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Optimizing Multivariate Metal–Organic Frameworks for Electrochemical Sensing of Dihydroxybenzene Isomers

Cun-Di Hao, Zhan‐Yun Zhang, Ai-Xuan Yu, Jiajia Li, Qing Liu, Xiangjie Bo, Dong‐Ying Du, Shuai Yuan, Zhongmin Su

2025Inorganic Chemistry13 citationsDOI

Abstract

Dihydroxybenzene isomers, including hydroquinone (HQ), catechol (CC), and resorcinol (RS), commonly used in pesticides, dyes, and cosmetics, are hazardous pollutants due to their high toxicity and carcinogenicity. Their simultaneous detection is important for environmental monitoring but remains challenging due to their structural similarities and mutual interference. Herein, a series of multivariate amino-functionalized bimetallic–organic frameworks, MIL-125(Ti–In)- x NH 2 ( x = 0%, 25%, 50%, 75% and 100%), were prepared and employed as electrochemical sensors for the determination of HQ, CC and RS. Integrating electrocatalytically active indium(III) centers and polar amino groups within the cavity of MIL-125(Ti–In)- x NH 2 regulates the electrocatalytic activity and selectivity toward dihydroxybenzene oxidation. By fine-tuning the content of indium(III) centers and amino-linkers within MIL-125(Ti–In)- x NH 2, the current response and selectivity toward different dihydroxybenzene isomers were judiciously optimized. Among them, MIL-125(Ti–In)–75%NH 2 exhibited the best performance, with outstanding wide linear response ranges (2–102, 2–120 and 30–350 μM) and low limits of detection (0.0891, 0.0162, and 3.686 μM, S/N = 3) for simultaneous detection of HQ, CC and RS, which provides an ideal platform for application in real water samples. In addition, electrochemical tests and density functional theory calculations highlighted the critical role of amino groups for the selective detection of dihydroxybenzene isomers.

Topics & Concepts

ChemistryElectrochemistryMetal-organic frameworkMultivariate statisticsMetalCombinatorial chemistryOrganic chemistryElectrodePhysical chemistryAdsorptionMathematicsStatisticsMetal-Organic Frameworks: Synthesis and ApplicationsElectrochemical Analysis and ApplicationsMachine Learning in Materials Science
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