Browsing by Author "Li, Bowen"

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    Battery metal recycling by flash Joule heating
    (AAAS, 2023) Chen, Weiyin; Chen, Jinhang; Bets, Ksenia V.; Salvatierra, Rodrigo V.; Wyss, Kevin M.; Gao, Guanhui; Choi, Chi Hun; Deng, Bing; Wang, Xin; Li, John Tianci; Kittrell, Carter; La, Nghi; Eddy, Lucas; Scotland, Phelecia; Cheng, Yi; Xu, Shichen; Li, Bowen; Tomson, Mason B.; Han, Yimo; Yakobson, Boris I.; Tour, James M.; Welch Institute for Advanced Materials; NanoCarbon Center; Applied Physics Program; Smalley-Curl Institute
    The staggering accumulation of end-of-life lithium-ion batteries (LIBs) and the growing scarcity of battery metal sources have triggered an urgent call for an effective recycling strategy. However, it is challenging to reclaim these metals with both high efficiency and low environmental footprint. We use here a pulsed dc flash Joule heating (FJH) strategy that heats the black mass, the combined anode and cathode, to >2100 kelvin within seconds, leading to ~1000-fold increase in subsequent leaching kinetics. There are high recovery yields of all the battery metals, regardless of their chemistries, using even diluted acids like 0.01 M HCl, thereby lessening the secondary waste stream. The ultrafast high temperature achieves thermal decomposition of the passivated solid electrolyte interphase and valence state reduction of the hard-to-dissolve metal compounds while mitigating diffusional loss of volatile metals. Life cycle analysis versus present recycling methods shows that FJH significantly reduces the environmental footprint of spent LIB processing while turning it into an economically attractive process.
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    Deep-3D microscope: 3D volumetric microscopy of thick scattering samples using a wide-field microscope and machine learning
    (Optica Publishing Group, 2022) Li, Bowen; Tan, Shiyu; Dong, Jiuyang; Lian, Xiaocong; Zhang, Yongbing; Ji, Xiangyang; Ji, Xiangyang; Veeraraghavan, Ashok; Veeraraghavan, Ashok
    Confocal microscopy is a standard approach for obtaining volumetric images of a sample with high axial and lateral resolution, especially when dealing with scattering samples. Unfortunately, a confocal microscope is quite expensive compared to traditional microscopes. In addition, the point scanning in confocal microscopy leads to slow imaging speed and photobleaching due to the high dose of laser energy. In this paper, we demonstrate how the advances in machine learning can be exploited to "teach" a traditional wide-field microscope, one that’s available in every lab, into producing 3D volumetric images like a confocal microscope. The key idea is to obtain multiple images with different focus settings using a wide-field microscope and use a 3D generative adversarial network (GAN) based neural network to learn the mapping between the blurry low-contrast image stacks obtained using a wide-field microscope and the sharp, high-contrast image stacks obtained using a confocal microscope. After training the network with widefield-confocal stack pairs, the network can reliably and accurately reconstruct 3D volumetric images that rival confocal images in terms of its lateral resolution, z-sectioning and image contrast. Our experimental results demonstrate generalization ability to handle unseen data, stability in the reconstruction results, high spatial resolution even when imaging thick (∼40 microns) highly-scattering samples. We believe that such learning-based microscopes have the potential to bring confocal imaging quality to every lab that has a wide-field microscope.
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    Electrothermal mineralization of per- and polyfluoroalkyl substances for soil remediation
    (Springer Nature, 2024) Cheng, Yi; Deng, Bing; Scotland, Phelecia; Eddy, Lucas; Hassan, Arman; Wang, Bo; Silva, Karla J.; Li, Bowen; Wyss, Kevin M.; Ucak-Astarlioglu, Mine G.; Chen, Jinhang; Liu, Qiming; Si, Tengda; Xu, Shichen; Gao, Xiaodong; JeBailey, Khalil; Jana, Debadrita; Torres, Mark Albert; Wong, Michael S.; Yakobson, Boris I.; Griggs, Christopher; McCary, Matthew A.; Zhao, Yufeng; Tour, James M.
    Per- and polyfluoroalkyl substances (PFAS) are persistent and bioaccumulative pollutants that can easily accumulate in soil, posing a threat to environment and human health. Current PFAS degradation processes often suffer from low efficiency, high energy and water consumption, or lack of generality. Here, we develop a rapid electrothermal mineralization (REM) process to remediate PFAS-contaminated soil. With environmentally compatible biochar as the conductive additive, the soil temperature increases to >1000 °C within seconds by current pulse input, converting PFAS to calcium fluoride with inherent calcium compounds in soil. This process is applicable for remediating various PFAS contaminants in soil, with high removal efficiencies ( >99%) and mineralization ratios ( >90%). While retaining soil particle size, composition, water infiltration rate, and cation exchange capacity, REM facilitates an increase of exchangeable nutrient supply and arthropod survival in soil, rendering it superior to the time-consuming calcination approach that severely degrades soil properties. REM is scaled up to remediate soil at two kilograms per batch and promising for large-scale, on-site soil remediation. Life-cycle assessment and techno-economic analysis demonstrate REM as an environmentally friendly and economic process, with a significant reduction of energy consumption, greenhouse gas emission, water consumption, and operation cost, when compared to existing soil remediation practices.