MARC 主機 00000nam a2200481K 4500 001 AAI22592186 005 20201109124831.5 006 m o d 007 cr mn ---uuuuu 008 201109s2019 miu sbm 000 0 eng d 020 9781088374467 035 (MiAaPQ)AAI22592186 040 MiAaPQ|beng|cMiAaPQ|dNTU 100 1 Yang, Yoona 245 10 Learning about Sequence-dependent DNA/Single-wall Carbon Nanotube Hybrids 264 0 |c2019 300 1 online resource (190 pages) 336 text|btxt|2rdacontent 337 computer|bc|2rdamedia 338 online resource|bcr|2rdacarrier 500 Source: Dissertations Abstracts International, Volume: 81- 04, Section: B 500 Advisor: Jagota, Anand 502 Thesis (Ph.D.)--Lehigh University, 2019 504 Includes bibliographical references 520 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 533 Electronic reproduction.|bAnn Arbor, Mich. :|cProQuest, |d2020 538 Mode of access: World Wide Web 650 4 Chemical engineering 650 4 Nanotechnology 650 4 Bioinformatics 653 Aqueous two-phase 653 Carbon nanotube 653 DNA/SWCNT 653 Machine learning 653 Molecular perception 655 7 Electronic books.|2local 690 0542 690 0652 690 0715 710 2 ProQuest Information and Learning Co 710 2 Lehigh University.|bChemical Engineering 773 0 |tDissertations Abstracts International|g81-04B 856 40 |uhttp://pqdd.sinica.edu.tw/twdaoapp/servlet/ advanced?query=22592186|zclick for full text (PQDT) 912 PQDT
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