Smart nano-architectures as potential sensing tools for detecting heavy metal ions in aqueous matrices

Rasheed, T, Shafi, S and Sher, F ORCID: https://orcid.org/0000-0003-2890-5912, 2022. Smart nano-architectures as potential sensing tools for detecting heavy metal ions in aqueous matrices. Trends in Environmental Analytical Chemistry: e00179. ISSN 2214-1588

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Abstract

The discharge of heavy metal ions into water resources as a result of human activities has become a global issue. Contamination with heavy metal ions poses a major threat to the environment and human health. Therefore, there is a dire need to probe the presence of heavy metal ions in a more selective, facile, quick, cost-effective and sensitive way. Conventional sensors are being utilized to sense heavy metal ions; however, various challenges and limitations like interference, overlapping of oxidation potential, selectivity and sensitivity are associated with them that limit their in-field applicability. Hence, nanomaterial based chemical sensors have emerged as an alternative substitute and are extensively employed for the detection of heavy metal ions as a potent analytical tool. The incorporation of nanomaterials in sensors increases their sensitivity, selectivity, portability, on-site detection capability and device performance. Nanomaterial based electrodes exhibit enhanced performance because surface of electrode at nano-scale level offers high catalytic potential, large active surface area and high conductivity. Therefore, this review addresses the recent progress on chemical sensors based on different nanomaterials such as carbon nanotubes (CNTs), metal nanoparticles, graphene, carbon quantum dots and nanocomposites for sensing heavy metals ions using different sensing approaches. Furthermore, various types of optical sensors such as fluorescence, luminescence and colorimetry sensors have been presented in detail.

Item Type:	Journal article
Publication Title:	Trends in Environmental Analytical Chemistry
Creators:	Rasheed, T., Shafi, S. and Sher, F.
Publisher:	Elsevier BV
Date:	21 September 2022
ISSN:	2214-1588
Identifiers:	Number Type 10.1016/j.teac.2022.e00179 DOI S2214158822000265 Publisher Item Identifier 1601650 Other
Rights:	© 2022 Elsevier B.V. All rights reserved.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	27 Sep 2022 08:37
Last Modified:	21 Sep 2023 03:00
URI:	https://irep.ntu.ac.uk/id/eprint/47129

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