imaging detection
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2021 ◽  
Author(s):  
Yang Zhao ◽  
Penghui Zhang ◽  
Peng Li ◽  
Zhiquan Zhou

2021 ◽  
Author(s):  
Mihajlo D Radmilović ◽  
Branka D. Murić ◽  
Dušan Gujić ◽  
Boban Zarkov ◽  
Marija Z. Nenadić ◽  
...  

Abstract Microoptical components are coming of age in a wide range of applications: lab-on-a-chip, imaging, detection... There are a large number of fabrication technologies capable of producing high quality individual components and their arrays. However, most of them require high-end and costly equipment, complex and time-consuming fabrication, harmful chemicals, resulting in expensive final products. Here we present a technology capable of producing high quality microoptical components, using low-end direct laser writing on a biocompatible, environmentally friendly hydrogel, without any waste substances. Gel is locally and controllably melted while surface tension forces shape the optical component, following the laser beam profile. Process is so quick that a single microlens is fabricated in less than a second, and can be used instantly without any further processing. The technology is neither subtractive nor additive, and the base material is simply displaced producing a smooth surface. We have been able to fabricate individual microlenses and their arrays (positive, negative, aspheric), gratings and diffractive components. The technology is tested by generating unique, difficult to counterfeit QR-codes. Turnaround time is fast and makes the technology suitable both for rapid prototyping and serial production.


Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Ning Zhang ◽  
Peng-cheng Li ◽  
Hubin Liu ◽  
Tian-cheng Huang ◽  
Han Liu ◽  
...  

Abstract Background Realizing imaging detection of water and nitrogen content in different regions of plant leaves in-site and real-time can provide an efficient new technology for determining crop drought resistance and nutrient regulation mechanisms, or for use in precision agriculture. Near-infrared imaging is the preferred technology for in-situ real-time detection owing to its non-destructive nature; moreover, it provides rich information. However, the use of hyperspectral imaging technology is limited as it is difficult to use it in field because of its high weight and power. Results We developed a smart imaging device using a near-infrared camera and an interference filter; it has a low weight, requires low power, and has a multi-wavelength resolution. The characteristic wavelengths of the filter that realize leaf moisture measurement are 1150 and 1400 nm, respectively, the characteristic wavelength of the filter that realizes nitrogen measurement is 1500 nm, and all filter bandwidths are 25 nm. The prediction result of the average leaf water content model obtained with the device was R2 = 0.930, RMSE = 1.030%; the prediction result of the average nitrogen content model was R2 = 0.750, RMSE = 0.263 g. Conclusions Using the average water and nitrogen content model, an image of distribution of water and nitrogen in different areas of corn leaf was obtained, and its distribution characteristics were consistent with the actual leaf conditions. The experimental materials used in this research were fresh leaves in the field, and the test was completed indoors. Further verification of applying the device and model to the field is underway.


2021 ◽  
Vol 9 ◽  
Author(s):  
Qi Zhou ◽  
Xiaoming Lyu ◽  
Bing Cao ◽  
Xueping Liu ◽  
Jing Liu ◽  
...  

Pathogenic microorganisms pose great challenges to public health, which is constantly urgent to develop extra strategies for the fast staining and efficient treatments. In addition, once bacteria form stubborn biofilm, extracellular polymeric substance (EPS) within biofilm can act as protective barriers to prevent external damage and inward diffusion of traditional antibiotics, which makes it frequently develop drug-resistant ones and even hard to treat. Therefore, it is imperative to develop more efficient methods for the imaging/detection and efficient inhibition of pathogenic microorganisms. Here, a water-soluble aggregation-induced emission (AIE)-active photosensitizer TPA-PyOH was employed for fast imaging and photodynamic treatment of several typical pathogens, such as S. aureus, methicillin-resistant Staphylococcus aureus, L. monocytogenes, C. albicans, and E. coli. TPA-PyOH was non-fluorescent in water, upon incubation with pathogen, positively charged TPA-PyOH rapidly adhered to pathogenic membrane, thus the molecular motion of TPA-PyOH was restricted to exhibit AIE-active fluorescence for turn-on imaging with minimal background. Upon further white light irradiation, efficient reactive oxygen species (ROS) was in-situ generated to damage the membrane and inhibit the pathogen eventually. Furthermore, S. aureus biofilm could be suppressed in vitro. Thus, water-soluble TPA-PyOH was a potent AIE-active photosensitizer for fast fluorescent imaging with minimal background and photodynamic inhibition of pathogenic microorganisms.


Author(s):  
Victoria Chernyak ◽  
Christian B. van der Pol ◽  
Federica Vernuccio ◽  
Kengo Yoshimitsu

Atmosphere ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1182
Author(s):  
Zhenduo Zhang ◽  
Wenbo Zheng ◽  
Ying Li ◽  
Kai Cao ◽  
Ming Xie ◽  
...  

The emission of SO2 from ships is an important source of atmospheric pollution. Therefore, the International Maritime Organization (IMO) has established strict requirements for the sulfur content of marine fuel oil. In this paper, a new optical noncontact detection technique for ship exhaust emissions analysis is studied. Firstly, the single-band simulation analysis model of the imaging detection technology for SO2 concentration in ship exhaust gas and the deep neural network model for the prediction of sulfur content were established. A bench test was designed to monitor the tail gas concentration simultaneously using online and imaging detection methods, so as to obtain the concentration data in the flue and the ultraviolet image data. The results showed that 300 nm had a higher inversion accuracy than the other two bands. Finally, a deep neural network model was trained with the SO2 concentration data from the inversion and the engine power, and the predictive model of sulfur content in marine fuel oil was thereby obtained. When the deep learning model was used to predict sulfur content, the prediction accuracy at 300, 310, and 330 nm was 73%, 94%, and 71%, respectively.


2021 ◽  
pp. 130711
Author(s):  
Wei Zhang ◽  
Tang Dang ◽  
Ying Li ◽  
Jiawei Liang ◽  
Hao Xu ◽  
...  

2021 ◽  
Vol 1986 (1) ◽  
pp. 012050
Author(s):  
Qianwen Liu ◽  
Yaowu Song ◽  
Guangwen Ye ◽  
Yanxi Zhang ◽  
Congyi Wang ◽  
...  

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