Optimizing the dielectric constant of the shell layer in core-shell structures for enhanced energy density of polymer nanocomposites.

Improved dielectric constant and breakdown strength facilitates excellent energy storage density of polymer dielectrics, which is positive to miniaturize dielectric capacitors in electronic and electrical systems. Although coating polar substances on nanoparticles enhances the dielectric constants of polymer nanocomposites, it usually causes local electric field concentration, leading to poor breakdown strength. Here, fluoropolymers with tailorable fluorine content (PF0, PF30 and PF60) are coated on BaTiO3 (BT) nanoparticles to construct typical core-shell structures that are further blended with poly(vinylidenefluoride-co-hexafluoropropylene) (P(VDF-HFP)) to obtain BT@PF/P(VDF-HFP) nanocomposites. Uniform dispersion of nanoparticles and excellent compatibility of interfaces are observed for the samples. In addition, the dielectric constant gradually increases from 8.03 to 8.26 to 9.12 for the nanocomposites filled with 3 wt% BT@PF0, BT@PF30 and BT@PF60, respectively. However, 3 wt% BT@PF30/P(VDF-HFP) has the highest breakdown strength (455 kV mm-1) among the nanocomposites, which is as good as neat P(VDF-HFP). More importantly, BT@PF30 rather than BT@PF60 possesses the maximum discharged energy density (11.56 J cm-3 at 485 kV mm-1), which is about 1.65 times that of neat P(VDF-HFP). This work proposes a facile experimental route to optimize the dielectric constants of the shell layer to couple the dielectric constants between the nanoparticles, shell layer and polymer matrix, which contributes to alleviating the local electric field concentration for excellent breakdown strength and electrical energy storage of polymer nanocomposites.

WGCNA combined with machine learning to find potential biomarkers of liver cancer.

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.