National Cheng Kung University

About: National Cheng Kung University is a education organization based out in Tainan City, Taiwan. It is known for research contribution in the topics: Population & Thin film. The organization has 49723 authors who have published 69799 publications receiving 1437420 citations. The organization is also known as: NCKU.

Preparation and characterization of a novel magnetic nano-adsorbent

Abstract: A novel magnetic nano-adsorbent has been developed using Fe3O4 nanoparticles (13.2 nm) as cores and polyacrylic acid (PAA) as ionic exchange groups. The Fe3O4 magnetic nanoparticles were prepared by co-precipitating Fe2+ and Fe3+ ions in an ammonia solution and treating under hydrothermal conditions. PAA was covalently bound onto the magnetic nanoparticles via carbodiimide activation. Transmission electron micrographs showed that the magnetic nanoparticles remained discrete and had no significant change in size after binding the PAA. The X-ray diffraction patterns indicated the magnetic nanoparticles were pure Fe3O4 with a spinel structure, and the binding of PAA did not result in a phase change. Magnetic measurement revealed the magnetic nanoparticles were superparamagnetic, and their saturation magnetization was reduced only slightly after PAA binding. Fourier transform infrared spectroscopy, thermogravimetric and differential thermal analyses, and X-ray photoelectron spectroscopy confirmed the binding of PAA to the magnetic nanoparticles, suggested a binding mechanism for the PAA, and revealed the maximum weight ratio of PAA bound to the magnetic nanoparticles was 0.12. In addition, the ionic exchange capacity of the resultant magnetic nano-adsorbents was estimated to be 1.64 mequiv g−1, much higher than those of commercial ionic exchange resins. When the magnetic nano-adsorbents were used for the recovery of lysozyme, the adsorption/desorption of lysozyme was completed within 1 min due to the absence of pore-diffusion resistance. Also, the adsorption/desorption efficiency could reach almost 100% under appropriate conditions, and the recovered lysozyme retained 95% activity.

GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media

Abstract: This paper solves the fake news detection problem under a more realistic scenario on social media. Given the source short-text tweet and the corresponding sequence of retweet users without text comments, we aim at predicting whether the source tweet is fake or not, and generating explanation by highlighting the evidences on suspicious retweeters and the words they concern. We develop a novel neural network-based model, Graph-aware Co-Attention Networks (GCAN), to achieve the goal. Extensive experiments conducted on real tweet datasets exhibit that GCAN can significantly outperform state-of-the-art methods by 16% in accuracy on average. In addition, the case studies also show that GCAN can produce reasonable explanations.