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Huang C, Jiang Y, Li Y, Zhang H. Droplet Detection and Sorting System in Microfluidics: A Review. Micromachines (Basel) 2022; 14:mi14010103. [PMID: 36677164 PMCID: PMC9867185 DOI: 10.3390/mi14010103] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 12/23/2022] [Accepted: 12/26/2022] [Indexed: 05/26/2023]
Abstract
Since being invented, droplet microfluidic technologies have been proven to be perfect tools for high-throughput chemical and biological functional screening applications, and they have been heavily studied and improved through the past two decades. Each droplet can be used as one single bioreactor to compartmentalize a big material or biological population, so millions of droplets can be individually screened based on demand, while the sorting function could extract the droplets of interest to a separate pool from the main droplet library. In this paper, we reviewed droplet detection and active sorting methods that are currently still being widely used for high-through screening applications in microfluidic systems, including the latest updates regarding each technology. We analyze and summarize the merits and drawbacks of each presented technology and conclude, with our perspectives, on future direction of development.
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Affiliation(s)
- Can Huang
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77842, USA
| | - Yuqian Jiang
- State Key Laboratory of Food Nutrition and Safety, College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
| | - Yuwen Li
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77842, USA
| | - Han Zhang
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77842, USA
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Li JZ, Dong LM, Zheng LL, Fu WL, Zhang JJ, Zhang L, Hu Q, Chen P, Gao ZF, Xia F. Molecular Visual Sensing, Boolean Logic Computing, and Data Security Using a Droplet-Based Superwetting Paradigm. ACS Appl Mater Interfaces 2022; 14:40447-40459. [PMID: 36006781 DOI: 10.1021/acsami.2c11532] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Inspired by information processing and logic operations of life, many artificial biochemical systems have been designed for applications in molecular information processing. However, encoding the binary synergism between matter, energy, and information in a superwetting system remains challenging. Herein, a superwetting paradigm was proposed for multifunctional applications including molecular visual sensing and data security on a superhydrophobic surface. A Triton X-100-encapsulated gelatin (TeG) hydrogel was prepared and selectively decomposed by trypsin, releasing the surfactant to decrease the surface tension of a droplet. Integrating the droplet with the superhydrophobic surface, the superwetting behavior was utilized for visual detection and information encoding. Interestingly, the proposed TeG hydrogel can function as an artificial gelneuron for molecular-level logic computing, where the combination of matters (superhydrophobic surface, trypsin, and leupeptin) acts as inputs to interact with energy (liquid surface tension and solid surface energy) and information (binary character), resulting in superwettability transitions (droplet surface tension, contact angle, rolling angle, and bounce) as outputs. Impressively, the TeG gelneuron can be further developed as molecular-level double cryptographic steganography to encode, encrypt, and hide specific information (including the maze escape route and content of the classical literature) due to its programmability, stimuli responsive ability, and droplet concealment. This study will encourage the development of advanced molecular paradigms and their applications, such as superwetting visual sensing, molecular computing, interaction, and data security.
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Affiliation(s)
- Jin Ze Li
- Shandong Provincial Key Laboratory of Detection Technology for Tumor Markers, College of Chemistry and Chemical Engineering, Linyi University, Linyi 276005, P. R. China
| | - Lu Ming Dong
- Shandong Provincial Key Laboratory of Detection Technology for Tumor Markers, College of Chemistry and Chemical Engineering, Linyi University, Linyi 276005, P. R. China
| | - Lin Lin Zheng
- Shandong Provincial Key Laboratory of Detection Technology for Tumor Markers, College of Chemistry and Chemical Engineering, Linyi University, Linyi 276005, P. R. China
| | - Wen Long Fu
- Advanced Materials Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, P. R. China
| | - Jing Jing Zhang
- Advanced Materials Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, P. R. China
| | - Lei Zhang
- Department of Chemical Engineering and Waterloo Institute for Nanotechnology, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L3G1, Canada
| | - Qiongzheng Hu
- School of Pharmaceutical Sciences, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, P. R. China
| | - Pu Chen
- Department of Chemical Engineering and Waterloo Institute for Nanotechnology, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L3G1, Canada
| | - Zhong Feng Gao
- Shandong Provincial Key Laboratory of Detection Technology for Tumor Markers, College of Chemistry and Chemical Engineering, Linyi University, Linyi 276005, P. R. China
- Advanced Materials Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, P. R. China
| | - Fan Xia
- Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, Wuhan 430074, P. R. China
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Dave PK, Rojas-Cessa R, Dong Z, Umpaichitra V. Survey of Saliva Components and Virus Sensors for Prevention of COVID-19 and Infectious Diseases. Biosensors (Basel) 2020; 11:14. [PMID: 33396519 PMCID: PMC7824170 DOI: 10.3390/bios11010014] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 12/18/2020] [Accepted: 12/24/2020] [Indexed: 12/20/2022]
Abstract
The United States Centers for Disease Control and Prevention considers saliva contact the lead transmission means of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19). Saliva droplets or aerosols expelled by heavy breathing, talking, sneezing, and coughing may carry this virus. People in close distance may be exposed directly or indirectly to these droplets, especially those droplets that fall on surrounding surfaces and people may end up contracting COVID-19 after touching the mucosa tissue on their faces. It is of great interest to quickly and effectively detect the presence of SARS-CoV-2 in an environment, but the existing methods only work in laboratory settings, to the best of our knowledge. However, it may be possible to detect the presence of saliva in the environment and proceed with prevention measures. However, detecting saliva itself has not been documented in the literature. On the other hand, many sensors that detect different organic components in saliva to monitor a person's health and diagnose different diseases that range from diabetes to dental health have been proposed and they may be used to detect the presence of saliva. This paper surveys sensors that detect organic and inorganic components of human saliva. Humidity sensors are also considered in the detection of saliva because a large portion of saliva is water. Moreover, sensors that detect infectious viruses are also included as they may also be embedded into saliva sensors for a confirmation of the virus' presence. A classification of sensors by their working principle and the substance they detect is presented. This comparison lists their specifications, sample size, and sensitivity. Indications of which sensors are portable and suitable for field application are presented. This paper also discusses future research and challenges that must be resolved to realize practical saliva sensors. Such sensors may help minimize the spread of not only COVID-19 but also other infectious diseases.
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Affiliation(s)
- Priya Kishor Dave
- Networking Research Laboratory, Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA;
| | - Roberto Rojas-Cessa
- Networking Research Laboratory, Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA;
| | - Ziqian Dong
- Department of Electrical and Computer Engineering, New York Institute of Technology, New York, NY 10023, USA;
| | - Vatcharapan Umpaichitra
- Department of Pediatrics, State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, NY 11203, USA;
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