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arXiv 2021年

作者： Farajpour, Nasim Deivanayagam, Ram Phakatkar, Abhijit Narayanan, Surya Shahbazian-Yassar, Reza Shokuhfar, Tolou Department of Bioengineering University of Illinois at Chicago ChicagoIL60607 United States Department of Electrical and Computer Engineering University of Illinois at Chicago ChicagoIL60607 United States Department of Mechanical and Industrial Engineering University of Illinois at Chicago ChicagoIL60607 United States

Glucose biosensors play an important role in the diagnosis and continued monitoring of the disease, diabetes mellitus. This report proposes the development of a novel enzymatic electrochemical glucose biosensor based on TiO2 nanotubes modified by AgO and Prussian blue (PB) nanoparticles (NPs), which has an additional advantage of possessing antimicrobial properties for implantable biosensor applications. In this study, we developed two high performance glucose biosensors based on the immobilization of glucose oxidase (GOx) onto Prussian blue (PB) modified TiO2 nanotube arrays functionalized by Au and AgO NPs. AgO-deposited TiO2 nanotubes were synthesized through an electrochemical anodization process followed by Ag electroplating process in the same electrolyte. Deposition of PB particles was performed from an acidic ferricyanide solution. The surface morphology and elemental composition of the two fabricated biosensors were investigated by scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) which indicate the successful deposition of Au and AgO nanoparticles as well as PB nanocrystals. Cyclic voltammetry and chronoamperometry were used to investigate the performance of the modified electrochemical biosensors. The results show that the developed electrochemical biosensors display excellent properties in terms of electron transmission, low detection limit as well as high stability for the determination of glucose. Under the optimized conditions, the amperometric response shows a linear dependence on the glucose concentration to a detection limit down to 4.91 µM with sensitivity of 185.1 mA M-1 cm-2 in Au modified biosensor and detection limit of 58.7 µM with 29.1 mA M-1 cm-2 sensitivity in AgO modified biosensor. © 2021, CC BY-NC-ND.

关键词： Glucose sensors

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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

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ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies

arXiv

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arXiv 2024年

作者： Huang, Jiahao Wu, Yinzhe Wang, Fanwen Fang, Yingying Nan, Yang Alkan, Cagan Abraham, Daniel Liao, Congyu Xu, Lei Gao, Zhifan Wu, Weiwen Zhu, Lei Chen, Zhaolin Lally, Peter Bangerter, Neal Setsompop, Kawin Guo, Yike Rueckert, Daniel Wang, Ge Yang, Guang Department of Bioengineering Faculty of Engineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Imperial-X and School of Biomedical Engineering & Imaging Sciences King's College London United Kingdom Department of Electrical Engineering Stanford University CA94305 United States Department of Radiology Beijing Anzhen Hospital Capital Medical University 2nd Anzhen Road Chaoyang District Beijing100029 China School of Biomedical Engineering Sun Yat-sen University Guangdong Guangzhou510006 China Hong Kong University of Science and Technology Hongkong Hong Kong Department of Data Science and AI Faculty of Information Technology Monash University Wellington Rd ClaytonVIC3800 Australia Electrical and Computer Engineering Department Boise State University ID83725 United States Department of Radiology Stanford University CA94305 United States Department of Computing Imperial College London South Kensington Campus LondonSW7 2AZ United Kingdom Technical University of Munich Munich80333 Germany New York United States

Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI acceleration via data and physics-driven models, leveraging techniques from algorithm unrolling models, enhancement-based methods, and plug-and-play models to the emerging full spectrum of generative model-based methods. We also explore the synergistic integration of data models with physics-based insights, encompassing the advancements in multi-coil hardware accelerations like parallel imaging and simultaneous multi-slice imaging, and the optimization of sampling patterns. We then focus on domain-specific challenges and opportunities, including image redundancy exploitation, image integrity, evaluation metrics, data heterogeneity, and model generalization. This work also discusses potential solutions and future research directions, with an emphasis on the role of data harmonization and federated learning for further improving the general applicability and performance of these methods in MRI reconstruction. © 2024, CC BY.

