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Record W2794946694 · doi:10.1021/acs.langmuir.7b03746

Superhydrophobic Silicon Nanocrystal–Silica Aerogel Hybrid Materials: Synthesis, Properties, and Sensing Application

2018· article· en· W2794946694 on OpenAlexafffund
Julian Kehrle, Tapas K. Purkait, Simon Kaiser, Konstantinos N. Raftopoulos, Malte Winnacker, Theresa Ludwig, Maryam Aghajamali, Marianne Hanzlik, Katia Rodewald, Tobias Helbich, Christine M. Papadakis, Jonathan G. C. Veinot, Bernhard Rieger

Bibliographic record

VenueLangmuir · 2018
Typearticle
Languageen
FieldChemistry
TopicAerogels and thermal insulation
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaStudienstiftung des Deutschen VolkesDeutsche Forschungsgemeinschaft
KeywordsPhotoluminescenceAerogelMaterials scienceNanocrystalNanotechnologyHybrid materialSiliconCovalent bondSinc functionPorous siliconChemical engineeringOptoelectronicsChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Silicon nanocrystals (SiNCs) are abundant and exhibit exquisitely tailorable optoelectronic properties. The incorporation of SiNCs into highly porous and lightweight substrates such as aerogels leads to hybrid materials possessing the attractive features of both materials. This study describes the covalent deposition of SiNCs on and intercalation into silica aerogels, explores the properties, and demonstrates a prototype sensing application of the composite material. SiNCs of different sizes were functionalized with triethoxyvinylsilane (TEVS) via a radical grafting approach and subsequently used for the synthesis of photoluminescent silica hybrids. The resulting SiNC-containing aerogels possess high porosities, SiNC-based size-dependent photoluminescence, transparency, and a superhydrophobic macroscopic surface. The materials were used to examine the photoluminescence response toward low concentrations of 3-nitrotoluene (270 μM), demonstrating their potential as a sensing platform for high-energy materials.

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 score0.009
Threshold uncertainty score0.533

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 head0.018
GPT teacher head0.210
Teacher spread0.192 · 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

Citations31
Published2018
Admission routes2
Has abstractyes

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