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Bio-Inspired Molecularly Imprinted Polymer Electrochemical Sensor for Cortisol Detection Based on O-Phenylenediamine Optimization.
Kim, Minwoo; Park, Daeil; Park, Joohyung; Park, Jinsung.
Afiliação
  • Kim M; Department of Biomechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
  • Park D; Department of Biomechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
  • Park J; Department of Biomechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
  • Park J; Department of Biomechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Biomimetics (Basel) ; 8(3)2023 Jul 01.
Article em En | MEDLINE | ID: mdl-37504170
ABSTRACT
This paper presents a comprehensive investigation of the various parameters involved in the fabrication of a molecularly imprinted polymer (MIP) sensor for the detection of cortisol. Parameters such as monomer concentration, electropolymerization cycles, pH, monomer-template ratio, template removal technique, and rebinding time were optimized to establish a more consistent and effective method for the fabrication of MIP sensors. Under the optimized conditions, the MIP sensor demonstrated a proportional decrease in differential pulse voltammetry peak currents with increasing cortisol concentration in the range of 0.1 to 100 nM. The sensor exhibited excellent sensitivity, with a limit of detection of 0.036 nM. Selectivity experiments using a non-imprinted polymer sensor confirmed the specific binding affinity of the MIP sensor for cortisol, distinguishing it from other steroid hormones. This study provides crucial insights into the development of a reliable and sensitive strategy for cortisol detection using O-PD-based MIPs. These findings laid the foundation for further advancements in MIP research.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article