关键词： Magnetic resonance imaging

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Self-supervised learning of hologram reconstruction using physics consistency

arXiv

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arXiv 2022年

作者： Huang, Luzhe Chen, Hanlong Liu, Tairan Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States David Geffen School of Medicine University of California Los Angeles Los AngelesCA90095 United States

The past decade has witnessed transformative applications of deep learning in various computational imaging, sensing and microscopy tasks. Due to the supervised learning schemes employed, these methods mostly depend on large-scale, diverse, and labeled training data. The acquisition and preparation of such training image datasets are often laborious and costly, also leading to biased estimation and limited generalization to new sample types. Here, we report a self-supervised learning model, termed GedankenNet, that eliminates the need for labeled or experimental training data, and demonstrate its effectiveness and superior generalization on hologram reconstruction tasks. Without prior knowledge about the sample types to be imaged, the self-supervised learning model was trained using a physics-consistency loss and artificial random images that are synthetically generated without any experiments or resemblance to real-world samples. After its self-supervised training, GedankenNet successfully generalized to experimental holograms of various unseen biological samples, reconstructing the phase and amplitude images of different types of objects using experimentally acquired test holograms. Without access to experimental data or knowledge of real samples of interest or their spatial features, GedankenNet's self-supervised learning achieved complex-valued image reconstructions that are consistent with the Maxwell's equations, and its output inference and object solutions accurately represent the wave propagation in free-space. GedankenNet framework also exhibits resilience to random, unknown perturbations in the physical forward model, including changes in the hologram distances, pixel size and illumination wavelength. This self-supervised learning of image reconstruction tasks creates new opportunities for various inverse problems in holography, microscopy and computational imaging fields. Copyright © 2022, The Authors. All rights reserved.

关键词： Holograms

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Neural population dynamics in songbird RA and HVC during learned motor-vocal behavior

arXiv

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arXiv 2024年

作者： Tostado-Marcos, Pablo Arneodo, Ezequiel M. Ostrowski, Lauren Brown, Daril E. Perez, Xavier A. Kadwory, Adam Stanwicks, Lauren L. Alothman, Abdullah Gentner, Timothy Q. Gilja, Vikash Department of Bioengineering University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Electrical and Computer Engineering University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Psychology University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Neurobiology University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States Neurosciences Graduate Program University of California San Diego 9500 Gilman Drive La Jolla CA92093 United States

Complex, learned motor behaviors involve the coordination of large-scale neural activity across multiple brain regions, but our understanding of the population-level dynamics within different regions tied to the same behavior remains limited. Here, we investigate the neural population dynamics underlying learned vocal production in awake-singing songbirds. We use Neuropixels probes to record the simultaneous extracellular activity of populations of neurons in two regions of the vocal motor pathway. In line with observations made in non-human primates during limb-based motor tasks, we show that the population-level activity in both the premotor nucleus HVC and the motor nucleus RA is organized on low-dimensional neural manifolds upon which coordinated neural activity is well described by temporally structured trajectories during singing behavior. Both the HVC and RA latent trajectories provide relevant information to predict vocal sequence transitions between song syllables. However, the dynamics of these latent trajectories differ between regions. Our state-space models suggest a unique and continuous-over-time correspondence between the latent space of RA and vocal output, whereas the corresponding relationship for HVC exhibits a higher degree of neural variability. We then demonstrate that comparable high-fidelity reconstruction of continuous vocal outputs can be achieved from HVC and RA neural latents and spiking activity. Unlike those that use spiking activity, however, decoding models using neural latents generalize to novel sub-populations in each region, consistent with the existence of preserved manifolds that confine vocal-motor activity in HVC and RA. © 2024, CC BY.

