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arXiv 2023年

作者： Tay, Fuyang Mojibpour, Ali Sanders, Stephen Liang, Shuang Xu, Hongjing Gardner, Geoff C. Baydin, Andrey Manfra, Michael J. Alabastri, Alessandro Hagenmüller, David Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley–Curl Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy Purdue University West LafayetteIN47907 United States Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Smalley–Curl Institute Rice University HoustonTX77005 United States School of Materials Engineering Purdue University West LafayetteIN47907 United States Université de Strasbourg CNRS Strasbourg67000 France Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States

Recent theoretical studies have highlighted the role of spatially varying cavity electromagnetic fields in exploring novel cavity quantum electrodynamics (cQED) phenomena, such as the potential realization of the elusive Dicke superradiant phase transition. One-dimensional photonic-crystal cavities (PCCs), widely used for studying solid-state cQED systems, have uniform spatial profiles in the lateral plane. Three-dimensional (3D) PCCs, which exhibit discrete in-plane translational symmetry, overcome this limitation, but fabrication challenges have hindered the achievement of strong coupling in 3D-PCCs. Here, we report the realization of multimode ultrastrong coupling in a 3D-PCC at terahertz frequencies. The multimode coupling between the 3D-PCC’s cavity modes and the cyclotron resonance of a Landau-quantized two-dimensional electron gas in GaAs is significantly influenced by the spatial profiles of the cavity modes, leading to distinct coupling scenarios depending on the probe polarization. Our experimental results are in excellent agreement with a multimode extended Hopfield model that accounts for the spatial inhomogeneity of the cavity field. Guided by the model, we discuss the possible strong ground-state correlations between different cavity modes and introduce relevant figures of merit for the multimode ultrastrong coupling regime. Our findings emphasize the importance of spatially nonuniform cavity mode profiles in probing nonintuitive quantum phenomena expected for the ground states of cQED systems in the ultrastrong coupling regime. Copyright © 2023, The Authors. All rights reserved.

关键词： Electron cyclotron resonance

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Terahertz cavity magnon polaritons

arXiv

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arXiv 2023年

作者： Kritzell, T. Elijah Baydin, Andrey Tay, Fuyang Rodriguez, Rodolfo Nojiri, Hiroyuki Everitt, Henry O. Barsukov, Igor Kono, Junichiro Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Physics and Astronomy University of California RiversideCA92521 United States Institute for Materials Research Tohoku University Sendai980-8577 Japan DEVCOM Army Research Laboratory-South HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States

Hybrid light-matter coupled states, or polaritons, in magnetic materials have attracted significant attention due to their potential for enabling novel applications in spintronics and quantum information processing. However, most studies to date have been carried out for ferromagnetic materials with magnon excitations at gigahertz frequencies. Here, we have investigated strong resonant photon-magnon coupling at frequencies above 1 terahertz for the first time in a prototypical room-temperature antiferromagnetic insulator, NiO, inside a Fabry-Pérot cavity. The cavity was formed by the crystal itself when it was thinned down to an optimized thickness. By using terahertz time-domain spectroscopy in high magnetic fields up to 25 T, we swept the magnon frequency through Fabry-Pérot cavity modes and observed photon-magnon anticrossing behavior, demonstrating clear vacuum Rabi splittings exceeding the polariton linewidths. These results show that NiO is a promising platform for exploring antiferromagnetic spintronics and cavity magnonics in the terahertz frequency range. Copyright © 2023, The Authors. All rights reserved.

关键词： Polariton

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Renewable Biodiesel Production from Oleaginous Yeast Biomass Using Industrial Wastes

Renewable Biodiesel Production from Oleaginous Yeast Biomass...

