Literature DB >> 33236831

Recent advances and applications of molecularly imprinted polymers in solid-phase extraction for real sample analysis.

Tianliang Hu1, Run Chen1, Qiang Wang1, Chiyang He1, Shaorong Liu2.   

Abstract

Sample pretreatment is essential for the analysis of complicated real samples due to their complex matrices and low analyte concentrations. Among all sample pretreatment methods, solid-phase extraction is arguably the most frequently used one. However, the majority of available solid-phase extraction adsorbents suffer from limited selectivity. Molecularly imprinted polymers are a type of tailor-made artificial antibodies and receptors with specific recognition sites for target molecules. Using molecularly imprinted polymers instead of conventional adsorbents can greatly improve the selectivity of solid-phase extraction, and therefore molecularly imprinted polymer-based solid-phase extraction has been widely applied to separation, clean up and/or preconcentration of target analytes in various kinds of real samples. In this article, after a brief introduction, the recent developments and applications of molecularly imprinted polymer-based solid-phase extraction for determination of different analytes in complicated real samples during the 2015-2020 are reviewed systematically, including the solid-phase extraction modes, molecularly imprinted adsorbent types and their preparations, and the practical applications of solid-phase extraction to various real samples (environmental, food, biological, and pharmaceutical samples). Finally, the challenges and opportunities of using molecularly imprinted polymer-based solid-phase extraction for real sample analysis are discussed.
© 2020 Wiley-VCH GmbH.

Entities:  

Keywords:  clean up; molecularly imprinted polymers; real samples; sample preparation; solid-phase extraction

Year:  2021        PMID: 33236831     DOI: 10.1002/jssc.202000832

Source DB:  PubMed          Journal:  J Sep Sci        ISSN: 1615-9306            Impact factor:   3.645


  7 in total

1.  Assessing organophosphorus and carbamate pesticides in maize samples using MIP extraction and PSI-MS analyzes.

Authors:  Carla Freitas; Lucas S Machado; Igor Pereira; Rodolfo R da Silva; Gabriel F Dos Santos; Andrea R Chaves; Rosineide C Simas; Gesiane S Lima; Boniek G Vaz
Journal:  J Food Sci Technol       Date:  2022-05-04       Impact factor: 3.117

Review 2.  Nanomaterials with Excellent Adsorption Characteristics for Sample Pretreatment: A Review.

Authors:  Wen-Xin Liu; Shuang Song; Ming-Li Ye; Yan Zhu; Yong-Gang Zhao; Yin Lu
Journal:  Nanomaterials (Basel)       Date:  2022-05-27       Impact factor: 5.719

3.  Characterization of molecularly imprinted polymers for the extraction of tobacco alkaloids and their metabolites in human urine.

Authors:  Haley A Mulder; Adam C Pearcy; Matthew S Halquist
Journal:  Biomed Chromatogr       Date:  2022-04-26       Impact factor: 1.911

Review 4.  Molecularly imprinted polymers via reversible addition-fragmentation chain-transfer synthesis in sensing and environmental applications.

Authors:  Irvin Veloz Martínez; Jackeline Iturbe Ek; Ethan C Ahn; Alan O Sustaita
Journal:  RSC Adv       Date:  2022-03-23       Impact factor: 3.361

Review 5.  Advances in Detection of Antibiotic Pollutants in Aqueous Media Using Molecular Imprinting Technique-A Review.

Authors:  Akinrinade George Ayankojo; Jekaterina Reut; Vu Bao Chau Nguyen; Roman Boroznjak; Vitali Syritski
Journal:  Biosensors (Basel)       Date:  2022-06-23

Review 6.  Dual-Functional Monomer MIPs and Their Comparison to Mono-Functional Monomer MIPs for SPE and as Sensors.

Authors:  Angela Alysia Elaine; Steven Imanuel Krisyanto; Aliya Nur Hasanah
Journal:  Polymers (Basel)       Date:  2022-08-26       Impact factor: 4.967

7.  Evaluation of 2-hydroxyethyl methacrylate as comonomer in the preparation of water-compatible molecularly imprinted polymers for triazinic herbicides.

Authors:  Myriam Díaz-Álvarez; Antonio Martín-Esteban; Esther Turiel
Journal:  J Sep Sci       Date:  2022-05-03       Impact factor: 3.614

  7 in total

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