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  1. Free, publicly-accessible full text available December 1, 2024
  2. Earables (ear wearables) are rapidly emerging as a new platform encompassing a diverse range of personal applications. The traditional authentication methods hence become less applicable and inconvenient for earables due to their limited input interface. Nevertheless, earables often feature rich around-the-head sensing capability that can be leveraged to capture new types of biometrics. In this work, we propose ToothSonic that leverages the toothprint-induced sonic effect produced by a user performing teeth gestures for earable authentication. In particular, we design representative teeth gestures that can produce effective sonic waves carrying the information of the toothprint. To reliably capture the acoustic toothprint, it leverages the occlusion effect of the ear canal and the inward-facing microphone of the earables. It then extracts multi-level acoustic features to reflect the intrinsic toothprint information for authentication. The key advantages of ToothSonic are that it is suitable for earables and is resistant to various spoofing attacks as the acoustic toothprint is captured via the user's private teeth-ear channel that modulates and encrypts the sonic waves. Our experiment studies with 25 participants show that ToothSonic achieves up to 95% accuracy with only one of the users' tooth gestures. 
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  3. This paper presents GoPose, a 3D skeleton-based human pose estimation system that uses WiFi devices at home. Our system leverages the WiFi signals reflected off the human body for 3D pose estimation. In contrast to prior systems that need specialized hardware or dedicated sensors, our system does not require a user to wear or carry any sensors and can reuse the WiFi devices that already exist in a home environment for mass adoption. To realize such a system, we leverage the 2D AoA spectrum of the signals reflected from the human body and the deep learning techniques. In particular, the 2D AoA spectrum is proposed to locate different parts of the human body as well as to enable environment-independent pose estimation. Deep learning is incorporated to model the complex relationship between the 2D AoA spectrums and the 3D skeletons of the human body for pose tracking. Our evaluation results show GoPose achieves around 4.7cm of accuracy under various scenarios including tracking unseen activities and under NLoS scenarios. 
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  4. Abstract The mechanically induced symmetry-allowed disrotatory ring openings of cis- and trans-gem-dichlorocyclopropane (gDCC) diesters are demonstrated through sonication and single-molecule force spectroscopy (SMFS) studies. In contrast to the previously reported symmetry-forbidden conrotatory ring opening of alkyl-tethered trans-gDCC, we show that the diester-tethered trans-gDCC primarily undergoes a symmetry-allowed disrotatory pathway even at the high forces (>2 nN) and short-time scales (ms or less) of sonication and SMFS experiments. The quantitative force-rate data obtained from SMFS data is consistent with computational models of transition-state geometry for the symmetry-allowed process, and activation lengths of 1.41 ± 0.02 Å and 1.08 ± 0.03 Å are inferred for the cis-gDCC diester and trans-gDCC diester, respectively. The strong mechanochemical coupling in the trans-gDCC is notable, given that the directionality of the applied force may appear initially to oppose the disrotatory motion associated with the reaction. The stereochemical perturbations of mechanical coupling created by the ester attachments reinforce the complexity that is possible in covalent polymer mechanochemistry and illustrate the breadth of reactivity outcomes that are available through judicious mechanophore design. 
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  5. Abstract

    Aqueous zinc-ion batteries, in terms of integration with high safety, environmental benignity, and low cost, have attracted much attention for powering electronic devices and storage systems. However, the interface instability issues at the Zn anode caused by detrimental side reactions such as dendrite growth, hydrogen evolution, and metal corrosion at the solid (anode)/liquid (electrolyte) interface impede their practical applications in the fields requiring long-term performance persistence. Despite the rapid progress in suppressing the side reactions at the materials interface, the mechanism of ion storage and dendrite formation in practical aqueous zinc-ion batteries with dual-cation aqueous electrolytes is still unclear. Herein, we design an interface material consisting of forest-like three-dimensional zinc-copper alloy with engineered surfaces to explore the Zn plating/stripping mode in dual-cation electrolytes. The three-dimensional nanostructured surface of zinc-copper alloy is demonstrated to be in favor of effectively regulating the reaction kinetics of Zn plating/stripping processes. The developed interface materials suppress the dendrite growth on the anode surface towards high-performance persistent aqueous zinc-ion batteries in the aqueous electrolytes containing single and dual cations. This work remarkably enhances the fundamental understanding of dual-cation intercalation chemistry in aqueous electrochemical systems and provides a guide for exploring high-performance aqueous zinc-ion batteries and beyond.

     
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