Electropolymerized Molecularly Imprinted Polymer Synthesis Guided by an Integrated Data-Driven Framework for Cortisol Detection
Electropolymerized Molecularly Imprinted Polymer Synthesis Guided by an Integrated Data-Driven Framework for Cortisol Detection
复制标题
DOI：
10.1021/acsami.2c02474
复制
发表时间：
2022-06-08
影响因子：
9.5
通讯作者：
Liu, Yixin
中科院分区：
文献类型：
--
作者：
Dykstra, Grace;Reynolds, Benjamin;Liu, Yixin
分子印迹聚合物（MIP）通常被称为“合成抗体”，作为具有定制生物分子识别功能的人工受体来构建生物传感器，非常有吸引力。电聚合是一种快速简便的方法，可在工作电极上直接原位合成 MIP 传感元件，从而实现超低成本且易于制造的电化学生物传感器。然而，由于电聚合 MIP (e-MIP) 的高维设计空间，在没有适当指导的情况下，基于反复试验的 eMIP 开发充满挑战且漫长。利用机器学习技术建立合成参数与相应传感性能之间的定量关系，可以促进 e-MIP 的开发和优化。我们在此展示了一个用于皮质醇检测的皮质醇印迹聚吡咯合成的案例研究，其中 e-MIP 是用 72 组合成参数和复制品制造的。使用 12 通道恒电位仪测量它们的传感性能，以构建后续的数据驱动框架。高斯过程（GP）被用作集成框架的支柱，它可以解释合成和测量中的各种不确定性。然后在 GP 代理模型上执行基于 Sobol 指数的全局灵敏度，以阐明 e-MIP 的合成参数对传感性能和参数之间相互关系的影响。基于所建立的GP模型的预测和局部灵敏度分析，通过实验对合成参数进行优化和验证，从而导致传感性能显着增强（灵敏度提高1.5倍）。所提出的框架在生物传感器开发中是新颖的，具有可扩展性，也普遍适用于其他传感材料的开发。
Molecularly imprinted polymers (MIPs), often called "synthetic antibodies", are highly attractive as artificial receptors with tailored biomolecular recognition to construct biosensors. Electropolymerization is a fast and facile method to directly synthesize MIP sensing elements in situ on the working electrode, enabling ultra-low-cost and easy-to-manufacture electrochemical biosensors. However, due to the high dimensional design space of electropolymerized MIPs (e-MIPs), the development of eMIPs is challenging and lengthy based on trial and error without proper guidelines. Leveraging machine learning techniques in building the quantitative relationship between synthesis parameters and corresponding sensing performance, e-MIPs' development and optimization can be facilitated. We herein demonstrate a case study on the synthesis of cortisol-imprinted polypyrrole for cortisol detection, where e-MIPs are fabricated with 72 sets of synthesis parameters with replicates. Their sensing performances are measured using a 12-channel potentiostat to construct the subsequent data-driven framework. The Gaussian process (GP) is employed as the mainstay of the integrated framework, which can account for various uncertainties in the synthesis and measurements. The Sobol index-based global sensitivity is then performed upon the GP surrogate model to elucidate the impact of e-MIPs' synthesis parameters on sensing performance and interrelations among parameters. Based on the prediction of the established GP model and local sensitivity analysis, synthesis parameters are optimized and validated by experiment, which leads to remarkable sensing performance enhancement (1.5-fold increase in sensitivity). The proposed framework is novel in biosensor development, which is expandable and also generally applicable to the development of other sensing materials.