Issue 7, 2022

Nanoclay-based sensor composites for the facile detection of molecular antioxidants

Abstract

The detection and quantification of antioxidant molecules is an important task in food science, the fine chemical industry and healthcare. Antioxidants help in preventing the deterioration of nutrition and healthcare products, while eliminating over-the-limit exogenic reactive species, which may lead to illnesses. In our contribution, an inexpensive and rapid method to determine the concentration of various molecular antioxidants was developed. The principle of the analysis relies on the cupric ion reducing antioxidant capacity (CuPRAC) method, which is based on the color-changing reduction of chelated Cu2+ ions. This complex was successfully immobilized on an alginate-functionalized layered double hydroxide (dLDH) nanosheet via electrostatic interactions. The synthesis conditions of alginate (NaAlg) and the cupric complex were optimized, and the optimized composite was fabricated on cellulose paper to obtain a sensing platform. The paper-based sensor was superior to the ones prepared without the dLDH support, as the limit of detection (LOD) values decreased, and the linearity ranges broadened. The results offer a single-point measurement to evaluate the antioxidant efficiency in a cuvette-based method. The superior ability of the sensor was assigned to the presence of solid dLDH particles, as they offer adsorption sites for the dissolved antioxidant molecules, which contributes significantly to the decrease of the diffusion limitation during the detection process.

Graphical abstract: Nanoclay-based sensor composites for the facile detection of molecular antioxidants

Supplementary files

Article information

Article type
Paper
Submitted
28 Dec 2021
Accepted
19 Feb 2022
First published
21 Feb 2022
This article is Open Access
Creative Commons BY license

Analyst, 2022,147, 1367-1374

Nanoclay-based sensor composites for the facile detection of molecular antioxidants

A. Szerlauth, L. Szalma, S. Muráth, S. Sáringer, G. Varga, L. Li and I. Szilágyi, Analyst, 2022, 147, 1367 DOI: 10.1039/D1AN02352G

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