Yonsei University · 工学
Professor Jinyoung Kim's research lab specializes in advanced microfluidic systems and nanomaterials for biomedical applications. The lab focuses on developing innovative organ-on-a-chip platforms, droplet-based microfluidics, and functional nanomaterials for disease modeling, drug screening, and point-of-care diagnostics. Key research directions include the integration of microfluidic devices with analytical techniques like HPLC, the fabrication of high-pressure-resistant microfluidic systems using novel materials such as thermoset polyester, and the application of nanomaterials like PoGNP for antiviral therapy and detection. The lab also explores symmetry-based analysis of complex materials to understand structural transitions in functional oxides.
Figures are computed from collected data and may differ slightly.
We believe that the technique described herein can be further developed for PoGNP-utilized antiviral protection as well as metal nanoparticle-based therapy to treat viral infection. Additionally, facile detection of IAV can be achieved by developing PoGNP as a multiplatform for detection of the virus.
In this article, we present a microfluidic platform, compatible with conventional 96-well formats, that enables facile and parallelized culturing and testing of spherical microtissues in a standard incubator. The platform can accommodate multiple microtissues (up to 66) of different cell types, formed externally by using the hanging-drop method, and enables microtissue interconnection through microfluidic channels for continuous media perfusion or dosage of substances. The platform contains 11 s
We demonstrate the integration of a droplet-based microfluidic device with high performance liquid chromatography (HPLC) in a monolithic format. Sequential operations of separation, compartmentalisation and concentration counter were conducted on a monolithic chip. This describes the use of droplet-based microfluidics for the preservation of chromatographic separations, and its potential application as a high frequency fraction collector.
The vast majority of droplet-based microfluidic devices are made from polydimethylsiloxane (PDMS). Unfortunately PDMS is not suitable for high frequency droplet generation at high operating pressure due to its low shear modulus. In this paper, we report the fabrication and testing of microfluidic devices using thermoset polyester (TPE). The optical characteristics of the fabricated devices were assessed and substrate resistance to pressure also investigated. TPE devices bonded using an O(2) plas
The<i>in vitro</i>simulation of organs resolves the accuracy, ethical, and cost challenges accompanying<i>in vivo</i>experiments. Organoids and organs-on-chips have been developed to model the<i>in vitro</i>, real-time biological and physiological features of organs. Numerous studies have deployed these systems to assess the<i>in vitro</i>, real-time responses of an organ to external stimuli. Particularly, organs-on-chips can be most efficiently employed in pharmaceutical drug development to pre
Group theoretical methods were applied to elucidate the structural transition path and the polarization process in YMnO3. The atomic displacements derived from in situ high-temperature synchrotron x-ray powder diffraction data were decomposed into three symmetry-adapted modes (Γ2−, K1, and K3). The temperature dependence of the mode amplitudes confirmed the existence of two step-transitions. First, a coupled K3 and Γ2− mode lowered the symmetry from P63/mmc to P63cm at the phase-transition tempe
Recently, deep learning models proliferate in the prediction of power demand for efficient planning of power consumption. However, the “black-box” characteristics of deep learning hinders from establishing a specific plan because it cannot explain the cause of the prediction. Recently, there are several attempts to explain the result of deep learning through the analysis of the input attributes that influence the prediction, but they lack of appropriate explanation because of ignoring the time-s
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