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
