scholarly journals A Potassium Ion-Exchanged Glass Optical Waveguide Sensor Locally Coated with a Crystal Violet-SiO2 Gel Film for Real-Time Detection of Organophosphorus Pesticides Simulant

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4219 ◽  
Author(s):  
Bin Du ◽  
Zhaoyang Tong ◽  
Xihui Mu ◽  
Jianjie Xu ◽  
Shuai Liu ◽  
...  

An optical waveguide (OWG) sensor was developed for real-time detection of diethyl chlorophosphate (DCP) vapor, which is a typical simulant for organophosphorus pesticides and chemical weapon agents. Silica gel, crystal violet (CV), and potassium ion-exchange (PIE) OWG were used to fabricate the sensor’s device. In the real-time detection of the DCP vapor, the volume fraction of DCP vapor was recorded to be as low as 1.68 × 10−9. Moreover, the detection mechanism of CV-SiO2 gel film coated the PIE OWG sensor for DCP, which was evaluated by absorption spectra. These results demonstrated that the change of output light intensity of the OWG sensor significantly increased with the augment of the DCP concentration. Repeatability as well as selectivity of the sensors were tested using 0.042 × 10−6 and 26.32 × 10−6 volume fraction of the DCP vapor. No clear interference with the DCP detection was observed in the presence of other common solvents (e.g., acetone, methanol, dichloromethane, dimethylsulfoxide, and tetrahydrofuran), benzene series (e.g., benzene, toluene, chlorobenzene, and aniline), phosphorus-containing reagents (e.g., dimethyl methylphosphonate and trimethyl phosphate), acid, and basic gas (e.g., acetic acid and 25% ammonium hydroxide), which demonstrates that the OWG sensor could provide real-time, fast, and accurate measurement results for the detection of DCP.

Nanoscale ◽  
2020 ◽  
Vol 12 (16) ◽  
pp. 9194-9207 ◽  
Author(s):  
Munezza A. Khan ◽  
Mohammad Mujahid ◽  
Say Chye Joachim Loo ◽  
Vidya N. Chamundeswari

Magneto-photonic crystals/MPCs are promising candidates for devising high-fidelity embedded biosensor systems which offer facile & real time detection of diagnostic proteins.


2012 ◽  
Author(s):  
Hsing-Ying Lin ◽  
Chen-Han Huang ◽  
Yu-Chia Liu ◽  
Shin-Huei Chen ◽  
Lai-Kwan Chau

2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown

Author(s):  
Muhammad Hanif Ahmad Nizar ◽  
Chow Khuen Chan ◽  
Azira Khalil ◽  
Ahmad Khairuddin Mohamed Yusof ◽  
Khin Wee Lai

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.


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