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Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection
Published on: January 26, 2024
Sensitive colorimetric assay for uric acid and glucose detection based on multilayer-modified paper with smartphone
1Anhui Province Key Laboratory of Advanced Catalytic Materials and Reaction Engineering, School of Chemistry and Chemical Engineering, Hefei University of Technology, Hefei, Anhui, 230009, China.
Abstract:
In this work, a multilayer-modified paper-based colorimetric sensing platform with improved color uniformity and intensity was developed for the sensitive and selective determination of uric acid and glucose with smartphone as signal readout. In detail, chitosan, different kinds of chromogenic reagents, and horseradish peroxidase (HRP) combined with a specific oxidase, e.g., uricase or glucose oxidase (GOD), were immoblized onto the paper substrate to form a multilayer-modified test paper. Hydrogen peroxide produced by the oxidases (uricase or GOD) reacts with the substrates (uric acid or glucose), and could oxidize the co-immoblized chromogenic reagents to form colored products with HRP as catalyst. A simple strategy by placing the test paper on top of a light-emitting diode lamp was adopted to efficiently prevent influence from the external light. The color images were recorded by the smartphone camera, and then the gray values of the color images were calculated for quantitative analysis. The developed method provided a wide linear response from 0.01 to 1.0 mM for uric acid detection and from 0.02 to 4.0 mM for glucose detection, with a limit of detection (LOD) as low as 0.003 and 0.014 mM, respectively, which was much lower than for previously reported paper-based colorimetric assays. The proposed assays were successfully applied to uric acid and glucose detection in real serum samples. Furthermore, the enhanced analytical performance of the proposed method allowed the non-invasive detection of glucose levels in tear samples, which holds great potential for point-of-care analysis. Graphical abstract ᅟ.
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