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() Enzyme-Mimics for Sensitive and Selective Steroid Metabolite Detection | ACS Applied Materials & Interfaces
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Enzyme-Mimics for Sensitive and Selective Steroid Metabolite Detection

  • Sanjida Yeasmin
    Sanjida Yeasmin
    School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 97331, United States
  • Ahasan Ullah
    Ahasan Ullah
    School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 97331, United States
    More by Ahasan Ullah
  • Bo Wu
    Bo Wu
    School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 97331, United States
    More by Bo Wu
  • Xueqiao Zhang
    Xueqiao Zhang
    School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 97331, United States
  • , and 
  • Li-Jing Cheng*
    Li-Jing Cheng
    School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, Oregon 97331, United States
    *Email: [email protected]
Cite this: ACS Appl. Mater. Interfaces 2023, 15, 11, 13971–13982
Publication Date (Web):March 12, 2023
https://doi.org/10.1021/acsami.2c21980
Copyright © 2023 American Chemical Society
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Supporting Info (1)»

Abstract

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We present an enzyme-like functional polymer that recognizes nonelectroactive targets and catalyzes their redox reactions for simple, selective steroid metabolite detection. Measuring steroid metabolites, such as cortisol, has been widely adopted to diagnose stress and chronic diseases. Conventional detection method based on competitive immunoassay requires time-consuming labeling processes for signal transduction and unstable biological receptors for biorecognition yet with limited selectivity. Inspired by natural enzymes’ target specificity and catalytic nature, we report an enzyme-mimic using electrocatalytic molecularly imprinted polymers (EC-MIP) to achieve label-free, external redox reagent-free, sensitive, and selective electrochemical detection of cortisol. The EC-MIP sensor contains molecularly imprinted cavities for specific cortisol binding and embedded copper phthalocyanine tetrasulfonate (CuPcTS) for electrocatalytic reduction of the ketones on the captured cortisol into alcohols. The direct sensing approach resolves the intrinsic limitations of conventional MIP-based sensors, most notably the use of external redox probes and weak sensing signals. The sensor exhibited a detection limit of 181 pM with significantly enhanced selectivity using a differential sensing mechanism. The new enzyme-like sensor can be modified to detect other targets, offering a simple, robust approach to future health monitoring technologies.

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The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsami.2c21980.

  • CVs during sensor preparation, SEM and EDX characterization of sensor, CV characteristics of CuPcTS-coated and PPy-coated electrodes, sensor fabrication, catalyst optimization, sensor electrochemical characterization, CV characteristics of CuPcTS-coated electrode in response to cortsiol, effects of scan rates and incubation time on sensing response, sensor regeneration test, pH-dependent and ionic strength-dependent sensor responses, sensor reproducibility test, pH test in PBS and artificial sweat, real sweat sample analysis and validation using MRM LC-MS/MS, derivation of the limit of detection equation, derivation of an analytical diffusion-reaction model, parameter extraction, and comparisons of sensor performance with prior works (PDF)

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