Chemical and Biomolecular Engineering Research Works
Permanent URI for this collectionhttp://hdl.handle.net/1903/1656
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Item Gradient Structural and Compositional Design of Conductive MXene Aerogels for Stable Zn Metal Anodes(Wiley, 2023-11-12) Li, Yang; Pang, Zhenqian; Ghani, Awais; Little, Joshua M.; Wang, Liping; Yang, Haochen; Zhao, Yusheng; Chen, Po-YenAqueous rechargeable zinc-ion batteries (ZIBs) are a safe and low-cost energy storage technology. However, practical ZIB exploitation faces critical challenges in achieving stable Zn metal anodes, which suffer from hydrogen evolution reaction (HER) corrosion and Zn dendrite growth. To address these challenges, a Zn2+-induced assembly process to fabricate Ti3C2Tx MXene-reduced graphene oxide aerogels with ZnO crust layers on Zn plates (abbreviated as ZnO/MG aerogel–Zn) that serve as stable Zn metal anodes is reported. By applying a constant voltage to a Zn plate, Zn2+ is gradually released to ionically crosslink MG nanosheets. After spontaneous hydrolysis and freeze-drying, a crust layer composed of ZnO nanoparticles is in situ formed. Additionally, the gradient Zn−O/Zn−F profiles across the ZnO/MG aerogel can facilitate Zn2+ transport and collectively suppress HER, enabling fast electrochemical kinetics and dendrite-free Zn deposition. Symmetric cells with ZnO/MG aerogel–Zn electrodes present stable cycling for 1200 h at 10 mA cm−2, and full cells achieve long lifespans at high rates (>500 cycles at 1.0 A g−1). Combining the advantages of an insulating protective layer and a conductive structured host, the ZnO/MG aerogel–Zn electrode with gradient structures and compositions creates synergistic advances in stable Zn metal anodes.Item Unclonable MXene Topographies as Robust Anti-Counterfeiting Tags via Fast Laser Scanning and Siamese Neural Networks(Wiley, 2023-05-19) Jing, Lin; Si, Huachun; Chen, Tianle; Hsiao, Li-Yin; Yang, Haochen; Little, Joshua M.; Li, Kerui; Li, Shuo; Xie, Qian; Chen, Po-YenAn ideal anti-counterfeiting technology is desired to be unclonable, nondestructive, mass-producible, and accompanied with fast and robust authentication under various external influences. Although multiple anti-counterfeiting technologies have been reported, few meet all of the above-mentioned features. Herein, a mechanically driven patterning process is reported to produce higher dimensional Ti3C2Tx MXene topographies in a scalable yet unclonable manner, which can be used as anti-counterfeiting tags. By using a high-speed confocal laser microscopy, the complex topographies can be extracted within one minute and then reconstructed into 3D physical unclonable function (PUF) keys. Meanwhile, a Siamese neural network model and a feature-tracking software are built to achieve a pick-and-check strategy, enabling highly accurate, robust, disturbance-insensitive tag authentication in practical exploitations. The 3D PUF key-based anti-counterfeiting technology features with several advances, including ultrahigh encoding capacities (≈10144 000-107 800 000), fast processing times (<1 min), and high authentication accuracy under various external disturbances, including tag rotations (≈0°‒360°), tag dislocation(s) in x(y) directions (≈0%‒100%), tag shifts in z-direction (≈0%‒28%), tag tilts (≈0°‒5°), differences in contrasts (20%‒60%) and laser power (6.0‒9.0 µW). The anti-counterfeiting technology promises information security, encoding capacity, and authentication efficiency for the manufacturer-distributor-customer distribution processes.Item Thermal Percolation of Antiperovskite Superionic Conductor into Porous MXene Scaffold for High-Capacity and Stable Lithium Metal Battery(Wiley, 2022-10-09) Li, Yang; Kong, Long; Yang, Haochen; Li, Shuai; Deng, Zhi; Li, Shuo; Wang, Liping; Lee, Jim Yang; Zhao, Yusheng; Chen, Po-YenLithium metal battery is considered an emerging energy storage technology due to its high theoretical capacity and low electrochemical potential. However, the practical exploitations of lithium metal batteries are not realized because of uncontrollable lithium deposition and severe dendrite formation. Herein, a thermal percolation strategy is developed to fabricate a dual-conductive framework using electronically conductive Ti3C2Tx MXene aerogels (MXAs) and Li2OHCl antiperovskite superionic conductor. By melting Li2OHCl at a low temperature, the molten antiperovskite phase can penetrate the MXA scaffold, resulting in percolative electron/ion pathways. Through density functional theory calculations and electrochemical characterizations, the hybridized lithiophilic (MXA)−lithiophobic (antiperovskite) interfaces can spatially guide the deposition of lithium metals and suppress the growth of lithium dendrites. The symmetric cell with MXA–antiperovskite electrodes exhibits superior cycling stability at high areal capacities of 4 mAh cm−2 over 1000 h. Moreover, the full cell with MXA−antiperovskite anode and high-loading LiFePO4 cathode demonstrates high energy and power densities (415.7 Wh kgcell−1 and 231.0 W kgcell−1) with ultralong lifespans. The thermal percolation of lithium superionic conductor into electronically conductive scaffolds promises an efficient strategy to fabricate dual-conductive electrodes, which benefits the development of dendrite-free lithium metal anodes with high energy/power densities.Item Topographic design in wearable MXene sensors with in-sensor machine learning for full-body avatar reconstruction(Nature Portfolio, 2022-09-09) Yang, Haitao; Li, Jiali; Xiao, Xiao; Wang, Jiahao; Li, Yufei; Li, Kerui; Li, Zhipeng; Yang, Haochen; Wang, Qian; Yang, Jie; Ho, John S.; Yeh, Po-Len; Mouthaan, Koen; Wang, Xiaonan; Shah, Sahil; Chen, Po-YenWearable strain sensors that detect joint/muscle strain changes become prevalent at human–machine interfaces for full-body motion monitoring. However, most wearable devices cannot offer customizable opportunities to match the sensor characteristics with specific deformation ranges of joints/muscles, resulting in suboptimal performance. Adequate wearable strain sensor design is highly required to achieve user-designated working windows without sacrificing high sensitivity, accompanied with real-time data processing. Herein, wearable Ti3C2Tx MXene sensor modules are fabricated with in-sensor machine learning (ML) models, either functioning via wireless streaming or edge computing, for full-body motion classifications and avatar reconstruction. Through topographic design on piezoresistive nanolayers, the wearable strain sensor modules exhibited ultrahigh sensitivities within the working windows that meet all joint deformation ranges. By integrating the wearable sensors with a ML chip, an edge sensor module is fabricated, enabling insensor reconstruction of high-precision avatar animations that mimic continuous full-body motions with an average avatar determination error of 3.5 cm, without additional computing devices.