Literature DB >> 35661731

State-of-the-art progress of metal-organic framework-based electrochemical and optical sensing platforms for determination of bisphenol A as an endocrine disruptor.

Alireza Khataee1, Hessamaddin Sohrabi2, Maryam Ehsani3, Mahdiyeh Agaei3, Abdollah Jamal Sisi3, Jafar Abdi4, Yeojoon Yoon5.   

Abstract

Considering the low concentration levels of bisphenol compounds present in environmental, food, and biological samples, and the difficulty in analyzing the matrices, the main challenge is with the cleanup and extraction process, as well as developing highly sensitive determination methods. Recent advances in the field of metal-organic frameworks (MOFs) due to their large surface area, low weight, and other extraordinary physical, chemical, and mechanical features have made these porous materials a crucial agent in developing biosensing assays. This review focuses on MOFs across their definition, structural features, various types, synthetic routes, and their significant utilization in sensing assays for bisphenol A (BPA) determination. Additionally, recent improvements in characteristics and physio-chemical features of MOFs and their functional applications in developing electrochemical and optical sensing assays via different recognition elements for detecting BPA are comprehensively discussed. Finally, the existing boundaries of the current advances including future challenges concerning successful construction of sensing approaches by employing functionalized MOFs are addressed.
Copyright © 2022 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Bisphenol A; Electrochemical sensing assays; Environmental pollutants; Metal-organic frameworks; Optical sensing assays

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Year:  2022        PMID: 35661731     DOI: 10.1016/j.envres.2022.113536

Source DB:  PubMed          Journal:  Environ Res        ISSN: 0013-9351            Impact factor:   8.431


  2 in total

Review 1.  Recent Advances in Early Diagnosis of Viruses Associated with Gastroenteritis by Biosensors.

Authors:  Abouzar Babaei; Nastaran Rafiee; Behnaz Taheri; Hessamaddin Sohrabi; Ahad Mokhtarzadeh
Journal:  Biosensors (Basel)       Date:  2022-07-08

2.  Machine learning approaches for predicting arsenic adsorption from water using porous metal-organic frameworks.

Authors:  Jafar Abdi; Golshan Mazloom
Journal:  Sci Rep       Date:  2022-09-30       Impact factor: 4.996

  2 in total

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