Joshua Doctor
Pronouns: he/him/his
Research Mentor(s): Albert Liu
Research Mentor School/College/Department: Chemical Engineering / Engineering
Program: UROP
Session: Session 1 (9:00am – 9:50am)
Authors: Joshua Doctor, Matthew Manion, Albert Liu
Abstract
Use of micro-electronic materials and low-power circuit elements, such as a memristor, allow for the development of one-dimensional arrays capable of independent time-based tracking. When paired with a series of chemiresistors, these microparticles are capable of providing data for chemical localization and extent. One potential use for this technology is within the human GI tract for tracking gut microbiota and their metabolites, specifically short-chain fatty acids (SCFAs). Irregularities in SCFAs have been linked to a number of neurodegenerative diseases and these microparticles could provide a cheaper and more effective diagnostic alternative. There is immense value in designing a simulation that can model the complex behavior of these microparticles and help optimize their design parameters. The most significant parameters pertaining to these particles are the memristor length, which is responsible for the accuracy of the recorded time intervals, and the quantity of sensors dispersed. Additionally, incorporating multiple binding sites into the chemiresistors, adding reversibility, or including circuits that solely detect excursion time could allow for further versatility in the microparticle’s data collection. In order to fully encapsulate the dynamics of these widely-dispersed individual particles, a stochastic-based simulation is utilized through the Gillespie method. This simulation would be able to model the memristor output for various input parameters and known SCFA concentrations for digestive tract regions. As individually tracking parameters for a large array of microparticles is computationally intensive, variations to the Gillespie method will be adapted to reduce runtime while maintaining the accuracy of the simulation. The ultimate purpose of this model is to optimize the particle’s parameters to output a data profile that is as true to the input concentrations as possible while simultaneously minimizing the amount of resources used. Beyond SCFA detection, determining the optimal parameters for these microparticles could greatly benefit their use in medicine, environmental monitoring, reaction engineering, and other applications.
