A Multi-Modal Lateral Flow Assay Driven by MWCNTs/Fe-N-C for the Detection of Bisphenol A in Lake

Abstract

Bisphenol A (BPA), a prototypical environmental endocrine disruptor with ubiquitous presence in aquatic systems, has prompted significant environmental and public health concerns. Addressing the urgent need for rapid and sensitive monitoring, we developed an innovative tri-mode lateral flow assay (LFA) platform powered by multifunctional MWCNTs/Fe-N-C nanozymes. The designed nanozyme enables the simultaneous output of three signal modes: intrinsic colorimetry, catalytic colorimetry, and catalytic photothermal colorimetry. The three signal channels act synergistically: intrinsic coloration provides basic visual readout, peroxidase-like activity enhances signal intensity by catalyzing TMB chromogenic reaction, and the oxTMB-mediated photothermal effect further amplifies the detection signal. These three modes constitute a self-validating system through mutual cross-verification. This cross-checking mechanism effectively reduces background interference and improves detection accuracy and reliability. The platform achieves a detection limit of 0.13 ng/mL (intrinsic colorimetric), 0.082 ng/mL (catalytic colorimetric) and 0.047 ng/mL (catalytic photothermal colorimetric) for BPA, with progressively enhanced sensitivity across modalities. Besides, validated through spiked recovery tests in real lake water showing 92.8-107.3% recovery rates, the method proves robust against environmental interferences. This work establishes an approach for environmental contaminant monitoring by harmonizing nanozyme multifunctionality with multimodal sensing architectures, offering a versatile analytical framework for environmental contaminant monitoring.

Supplementary files

Article information

Article type
Paper
Submitted
08 Apr 2026
Accepted
09 Jun 2026
First published
11 Jun 2026

Anal. Methods, 2026, Accepted Manuscript

A Multi-Modal Lateral Flow Assay Driven by MWCNTs/Fe-N-C for the Detection of Bisphenol A in Lake

Z. Bi, M. Shi, T. Chen, Y. Sun, Y. Zhou, L. Wang, H. Zhang and G. Liu, Anal. Methods, 2026, Accepted Manuscript , DOI: 10.1039/D6AY00643D

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