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SERS-Based ssDNA Composition Analysis Using Chemical Enhancement and Machine Learning Methods.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	SERS-Based ssDNA Composition Analysis Using Chemical Enhancement and Machine Learning Methods./
作者:	Nguyen Hoang, Lan Phuong.
面頁冊數:	1 online resource (117 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Electrical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28963345click for full text (PQDT)
ISBN:	9798209914075

SERS-Based ssDNA Composition Analysis Using Chemical Enhancement and Machine Learning Methods.
Nguyen Hoang, Lan Phuong.

SERS-Based ssDNA Composition Analysis Using Chemical Enhancement and Machine Learning Methods. - 1 online resource (117 pages)

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--University of California, San Diego, 2022.

Includes bibliographical references

Surface-enhanced Raman spectroscopy (SERS) is an attractive method for bio-chemical sensing due to its potential for single molecule sensitivity and the prospect of DNA composition analysis. When employed in conjunction with post-processing machine learning (ML) methods, it becomes a promising technique for effective data analysis, allowing enhanced molecular and chemical composition analysis of information rich DNA molecules. In this work, we leverage metal specific chemical enhancement effect to detect differences in SERS spectra of 200-base length single-stranded DNA (ssDNA) molecules adsorbed on gold or silver nanorod substrates, and then develop and train multiple ML models to predict the composition of ssDNA. Our results indicate that employing substrates of different metals that host a given adsorbed molecule leads to distinct SERS spectra, allowing to probe metal-molecule interactions under distinct chemical enhancement regimes. Leveraging this difference and combining spectra from different metals as an input for our ML models, allows to significantly lower the detection errors compared to manual feature-choosing analysis as well as compared to the case where data from single metal is used. Additionally, we also were able to report on a room temperature inhomogeneous broadening as a function of increased adenine concentration, and employ this feature to develop one- and two-dimensional chemical composition classification models. Last but not least, we report on experimental results indicating that SERS spectra of adsorbed single-stranded DNA (ssDNA) isomers depend on the order on which individual bases appear in the 3-base long ssDNA due to intra-molecular interaction between DNA bases. Furthermore, we experimentally demonstrate that the effect holds under more general conditions when the molecules don't experience chemical enhancement due to resonant charge transfer effect and also under standard Raman scattering without electromagnetic or chemical enhancements. Our numerical simulations qualitatively support the experimental findings and indicate that base permutation results in modification of both Raman and chemically enhanced Raman spectra.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798209914075Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Chemical EnhancementsIndex Terms--Genre/Form:

542853
Electronic books.

SERS-Based ssDNA Composition Analysis Using Chemical Enhancement and Machine Learning Methods.
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504 $a Includes bibliographical references
520 $a Surface-enhanced Raman spectroscopy (SERS) is an attractive method for bio-chemical sensing due to its potential for single molecule sensitivity and the prospect of DNA composition analysis. When employed in conjunction with post-processing machine learning (ML) methods, it becomes a promising technique for effective data analysis, allowing enhanced molecular and chemical composition analysis of information rich DNA molecules. In this work, we leverage metal specific chemical enhancement effect to detect differences in SERS spectra of 200-base length single-stranded DNA (ssDNA) molecules adsorbed on gold or silver nanorod substrates, and then develop and train multiple ML models to predict the composition of ssDNA. Our results indicate that employing substrates of different metals that host a given adsorbed molecule leads to distinct SERS spectra, allowing to probe metal-molecule interactions under distinct chemical enhancement regimes. Leveraging this difference and combining spectra from different metals as an input for our ML models, allows to significantly lower the detection errors compared to manual feature-choosing analysis as well as compared to the case where data from single metal is used. Additionally, we also were able to report on a room temperature inhomogeneous broadening as a function of increased adenine concentration, and employ this feature to develop one- and two-dimensional chemical composition classification models. Last but not least, we report on experimental results indicating that SERS spectra of adsorbed single-stranded DNA (ssDNA) isomers depend on the order on which individual bases appear in the 3-base long ssDNA due to intra-molecular interaction between DNA bases. Furthermore, we experimentally demonstrate that the effect holds under more general conditions when the molecules don't experience chemical enhancement due to resonant charge transfer effect and also under standard Raman scattering without electromagnetic or chemical enhancements. Our numerical simulations qualitatively support the experimental findings and indicate that base permutation results in modification of both Raman and chemically enhanced Raman spectra.
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