Issue 1, 2024

Optical sensors (optodes) for multiparameter chemical imaging: classification, challenges, and prospects

Abstract

Chemical gradients and uneven distribution of analytes are common in natural and artificial systems. As a result, the ability to visualize chemical distributions in two or more dimensions has gained significant importance in recent years. This has led to the integration of chemical imaging techniques into all domains of analytical chemistry. In this review, we focus on the use of optical sensors, so-called optodes, to obtain real-time and multidimensional images of two or more parameters simultaneously. It is important to emphasize that multiparameter imaging in this context is not confined solely to multiple chemical parameters (analytes) but also encompasses physical (e.g., temperature or flow) or biological (e.g., metabolic activity) parameters. First, we discuss the technological milestones that have paved the way for chemical imaging using optodes. Later, we delve into various strategies that can be taken to enable multiparameter imaging. The latter spans from developing novel receptors that enable the recognition of multiple parameters to chemometrics and machine learning-based techniques for data analysis. We also explore ongoing trends, challenges, and prospects for future developments in this field. Optode-based multiparameter imaging is a rapidly expanding field that is being fueled by cutting-edge technologies. Chemical imaging possesses the potential to provide novel insights into complex samples, bridging not only across various scientific disciplines but also between research and society.

Graphical abstract: Optical sensors (optodes) for multiparameter chemical imaging: classification, challenges, and prospects

Article information

Article type
Critical Review
Submitted
28 септ. 2023
Accepted
10 ноем. 2023
First published
13 ноем. 2023

Analyst, 2024,149, 29-45

Optical sensors (optodes) for multiparameter chemical imaging: classification, challenges, and prospects

A. V. Kalinichev, S. E. Zieger and K. Koren, Analyst, 2024, 149, 29 DOI: 10.1039/D3AN01661G

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