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Enhanced Optical Detection of Chemical and Biological Species with Volume-Shrinkable Hydrogel and AI-Assisted Spectrum Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Enhanced Optical Detection of Chemical and Biological Species with Volume-Shrinkable Hydrogel and AI-Assisted Spectrum Analysis./
Author:	Zheng, Yiting.
Description:	1 online resource (86 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
Subject:	Chemical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30247773click for full text (PQDT)
ISBN:	9798379474331

Enhanced Optical Detection of Chemical and Biological Species with Volume-Shrinkable Hydrogel and AI-Assisted Spectrum Analysis.
Zheng, Yiting.

Enhanced Optical Detection of Chemical and Biological Species with Volume-Shrinkable Hydrogel and AI-Assisted Spectrum Analysis. - 1 online resource (86 pages)

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

Thesis (Ph.D.)--Northeastern University, 2023.

Includes bibliographical references

The need to develop chemical and biological sensors with high sensitivity persists and calls for new mechanisms for signal transduction and amplification. A variety of techniques have been developed to enhance sensitivity and reduce detection limit including pre-concentrating analytes, using nanoparticle probes, amplifying electrical and optical signals. However, these techniques are inappropriate to detect multiple analytes in one test, require preconcentration operations that may cause analyte loss and contamination, need additional signal amplification at a low concentration of analytes. It is imperative to develop new mechanisms for signal transduction and amplification.Hydrogel is a network of crosslinked polymer chains with abundant water. With its rich and tunable properties, hydrogels are widely used in tissue engineering and regenerative medicine. A unique property of hydrogel is the large volume change (hundred times) upon dehydration. However, this large volume change has not been fully exploited, except in one case where the volume increase of dehydrated gels has been used to map/image the distribution of proteins inside cells. In the context of chemical and biological sensing, the large volume reduction upon dehydration could be useful to enrich or pre-concentrate species contained in or immobilized on hydrogels. If the analytes to be detected could be captured on hydrogel, the volume reduction of hydrogel will naturally concentrate analytes to over thousand times, thus enhancing the detection sensitivity. In reality, there is no prior research on signal amplification based on volume reduction of hydrogels.My doctoral research aims to develop a new and general strategy to enhance the sensitivity of chemical and biological sensing with volume shrinkable hydrogel. Specifically, this thesis explores the concept with fluorescence chemical sensing of ionic species, and nanoparticle-based X-ray fluorescence sensing of biomarkers using widely available agarose. To increase the density of surface reactive groups, agarose has been carboxylated to have more reactive groups, which is followed by grafting probes. A turn-on fluorescence probe that can fluoresce upon binding of lead ions is made and used to report binding events. After binding lead ions to the fluorescence probes, the volume of hydrogel is reduced by dehydration and the fluorescence signal is found to increase by 10 times. In order to detect biomarkers, iron oxide nanoparticles with large X-ray fluorescence are modified with specific antibodies and used to report the target biomarkers onto hydrogel. After removing unbounded nanoparticles, the hydrogels are dehydrated to concentrate nanoparticles in the dehydrated gel prior to detection of X-ray fluorescence signals. A 6-fold increase in the intensity of the characteristic X-ray emission of iron is achieved after dehydration.The volume shrinking hydrogel can be used for the simultaneous detection of multiple biomarkers by using multiple nanoparticles with characteristic X-ray fluorescence. However, a long-standing issue for spectral detection of multiple analytes is peak overlapping, which often requires spectral information and specific algorithms for peak deconvolution. In order to quantify the amount of each analyte in a mixture, multitask learning with recurrent neural networks has been developed and trained with spectrum information of each analyte. The trained model is then used to predict the identity and quantification of each analyte in a mixture. This machine learning approach has significantly accelerated the multiplex detection using X-ray fluorescence. Further, given the similarity of all spectrum analysis, the machine learning approach can be reasonably extended to all other spectra such as UV-visible spectrum, Fourier transform infrared spectrum. mass spectroscopy and Raman spectroscopy, among many others.

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

Mode of access: World Wide Web

ISBN: 9798379474331Subjects--Topical Terms:

560457
Chemical engineering.
Subjects--Index Terms:

Biological speciesIndex Terms--Genre/Form:

542853
Electronic books.

Enhanced Optical Detection of Chemical and Biological Species with Volume-Shrinkable Hydrogel and AI-Assisted Spectrum Analysis.
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