Search results for: Sang-Woo Kim

Authors: Sangwoo Oh, Jaewoo Kim, Dongmin Seo, Jaewon Park, Yongha Hwang, Sungkyu Seo

Abstract:

Plasmonics has been regarded one of the most powerful bio-sensing modalities to evaluate bio-molecular interactions in real-time. However, most of the plasmonic sensing methods are based on labeling metallic nanoparticles, e.g. gold or silver, as optical modulation markers, which are non-recyclable and expensive. This plasmonic modulation can be usually achieved through various nano structures, e.g., nano-hole arrays. Among those structures, plasmonic lens has been regarded as a unique plasmonic structure due to its light focusing characteristics. In this study, we introduce a custom designed plasmonic lens array for bio-sensing, which was simulated by finite-difference-time-domain (FDTD) approach and fabricated by top-down approach. In our work, we performed the FDTD simulations of various plasmonic lens designs for bacteria sensor, i.e., Samonella and Hominis. We optimized the design parameters, i.e., radius, shape, and material, of the plasmonic lens. The simulation results showed the change in the peak intensity value with the introduction of each bacteria and antigen i.e., peak intensity 1.8711 a.u. with the introduction of antibody layer of thickness of 15nm. For Salmonella, the peak intensity changed from 1.8711 a.u. to 2.3654 a.u. and for Hominis, the peak intensity changed from 1.8711 a.u. to 3.2355 a.u. This significant shift in the intensity due to the interaction between bacteria and antigen showed a promising sensing capability of the plasmonic lens. With the batch processing and bulk production of this nano scale design, the cost of biological sensing can be significantly reduced, holding great promise in the fields of clinical diagnostics and bio-defense.

Keywords: plasmonic lens, FDTD, fabrication, bacteria sensor, salmonella, hominis

Procedia PDF Downloads 246

Authors: Kyunghun Kang, Sangwoo Oh, Yongha Hwang

Abstract:

PDMS (Polydimethylsiloxane)-based microfluidic device has been recently applied to area of biomedical research, tissue engineering, and diagnostics because PDMS is low cost, nontoxic, optically transparent, gas-permeable, and especially biocompatible. Generally, PDMS microfluidic devices are fabricated by conventional soft lithography. Microfabrication requires expensive cleanroom facilities and a lot of time; however, only two-dimensional or simple three-dimensional structures can be fabricated. In this study, we introduce fabricating method for complex three-dimensional microfluidic channels using soluble wax mold. Using the 3D printing technique, we firstly fabricated three-dimensional mold which consists of soluble wax material. The PDMS pre-polymer is cast around, followed by PDMS casting and curing. The three-dimensional casting mold was removed from PDMS by chemically dissolved with methanol and acetone. In this work, two preliminary experiments were carried out. Firstly, the solubility of several waxes was tested using various solvents, such as acetone, methanol, hexane, and IPA. We found the combination between wax and solvent which dissolves the wax. Next, side effects of the solvent were investigated during the curing process of PDMS pre-polymer. While some solvents let PDMS drastically swell, methanol and acetone let PDMS swell only 2% and 6%, respectively. Thus, methanol and acetone can be used to dissolve wax in PDMS without any serious impact. Based on the preliminary tests, three-dimensional PDMS microfluidic channels was fabricated using the mold which was printed out using 3D printer. With the proposed fabricating technique, PDMS-based microfluidic devices have advantages of fast prototyping, low cost, optically transparence, as well as having complex three-dimensional geometry. Acknowledgements: This research was supported by Supported by a Korea University Grant and Basic Science Research Program through the National Research Foundation of Korea(NRF).

Keywords: microfluidic channel, polydimethylsiloxane, 3D printing, casting

Procedia PDF Downloads 249

Authors: Sangwoo Han, Saeed Khaleghi Rahimian, Ying Liu

Abstract:

Vehicle electrification is gaining momentum, and many car manufacturers promise to deliver more electric vehicle (EV) models to consumers in the coming years. In controlling the battery pack, the battery management system (BMS) must maintain optimal battery performance while ensuring the safety of a battery pack. Tasks related to battery performance include determining state-of-charge (SOC), state-of-power (SOP), state-of-health (SOH), cell balancing, and battery charging. Safety related functions include making sure cells operate within specified, static and dynamic voltage window and temperature range, derating power, detecting faulty cells, and warning the user if necessary. The BMS often utilizes an RC circuit model to model a Li-ion cell because of its robustness and low computation cost among other benefits. Because an equivalent circuit model such as the RC model is not a physics-based model, it can never be a prognostic model to predict battery state-of-health and avoid any safety risk even before it occurs. A physics-based Li-ion cell model, on the other hand, is more capable at the expense of computation cost. To avoid the high computation cost associated with a full-order model, many researchers have demonstrated the use of a single particle model (SPM) for BMS applications. One drawback associated with the single particle modeling approach is that it forces to use the average current density in the calculation. The SPM would be appropriate for simulating drive cycles where there is insufficient time to develop a significant current distribution within an electrode. However, under a continuous or high-pulse electrical load, the model may fail to predict cell voltage or Li⁺ plating potential. To overcome this issue, a multi-particle reduced-order model is proposed here. The use of multiple particles combined with either linear or nonlinear charge-transfer reaction kinetics enables to capture current density distribution within an electrode under any type of electrical load. To maintain computational complexity like that of an SPM, governing equations are solved sequentially to minimize iterative solving processes. Furthermore, the model is validated against a full-order model implemented in COMSOL Multiphysics.

Keywords: battery management system, physics-based li-ion cell model, reduced-order model, single-particle and multi-particle model

Procedia PDF Downloads 78