of base materials whose derivatives rival the properties of their petro-based counterparts. Gaps exist however and one way to fill them is by employing sol-gel processes to synthesize organic-inorganic hybrid materials, often derived from silane/siloxane compounds. Creating Si─O─Si inorganic networks in the organic VO matrix permits the attainment of necessary strength, among other property enhancements. Consequently, many efforts have been directed to optimally achieve organic-inorganic hybrid materials with VOs. However, compatibilization is challenging, and desirable conditions for matching the inorganic filler in the organic matrix remain a key stumbling block toward wider application. Therefore, this review aims to detail recent progress on these new hybrids, focusing on the main strategies to polymerize and functionalize the raw VO, followed by routes highlighting the addition of the inorganic fillers to obtain desirable composites.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0050.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.014

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 head0.041
GPT teacher head0.309
Teacher spread0.268 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreReview

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

Citations5
Published2024
Admission routes2
Has abstractyes

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