Dissecting the Complex Behavior of Hyperelastic Conductive Materials Through Materials Model
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Authors
Hossain, Kazi Zihan
Issue Date
2024
Type
Dissertation
Language
Keywords
Gallium Oxide , Hollow Polymer , Hyperelastic Conductive Materials , Liquid Metal , Materials Model , Stretchable Electronics
Alternative Title
Abstract
The hyperelastic conductive system that can remain conductive despite extensive stretching or large deformation holds immense potential, such as being used as wearable, soft-stretchable electronics, stretchable sensors, soft robotic systems, and flexible biomedical devices. Despite these vast prospects, challenges lie in material selection, maintaining conductivity or structural integrity, and accurately predicting the functional behavior during extensive deformation. Because of the absence of comprehensive predictive platforms, researchers depend on multiple trial-and-error approaches, hindering the development of well-engineered soft conductive systems. One promising approach to achieve such a system is injecting a well-understood conductive fluid into a pre-engineered hollow hyperelastic polymer. While it is crucial to understand the conductive fluid to predict its electrical response during usage, designing the polymer is also essential to maintaining the performance and seamlessly integrating the system into our lives. This dissertation has contributed to this gap by dissecting the fundamental characteristics of two key components – liquid metal and hollow thermoplastic polymers – widely used for soft electronics. Gallium alloys, often called liquid metals, are preferred by researchers over other conductive fluids due to their excellent metal-like conductivity and fluidity at room temperature. However, liquid metal gains an oxide layer by interacting with air, which is vital in manipulating the fluidity and predicting its behavior. Notably, it may create perturbed pendant droplets because of the oxide layer, which traditional surface tension measurement techniques struggle to characterize. A machine learning-based technique has; been developed to approximate the surface tension of perturbed droplets. This was further harnessed to observe the modulus decrease of the oxide layer due to repeated oscillation, indicating that the oxide may get softer under repeated strain. Moreover, to gain deeper insights into the change in the oxide layer from chemical interaction, the reaction dynamics of the oxide with chlorosilane have been explored, leading to the fabrication of open-ended microfluidic channels on various surfaces. In parallel with contributing to a better understanding of the liquid metal, efforts have been directed towards investigating hollow thermoplastic polymers that are commonly utilized for embedding liquid metal to fabricate soft electronics. The limited understanding of hyperelastic materials has led researchers to depend on trial-and-error approaches to determine proper materials and geometry for a particular application, obstructing the growth of the soft electronics field. An experimental-computational platform has been established to overcome this problem. A rheological model was calibrated from simple experimental data with more than 99% confidence, enabling further investigation in finite element analysis, COMSOL. Additionally, stitching is commonly used to integrate soft electronics or wearables into our fabric. To assess how this process might impact rheological behavior, the same approach has been used to investigate the rheological alteration of a cotton-stitched fabric composite, showcasing the versatility of our platform in evaluating composite materials. Overall, this dissertation contributes to advancing our understanding of hyperelastic conductive materials and advancing the existing knowledge towards the design and development of soft electronics.
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International