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Title: Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring
Authors: Leong, Shi Xuan
Leong, Yong Xiang
Koh, Charlynn Sher Lin
Tan, Emily Xi
Nguyen, Lam Bang Thanh
Chen, Jaslyn Ru Ting
Chong, Carice
Pang, Desmond Wei Cheng
Sim, Howard Yi Fan
Liang, Xiaochen
Tan, Nguan Soon
Ling, Xing Yi
Keywords: Science::Chemistry
Issue Date: 2022
Source: Leong, S. X., Leong, Y. X., Koh, C. S. L., Tan, E. X., Nguyen, L. B. T., Chen, J. R. T., Chong, C., Pang, D. W. C., Sim, H. Y. F., Liang, X., Tan, N. S. & Ling, X. Y. (2022). Emerging nanosensor platforms and machine learning strategies toward rapid, point-of-need small-molecule metabolite detection and monitoring. Chemical Science.
Project: MOH-000584 
Journal: Chemical Science 
Abstract: Speedy, point-of-need detection and monitoring of small-molecule metabolites are vital across diverse applications ranging from biomedicine to agri-food and environmental surveillance. Nanomaterial-based sensor (nanosensor) platforms are rapidly emerging as excellent candidates for versatile and ultrasensitive detection owing to their highly configurable optical, electrical and electrochemical properties, fast readout, as well as portability and ease of use. To translate nanosensor technologies for real-world applications, key challenges to overcome include ultralow analyte concentration down to ppb or nM levels, complex sample matrices with numerous interfering species, difficulty in differentiating isomers and structural analogues, as well as complex, multidimensional datasets of high sample variability. In this Perspective, we focus on contemporary and emerging strategies to address the aforementioned challenges and enhance nanosensor detection performance in terms of sensitivity, selectivity and multiplexing capability. We outline 3 main concepts: (1) customization of designer nanosensor platform configurations via chemical- and physical-based modification strategies, (2) development of hybrid techniques including multimodal and hyphenated techniques, and (3) synergistic use of machine learning such as clustering, classification and regression algorithms for data exploration and predictions. These concepts can be further integrated as multifaceted strategies to further boost nanosensor performances. Finally, we present a critical outlook that explores future opportunities toward the design of next-generation nanosensor platforms for rapid, point-of-need detection of various smallmolecule metabolites.
ISSN: 2041-6520
DOI: 10.1039/D2SC02981B
Schools: Lee Kong Chian School of Medicine (LKCMedicine) 
School of Biological Sciences 
School of Chemistry, Chemical Engineering and Biotechnology 
Rights: © 2022 The Author(s). Published by the Royal Society of Chemistry. This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CCEB Journal Articles
LKCMedicine Journal Articles
SBS Journal Articles

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