引用

2019 Research, Invention, and Innovation Congress, RI2C 2019

作者： Kongruang, Sasithorn Roytrakul, Sittiruk Sriariyanun, Malinee Bioprocess Engineering and Biotechnology Center Department of Biotechnology Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut' S University of Technology North Bangkok Bangkok Thailand Chemical and Process Engineering Program Sirindhorn International Thai-German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

The accumulation lipid from oleaginous microorganisms is recognized as a second generation fuel. Biooil is known to as intracellular product of oily yeast utilizing various carbon substrates and converting different quantities of lipids in the form of triacylglycerols. This second generation fuel can be used to make biodiesel via a transesterification process. This study investigated the morphological characteristics of eight strains of Thai oleaginous yeasts via microscopy and analyzed the fatty acid profiling of yeasts cultured in three carbon sources: glucose, sugar cane molasses and crude glycerol in order to estimate biodiesel properties. To approach this goal, batch fermentations were used to culture eight yeast strains, Rhodosporidium toruloides TISTR 5123, TISTR 5154, TISTR 5149, Yarrowia lipolytica TISTR 5054, TISTR 5151, TISTR 5621, Rhodotorula glutinis TISTR 5159 and Rhodotorula graminis TISTR 5124 for 96 h under 30°C at 250 rpm. Result revealed that eight yeast strains contained significant amounts of fatty acids and lipids and accumulated mainly palmitic acid (C16:0), stearic acid (C18:0), oleic acid (C 18:1) and linoleic acid (C18:2), and they are suitable for the production of biodiesel. Fatty acid productions and profiles indicated that these yeast strains can be potentially used as the triacylglycerols producers for biodiesel production. © 2020 The Authors, published by EDP sciences.

关键词： Lipids

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Identifying Materials-Level Sources of Performance Variation in Superconducting Transmon Qubits

arXiv

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arXiv 2025年

作者： Murthy, Akshay A. Bal, Mustafa Bedzyk, Michael J. Cansizoglu, Hilal Chan, Randall K. Chandrasekhar, Venkat Crisa, Francesco Datta, Amlan Deng, Yanpei Matute Diaz, Celeo D. Dravid, Vinayak P. Garcia-Wetten, David A. Garattoni, Sabrina Ghimire, Sunil Goronzy, Dominic P. de Graaf, Sebastian E. Haeuser, Sam Hersam, Mark C. Isheim, Dieter Joshi, Kamal Kim, Richard Kolachina, Saagar Kopas, Cameron J. Kramer, Matthew J. Lachman, Ella O. Lee, Jaeyel Lim, Peter G. Lunin, Andrei Mah, William Marshall, Jayss Mutus, Josh Y. Oh, Jin-Su Pappas, David P. Park, Joong-Mok Prozorov, Ruslan dos Reis, Roberto Seidman, David N. Sung, Zuhawn Tanatar, Makariy Walker, Mitchell J. Wang, Jigang Wu, Haotian Zhou, Lin Zhu, Shaojiang Grassellino, Anna Romanenko, Alexander BataviaIL60510 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Rigetti Computing BerkeleyCA94710 United States Ames National Laboratory AmesIA50011 United States Department of Physics & Astronomy Iowa State University AmesIA50011 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States The NUANCE Center Northwestern University EvanstonIL60208 United States International Institute of Nanotechnology Northwestern University EvanstonIL60208 United States National Physical Laboratory TeddingtonTW11 0LW United Kingdom Department of Chemistry Northwestern University EvanstonIL60208 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Applied Physics Graduate Program Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Iowa State University AmesIA50011 United States

The Superconducting Materials and Systems (SQMS) Center, a DOE National Quantum Information science Research Center, has conducted a comprehensive and coordinated study using superconducting transmon qubit chips with known performance metrics to identify the underlying materials-level sources of device-to-device performance variation. Following qubit coherence measurements, these qubits of varying base superconducting metals and substrates have been examined with various nondestructive and invasive material characterization techniques at Northwestern University, Ames National Laboratory, and Fermilab as part of a blind study. We find trends in variations of the depth of the etched substrate trench, the thickness of the surface oxide, and the geometry of the sidewall, which when combined, lead to correlations with the T1 lifetime across different devices. In addition, we provide a list of features that varied from device to device, for which the impact on performance requires further studies. Finally, we identify two low-temperature characterization techniques that may potentially serve as proxy tools for qubit measurements. These insights provide materials-oriented solutions to not only reduce performance variations across neighboring devices, but also to engineer and fabricate devices with optimal geometries to achieve performance metrics beyond the state-of-the-art values. © 2025, CC BY.

