Issue 5, 2024

Superassembled MXene–carboxymethyl chitosan nanochannels for the highly sensitive recognition and detection of copper ions

Abstract

Copper ions (Cu2+), as a crucial trace element, play a vital role in living organisms. Thus, the detection of Cu2+ is of great significance for disease prevention and diagnosis. Nanochannel devices with an excellent nanoconfinement effect show great potential in recognizing and detecting Cu2+ ions. However, these devices often require complicated modification and treatment, which not only damages the membrane structure, but also induces nonspecific, low-sensitivity and non-repeatable detection. Herein, a 2D MXene–carboxymethyl chitosan (MXene/CMC) freestanding membrane with ordered lamellar channels was developed by a super-assembly strategy. The introduction of CMC provides abundant space charges, improving the nanoconfinement effect of the nanochannel. Importantly, the CMC can chelate with Cu2+ ions, endowing the MXene/CMC with the ability to detect Cu2+. The formation of CMC–Cu2+ complexes decreases the space charges, leading to a discernible variation in the current signal. Therefore, MXene/CMC can achieve highly sensitive and stable Cu2+ detection based on the characteristics of nanochannel composition. The linear response range for Cu2+ detection is 10−9 to 10−5 M with a low detection limit of 0.095 nM. Notably, MXene/CMC was successfully applied for Cu2+ detection in real water and fetal bovine serum samples. This work provides a simple, highly sensitive and stable detection platform based on the properties of the nanochannel composition.

Graphical abstract: Superassembled MXene–carboxymethyl chitosan nanochannels for the highly sensitive recognition and detection of copper ions

Supplementary files

Article information

Article type
Paper
Submitted
20 Dec 2023
Accepted
16 Jan 2024
First published
29 Jan 2024

Analyst, 2024,149, 1464-1472

Superassembled MXene–carboxymethyl chitosan nanochannels for the highly sensitive recognition and detection of copper ions

X. Hu, S. Zhou, X. Zhang, H. Zeng, Y. Guo, Y. Xu, Q. Liang, J. Wang, L. Jiang and B. Kong, Analyst, 2024, 149, 1464 DOI: 10.1039/D3AN02190D

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