Synthesis of capped TiO2 nanocrystals of controlled shape and their use with MEH-PPV conjugated polymer to develop nanocomposite films for photovoltaic applications
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".