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

The present study presents the synthesis details of titanium dioxide (TiO2) nanoparticles (NPs) of different morphologies using oleic acid (OA) and oleyl amine (OM) as capping agents. Different shapes of NPs, such as nanospheres, nanorods, and nanorhombics, were achieved. 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. Obtained results showed promising properties for photovoltaic devices, especially solar radiation absorption properties and charge transfer at the interface of the conjugated MEH-PPV matrix and TiO2 dispersed NPs.

Protein function prediction with gene ontology: from traditional to deep learning models.

Protein function prediction is a crucial part of genome annotation. Prediction methods have recently witnessed rapid development, owing to the emergence of high-throughput sequencing technologies. Among the available databases for identifying protein function terms, Gene Ontology (GO) is an important resource that describes the functional properties of proteins. Researchers are employing various approaches to efficiently predict the GO terms. Meanwhile, deep learning, a fast-evolving discipline in data-driven approach, exhibits impressive potential with respect to assigning GO terms to amino acid sequences. Herein, we reviewed the currently available computational GO annotation methods for proteins, ranging from conventional to deep learning approach. Further, we selected some suitable predictors from among the reviewed tools and conducted a mini comparison of their performance using a worldwide challenge dataset. Finally, we discussed the remaining major challenges in the field, and emphasized the future directions for protein function prediction with GO.