Award Abstract # 2138523
ERI: In-Situ Fabrication of Dual-Template Imprinted Nanocomposites for Simultaneous Detection of Glucose and Cortisol

NSF Org: CBET
Division of Chemical, Bioengineering, Environmental, and Transport Systems
Recipient: MICHIGAN TECHNOLOGICAL UNIVERSITY
Initial Amendment Date: January 27, 2022
Latest Amendment Date: January 27, 2022
Award Number: 2138523
Award Instrument: Standard Grant
Program Manager: Aleksandr Simonian
asimonia@nsf.gov
 (703)292-2191
CBET
 Division of Chemical, Bioengineering, Environmental, and Transport Systems
ENG
 Directorate for Engineering
Start Date: February 1, 2022
End Date: January 31, 2025 (Estimated)
Total Intended Award Amount: $199,972.00
Total Awarded Amount to Date: $199,972.00
Funds Obligated to Date: FY 2022 = $199,972.00
History of Investigator:
  • Yixin Liu (Principal Investigator)
    yixinliu@mtu.edu
Recipient Sponsored Research Office: Michigan Technological University
1400 TOWNSEND DR
HOUGHTON
MI  US  49931-1200
(906)487-1885
Sponsor Congressional District: 01
Primary Place of Performance: Michigan Technological University
1400 Townsend Drive
Houghton
MI  US  49931-1295
Primary Place of Performance
Congressional District:
01
Unique Entity Identifier (UEI): GKMSN3DA6P91
Parent UEI: GKMSN3DA6P91
NSF Program(s): ERI-Eng. Research Initiation
Primary Program Source: 010V2122DB R&RA ARP Act DEFC V
Program Reference Code(s): 9102
Program Element Code(s): 180Y00
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

People with diabetes are 2-3 times more likely to have depression than people without diabetes. Meanwhile, depressive or anxiety symptoms, often associated with elevated cortisol (the ?stress hormone?), can lead to the onset of type 2 diabetes (T2D). Monitoring both glucose and cortisol levels regularly in a cost-effective and effortless way is highly desired to manage diabetes and stress, and prevent prediabetes from progressing to full-blown T2D. Enzyme-based glucose sensors monopolize the current glucose monitor industry, and the traditional detection of cortisol is carried out in centralized laboratory settings based on immunoassays using antibodies and enzymes. Natural receptors such as enzymes and antibodies often suffer from high cost, poor stability, and complexity. This project aims to develop an enzyme-free and antibody-free electrochemical sensor to simultaneously detect glucose and cortisol coupled with machine learning techniques. The knowledge gained from this research will lead to low-cost biosensing devices and manufacturing processes that will not only increase access to decentralized, personalized, and preventive healthcare but may also be applied to other chemicals, biomarkers, and pathogens detection. This project will contribute significantly to workforce training by promoting the interdisciplinary research of sensing, computing, and machine learning-based data analytics.

