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Computational Design and Analysis of MOF-based Electronic Noses for Disease Detection by Breath

Day, Brian (2022) Computational Design and Analysis of MOF-based Electronic Noses for Disease Detection by Breath. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Despite the existence of sophisticated analytical gas sensing technologies like gas chromatography – mass spectrometry (GCMS), there are many applications for which sufficient gas sensors are lacking, such as environmental monitoring and disease detection by breath, where there is a need for low-cost, portable devices with high sensitivities and fast response times. A promising strategy for achieving these features is the development of gas sensor arrays, better known as electronic noses, in which multiple sensing elements are used cooperatively to improve detection capabilities.
This dissertation describes my research on the use of metal-organic frameworks (MOFs) as the sensing materials for electronic noses. MOFs are a novel class of nanoporous crystalline materials with high internal surface areas and a large degree of chemical and structural diversity, resulting in similarly impressive and diverse gas adsorption properties. Prior to the work of this lab, there had been few investigations into MOF-based sensor arrays, and they were limited to experimental trial-and-error approaches. In response, our lab pioneered the use computational approaches for high-throughput screening of MOFs and rational design of sensor arrays, resulting in significant improvements to sensing performance.
The focus of this dissertation is on strategies for further improving the design and analysis of MOF-based electronic noses, specifically for the detection of trace gas species in complex gas mixtures for disease detection by breath. We first examined the ternary gas mixtures of carbon dioxide, nitrogen, and oxygen, the majority species of breath, which provided a valuable starting point for breath analysis and highlighted limitations of our existing method. In order to address the scalability challenges related to the combinatorics of multicomponent mixtures, we developed a novel coefficient-based method for evaluating the adsorption of trace gas species in complex mixtures, as well as a corresponding algorithm for signal analysis, which we used to study five- component gas mixtures relating to the detection of chronic kidney disease by breath. Finally, we developed a strategy for improving the sensitivity and selectivity of arrays, and increasing overall information content, by sampling the gas mixture at various pressures, with low pressures enabling the desaturation of sensors by strongly adsorbing gases, and high pressures enabling us to increase mass uptake for weakly adsorbing gases.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Day, Brianbrd84@pitt.edubrd840000-0003-3769-6462
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorWilmer,
Committee MemberBeckman,
Committee MemberFullerton,
Committee MemberStar,
Date: 6 September 2022
Date Type: Publication
Defense Date: 26 July 2022
Approval Date: 6 September 2022
Submission Date: 12 July 2022
Access Restriction: 1 year -- Restrict access to University of Pittsburgh for a period of 1 year.
Number of Pages: 144
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Chemical Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Computational Chemistry, Data Science, Metal-Organic Frameworks, Disease Detection
Date Deposited: 06 Sep 2023 05:00
Last Modified: 06 Sep 2023 05:15


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