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Record W2766701207 · doi:10.1002/chem.201704941

High Surface Area, Thermally Stable, Hydrophobic, Microporous, Rigid Gels Generated at Ambient from MeSi(OEt)<sub>3</sub>/(EtO)<sub>3</sub>SiCH<sub>2</sub>CH<sub>2</sub>Si(OEt)<sub>3</sub> Mixtures by F<sup>−</sup>‐Catalyzed Hydrolysis

2017· article· en· W2766701207 on OpenAlexfundno aff
Joseph C. Furgal, Honami Yamane, Timothy R. Odykirk, Eongyu Yi, Yoshiki Chujo, Richard M. Laine

Bibliographic record

VenueChemistry - A European Journal · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSilicone and Siloxane Chemistry
Canadian institutionsnot available
FundersMinistère de l'Économie, de la Science et de l'Innovation - QuébecBowling Green State UniversitySemiconductor Research Corporation
KeywordsMicroporous materialPolymerDichloromethaneMaterials scienceHydrolysisChemical engineeringSolventFluorideCatalysisSpecific surface areaPolymer chemistryChemistryOrganic chemistryInorganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract High surface area materials are of considerable interest for gas storage/capture, molecular sieving, catalyst supports, as well as for slow‐release drug‐delivery systems. We report here a very simple and fast route to very high surface area, mechanically robust, hydrophobic polymer gels prepared by fluoride‐catalyzed hydrolysis of mixtures of MeSi(OEt) 3 and bis‐triethoxysilylethane (BTSE) at room temperature. These materials offer specific surface areas up to 1300 m 2 g −1 , peak pore sizes of 0.8 nm and thermal stabilities above 200 °C. The gelation times and surface areas can be controlled by adjusting the solvent volume (dichloromethane), percent fluoride (as n Bu 4 NF or TBAF) and the BTSE contents. Polymers with other corners and linkers were also explored. These materials will further expand the materials databank for use in vacuum insulation panels and as thermally stable release and capture media.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
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 score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0050.005
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.001
Science and technology studies0.0060.003
Scholarly communication0.0050.002
Open science0.0070.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0010.003

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 head0.010
GPT teacher head0.199
Teacher spread0.189 · 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
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

Citations7
Published2017
Admission routes1
Has abstractyes

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Same venueChemistry - A European JournalSame topicSilicone and Siloxane ChemistryFrench-language works237,207