关键词： Dynamics

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Direct measure of DNA bending by quantum magnetic imaging of a nano-mechanical torque-balance

arXiv

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arXiv 2024年

作者： Kazi, Zeeshawn Shelby, Isaac M. Nirodi, Ruhee Turnbull, Joseph Watanabe, Hideyuki Itoh, Kohei M. Wiggins, Paul A. Fu, Kai-Mei C. Department of Physics University of Washington SeattleWA98105 United States National Institute of Advanced Industrial Science and Technology Tsukuba Central 2 1-1-1 Umezono Tsukuba Ibaraki305-8568 Japan School of Fundamental Science and Technology Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan Department of Bioengineering University of Washington SeattleWA98105 United States Department of Electrical and Computer Engineering University of Washington SeattleWA98105 United States Physical Sciences Division Pacific Northwest National Laboratory RichlandWA99354 United States

DNA flexibility is a key determinant of biological function, from nucleosome positioning to transcriptional regulation, motivating a direct measurement of the bend-torque response of individual DNA molecules. In this work, DNA bending is detected using a nano-mechanical torque balance formed by tethering a ferromagnetic nanoparticle probe by an individual DNA molecule to a diamond magnetic field imager. The torque exerted by the DNA in response to bending caused by an applied magnetic torque is measured using wide-field imaging of quantum defects near the surface of the diamond. Qualitative measurements of differences in DNA bio-mechanical binding configuration are demonstrated, and as a proof-of-principle, a quantitative measurement of the bend response is made for individual DNA molecules. This quantum-enabled measurement approach could be applied to characterize the bend response of biophysically relevant short DNA molecules as well as the sequence dependence of DNA bending energy. © 2024, CC BY.

关键词： DNA

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Near-infrared femtosecond time lens diode laser with kW peak powers for two-photon microscopy

Near-infrared femtosecond time lens diode laser with kW peak...

引用

CLEO: Science and Innovations, CLEO_SI 2020

作者： Lange Simmons, Y. Underwood, Kenneth J. Heffernan, Brendan M. Supekar, Omkar D. Gibson, Emily A. Gopinath, Juliet T. Department of Physics University of Colorado BoulderCO80309 United States Department of Mechanical Engineering University of Colorado BoulderCO80309 United States Department of Bioengineering University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States

A diode-based time lens laser with nonlinear pulse compression at 976 nm produces 700 fs pulses with 17 kW peak power. Two-photon imaging of a mouse brain slice is demonstrated with the laser. © OSA 2020 © 2... 详细信息

ISBN: (数字)9781943580767

ISBN: (纸本)9781943580767

关键词： Infrared devices

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Electrocatalytic on-site oxygenation for transplanted cell-based-therapies

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Nature Communications 2023年第1期14卷 7019页

作者： Lee, Inkyu Surendran, Abhijith Fleury, Samantha Gimino, Ian Curtiss, Alexander Fell, Cody Shiwarski, Daniel J. Refy, Omar Rothrock, Blaine Jo, Seonghan Schwartzkopff, Tim Mehta, Abijeet Singh Wang, Yingqiao Sipe, Adam John, Sharon Ji, Xudong Nikiforidis, Georgios Feinberg, Adam W. Hester, Josiah Weber, Douglas J. Veiseh, Omid Rivnay, Jonathan Cohen-Karni, Tzahi Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh PA United States Department of Biomedical Engineering Northwestern University Evanston IL United States Department of Bioengineering Rice University Houston TX United States Department of Chemical Engineering Carnegie Mellon University Pittsburgh PA United States Department of Electrical and Computer Engineering Northwestern University Evanston IL United States Department of Biomedical Engineering Carnegie Mellon University Pittsburgh PA United States Department of Physics Carnegie Mellon University Pittsburgh PA United States Department of Computer Science Northwestern University Evanston IL United States Department of Material Science and Engineering The Pennsylvania State University State College PA United States Neuroscience Institute Carnegie Mellon University Pittsburgh PA United States Simpson Querrey Institute Northwestern University Chicago IL United States Interactive Computing and Computer Science Georgia Institute of Technology Atlanta GA United States Department of Mechanical Engineering Carnegie Mellon University Pittsburgh PA United States Department of Materials Science and Engineering Northwestern University Evanston 60208 IL United States

