April 22 - 26, 2024
Seattle, Washington
May 7 - 9, 2024 (Virtual)

Event Supporters

2024 MRS Spring Meeting
SB10.10.10

QuantumDock: An Automated Computational Framework for Wearable Sensor Design

When and Where

Apr 26, 2024
10:45am - 11:00am
Room 429, Level 4, Summit

Presenter(s)

Co-Author(s)

Daniel Mukasa1,Minqiang Wang1,Jihong Min1,Yiran Yang1,Samuel Solomon1,Hong Han1,Cui Ye1,Wei Gao1

California Institute of Technology1

Abstract

Daniel Mukasa1,Minqiang Wang1,Jihong Min1,Yiran Yang1,Samuel Solomon1,Hong Han1,Cui Ye1,Wei Gao1

California Institute of Technology1
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. Here, QuantumDock is introduced, an automated computational framework for universal MIP development toward wearable applications. QuantumDock utilizes density functional theory to probe molecular interactions between monomers and the target/interferent molecules to optimize selectivity, a fundamentally limiting factor for MIP development toward wearable sensing. A molecular docking approach is employed to explore a wide range of monomers, and to identify the optimal monomer/cross-linker choice for subsequent MIP fabrication. We further employ a molecular generation scheme that allows us to generate molecules with much higher sensitivities and selectivities than traditionally used monomers. Using an essential amino acid phenylalanine as the exemplar, experimental validation of QuantumDock is performed successfully using solution-synthesized MIP nanoparticles coupled with ultraviolet–visible spectroscopy. Moreover, a QuantumDock-optimized graphene-based wearable device is designed that can perform autonomous sweat induction, sampling, and sensing. For the first time, wearable non-invasive phenylalanine monitoring is demonstrated in human subjects toward personalized healthcare applications.

Keywords

adsorption | polymerization

Symposium Organizers

Simone Fabiano, Linkoping University
Sahika Inal, King Abdullah University of Science and Technology
Naoji Matsuhisa, University of Tokyo
Sihong Wang, University of Chicago

Symposium Support

Bronze
IOP Publishing

Session Chairs

Donghee Son
Shunsuke Yamamoto

In this Session