Machine learning-driven sensor array based on luminescent metal-organic frameworks for simultaneous identification of multiple anions in environmental waters - Article 162796

Aug 4, 2026

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High correlation between anions in water and environmental quality, as well as human health, drives the need for simple, effective systems to discriminate multiple anions. A machine learning-assisted fluorescent sensor array was developed using two luminescent metal-organic frameworks, UiO-66-NH2 and UiO-66-OH, for simultaneous identification of five anions (F−, PO43−, ClO4−, NO3−, SO42−). Each framework produced distinct fluorescence fingerprints that anions modulated. Under optimized conditions, the array achieved LODs of 2.14–3.51 μM with satisfactory accuracy and enabled reliable identification and prediction in real water samples, supporting practical on-site pollution control.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages24

Reading Time1 h 0 min

AccessFree to download

Abstract

Introduction

Rationale for multi-anion detection

Limitations of existing instrumental and probe-based methods

Sensor arrays as an artificial tongue for multitarget sensing

Manuscript Click here to view linked References 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 1 Machine learning-driven sensor array based on luminescent metal- 2 organic frameworks for simultaneous identification of multiple 3 anions in environmental waters 4 5 Dali Wei1, Yudi Yang1, Ying Wang1, Yunxiang Fan1, Yuxuan Shen1, Chunmeng Deng1, Kun Zeng1, 6 Zhugen Yang2, Zhen Zhang1* 7 8 1 School of the Environment and Safety Engineering, School of the Emergency Management, 9 Jiangsu University, Zhenjiang 212013, China. 10 2 School of Water, Energy, and Environment, Cranfield University, Milton Keynes, MK43 0AL, 11 UK. 12 13 14 *Corresponding author: 15 Email: [zhenzhang@ujs.edu.cn](zhenzhang@ujs.edu.cn). Chemical Engineering Journal, Volume 512, May 2025, Article number 162796 DOI: 10.1016/j.cej.2025.162796 1 Published by Elsevier. This is the Author Accepted Manuscript issued with: Creative Commons Attribution License (CC:BY 4.0) . The final published version (version of record) is available online at DOI:10.1016/j.cej.2025.162796 . Please refer to any applicable publisher terms of use. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 16 Abstract 17 Due to the high correlation of anions in waters to environmental quality and human health, 18 thus there is urgent need for developing simple and effective sensors to discriminate multiple 19 anions. Herein, a machine learning-assisted fluorescent sensor array based on two luminescent 20 metal-organic frameworks (LMOFs, UiO-66-NH2 and UiO-66-OH) was developed for 21 simultaneous discrimination of five anions (F− , PO43−, ClO44−, NO3− , and SO42−) . Wherein, 22 UiO-66-NH2 and UiO-66-OH were designed by anchoring 2,5-diaminoterephthalic acid and 23 2,5-dihydroxyterephthalic acid on UiO-66, respectively, which exhibited blue and green 24 fluorescence emission, possessing good fluorescence property. Interestingly, the anions could 25 effectively enhance the fluorescence intensity of UiO-66-NH2 and UiO-66-OH to generate 26 diverse fluorescence responses and unique fingerprints, which could be utilized to develop a 27 fluorescence sensor array for the sensitive identification of five anions and their mixtures. 28 Under the optimized conditions, the limit of detection (LOD) of the proposed array for anions 29 detection was about at 2.14-3.51 μM with a satisfactory accuracy. More importantly, the 30 integration of machine learning algorithm and sensor array has successfully achieved accurate 31 identification and prediction of five anions in real water samples, affirming its practicability in 32 actual samples. Our findings provided a promising tool for detecting multiple anions, and 33 inspired potentials of the combination of sensor arrays and machine learning algorithm for 34 pollution control in real waters. 35 Keywords: anions detection, sensor array, metal-organic frameworks, environmental 36 monitoring 37 2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 38 1. Introduction 39 Anions play an irreplaceable role in the ecological system, while excessive anions can pose 40 a huge threat to environment quality and human health (Al-Saidi and Khan 2022;

FAQs

What problem does the proposed method target?

It addresses the urgent need to discriminate multiple anions in environmental waters because anion levels are closely linked to environmental quality and human health.

How does the sensor array work?

It uses two luminescent metal-organic frameworks (UiO-66-NH2 and UiO-66-OH) whose blue/green fluorescence responses are modulated by different anions, producing diverse fingerprints for identification.

What performance and validation results are reported?

With optimized conditions, the limit of detection is about 2.14–3.51 μM and the integrated machine learning approach achieves accurate identification and prediction of the five anions in real water samples.

Aug 4, 2026

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