The Controllable Ratio of the Polyaniline-Needle-Shaped Manganese Dioxide for the High-Performance Supercapacitor Application.

The nanohybrid development of metal oxide/conducting polymer as an energy storage material is an active research area, because of the device stability, conductive behavior, and easy fabrication. Herein, needle-like MnO2 was coupled with polyaniline fabricated through chemical polymerization followed by the hydrothermal process. The characterization results show that MnO2/polyaniline exhibited a needle-like morphology. Different characterization techniques such as X-ray diffraction patterns and scanning electron microscopy confirmed the formation of the MnO2/polyaniline nanohybrids. The electrochemical performance, including cyclic voltammetry (CV), galvanostatic charge-discharge (GCD), specific capacitance (Csp), and cyclic stability, was examined using a three-electrode assembly cell. The optimized electrode displayed a Csp of 522.20 F g-1 at a current load of 1.0 A g-1 compared with the other electrodes. The developed synergism during MnO2/polyaniline fabrication provided enhanced conductive channels and stability during the charge-discharge process.

Toward automated classification of monolayer versus few-layer nanomaterials using texture analysis and neural networks.

The need for a fast and robust method to characterize nanostructure thickness is growing due to the tremendous number of experiments and their associated applications. By automatically analyzing the microscopic image texture of MoS2 and WS2, it was possible to distinguish monolayer from few-layer nanostructures with high accuracy for both materials. Three methods of texture analysis (TA) were used: grey level histogram (GLH), grey levels co-occurrence matrix (GLCOM), and run-length matrix (RLM), which correspond to first, second, and higher-order statistical methods, respectively. The best discriminating features were automatically selected using the Fisher coefficient, for each method, and used as a base for classification. Two classifiers were used: artificial neural networks (ANN), and linear discriminant analysis (LDA). RLM with ANN was found to give high classification accuracy, which was 89% and 95% for MoS2 and WS2, respectively. The result of this work suggests that RLM, as a higher-order TA method, associated with an ANN classifier has a better ability to quantify and characterize the microscopic structure of nanolayers, and, therefore, categorize thickness to the proper class.

Growth of Complex 2D Material-Based Structures with Naturally Formed Contacts.

The difficulty of processing two-dimensional (2D) transition metal dichalcogenide (TMD) materials into working devices with any scalability is one of the largest impediments to capitalizing on their industrial promise. Here, we describe a versatile, simple, and scalable technique to directly grow self-contacted thin-film materials over a range of TMDs (MoS2, MoSe2, WS2, and WSe2), where predeposited bulk metallic contacts serve as the nucleation site for the TMD material to grow, forming naturally contacted device structures in a single step. The conditions for growth as well as optical and physical properties are reported. Because the material grows controllably around the lithographically defined patterns, wafer scale circuits and complex device geometries can be envisioned, including lateral heterostructures of different TMD materials.