Single-layered assembly of vanadium pentoxide nanowires on graphene for nanowire-based lithography technique

Graphene nanoribbon (GNR)-based materials are a promising device material because of their potential high carrier mobility and atomically thin structure. Various approaches have been reported for preparing the GNR-based materials, from bottom-up chemical synthetic procedures to top-down fabrication techniques using lithography of graphene. However, it is still difficult to prepare a large-scale GNR-based material. Here, we develop a procedure to prepare a large-scale GNR network using networked single-layer inorganic nanowires. Vanadium pentoxide (V2O5) nanowires were assembled on graphene with an interfacial layer of a cationic polymer via the electrostatic interaction. A large-scale nanowire network can be prepared on graphene and is stable enough for applying an oxygen plasma. Using plasma etching, a networked graphene structure can be generated. Removing the nanowires results in a networked flat structure whose both surface morphology and Raman spectrum indicate a GNR networked structure. The field-effect device indicates the semiconducting character of the GNR networked structure. This work would be useful for fabricating a large-scale GNR-based material as a platform for GNR junctions for physics and electronic circuits.

Unsupervised Feature Selection for Histogram-Valued Symbolic Data Using Hierarchical Conceptual Clustering

This paper presents an unsupervised feature selection method for multi-dimensional histogram-valued data. We define a multi-role measure, called the compactness, based on the concept size of given objects and/or clusters described using a fixed number of equal probability bin-rectangles. In each step of clustering, we agglomerate objects and/or clusters so as to minimize the compactness for the generated cluster. This means that the compactness plays the role of a similarity measure between objects and/or clusters to be merged. Minimizing the compactness is equivalent to maximizing the dis-similarity of the generated cluster, i.e., concept, against the whole concept in each step. In this sense, the compactness plays the role of cluster quality. We also show that the average compactness of each feature with respect to objects and/or clusters in several clustering steps is useful as a feature effectiveness criterion. Features having small average compactness are mutually covariate and are able to detect a geometrically thin structure embedded in the given multi-dimensional histogram-valued data. We obtain thorough understandings of the given data via visualization using dendrograms and scatter diagrams with respect to the selected informative features. We illustrate the effectiveness of the proposed method by using an artificial data set and real histogram-valued data sets.

Unsupervised Feature Selection for Histogram-Valued Symbolic Data by Hierarchical Conceptual Clustering

This paper presents an unsupervised feature selection method for multi-dimensional histogram-valued data. We define a multi-role measure, called the compactness, based on the concept size of given objects and/or clusters described by a fixed number of equal probability bin-rectangles. In each step of clustering, we agglomerate objects and/or clusters so as to minimize the compactness for the generated cluster. This means that the compactness plays the role of a similarity measure between objects and/or clusters to be merged. To minimize the compactness is equivalent to maximize the dis-similarity of the generated cluster, i.e., concept, against the whole concept in each step. In this sense, the compactness plays the role of cluster quality. We also show that the average compactness of each feature with respect to objects and/or clusters in several clustering steps is useful as feature effectiveness criterion. Features having small average compactness are mutually covariate, and are able to detect geometrically thin structure embedded in the given multi-dimensional histogram-valued data. We obtain thorough understandings of the given data by the visualization using dendrograms and scatter diagrams with respect to the selected informative features. We illustrate the effectiveness of the proposed method by using an artificial data set and real histogram-valued data sets.

A thin, deformable, high-performance supercapacitor implant that can be biodegraded and bioabsorbed within an animal body

It has been an outstanding challenge to achieve implantable energy modules that are mechanically soft (compatible with soft organs and tissues), have compact form factors, and are biodegradable (present for a desired time frame to power biodegradable, implantable medical electronics). Here, we present a fully biodegradable and bioabsorbable high-performance supercapacitor implant, which is lightweight and has a thin structure, mechanical flexibility, tunable degradation duration, and biocompatibility. The supercapacitor with a high areal capacitance (112.5 mF cm−2 at 1 mA cm−2) and energy density (15.64 μWh cm−2) uses two-dimensional, amorphous molybdenum oxide (MoOx) flakes as electrodes, which are grown in situ on water-soluble Mo foil using a green electrochemical strategy. Biodegradation behaviors and biocompatibility of the associated materials and the supercapacitor implant are systematically studied. Demonstrations of a supercapacitor implant that powers several electronic devices and that is completely degraded after implantation and absorbed in rat body shed light on its potential uses.

Extremums at temperature dependencies physico-mechanical properties of iron, as a result of transformation in it at ~ 650ºс

In the presented work on the basis of extrema on curves: efforts of resistance of draft, parameters of thin structure, metalgraphic structure depending on temperature and also numerous literary data on anomalies of physicomechanical properties of iron and staly transformation is declared at ~ 650 C. The research was conducted on samples from almost pure iron (0,008% C). An experiment (the metalgraphic research, the X-ray diffraction analysis, resistance to draft (Gleeble-3800) carried out through each 20 C.