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

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。患有糖尿病的人患抑郁症的可能性是没有糖尿病的人的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，可能导致2型糖尿病(T2D)的发病。以经济有效和毫不费力的方式定期监测血糖和皮质醇水平，对于管理糖尿病和压力，防止糖尿病前期进展到全面的T2D是非常必要的。基于酶的葡萄糖传感器垄断了当前的血糖监测行业，传统的皮质醇检测是在基于抗体和酶的免疫分析的集中式实验室环境中进行的。天然受体，如酶和抗体，往往存在成本高、稳定性差和复杂性的问题。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，不仅将增加获得分散、个性化和预防性医疗保健的机会，而且还可能应用于其他化学品、生物标记物和病原体检测。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基于电聚合分子印迹聚合物(e-MIP)的生物传感技术，作为检测人体生物液中生物标志物的平台，用于分散诊断和个人健康监测。为实现这一目标，本ERI项目的目标是试验一种原位制造程序，以构建一种无酶和e-MIPs为基础的电化学传感器，以高灵敏度和选择性同时检测葡萄糖和皮质醇。该传感器由金属/金属氧化物(M/MO)纳米结构和分子印迹聚合物(MIP)组成，金属/金属氧化物纳米结构模拟酶对葡萄糖氧化的催化活性，分子印迹聚合物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制造程序，直接在电极表面合成并将功能纳米材料与分子印迹聚合物集成在一起。这一过程快速、简便、重复性高，传感器无需进一步处理即可使用。所提出的传感器旨在提供与皮质醇和葡萄糖浓度相关的明显双重信号，这些信号可以通过配置良好的机器学习模型同时量化。这种新颖的双感知机制将建立一条新的途径，利用机器学习的强大推理能力来实现多路检测。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分子印迹聚合物设计提供有价值的指导。MIP的低成本和高性能、简单的制造工艺、微流控集成的就绪性和多路检测能力相结合，将带来具有成本效益的生物传感器和生物设备，不仅适用于护理点(POC)诊断和个人健康监测，还适用于其他应用，如智能农业、水质和食品安全监测。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.