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Record W2618408705 · doi:10.1021/acs.langmuir.7b00820

Covalent Surface Modification of Silicon Oxides with Alcohols in Polar Aprotic Solvents

2017· article· en· W2618408705 on OpenAlexafffund
Austin W. H. Lee, Byron D. Gates

Bibliographic record

VenueLangmuir · 2017
Typearticle
Languageen
FieldEngineering
TopicMolecular Junctions and Nanostructures
Canadian institutionsSimon Fraser University
FundersCanada Research ChairsSimon Fraser UniversityWestern Economic Diversification CanadaBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsReagentChemistrySiliconMonolayerSolventAlcoholPropylene carbonateCovalent bondOrganic chemistryPolarInorganic chemistryChemical engineeringPhysical chemistryElectrochemistry

Abstract

fetched live from OpenAlex

Alcohol-based monolayers were successfully formed on the surfaces of silicon oxides through reactions performed in polar aprotic solvents. Monolayers prepared from alcohol-based reagents have been previously introduced as an alternative approach to covalently modify the surfaces of silicon oxides. These reagents are readily available, widely distributed, and are minimally susceptible to side reactions with ambient moisture. A limitation of using alcohol-based compounds is that previous reactions required relatively high temperatures in neat solutions, which can degrade some alcohol compounds or could lead to other unwanted side reactions during the formation of the monolayers. To overcome these challenges, we investigate the condensation reaction of alcohols on silicon oxides carried out in polar aprotic solvents. In particular, propylene carbonate has been identified as a polar aprotic solvent that is relatively nontoxic, readily accessible, and can facilitate the formation of alcohol-based monolayers. We have successfully demonstrated this approach for tuning the surface chemistry of silicon oxide surfaces with a variety of alcohol containing compounds. The strategy introduced in this research can be utilized to create silicon oxide surfaces with hydrophobic, oleophobic, or charged functionalities.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.217

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.238
Teacher spread0.222 · 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

Citations10
Published2017
Admission routes2
Has abstractyes

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