Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Computational Optimization of Metal-Organic Framework (MOF) Arrays for Chemical Sensing

Gustafson, Jenna (2019) Computational Optimization of Metal-Organic Framework (MOF) Arrays for Chemical Sensing. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

This is the latest version of this item.

Download (1MB) | Preview


Although commercial gas sensors exist for applications such as product quality control, industrial food monitoring, and smoke detection, there are many potential applications for which adequate gas sensing technology is lacking. There is an unmet need for gas sensors to detect natural gas leaks, for disease detection via breath analysis, and for environmental monitoring, to name just a few examples. Current gas sensors do not exhibit the sensitivity and/or selectivity required to detect trace amounts of the required gases in complex gas mixture environments (e.g., ambient air or a patient’s breath). It is known that arrays of sensors, or electronic noses, improve chemical detection when compared to single sensor elements. Although some work has been done to optimize sensor device performance, there are many potential sensing materials that have not yet been extensively explored.
Herein, we explore the use of metal-organic framework (MOF) materials in sensor arrays, exploiting their high adsorption capabilities to yield more selective and sensitive electronic noses. As a relatively new class of materials, MOFs have not been thoroughly investigated for gas sensing applications. In particular, prior to our work, there had only been a few investigations of MOF sensor arrays and those were limited to purely experimental work that relied heavily on trial-and-error. We demonstrate that leveraging computational modeling and optimization to rationally design MOF sensor arrays can yield significantly improved sensing performance.
Our novel computational method was carried out first by predicting individual MOF sensor responses via molecular simulations. Then, we developed a method to analyze those individual responses and provide output signals for entire sensor arrays to predict unknown gas mixtures. Following this, the prediction ability of each array was evaluated according to the Kullback-Liebler divergence (KLD), where we determined the best arrays for detecting methane-in-air mixtures. Finally, we developed and validated a genetic algorithm that enables the optimization of large MOF arrays.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Gustafson, Jennajag227@pitt.edujag227
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairWilmer,
Committee MemberBeckman,
Committee MemberFullerton,
Committee MemberOhodnicki,
Date: 21 June 2019
Date Type: Publication
Defense Date: 19 February 2019
Approval Date: 21 June 2019
Submission Date: 1 March 2019
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 144
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Chemical and Petroleum Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: electronic nose, methane sensor, machine learning, molecular simulations, surface acoustic wave devices
Date Deposited: 21 Jun 2020 05:00
Last Modified: 21 Jun 2020 05:00

Available Versions of this Item


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item