Pusan National University

About: Pusan National University is a education organization based out in Busan, South Korea. It is known for research contribution in the topics: Catalysis & Population. The organization has 24124 authors who have published 45054 publications receiving 819356 citations. The organization is also known as: Busan National University & Pusan University.

Morphological and physical properties of a thermoplastic polyurethane reinforced with functionalized graphene sheet

Abstract: BACKGROUND: Functionalized graphene sheet (FGS) was recently introduced as a new nano-sized conductive filler, but little work has yet examined the possibility of using FGS as a nanofiller in the preparation of polymer nanocomposites. In particular, there are currently no published papers that evaluate polyurethane/FGS nanocomposites. The purpose of this study was to prepare a polyurethane/FGS nanocomposite and examine the morphological and physical properties of the material. RESULTS: A cast nanocomposite film was prepared from a mixture of thermoplastic polyurethane (TPU) solution and FGS suspended in methyl ethyl ketone. The FGS dispersed on the nanoscale throughout the TPU matrix and effectively enhanced the conductivity. A nanocomposite containing 2 parts of FGS per 100 parts of TPU had an electrical conductivity of 10−4 S cm−1, a 107 times increase over that of pristine TPU. The dynamic mechanical properties showed that the FGS efficiently reinforced the TPU matrix, particularly in the temperature region above the soft segment melt. CONCLUSION: Our results show that FGS has a high affinity for TPU, and it could therefore be used effectively in the preparation of TPU/FGS nanocomposites without any further chemical surface treatment. This indicates that FGS is an effective and convenient new material that could be used for the modification of polyurethane. It could also be used in place of other nano-sized conductive fillers, such as carbon nanotubes. Copyright © 2009 Society of Chemical Industry

Self-Organizing Map

Abstract: Ecological data are considered difficult to analyze because numerous biological and environmental factors are involved in ecological processes in a complex manner. The self-organizing map (SOM) has been an efficient alternative tool for analyzing ecological data without a priori knowledge. The unsupervised learning process was applied to provide a comprehensive view on ecological data through the use of ordination and classification. The SOM extracts information from multidimensional data and maps it onto two- or three-dimensional space. The network structure and learning algorithm are discussed to reveal the adaptive convergence of connection weights among computation nodes (i.e., neurons). Examples are provided to demonstrate the environmental impact gradient and sample unit clustering. SOM visualization is also presented to show profiles of the corresponding taxa and environmental variables.