Doctor of Philosophy
Since the single-wall carbon nanotubes (SWCNTs) were discovered in 1993, they have attracted significant interest with their extraordinary electrical and optical properties in addition to their remarkable mechanical strength and thermal conductivity. Single-stranded DNA conjugated SWCNT have shown outstanding functionality in terms of dispersibility and biocompatibility. In addition, some special DNA sequences have presented an ability to recognize specific SWCNT species, called recognition sequences. Ion-exchange chromatography and aqueous two-phase (ATP) separation technique have been widely used for SWCNT separation. However, little is known about the use of ATP as an analytical technique. Furthermore, for bio-applications, DNA/SWCNT hybrids have attracted significant interest due to their high solvatochromic sensitivity to changes in the local environment, which enables their use as sensors. Recognition properties can provide good candidates for molecular detection on the assumption that the recognition DNA/SWCNT hybrids have structurally well-defined DNA wrappings. Thus, there is a growing need for discovery of new recognition sequences. In this thesis, we explore new methods to quantify difference in solvation/binding characteristics using ATP, and a new approach to predicting recognition sequences using Machine Learning techniques. Finally, a new concept for a DNA/SWCNT-based sensing system is demonstrated.
Yang, Yoona, "Learning about Sequence-Dependent DNA/Single-Wall Carbon Nanotube Hybrids" (2019). Theses and Dissertations. 5730.