Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Energy Barrier Modulation vs Kinetic Acceleration: Tailoring WO<sub>3</sub> Nanofibers for Trace-Level Mustard Gas Simulant Detection.

ACS sensors·2026
Same author

Integrating advanced practice providers in acute care surgery: considerations, solutions, and fresh perspectives.

Trauma surgery & acute care open·2026
Same author

Electronic Trap-State Modulation in Sm-Doped SnO<sub>2</sub> Nanofibers Enables Ultrasensitive Hydrogen Sensing.

ACS sensors·2026
Same author

Aging the Imprint: Enhanced Performance of a Silver Prussian Blue Analogue-MIP Electrochemical Sensor for Sulfamethoxazole Detection in Milk.

ACS sensors·2025
Same author

Exploration of electrochemical hydrogen pumping with ultralow feed concentration down to 1 ppm.

Chemical communications (Cambridge, England)·2025
Same author

Modified Simple Limbal Epithelial Transplant for the Treatment of Symblepharon in Cats: A Pilot Study.

Veterinary ophthalmology·2025

Related Experiment Video

Updated: Sep 24, 2025

Ultrasensitive Detection of Biomarkers by Using a Molecular Imprinting Based Capacitive Biosensor
08:22

Ultrasensitive Detection of Biomarkers by Using a Molecular Imprinting Based Capacitive Biosensor

Published on: February 16, 2018

12.2K

Electropolymerized Molecularly Imprinted Polymer Synthesis Guided by an Integrated Data-Driven Framework for Cortisol

Grace Dykstra1, Benjamin Reynolds1, Riley Smith1

  • 1Department of Chemical Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, Michigan 49931, United States.

ACS Applied Materials & Interfaces
|May 10, 2022
PubMed
Summary

Machine learning accelerates the development of molecularly imprinted polymers (MIPs) for biosensors. This study uses Gaussian processes to optimize synthesis parameters, significantly enhancing cortisol detection sensitivity.

Keywords:
Gaussian processcortisol sensingmachine learningmolecularly imprinted polymerssensitivity analysissynthesis optimization

More Related Videos

A Method for Selecting Structure-switching Aptamers Applied to a Colorimetric Gold Nanoparticle Assay
12:31

A Method for Selecting Structure-switching Aptamers Applied to a Colorimetric Gold Nanoparticle Assay

Published on: February 28, 2015

15.3K
Dry Film Photoresist-based Electrochemical Microfluidic Biosensor Platform: Device Fabrication, On-chip Assay Preparation, and System Operation
13:42

Dry Film Photoresist-based Electrochemical Microfluidic Biosensor Platform: Device Fabrication, On-chip Assay Preparation, and System Operation

Published on: September 19, 2017

11.9K

Related Experiment Videos

Last Updated: Sep 24, 2025

Ultrasensitive Detection of Biomarkers by Using a Molecular Imprinting Based Capacitive Biosensor
08:22

Ultrasensitive Detection of Biomarkers by Using a Molecular Imprinting Based Capacitive Biosensor

Published on: February 16, 2018

12.2K
A Method for Selecting Structure-switching Aptamers Applied to a Colorimetric Gold Nanoparticle Assay
12:31

A Method for Selecting Structure-switching Aptamers Applied to a Colorimetric Gold Nanoparticle Assay

Published on: February 28, 2015

15.3K
Dry Film Photoresist-based Electrochemical Microfluidic Biosensor Platform: Device Fabrication, On-chip Assay Preparation, and System Operation
13:42

Dry Film Photoresist-based Electrochemical Microfluidic Biosensor Platform: Device Fabrication, On-chip Assay Preparation, and System Operation

Published on: September 19, 2017

11.9K

Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Biomedical Engineering

Background:

  • Molecularly imprinted polymers (MIPs) are synthetic receptors mimicking antibodies for biosensor applications.
  • Electropolymerization offers a cost-effective method for *in situ* MIP synthesis on electrodes.
  • Optimizing electropolymerized MIPs (e-MIPs) is complex due to numerous synthesis parameters, often relying on trial-and-error.

Purpose of the Study:

  • To develop a data-driven framework using machine learning to optimize e-MIP synthesis for enhanced biosensor performance.
  • To establish quantitative relationships between e-MIP synthesis parameters and their sensing capabilities.
  • To demonstrate a novel, generally applicable approach for accelerating biosensor material development.

Main Methods:

  • Fabrication of cortisol-imprinted polypyrrole e-MIPs using 72 parameter sets.
  • Performance evaluation using a 12-channel potentiostat.
  • Application of Gaussian process (GP) modeling for surrogate modeling and uncertainty quantification.
  • Global sensitivity analysis using Sobol indices to identify key synthesis parameters.

Main Results:

  • A GP surrogate model was built to predict e-MIP sensing performance.
  • Sensitivity analysis revealed critical synthesis parameters and their interrelations.
  • Optimized synthesis parameters, guided by GP predictions and local sensitivity analysis, led to a 1.5-fold increase in sensitivity.
  • Experimental validation confirmed the enhanced performance of the optimized e-MIPs.

Conclusions:

  • A machine learning framework, integrating GP modeling and sensitivity analysis, effectively optimizes e-MIP synthesis for biosensors.
  • This approach significantly improves biosensor sensitivity and reduces development time compared to traditional methods.
  • The framework is expandable and applicable to the development of various other sensing materials and biosensor platforms.