Wuhan University

About: Wuhan University is a education organization based out in Wuhan, China. It is known for research contribution in the topics: Population & Feature extraction. The organization has 92849 authors who have published 92882 publications receiving 1691049 citations. The organization is also known as: WHU & Wuhan College.

Enhanced photocatalytic performance of direct Z-scheme g-C3N4–TiO2 photocatalysts for the decomposition of formaldehyde in air

Abstract: Formaldehyde (HCHO) is a major indoor pollutant and long-term exposure to HCHO may cause health problems such as nasal tumors and skin irritation. Photocatalytic oxidation is considered as the most promising strategy for the decomposition of HCHO. Herein, for the first time, a direct g-C3N4–TiO2 Z-scheme photocatalyst without an electron mediator was prepared by a facile calcination route utilizing affordable P25 and urea as the feedstocks. Photocatalytic activities of the as-prepared samples were evaluated by the photocatalytic oxidation decomposition of HCHO in air. It was shown that the photocatalytic activity of the prepared Z-scheme photocatalysts was highly dependent on the g-C3N4 content. At the optimal g-C3N4 content (sample U100 in this study), the apparent reaction rate constant was 7.36 × 10−2 min−1 for HCHO decomposition, which exceeded that of pure P25 (3.53 × 10−2 min−1) by a factor of 2.1. The enhanced photocatalytic activity could be ascribed to the formation of a g-C3N4–TiO2 Z-scheme photocatalyst, which results in the efficient space separation of photo-induced charge carriers. Considering the ease of the preparation method, this work will provide new insights into the design of high-performance Z-scheme photocatalysts for indoor air purification.

Remote sensing imagery in vegetation mapping: a review

Abstract: Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover. Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments. Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.