Study on the microfluidic devices and nano materials for biological applications

Abstract

Microfluidic devices and nano materials play a key role in the emerging biological applications such as lab-on-a-chip, which include pretreatment and purification of protein sample, and diagnosis and therapy of disease. Recently, microfluidic devices for biological sample pretreatment have become important in the design of analytical devices for high-throughput applications such as functional validation of proteins and peptides in large-scale proteome characterizations. In addition, nano materials for diagnosis and therapy are young and rapid emerging field such as point-of-care (POC) biosensor, tumor pretargeting agent, drug delivery vehicle and diagnostic imaging agent. In this study, micro-dialysis ($\mu$ -dialysis) and microfluidic free-flow isoelectric focusing ($\mu$ -FFIEF) systems were developed for the protein sample preparation, and porous AAO-integrated microfluidic system for enzyme-linked immunosorbent assay (AAO- $\mu$ -ELISA) and diacetylene-modified single-walled carbon nanotube (PDA-SWNT) were prepared for the nano materials for biological applications. First, $\mu$ -dialysis system was developed for the removal of urea or guanidine hydrochloride (GdnHCl) from red fluorescence protein (RFP) extract. In this system, affinity streams facilitated by copper (II) ion was employed in the five lamina streams expressed by ‘affinity stream (AS)<->buffer stream (BS)<->sample stream (SS)<->buffer stream (BS)<->affinity stream (AS)’. In this system, were employed at outer position of the buffer streams (BS). Simple diffusion expressed by ‘BS<->SS<->BS’ showed about 55% of urea removal with 85% of protein recovery from SS containing protein. Diffused Cu(II) ions into the BS react with urea or GdnHCl molecules. Affinity diffusion system resulted in about 72% desalting efficiency while maintaining the recovery of protein similar to the simple diffusion device. Furthermore, fluorescence intensity and spectral quality was also improved. Second, $\mu$ -FFIE...