Edith Cowan University

About: Edith Cowan University is a education organization based out in Perth, Western Australia, Australia. It is known for research contribution in the topics: Population & Tourism. The organization has 4040 authors who have published 13529 publications receiving 339582 citations. The organization is also known as: Edith Cowan & ECU.

Activation of peroxymonosulfate by carbonaceous oxygen groups: experimental and density functional theory calculations

Abstract: The active sites for metal-free carbocatalysis in environmental remediation are intricate compared to those for traditional metal-based catalysis. In this study, we report a facile fabrication of amorphous carbon spheres with varying oxygen functional groups by hydrothermal treatment of glucose solutions. With air/N2 annealing and regeneration in the glucose solution of the as-synthesized carbon spheres, the concentrations of oxygen-containing groups were tailored on the amorphous carbon spheres in an Excess-On-Off-On manner. Accordingly, an Off-On-Off-On catalytic behavior in peroxymonosulfate (PMS) activation using these amorphous carbon spheres was observed. To uncover the mechanism of catalytic activity, electron spin resonance (EPR) spectra were recorded to investigate the variation of the generated OH and SO4 −radicals. Moreover, density functional theory (DFT) studies were employed to identify the role of oxygen-containing groups on the amorphous carbon spheres in adsorptive O O bond activation of PMS. Results revealed that ketone groups (C O) are the dominant active sites for PMS activation among oxygen-containing functional groups. In order to simulate real wastewater treatment, influences of chloride anions and humic acid on PMS activation for phenol degradation were further evaluated. This study provides an in-depth insight to discovering the role of oxygen-containing functional groups as the active sites in metal-free carbocatalysis.

A Bayesian approach to skin color classification in YCbCr color space

Abstract: This paper addresses an image classification technique that uses the Bayes decision rule for minimum cost to classify pixels into skin color and non-skin color. Color statistics are collected from YCbCr color space. The Bayesian approach to skin color classification is discussed along with an overview of YCbCr color space. Experimental results demonstrate that this approach can achieve good classification outcomes, and it is robust against different skin colors.