Multiple chemical warfare agent simulant decontamination by self-driven microplasma

Low-temperature plasma is a green and high-efficiency technology for chemical warfare agent(CWA)decontamination.However,traditional plasma devices suffer from the problems of highpower composition and large power-supply size,which limit their practical applications.In this paper,a self-driven microplasma decontamination system,induced by a dielectric-dielectric rotary triboelectric nanogenerator(dd-r TENG),was innovatively proposed for the decontamination of CWA simulants.The microplasma was characterized via electrical measurements,optical emission spectra and ozone concentration detection.With an output voltage of-3460 V,the dd-r TENG can successfully excite microplasma in air.Reactive species,such as OH,O(1D),Hαand O3were detected.With input average power of 0.116 W,the decontamination rate of 2-chloroethyl ethyl sulfide reached 100%within 3 min of plasma treatment,while the decontamination rates of malathion and dimethyl methylphosphonate reached(65.92±1.65)%and(60.88±1.92)%after 7 min of plasma treatment,respectively.In addition,the decontamination rates gradually decreased with the increase in the simulant concentrations.Typical products were identified and analyzed.This study demonstrates the broad spectrum and feasibility of the dd-r TENG-microplasma for CWA elimination,which provides significant guidance for their practical applications in the future.

Applications of magnetic nanoparticles in surface-enhanced Raman scattering(SERS)detection of environmental pollutants

Environmental pollution, a major problem worldwide, poses considerable threat to human health and ecological environment. Efficient and reliable detection technologies, which focus on the appearance of emerging environmental and trace pollutants, are urgently needed. Surface-enhanced Raman scattering(SERS) has become an attractive analytical tool for sensing trace targets in environmental field because of its inherent molecular fingerprint specificity and high sensitivity. In this review, we focused on the recent developments in the integration of magnetic nanoparticles(MNPs) with SERS for facilitating sensitive detection of environmental pollutants. An overview and classification of different types of MNPs for SERS detection were initially provided, enabling us to categorize the huge amount of literature that was available in the interdisciplinary research field of MNPs based SERS technology. Then, the basic working principles and applications of MNPs in SERS detection were presented. Subsequently, the detection technologies integrating MNPs with SERS that eventually were used for the detection of various environmental pollutions were reviewed. Finally, the advantages of MNP-basedSERS detection technology for environmental pollutants were concluded, and the current challenges and future outlook of this technology in practical applications were highlighted. The application of the MNPsbasedSERS techniques for environmental analysis will be significantly advanced with the great progresses of the nanotechnologies, optics, and materials.

Scene word recognition from pieces to whole

Convolutional neural networks (CNNs) have had great success with regard to the object classification problem. For character classification, we found that training and testing using accurately segmented character regions with CNNs resulted in higher accuracy than when roughly segmented regions were used. Therefore, we expect to extract complete character regions from seene images. Text in natural scene images has an obvious contrast with its attachments. Many methods attempt to extract characters through different segmentation techniques. However, for blurred, occluded, and complex background cases, those methods may result in adjoined or over segmented characters. In this paper, we propose a scene word recognition model that integrates words from small pieces to entire after-cluster-based segmentation. The segmented connected components are classified as four types: background, in dividual character proposals, adjoined characters, and stroke proposals. Individual character proposals are directly inputted to a CNN that is trained using accurately segmented character images. The sliding window strategy is applied to adjoined character regions. Stroke proposals are considered as fragments of entire characters whose locations are estimated by a stroke spatial distribution system. Then、the estimated characters from adjoined characters and stroke proposals are classified by a CNN that is trained on roughly segmented character images. Finally, a lexicondriven integration method is performed to obtain the final word recognition results. Compared to other word recognition methods, our method achieves a comparable performance on Street View Text and the ICDAR 2003 and ICDAR 2013 benchmark databases. Moreover, our method can deal with recognizing text images of occlusion and improperly segmented text images.