Well-Designed Smartphone-Based Imaging Biosensor
DOI:
https://doi.org/10.54097/hset.v14i.1835Keywords:
Smartphone, Imaging biosensor, Point-of-care, Microfluidics, Fluorescence.Abstract
With the development of hardware and software for smartphones, more and more well-designed smartphone-based imaging biosensors have been created and broadly applied in point-of-care testing (POCT). Imaging biosensors can get clear images through the high pixel density of smartphones’ camera systems. And smartphones also provide a chance for imaging processing thanks to smartphones' central processing units (CPUs) and graphics processing units (GPUs). Different approaches have extensively explored smartphone-based imaging biosensors. The commonly used imaging methods are generally implemented by the bright field with the light source or by fluorescence with a fluorescence microscope. Smartphones have enabled the widespread application of imaging-based methods in clinical chemistry, environmental monitoring, flow cytometry, food analysis, drug screening, and medical diagnostics. In detail, this article discusses various imaging biosensors and specific applications of smartphone-based imaging biosensors for bright-field imaging and fluorescence bioimaging. Meanwhile, the opportunities and challenges of smartphone-based imaging biosensors are also analyzed here.
Downloads
References
Alsabbagh K, Hornung T, Voigt A, Sadir S, Rajabi T, Länge K. Microfluidic Impedance Biosensor Chips Using Sensing Layers Based on DNA-Based Self-Assembled Monolayers for Label-Free Detection of Proteins. Biosensors (Basel). 2021 Mar 13; 11 (3): 80. DOI: 10.3390/bios11030080.
Vashist SK, Mudanyali O, Schneider EM, Zengerle R, Ozcan A. Cellphone-based devices for bioanalytical sciences. Anal Bioanal Chem. 2014 May; 406 (14): 3263 - 77. DOI: 10.1007/s00216-013-7473 - 1. Epub 2013 Nov 28.
Guan T, Xu Z, Wang J, Liu Y, Shen X, Li X, Sun Y, Lei H. Multiplex optical bioassays for food safety analysis: Toward on-site detection. Compr Rev Food Sci Food Saf. 2022 Mar; 21 (2): 1627 - 1656. DOI: 10.1111/1541 - 4337.12914.
Mudanyali O, Dimitrov S, Sikora U, Padmanabhan S, Navruz I, Ozcan A. Integrated rapid-diagnostic-test reader platform on a cellphone. Lab Chip. 2012 Aug 7; 12 (15): 2678 - 86. DOI: 10.1039/c2lc40235a.
Yang JM, Yang NZ, Chen CH, Huang CS. Gradient Waveguide Thickness Guided-Mode Resonance Biosensor. Sensors (Basel). 2021 Jan 7; 21 (2): 376. DOI: 10.3390/s21020376.
Zhu H, Mavandadi S, Coskun AF, Yaglidere O, Ozcan A. Optofluidic fluorescent imaging cytometry on a cell phone. Anal Chem. 2011 Sep 1; 83 (17): 6641 - 7. DOI: 10.1021/ac201587a.
Zhang Y, Tseng TM, Schlichtmann U. ColoriSens: An open-source and low-cost portable color sensor board for microfluidic integration with wireless communication and fluorescence detection. HardwareX. 2022 Apr 28; 11: e00312. DOI: 10.1016/j.ohx.2022.e00312.
Wang F, Chen L, Zhu J, Hu X, Yang Y. A Phosphorescence Quenching-Based Intelligent Dissolved Oxygen Sensor on an Optofluidic Platform. Micromachines (Basel). 2021 Mar 8; 12 (3): 281. DOI: 10.3390/mi12030281.
Kim KR, Lee KW, Chun HJ, Lee D, Kim JH, Yoon HC. Wash-free operation of smartphone-integrated optical immunosensor using retroreflective microparticles. Biosens Bioelectron. 2022 Jan 15; 196: 113722. DOI: 10.1016/j.bios.2021.113722.
Kim JD, Park CY, Kim YS, Hwang JS. Quantitative Analysis of Fluorescence Detection Using a Smartphone Camera for a PCR Chip. Sensors (Basel). 2021 Jun 6; 21 (11):3917. DOI: 10.3390/s21113917.
