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Meeting ID: 990 0593 3307
Passcode: 772880

Abstract: The pursuit of advanced flexible and wearable electronics necessitates innovative approaches to overcome the limitations of traditional materials and methods. Ga-based room-temperature liquid metals (LM), with their unique deformability and conductive properties, present a promising solution. We introduced a method for high-frequency AC-enhanced resistive sensing using deformable liquid metals to improve low-power detection in wearable electronics. By modulating electromagnetic effects, such as current crowding due to the skin effect, this method can distinguish mechanical deformation modes. Additionally, we explored reducing resistive losses and enhancing the resonant radio frequency performance of LM inductors by twisting LM traces in a 3D woven 'litz' transmission line configuration. Simulations and experiments showed this approach increases the quality factor (Q) by 80% compared to the control. Finally, we demonstrated an automated method to extract and deposit a transparent, ultrathin Indium Tin Oxide (ITO) shell from the In-Tin liquid metal alloy onto flexible substrates. Non-invasive dry bioelectrodes made of transparent oxide films provide superior strain compliance, adhesion, and abrasion resistance than standard alternatives. The conductivity and transparency of 2D ITO were used for synchronous, multimodal measurements via electrocardiography (ECG) and pulse plethysmography (PPG).

We propose utilizing these bendable and transparent oxide films as bioelectrode material in a multimodal biosignal acquisition setup, with LM traces as interconnects for data and power transfer to an on-body computing platform, creating a highly stretchable and ultra-comfortable electronic wearable system. Specifically, we propose a peripheral circulation monitoring device to assess peripheral artery disease and venous insufficiency, using PPG to measure peripheral blood flow and vascular health and bioimpedance to measure tissue composition and fluid distribution. In addition, algorithms will be developed to integrate and evaluate bioimpedance and PPG sensor data, using machine learning and data analytics to improve signal accuracy and utility.

Thesis Committee: Prof. William Scheideler (Chair), Prof. Jifeng Liu, Prof. Hui Fang, and Prof. Thomas Wallin (MIT)