The investigator?s long-term research goal is to develop a low-cost, easy-to-manufacture and high-performance biosensing technology based on electropolymerized MIPs (e-MIPs) as the platform to detect biomarkers in human biofluids for decentralized diagnostics and personal health monitoring. Towards this goal, the aim of this ERI project is to pilot an in-situ fabrication procedure to construct an enzyme-free and e-MIPs-based electrochemical sensor to simultaneously detect glucose and cortisol with high sensitivity and selectivity. The proposed sensor consists of metal/metal oxide (M/MO) nanostructures to mimic enzymes? catalytic activity for glucose oxidation and a molecularly imprinted polymer (MIP) to mimic antibodies? selective biomolecular recognition for glucose and cortisol. The project will explore a fully in-situ fabrication procedure to synthesize and integrate functional nanomaterials with MIPs directly on the electrode?s surface. This process is fast, facile, and highly reproducible, and the sensor is immediately ready for use without further processing. The proposed sensor is designed to provide distinct dual signals correlated with cortisol and glucose concentrations, which can be quantified simultaneously by a well-configured machine learning model. The novel dual-sensing mechanism will establish a new path to enable multiplex detection leveraging upon the powerful inference capability of machine learning. This project will also deliver an in-depth understanding of the critical factors that impact the sensing performance, which will provide valuable guidelines for future MIPs design for biosensors. Low cost and high performance of MIPs, facile fabrication process, microfluidic-integration readiness, and multiplex detection capability all together will lead to cost-effective biosensors and biodevices not only for Point-of-Care (POC) diagnosis and personal health monitoring but also for other applications such as smart agriculture, water quality, and food safety monitoring.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Dykstra, Grace and Chapa, Isabel and Liu, Yixin "Reagent-Free Lactate Detection Using Prussian Blue and Electropolymerized-Molecularly Imprinted Polymers-Based Electrochemical Biosensors" ACS Applied Materials & Interfaces , v.16 , 2024 https://doi.org/10.1021/acsami.3c19448 Citation Details
Dykstra, Grace and Reynolds, Benjamin and Smith, Riley and Zhou, Kai and Liu, Yixin "Electropolymerized Molecularly Imprinted Polymer Synthesis Guided by an Integrated Data-Driven Framework for Cortisol Detection" ACS Applied Materials & Interfaces , v.14 , 2022 https://doi.org/10.1021/acsami.2c02474 Citation Details
Dykstra, Grace and Vera, Verdict and Chapa, Isabel and Rao, Smitha and Liu, Yixin "Engineering electropolymerized molecularly imprinted polymer films for redox-integrated, reagent-free cortisol detection: The critical role of scan rate" Biosensors and Bioelectronics , v.286 , 2025 https://doi.org/10.1016/j.bios.2025.117623 Citation Details
Sylvain, Rourke and Dykstra, Grace and Fungura, Asky and Rao, Smitha and Liu, Yixin "In-situ electrochemical synthesis of Ni/Ni(OH)2/molecularly imprinted polymer nanocomposite for high-performance glucose detection" Sensors and Actuators B: Chemical , v.424 , 2025 https://doi.org/10.1016/j.snb.2024.136921 Citation Details
Sylvain, Rourke and Waslawski, Tyler and Vera, Verdict and Dykstra, Grace and Heintz, Georgia and Rao, Smitha and Liu, Yixin "Optimization and Application of Laser-Induced Graphene Electrodes with Nickel Hydroxide Nanoparticles for Ultrasensitive Non-Enzymatic Glucose Sensing" , 2024 https://doi.org/10.1109/SENSORS60989.2024.10784638 Citation Details
Vera, Verdict and Sylvain, Rourke and Waslawski, Tyler and Dykstra, Grace and Rao, Smitha and Liu, Yixin "Reagent-Less Low-Concentration Cortisol Detection Enabled by Laser-Induced Graphene Electrodes" , 2024 https://doi.org/10.1109/BSN63547.2024.10780732 Citation Details

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This project aimed to develop low-cost, non-enzymatic, and antibody-free electrochemical biosensors capable of simultaneously detecting glucose and cortisol, two key biomarkers related to metabolic health and stress.

Key scientific advances include: 1) Fully In-Situ Electrochemical Fabrication:We developed a streamlined, entirely in-situ electrochemical process to fabricate sensor interfaces, enabling precise control over material deposition and film formation without requiring complex post-processing or external functionalization steps. 2) Non-Enzymatic Catalysts Integrated with MIPs: We successfully combined molecularly imprinted polymers (MIPs) with nickel-based nanostructured catalysts to develop non-enzymatic glucose sensors that offer high stability, long shelf life, and enhanced selectivity toward glucose under alkaline conditions. 3) Redox-Integrated MIPs for Cortisol Detection:By embedding Prussian Blue directly beneath the MIP layer, we created a reagent-free cortisol sensor with internal redox capability. This integration simplified the sensing mechanism and improved device reliability by eliminating the need for external redox mediators. 4) Data-Driven Material Optimization and Real-Time Characterization: We applied machine learning techniques to optimize MIP synthesis based on experimental sensing data, leading to enhanced sensitivity based on predicted fabrication parameters. In parallel, electrochemical quartz crystal microbalance (EC-QCM) was used to monitor polymer growth in real time, providing quantitative insights into the relationship between fabrication parameters and sensor performance. 5) Advanced Electrode Platform Development: Laser-induced graphene (LIG) was adapted and optimized as a tunable, conductive, and scalable electrode platform compatible with imprinting, nanomaterial deposition, and low-cost manufacturing. The platform supports diverse sensing architectures and biofluid compatibility. 6) Dual-Sensing Architecture for Glucose and Cortisol: We designed and validated a dual-sensing platform that addresses the distinct chemical requirements of glucose (alkaline) and cortisol (neutral or slightly acidic) detection. The system spatially separates the sensing regions and tailors local environments to optimize each sensor's performance, enabling accurate, simultaneous detection of both analytes directly from biofluid samples without additional chemicals.
This project lays the foundation for non-enzymatic glucose sensors and synthetic receptor-based cortisol sensor for wearable health monitoring, that are affordable and long-lasting. Future applications include stress management, metabolic tracking, and early disease detection, especially for individuals managing chronic conditions or seeking preventative care tools outside of clinical settings.
Beyond the lab, this project provided hands-on training to both undergraduate and graduate students, equipping them with interdisciplinary skills in materials science, electrochemistry, and chemical and biomedical engineering. We also engaged high school students through outreach events and summer youth programs, introducing them to emerging sensor technologies and encouraging interest in STEM careers.







Last Modified: 05/29/2025
Modified by: Yixin Liu

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