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

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。糖尿病患者患抑郁症的可能性是非糖尿病患者的2-3倍。同时，抑郁或焦虑症状，通常与皮质醇（“应激激素”）升高有关，可导致2型糖尿病（T2 D）的发作。以具有成本效益且毫不费力的方式定期监测葡萄糖和皮质醇水平是非常期望的，以管理糖尿病和压力，并防止前驱糖尿病进展为全面的T2 D。基于酶的葡萄糖传感器垄断了当前的葡萄糖监测器行业，并且传统的皮质醇检测是在基于使用抗体和酶的免疫测定的集中实验室环境中进行的。天然受体如酶和抗体通常具有成本高、稳定性差和复杂性的缺点。该项目旨在开发一种无酶无抗体的电化学传感器，结合机器学习技术同时检测葡萄糖和皮质醇。从这项研究中获得的知识将导致低成本的生物传感设备和制造工艺，不仅可以增加分散，个性化和预防性医疗保健的获得，而且还可以应用于其他化学品，生物标志物和病原体检测。该项目将通过促进传感，计算和基于机器学习的数据分析的跨学科研究，为劳动力培训做出重大贡献。研究人员的长期研究目标是开发一种低成本，易于制造和高性能的生物传感技术，该技术基于电聚合MIP（e-MIP）作为平台，用于检测人体生物流体中的生物标志物，用于分散诊断和个人健康监测。 为了实现这一目标，该ERI项目的目的是试点原位制造过程，以构建无酶和基于e-MIP的电化学传感器，以同时检测葡萄糖和皮质醇，具有高灵敏度和选择性。 所提出的传感器由金属/金属氧化物（M/MO）纳米结构来模拟酶对葡萄糖氧化的催化活性和分子印迹聚合物（MIP）来模拟抗体对葡萄糖和皮质醇的选择性生物分子识别。该项目将探索一种完全原位的制造过程，以直接在电极表面合成和整合功能纳米材料与MIP。该过程快速、简便、可重复性高，传感器无需进一步处理即可立即投入使用。所提出的传感器旨在提供与皮质醇和葡萄糖浓度相关的独特双信号，这些信号可以通过配置良好的机器学习模型同时量化。新的双传感机制将建立一个新的路径，使多重检测利用强大的推理能力的机器学习。该项目还将深入了解影响传感性能的关键因素，这将为未来生物传感器的MIP设计提供有价值的指导。MIP的低成本和高性能，简易的制造工艺，微流体集成准备和多重检测能力将共同导致具有成本效益的生物传感器和生物设备，不仅用于护理点（POC）诊断和个人健康监测，还用于其他应用，如智能农业，水质，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.