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Record W1981391003 · doi:10.1063/1.4873778

Synthesis of capped TiO2 nanocrystals of controlled shape and their use with MEH-PPV conjugated polymer to develop nanocomposite films for photovoltaic applications

2014· article· en· W1981391003 on OpenAlexaff
Frej Mighri, Duong Vu, D. Trong On, Abdellah Ajji

Bibliographic record

VenueAIP conference proceedings · 2014
Typearticle
Languageen
FieldMaterials Science
TopicChemical and Physical Properties of Materials
Canadian institutionsPolytechnique MontréalUniversité Laval
Fundersnot available
KeywordsMaterials scienceNanocompositeThermogravimetric analysisNanorodPhotoluminescenceChemical engineeringTitanium dioxidePolymerNanoparticleConjugated systemNanotechnologyComposite materialOptoelectronics

Abstract

fetched live from OpenAlex

This study presents the synthesis details of titanium dioxide (⁠TiO2⁠) nanoparticles (NPs) of different shapes (nanospheres, nanorods and nanorhombics) using oleic acid (OA) and oleyl amine (OM) as capping agents. In order to develop nanocomposite thin films for photovoltaic cells, these TiO2 NPs were carefully dispersed in 2-methoxy-5-(2'-ethylhexyloxy)-p-phenylene vinylene (MEH-PPV) matrix. The properties of synthesized TiO2 NPs and MEH-PPV/TiO2 nanocomposites were characterized using transmission electron microscopy (TEM), thermogravimetric analysis (TGA), UV-Visible spectroscopy, and Photoluminescence technique. It was found that the shape of NPs and the amount of OA and OM surfactants capped on their surface have an effect on their energy bandgap and also on the dispersion quality of MEH-PPV/TiO2 nanocomposites. Even though there was no evidence of chemical bonding between MEH-PPV matrix and TiO2 dispersed NPs, MEH-PPV/TiO2 nanocomposites showed very promising results for light absorption properties and charge transfer at the interface of the conjugated MEH-PPV matrix and TiO2 dispersed NPs, which are two main characteristics for photovoltaic 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 score0.001
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.218
Teacher spread0.201 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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