A Multi-Scale and Multi-Physics Framework for Integrated Electronics Operating in Harsh Environment: A Sensor-to-system Perspective

Xin Jin, Purdue University

Abstract

In a modern IoT network, the design of edge computing electronics operating in harsh environment faces great challenges. In this doctoral thesis, we are developing an end-to-end modeling framework for two IoT-based applications: personalized medicine and precision agriculture. By coupling the physics of analyte mass transfer, electrochemical reactions, and electrostatics, the framework paves the way for the development of the following new generation electrochemical/biosensors: 1) high sensitivity nano-electrode non-enzymatic/enzymatic amperometric glucose sensors, 2) self-powered enzymatic biofuel cell (EBFC)-based lactate sensors, and 3) roll-to-roll printed thin-film ion-selective electrode (ISE)-based soil nitrate sensors. Glucose sensors have transformed diabetes control. Amperometric glucose sensors with nanoparticle electrodes promise fast and highly sensitive detection of glucose concentration in both in vivo and in vitro applications. Unfortunately, the sensitivity and response of the sensor, as a function of nanoparticle geometry and glucose oxidase distribution, is not fully understood, making it difficult to optimize the sensor performance. In this work, we derive an analytical relationship that explicitly correlates sensor performance to the elementary properties of the electrodes and oxidase. The model facilitates predictive design and optimization of nanoparticle-based amperometric biosensors that can eventually be integrated into the wearable platform. Most glucose sensors are enzymatic, but a non-enzymatic metal oxide-based glucose sensor on a nanostructured substrate is of considerable interest for future alwayson wearable closed-loop sensing for hypoglycemia management. Recently, various research groups have demonstrated that different nanostructured substrates (fabricated by a variety of innovative techniques) boost the sensitivity of non-enzymatic glucose sensor. In this work, we develop a physics-based model to correlate the geometrical and chemical design parameters to the non-linear amperometric response of non-enzymatic glucose sensor on the geometrically complex substrate. Using this model, we can interpret the scattered results in the literature within a common conceptual framework. Our model will predictably improve the design of non-enzymatic glucose sensors for integrating into continuous glucose monitoring system (CGMS) in wearable and implantable platforms. Enzymatic biofuel (EBFC)-based self-powered sensors represent an interesting class of biochemical sensors as they obviate the need for external power sources thus enabling device miniaturization. While recent efforts driven by experimentalists illustrate the potential of EBFC-based sensors for real-time monitoring of physiologically relevant biochemicals, a robust mathematical model that helps understand the contributions of sensor components and empowers experimentalists to pre-dict sensor performance remains missing. In this work, we provide an elegant yet simple equivalent circuit model that cap-tures the complex, three-dimensional interplay between coupled catalytic redox reactions occurring in an EBFC-based sensor and predicts its output signal with high correlations to experimental observations. Systematic experiments validate the accuracy of the described model. The mathematical model derived in this work can be easily adapted to understand a wide range of two-electrode systems, including sensors, fuel cells, and energy storage devices. To improve farm-to-folk productivity, we develop design guidelines for roll-to-roll thin-film ion-selective electrode sensors. The sensor detects the local soil nitrate level on demand. The fabrication process involves roll-to-roll (R2R) nano-manufacturing facility which enables high throughput at low cost. We developed a fundamental physics-based model to describe both steady-state response and transient response of ISE sensor.

Degree

Ph.D.

Advisors

Alam, Purdue University.

Subject Area

Physics|Alternative Energy|Energy|Nanotechnology

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