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Updated: Feb 4, 2026

Multimodal Optical Imaging Platform for Studying Cellular Metabolism
Published on: June 6, 2025
Bifunctional Carbon Dot Nanozymes for Ratiometric Optical, Photothermal, and Smartphone-Assisted Multimodal Detection
Jianan Pei1,2, Jiayi Li1, Yuhui Guo1
1Institute of Environmental Science and School of Chemistry and Chemical Engineering, Shanxi University, Taiyuan 030006, P.R. China.
Abstract:
Accurate and reliable nitrite (NO2-) detection is crucial for food safety but remains challenging. Herein, we develop a triple-signal sensing strategy utilizing bifunctional carbon dot (CD) nanozymes for NO2- analysis in diverse food matrices. CDs with blue fluorescence and photoresponsive oxidase-mimicking activity are successfully synthesized by a simple one-step hydrothermal treatment with citric acid monohydrate (CA) and triethanolamine (TEA) as precursors. The oxidase-mimicking property enables efficient catalytic oxidation of 3,3',5,5'-tetramethylbenzidine (TMB) from colorless to blue ox-TMB. Capitalizing on the specific diazotization reaction between NO2- and o-phenylenediamine (OPD)/ox-TMB, the engineered sensor delivers ratiometric fluorescence, ratiometric colorimetric, and photothermal triple-signal outputs, displaying high performance in selectivity and anti-interference capability. The linear detection ranges for NO2- are 0.5-400 μM in ratiometric fluorescence sensing, 0.5-100 μM in ratiometric colorimetric sensing, and 5-100 μM in photothermal sensing, with corresponding limits of detection of 0.23 μM, 0.19 μM, and 1.43 μM, respectively. Furthermore, by leveraging the distinct color transitions from ratiometric fluorescence and colorimetric responses, a dual-modality sensing platform assisted by smartphones is engineered to achieve convenient, visual, and on-site NO2- detection in food samples. Notably, the integrated photothermal detection specifically overcomes the limitations of conventional optical methods for analyzing colored or autofluorescent samples, while the cross-validation capability of the multimodal strategy ensures reliable and stable results. This synergy provides a comprehensive and promising solution for the accurate and robust monitoring of NO2- in multiple types of food matrices.
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