4.7 Article

Buffer species-dependent catalytic activity of Cu-Adenine as a laccase mimic for constructing sensor array to identify multiple phenols

期刊

ANALYTICA CHIMICA ACTA
卷 1204, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.aca.2022.339725

关键词

Cu-adenine nanosheets; Laccase-mimicking nanozyme; Buffer species-dependent; Colorimetric sensor array; Phenols detection

资金

  1. National Natural Science Foundation of China [21673022]
  2. Beijing Natural Science Founda-tion [2222013]
  3. Fundamental Research Funds for the Central Universities

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Through the synthesis of Cu-Adenine nanosheets, we have developed an excellent colorimetric sensor array that can simultaneously identify and quantify phenolic substances in different buffer solutions, demonstrating great potential for wide-ranging applications in water samples.
Laccase mimics are multicopper oxidase highly important for biotechnology and environmental evaluation/remediation. However, buffer species-dependent catalytic activity is rarely investigated. Herein, through Cu2+ coordinating with adenine (A), gram-scale Cu-Adenine nanosheets are synthesized. As a laccase mimic, Cu-Adenine exhibits excellent catalytic performance in 10 mM Tris-HAc, and greatly decreases in 10 mM MES (4-morpholine ethanesulfonic acid) and further decreases in 10 mM HEPES (N2-hydroxyethyl piperazine-N'-2-ethanesulfonic acid) (pH = 7, 25 ?). Moreover, upon gradually increasing NaCl to 500 mM, the catalytic activity further enhances. Such the buffer species/NaCl concentration-dependent catalytic activity is explained with the help of Km (Michaelis constant) and V-max (maximum rate value) Based on the fact that Cu-Adenine could differentially catalyze phenolic substrates (2,4-dichlorophenol (2,4-DP), o-chlorophenol (o-CP), m-chlorophenol (m-CP), p-chlorophenol (p-CP), phenol and catechol), a colorimetric sensor array with three buffer solutions as sensing channels is designed for simultaneously discriminating 2,4-DP, o-CP, m-CP, p-CP, phenol and catechol as low as 10 mM, even quantifying 2,4-DP (as a model analyst). The performance is further validated through accurately identifying binary and ternary mixtures, and even 18 blind samples containing one of six phenols and ternary mixtures. Finally, the designed sensor array is successfully applied for identifying six representative phenols in various simulated water samples, presenting great potential and valuable applications for large-scale scanning levels of phenols in water samples.(c) 2022 Elsevier B.V. All rights reserved.

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