Ardalan S, Hosseinifard M, Vosough M, Golmohammadi H. Towards smart personalized perspiration analysis: An IoT-integrated cellulose-based microfluidic wearable patch for smartphone fluorimetric multi-sensing of sweat biomarkers. Biosens Bioelectron. 2020 Nov 15; 168: 112450. DOI: 10.1016/j.bios.2020.112450.
Bremer K, Roth B. Fibre optic surface plasmon resonance sensor system designed for smartphones. Opt Express. 2015 Jun 29; 23 (13): 17179 - 84. DOI: 10.1364/OE.23.017179.
S. Dutta, K. Saikia, P. Nath, Smartphone based LSPR sensing platform for bio-conjugation detection and quantification, RSC Adv. 6 (2016) 21871 – 21880, DOI: 10.1039/C6RA01113F.
Xu D, Huang X, Guo J, Ma X. Automatic smartphone-based microfluidic biosensor system at the point of care. Biosens Bioelectron. 2018 Jul 1; 110: 78 - 88. DOI: 10.1016/j.bios.2018.03.018.
Lee SA, Yang C. A smartphone-based chip-scale microscope using ambient illumination. Lab Chip. 2014 Aug 21; 14 (16): 3056 - 63. DOI: 10.1039/c4lc00523f.
Navruz I, Coskun AF, Wong J, Mohammad S, Tseng D, Nagi R, Phillips S, Ozcan A. Smart-phone based computational microscopy using multi-frame contact imaging on a fiber-optic array. Lab Chip. 2013 Oct 21; 13 (20): 4015 - 23. DOI: 10.1039/c3lc50589h.
Liu Y, Liu Q, Chen S, Cheng F, Wang H, Peng W. Surface Plasmon Resonance Biosensor Based on Smart Phone Platforms. Sci Rep. 2015 Aug 10; 5: 12864. DOI: 10.1038/srep12864.
Ong B H, Yuan X, Tjin S C, et al. Optimised film thickness for maximum evanescent field enhancement of a bimetallic film surface plasmon resonance biosensor. Sensors and Actuators B: Chemical, 2006, 114 (2): 1028 - 1034. DOI: 10.1016/j.snb.2005.07.064.
Xia L, Yin S, Gao H, et al. Sensitivity enhancement for surface plasmon resonance imaging biosensor by utilizing gold–silver bimetallic film configuration. Plasmonics, 2011, 6 (2): 245 - 250. DOI: 10.1007/ s11468-010-9195-y.
Chen Y, Zheng RS, Zhang DG, Lu YH, Wang P, Ming H, Luo ZF, Kan Q. Bimetallic chips for a surface plasmon resonance instrument. Appl Opt. 2011 Jan 20; 50 (3): 387 - 91. DOI: 10.1364/AO.50.000387.
Li CT, Lo KC, Chang HY, Wu HT, Ho JH, Yen TJ. Ag/Au bi-metallic film-based color surface plasmon resonance biosensor with enhanced sensitivity, color contrast and great linearity. Biosens Bioelectron. 2012 Jun-Jul; 36 (1): 192 - 8. DOI: 10.1016/j.bios.2012.04.016.
Ehler T T, Noe L J. Surface plasmon studies of thin silver/gold bimetallic films [J]. Langmuir, 1995, 11 (10): 4177 - 4179. DOI: 10.1021/la00010a088.
Koydemir HC, Gorocs Z, Tseng D, Cortazar B, Feng S, Chan RY, Burbano J, McLeod E, Ozcan A. Rapid imaging, detection and quantification of Giardia lamblia cysts using mobile-phone based fluorescent microscopy and machine learning. Lab Chip. 2015 Mar 7; 15 (5): 1284 - 93. DOI: 10.1039/c4lc01358a.
Knowlton S, Joshi A, Syrrist P, Coskun AF, Tasoglu S. 3D-printed smartphone-based point of care tool for fluorescence- and magnetophoresis-based cytometry. Lab Chip. 2017 Aug 8; 17 (16): 2839 - 2851. DOI: 10.1039/c7lc00706j.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







