3D Spiky Needle-Clustered Ag@Au Plasmonic Nanoarchitecture for Highly Sensitive and Machine Learning-Assisted Detection of Multiple Hazardous Molecules

Hyo Jeong Seo, Jun Young Kim, Jun-Yeong Yang, Chaewon Mun, Seunghun Lee, Eun Hye Koh, Vo Thi Nhat Linh, Mijeong Kang, Ho Sang Jung
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Abstract

To develop a field applicable hazardous molecular detection system, highly sensitive and multiplex detection capability is required for practical utilization. Here, a paper-based 3D spiky needle-clustered gold grown on silver (Ag@Au) plasmonic nanoarchitecture (3D-SNCP) is fabricated through whole solution process. The developed substrate is investigated by scanning electron microscopy (SEM), transmission electron microscopy (TEM) and X-ray diffraction (XRD) to find out morphological development mechanism. Also, finite-domain time difference (FDTD) simulation is conducted for the observation of electromagnetic field (E-field) distribution. After surface-enhanced Raman scattering (SERS) characterization, the 3D-SNCP is utilized for ultra-sensitive and multiplex hazardous molecular detection, such as bipyridine pesticides including paraquat (PQ), diquat (DQ), and difenzoquat (DIF). Then, each of pesticide molecular Raman signals are trained by a machine learning technique of multinomial logistic regression (MLR), followed by multiplex classificationf of blank, PQ, DQ, DIF, and four mixture types of each pesticide, spiked in real agricultural matrix. The developed 3D-SNCP substrate combined with the machine learning method successfully verifies the multiple pesticides and it is expected to be applied for various hazardous molecular detection in much complicated matrix environments.

Abstract Image

用于高灵敏度和机器学习辅助检测多种有害分子的三维尖针簇状 Ag@Au 质子纳米结构
要开发一种适用于现场的危险分子检测系统,就必须具备高灵敏度和多重检测能力。本文通过全溶液工艺制备了一种基于纸的银上生长的三维尖针状金(Ag@Au)质子纳米结构(3D-SNCP)。通过扫描电子显微镜(SEM)、透射电子显微镜(TEM)和 X 射线衍射(XRD)对所制备的基底进行了研究,以找出其形态发展机理。此外,还进行了有限域时差(FDTD)模拟,以观察电磁场(E-field)的分布。经过表面增强拉曼散射(SERS)表征后,3D-SNCP 被用于超灵敏和多重有害分子检测,如百草枯(PQ)、敌草快(DQ)和敌草快(DIF)等联吡啶类农药。然后,利用多叉逻辑回归(MLR)的机器学习技术对每种农药分子拉曼信号进行训练,再对实际农业基质中添加的空白、PQ、DQ、DIF 和每种农药的四种混合物进行多重分类。所开发的 3D-SNCP 基质与机器学习方法相结合,成功地验证了多种农药,有望应用于复杂基质环境中各种有害分子的检测。
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