关键词： Qubits

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Training of Physical Neural Networks

arXiv

引用

arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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On the Efficient Evaluation of the Azimuthal Fourier Components of the Green's Function for Helmholtz's Equation in Cylindrical Coordinates

arXiv

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arXiv 2021年

作者： Garritano, James Kluger, Yuval Rokhlin, Vladimir Serkh, Kirill Program in Applied Mathematics Yale University New HavenCT06511 United States Yale School of Medicine New HavenCT06511 United States Dept. of Mathematics Yale University New HavenCT06511 United States Dept. of Math. and Computer Science University of Toronto TorontoONM5S 2E4 Canada

In this manuscript, we develop an efficient algorithm to evaluate the azimuthal Fourier components of the Green's function for the Helmholtz equation in cylindrical coordinates. A computationally efficient algorithm for this modal Green's function is essential for solvers for electromagnetic scattering from bodies of revolution (e.g., radar cross sections, antennas). Current algorithms to evaluate this modal Green's function become computationally intractable when the source and target are close or when the wavenumber is large. Furthermore, most state of the art methods cannot be easily parallelized. In this manuscript, we present an algorithm for evaluating the modal Green's function that has performance independent of both source-to-target proximity and wavenumber, and whose cost grows as $O(m)$, where $m$ is the Fourier mode. Furthermore, our algorithm is embarrassingly parallelizable. © 2021, CC BY.

关键词： Bodies of revolution

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Autonomous Multi-Modal Chemical Tomography using Bayesian Optimization

引用

Microscopy and Microanalysis 2024年第SUPPLEMENT_1期30卷

作者： Millsaps, William Schwartz, Jonathan Manassa, Jason Wendy Di, Zichao Jiang, Yi Hovden, Robert Department of Nuclear Engineering Radiological Sciences University of Michigan Ann Arbor MI US Chan Zuckerberg Imaging Institute Redwood City CA USA Department of Materials Science and Engineering University of Michigan Ann Arbor MI US Mathematics and Computer Science Division Argonne National Laboratory Lemont IL US Advanced Photon Source Facility Argonne National Laboratory Lemont IL US Applied Physics Program University of Michigan Ann Arbor MI US

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Observation of current-induced switching in non-collinear antiferromagnetic IrMn3 by differential voltage measurements

arXiv

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arXiv 2021年

作者： Arpaci, Sevdenur Lopez-Dominguez, Victor Shi, Jiacheng Sánchez-Tejerina, Luis Garesci, Francesca Wang, Chulin Yan, Xueting Sangwan, Vinod K. Grayson, Matthew Hersam, Mark C. Finocchio, Giovanni Amiri, Pedram Khalili Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina98166 Italy Department of Engineering University of Messina Messina98166 Italy Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States

There is accelerating interest in developing memory devices using antiferromagnetic (AFM) materials, motivated by the possibility for electrically controlling AFM order via spin-orbit torques, and its read-out via magnetoresistive effects. Recent studies have shown, however, that high current densities create non-magnetic contributions to resistive switching signals in AFM/heavy metal (AFM/HM) bilayers, complicating their interpretation. Here we introduce an experimental protocol to unambiguously distinguish current-induced magnetic and nonmagnetic switching signals in AFM/HM structures, and demonstrate it in IrMn3/Pt devices. A six-terminal double-cross device is constructed, with an IrMn3 pillar placed on one cross. The differential voltage is measured between the two crosses with and without IrMn3 after each switching attempt. For a wide range of current densities, reversible switching is observed only when write currents pass through the cross with the IrMn3 pillar, eliminating any possibility of non-magnetic switching artifacts. Micromagnetic simulations support our findings, indicating a complex domain-mediated switching process. © 2021, CC BY.

关键词： Binary alloys

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Decision Making using Internet of Things and Machine Learning: A bibliometric approach to tracking main research themes

Decision Making using Internet of Things and Machine Learnin...

