Issue 16, 2024

Advancements in nanomaterials for nanosensors: a comprehensive review

Abstract

Nanomaterials (NMs) exhibit unique properties that render them highly suitable for developing sensitive and selective nanosensors across various domains. This review aims to provide a comprehensive overview of nanomaterial-based nanosensors, highlighting their applications and the classification of frequently employed NMs to enhance sensitivity and selectivity. The review introduces various classifications of NMs commonly used in nanosensors, such as carbon-based NMs, metal-based NMs, and others, elucidating their exceptional properties, including high thermal and electrical conductivity, large surface area-to-volume ratio and good biocompatibility. A thorough examination of literature sources was conducted to gather information on NMs-based nanosensors' characteristics, properties, and fabrication methods and their application in diverse sectors such as healthcare, environmental monitoring, industrial processes, and security. Additionally, advanced applications incorporating machine learning techniques were analyzed to enhance the sensor's performance. This review advances the understanding and development of nanosensor technologies by providing insights into fabrication techniques, characterization methods, applications, and future outlook. Key challenges such as robustness, biocompatibility, and scalable manufacturing are also discussed, offering avenues for future research and development in this field.

Graphical abstract: Advancements in nanomaterials for nanosensors: a comprehensive review

Article information

Article type
Review Article
Submitted
14 Meur. 2024
Accepted
23 Mae 2024
First published
24 Mae 2024
This article is Open Access
Creative Commons BY license

Nanoscale Adv., 2024,6, 4015-4046

Advancements in nanomaterials for nanosensors: a comprehensive review

M. A. Darwish, W. Abd-Elaziem, A. Elsheikh and A. A. Zayed, Nanoscale Adv., 2024, 6, 4015 DOI: 10.1039/D4NA00214H

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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