Kwangwoon University

About: Kwangwoon University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Thin film & Resonator. The organization has 4020 authors who have published 8217 publications receiving 104365 citations.

Flexible electrochemical biosensors for healthcare monitoring

Abstract: As the interest in wearable devices has increased recently, increasing biosensor flexibility has begun to attract considerable attention. Among the various types of biosensors, electrochemical biosensors are uniquely suited for the development of such flexible biosensors due to their many advantages, including their fast response, inherent miniaturization, convenient operation, and portability. Therefore, many studies on flexible electrochemical biosensors have been conducted in recent years to achieve non-invasive and real-time monitoring of body fluids such as tears, sweat, and saliva. To achieve this, various substrates, novel nanomaterials, and detection techniques have been utilized to develop conductive flexible platforms that can be applied to create flexible electrochemical biosensors. In this review, we discussed recently reported flexible electrochemical biosensors and divided them into specific categories including materials for flexible substrate, fabrication techniques for flexible biosensor development, and recently developed flexible electrochemical biosensors to externally monitor target molecules, thereby providing a means to noninvasively examine cells and body fluid samples. In conclusion, this review will discuss the materials, methods, recent studies, and perspectives on flexible electrochemical biosensors for healthcare monitoring and wearable biosensing systems.

Twofold Polarization-Selective All-Dielectric Trifoci Metalens for Linearly Polarized Visible Light

Abstract: This work was supported by National Research Foundation of Korea (NRF) grants funded by the Ministry of Education (Grant No. 2018R1A6A1A03025242). The work was partly supported by the Australian Research Council Future Fellowship (FT110100853, D.-Y.C.) and was performed in part at the ACT node of the Australian National Fabrication Facility

Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy.

Abstract: Terahertz imaging and time-domain spectroscopy have been widely used to characterize the properties of test samples in various biomedical and engineering fields. Many of these tasks require the analysis of acquired terahertz signals to extract embedded information, which can be achieved using machine learning. Recently, machine learning techniques have developed rapidly, and many new learning models and learning algorithms have been investigated. Therefore, combined with state-of-the-art machine learning techniques, terahertz applications can be performed with high performance that cannot be achieved using modeling techniques that precede the machine learning era. In this review, we introduce the concept of machine learning and basic machine learning techniques and examine the methods for performance evaluation. We then summarize representative examples of terahertz imaging and time-domain spectroscopy that are conducted using machine learning.