Surface free energy is an important parameter in surface and interface properties of fiber reinforced polymer composite. The BET (Brunauer, Emmett, and Teller) surface area and surface energy of the sample can be obtained by Inverse Gas Chromatography (IGC) based on the adsorption principle. In this paper, surface energy of carbon fiber bundle was tested by means of IGC under different conditions to find reliable test parameters. The main parameters involved include length, mass, and packing density of sample, target fractional surface coverage, flow rate, and maximum elution time. It is demonstrated that IGC has the advantages of simple sample preparation, stable test data, high automation, and high sensitivity for carbon fiber. Among all test conditions, packing density and flow rate have the greatest influences on the experimental results. The optimized test parameters are suitable for various kinds of carbon fiber bundles, including polyacrylonitrilebased and pitch-based carbon fibers with different tensile properties and tow sizes. Moreover, IGC can acutely characterize the surface properties of carbon fibers after carbon nanotube modification and heat treatment, which are hard to carry out using contact angle method.

The purpose of this paper is to understand the digital differentiation in social members’ information use via digital devices. Though the attention to the digital differentiation becomes far more increasing, there are only few literatures dealing with quantitative approaches about the digital differentiation. The term ‘digital differentiation’ represents the availability of the information user. It is different from ‘digital divide’ of which the main parameter is accessibility to the information. Once accessibility meets a certain level, availability is considered as a more important factor than accessibility when evaluating the progress of ICT(Information and Communication Technology). We present a model that can describe the digital differentiation phenomenon by using the methodology borrowed from the graph theory, inverse optimization and other established research theory related to digital differentiation. We provide some insights to reduce digital differentiations and therefore our analysis can be used as a guideline for policy maker who desires to mitigate digital differentiations.