Implantable cell therapies and tissue transplants require sufficient oxygen supply to function and are limited by a delay or lack of vascularization from the transplant host. Previous exogenous oxygenation strategies have been bulky and had limited oxygen production or regulation. Here, we show an electrocatalytic approach that enables bioelectronic control of oxygen generation in complex cellular environments to sustain engineered cell viability and therapy under hypoxic stress and at high cell densities. We find that nanostructured sputtered iridium oxide serves as an ideal catalyst for oxygen evolution reaction at neutral pH. We demonstrate that this approach exhibits a lower oxygenation onset and selective oxygen production without evolution of toxic byproducts. We show that this electrocatalytic on site oxygenator can sustain high cell loadings (>60k cells/mm³) in hypoxic conditions in vitro and in vivo. Our results showcase that exogenous oxygen production devices can be readily integrated into bioelectronic platforms, enabling high cell loadings in smaller devices with broad applicability. © 2023, The Author(s).

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computer-free, all-optical reconstruction of holograms using diffractive networks

arXiv

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arXiv 2021年

作者： Rahman, Md Sadman Sakib Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States

Reconstruction of in-line holograms of unknown objects in general suffers from twin-image artifacts due to the appearance of an out-of-focus image overlapping with the desired image to be reconstructed. computer-based iterative phase retrieval algorithms and learning-based methods have been used for the suppression of such image artifacts in digital holography. Here we report an all-optical hologram reconstruction method that can instantly retrieve the image of an unknown object from its in-line hologram and eliminate twin-image artifacts without using a digital processor or a computer. Multiple transmissive diffractive layers are trained using deep learning so that the diffracted light from an arbitrary input hologram is processed all-optically, through light-matter interaction, to reconstruct the image of an unknown object at the speed of light propagation and without the need for any external power. This passive all-optical processor composed of spatially-engineered transmissive layers forms a diffractive network, which successfully generalizes to reconstruct in-line holograms of unknown, new objects and exhibits improved diffraction efficiency as well as extended depth-of-field at the hologram recording distance. This all-optical hologram processor and the underlying design framework can find numerous applications in coherent imaging and holographic display-related applications owing to its major advantages in terms of image reconstruction speed and computer-free operation. Copyright © 2021, The Authors. All rights reserved.

关键词： Holograms

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Cascadable all-optical NAND gates using diffractive networks

arXiv

引用

arXiv 2021年

作者： Luo, Yi Mengu, Deniz Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States

Owing to its potential advantages such as scalability, low latency and power efficiency, optical computing has seen rapid advances over the last decades. A core unit of a potential all-optical processor would be the NAND gate, which can be cascaded to perform an arbitrary logical operation. Here, we present the design and analysis of cascadable all-optical NAND gates using diffractive neural networks. We encoded the logical values at the input and output planes of a diffractive NAND gate using the relative optical power of two spatially-separated apertures. Based on this architecture, we numerically optimized the design of a diffractive neural network composed of 4 passive layers, spread over an axial length of 250λ, to all-optically perform NAND operation using diffraction of light at a wavelength of λ, and cascaded these diffractive NAND gates to perform complex logical functions by successively feeding the output of one diffractive NAND gate into another. We demonstrated the cascadability of our diffractive NAND gates by using identical diffractive designs to all-optically perform AND and OR operations, as well as a half-adder. Cascadable all-optical NAND gates composed of spatially-engineered passive diffractive layers can serve as a core component of various optical computing platforms. Copyright © 2021, The Authors. All rights reserved.

关键词： Optical data processing

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