Dataset Information

Low-Cost Graphene-Based Digital Microfluidic System.

ABSTRACT: In this work, the laser-scribing technique was used as a low-cost, rapid and facile method for fabricating digital microfluidic (DMF) systems. Laser-scribed graphene (LSG) electrodes are directly synthesized on flexible substrates to pattern the DMF electrode arrays. This facilitates the DMF electrodes' fabrication process by eliminating many microfabrication steps. An electrowetting test was performed to investigate the effectiveness of the LSG DMF electrodes in changing the contact angles of droplets. Different DMF operations were successfully performed using the proposed LSG DMF chips in both open and closed DMF systems. The quality and output resolution were examined to assess the performance of such patterned electrodes in the DMF systems. To verify the efficacy of the LSG DMF chips, a one-step direct assay for the detection of Legionellapneumophila deoxyribonucleic acid (DNA) was performed on the chip without the need for any washing step. The high specificity in distinguishing a single-nucleotide mismatch was achieved by detecting target DNA concentrations as low as 1 nM. Our findings suggest that the proposed rapid and easy fabrication method for LSG DMF electrodes offers a great platform for low-cost and easily accessible point-of-care diagnostic devices.

SUBMITTER: Yafia M

PROVIDER: S-EPMC7569958 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Low-Cost Graphene-Based Digital Microfluidic System.

Yafia Mohamed M Foudeh Amir M AM Tabrizian Maryam M Najjaran Homayoun H

Micromachines 20200922 9

In this work, the laser-scribing technique was used as a low-cost, rapid and facile method for fabricating digital microfluidic (DMF) systems. Laser-scribed graphene (LSG) electrodes are directly synthesized on flexible substrates to pattern the DMF electrode arrays. This facilitates the DMF electrodes' fabrication process by eliminating many microfabrication steps. An electrowetting test was performed to investigate the effectiveness of the LSG DMF electrodes in changing the contact angles of d ...[more]

PMID: 32971896

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Project description:Smart surveillance systems are used to monitor specific areas, such as homes, buildings, and borders, and these systems can effectively detect any threats. In this work, we investigate the design of low-cost multiunit surveillance systems that can control numerous surveillance cameras to track multiple objects (i.e., people, cars, and guns) and promptly detect human activity in real time using low computational systems, such as compact or single board computers. Deep learning techniques are employed to detect certain objects to surveil homes/buildings and recognize suspicious and vital events to ensure that the system can alarm officers of relevant events, such as stranger intrusions, the presence of guns, suspicious movements, and identified fugitives. The proposed model is tested on two computational systems, specifically, a single board computer (Raspberry Pi) with the Raspbian OS and a compact computer (Intel NUC) with the Windows OS. In both systems, we employ components, such as a camera to stream real-time video and an ultrasonic sensor to alarm personnel of threats when movement is detected in restricted areas or near walls. The system program is coded in Python, and a convolutional neural network (CNN) is used to perform recognition. The program is optimized by using a foreground object detection algorithm to improve recognition in terms of both accuracy and speed. The saliency algorithm is used to slice certain required objects from scenes, such as humans, cars, and airplanes. In this regard, two saliency algorithms, based on local and global patch saliency detection are considered. We develop a system that combines two saliency approaches and recognizes the features extracted using these saliency techniques with a conventional neural network. The field results demonstrate a significant improvement in detection, ranging between 34% and 99.9% for different situations. The low percentage is related to the presence of unclear objects or activities that are different from those involving humans. However, even in the case of low accuracy, recognition and threat identification are performed with an accuracy of 100% in approximately 0.7 s, even when using computer systems with relatively weak hardware specifications, such as a single board computer (Raspberry Pi). These results prove that the proposed system can be practically used to design a low-cost and intelligent security and tracking system.

Dataset Information

Low-Cost Graphene-Based Digital Microfluidic System.

Publications

Low-Cost Graphene-Based Digital Microfluidic System.

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