Optical Image Sensors for Smart Analytical Chemiluminescence Biosensors

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Optical biosensors have emerged as a powerful tool in analytical biochemistry, offering high sensitivity and specificity in the detection of various biomolecules. This article explores the advancements in the integration of optical biosensors with microfluidic technologies, creating lab-on-a-chip (LOC) platforms that enable rapid, efficient, and miniaturized analysis at the point of need. These LOC platforms leverage optical phenomena such as chemiluminescence and electrochemiluminescence to achieve real-time detection and quantification of analytes, making them ideal for applications in medical diagnostics, environmental monitoring, and food safety. Various optical detectors used for detecting chemiluminescence are reviewed, including single-point detectors such as photomultiplier tubes (PMT) and avalanche photodiodes (APD), and pixelated detectors such as charge-coupled devices (CCD) and complementary metal–oxide–semiconductor (CMOS) sensors. A significant advancement discussed in this review is the integration of optical biosensors with pixelated image sensors, particularly CMOS image sensors. These sensors provide numerous advantages over traditional single-point detectors, including high-resolution imaging, spatially resolved measurements, and the ability to simultaneously detect multiple analytes. Their compact size, low power consumption, and cost-effectiveness further enhance their suitability for portable and point-of-care diagnostic devices. In the future, the integration of machine learning algorithms with these technologies promises to enhance data analysis and interpretation, driving the development of more sophisticated, efficient, and accessible diagnostic tools for diverse applications.
Funding Information
  • National Sciences and Engineering Research Council of Canada (NSERC) (RGPIN-2018-05675)
  • Faculty of Engineering, McGill University (RGPIN-2018-05675)
  • McGill Engineering Doctoral Award (MEDA) and Fonds de Recherche du Quebec—Nature et Technologies (FRQNT) (RGPIN-2018-05675)
  • McGill University Faculty of Engineering (RGPIN-2018-05675)

This publication has 314 references indexed in Scilit: