Computational Design of Wearable Chemical Sensors for Personalized Healthcare

Citation

Mukasa, Daniel (2025) Computational Design of Wearable Chemical Sensors for Personalized Healthcare. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/r46k-sw73. https://resolver.caltech.edu/CaltechTHESIS:06292024-045659387

Abstract

Wearable sweat sensors have the potential to revolutionize precision medicine as they can non-invasively collect molecular information closely associated with an individual’s health status. However, the majority of clinically relevant biomarkers cannot be continuously detected in situ using existing wearable approaches. Molecularly imprinted polymers (MIPs) are a promising candidate to address this challenge but haven’t yet gained widespread use due to their complex design and optimization process yielding variable selectivity. Despite their promise, MIPs have historically been known to be exceedingly difficult to optimize. Changes in the monomer/monomers used, polymerization solvent, and crosslinking agent have been shown to change the performance of MIP sensors significantly. This is particularly a concern in sweat-based sensors where the concentration of analytes is very low and chemical diversity is very high as a drop of sweat can contain vitamins, hormones, and amino acids. Consequentially, any sweat based sensor must exhibit high sensitivity (ability to detect low analyte concentrations) and selectivity (ability to distinguish one analyte from another). Computational methods have been introduced to design MIP sensitivity alone, however these prior methods do not cover all aspects essential for using a sensor in a wearable device such as selectivity optimization, detection of non-electroactive analytes, and scalable manufacturing. Here, we introduce a full computational method that allows for high throughput materials discovery for wearable devices. We will describe how to design novel sensing materials with QuantumDock, an automated computational framework for universal MIP development toward wearable applications. Then we delve into further technical details on signal transduction and scalable manufacturing approaches for these wearable devices. We present a number of novel devices designed with these computational methods including a wearable non-invasive phenylalanine monitoring system (the first of its kind), a wearable nutritional tracker ‘Nutritrek’ capable of monitoring a range of metabolic disorders, and an implantable pharmaceutical drug monitoring system for cancer patients.

Thesis Files