引用

2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy, ICDABI 2020

作者： Gamboa-Rosales, N.K. Castorena-Robles, A. Casas-Valadez, M.A. Cobo, M.J. Castaneda-Miranda, R. Lopez-Robles, J.R. CONACYT-Autonomous University of Zacatecas Academic Unit of Electrical Engineering Zacatecas Mexico Monterrey Institute of Technology and Higher Education Biotechnology Engineering Queretaro Mexico Autonomous University of Zacatecas Academic Unit of Accounting and Administration Zacatecas Mexico University of Cadiz Department of Computer Science and Engineering Cadiz Spain Autonomous University of Zacatecas Post. Program of Eng. and Applied Tech. National Lab. CONACYT-SEDEAM Zacatecas Mexico

ISBN: (纸本)9781728196756

The internet has become an integral part of our life, we connect daily for informational, social, entertainment and even work and economic purposes. Therefore, it is completely normal to connect to the internet;But it is not only people, also these everyday objects or things in our environment who connect to the network to take advantage of its benefits. This next stage in the evolution of the internet, in which connectivity extends to the objects which surround us, is known as the Internet of Things. Sensors, Machines, Devices, Wearables are part of the Internet of Things and these can be identified to be recognized and interact without the need for a human being. With the Internet of Things, the connection extends to any type of object. All these interconnected devices and people produce an immense amount of data, when analyzed, they become a tool that allows organizations to make wise decisions in real time by having information on various market situations and ensuring a better understanding of behavior and consumer preferences. One of the disciplines of Artificial Intelligence responsible for analyzing this large amount of data is Machine Learning, whose sole purpose is to improve the Decision-Making process. To understand the relationship between Internet of Things, application of Machine Learning and the Decision Making process, a bibliometric analysis is carried out from 2013 to 2020 to know its evolution, trends, research areas, authors and other aspects using Scopus. © 2020 IEEE.

关键词： Decision making

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Electrochemical biosensor based on an atomic layered composite of graphene oxide/graphene as an electrode material towards selective and sensitive detection of miRNA-21

引用

Microchemical Journal 2024年 199卷

作者： Huang, Min-Shin Govindasamy, Mani Chinnapaiyan, Sathishkumar Lin, Yi-Ting Lu, Shao-Yang Samukawa, Seiji Huang, Chi-Hsien Department of Materials Engineering Ming Chi University of Technology New Taipei City 243303 Taiwan International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City 24303 Taiwan Centre for Applied Research Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai-602105 Tamil Nadu India Institute of Fluid Science Tohoku University Sendai 980-8577 Japan College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 300093 Taiwan Division of Rheumatology Allergy and Immunology Chang Gung Memorial Hospital Taoyuan City 33305 Taiwan College of Engineering Chang Gung University Taoyuan City 33302 Taiwan

Development of an efficient technique towards the detection of miRNAs is highly significant for the timely diagnosis and prognosis of cancers. In this study, we utilized the chemical vapor deposition (CVD) technique to grow bilayer graphene (BLG) on copper foil and subsequently transferred the BLG to fluorine doped tin oxide (FTO) substrate via a facile wet transfer technique. The upper layer of the BLG was modified to graphene oxide (GO) with a low damage plasma treatment (LDPT) process to serve as a biomolecule receiving layer and the lower graphene layer (G) serves as an electron transport layer. The developed GO/G/FTO platform offers superior sensing ability towards the miRNA-21. The results demonstrated that the proposed GO/G/FTO substrate detect the miRNA-21 with a wide linear range (10 fM ∼ 1 nM), low limit of detection (3.18 fM), high sensitivity (0.6 mApM⁻¹cm⁻²), selectivity and stability via the differential pulse voltammetry method. Additionally, the sensors can efficiently discriminate the target miRNA-21 from its complementary base mismatched counterparts. The proposed approach serves as an excellent tool for the early diagnosis of diseases. © 2024 Elsevier B.V.

关键词： Atomic layer oxidation Biosensor Cancer biomarkers Electrochemical Graphene oxide Plasma treatment

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