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Conference on Lasers and Electro-Optics (CLEO)

作者： Jacques Doumani Minhan Lou Oliver Dewey Nina Hong Jichao Fan Andrey Baydin Matteo Pasquali Yohei Yomogida Kazuhiro Yanagi Riichiro Saito Junichiro Kono Weilu Gao Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Electrical and Computer Engineering The University of Utah Salt Lake City UT USA Carbon Hub Rice University Houston TX USA Department of Chemical and Biomolecular Engineering Rice University Houston TX USA J.A. Woollam Co. Lincoln NE USA The Smalley-Curl Institute Rice University Houston TX USA Department of Chemistry Rice University Houston TX USA Department of Materials Science and NanoEngineering Rice University Houston TX USA Department of Physics and Astronomy Tokyo Metropolitan University Tokyo Japan Department Physics Tohoku University Sendai Japan Department Physics and Astronomy Rice University Houston TX USA

We demonstrate tunable, giant, and structure-induced deep-ultraviolet circular dichroism in macroscopically chiral assemblies of racemic carbon nanotubes prepared using two approaches: mechanical-rotation-assisted vac... 详细信息

ISBN: (纸本)9781957171258

关键词： Carbon nanotubes Chiral media Dichroism Graphene Optical activity Quantum information

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Meta-optic Accelerators for Object Classifiers

arXiv

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arXiv 2022年

作者： Zheng, Hanyu Liu, Quan Zhou, You Kravchenko, Ivan I. Huo, Yuankai Valentine, Jason Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN37212 United States Department of Computer Science Vanderbilt University NashvilleTN37212 United States Department of Mechanical Engineering Vanderbilt University NashvilleTN37212 United States Interdisciplinary Materials Science Program Vanderbilt University NashvilleTN37212 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory Oak RidgeTN37830 United States

Rapid advances in deep learning have led to paradigm shifts in a number of fields, from medical image analysis to autonomous systems. These advances, however, have resulted in digital neural networks with large computational requirements, resulting in high energy consumption and limitations in real-time decision making when computation resources are limited. Here, we demonstrate a meta-optic based neural network accelerator that can off-load computationally expensive convolution operations into high-speed and low-power optics. In this architecture, metasurfaces enable both spatial multiplexing and additional information channels, such as polarization, in object classification. End-to-end design is used to co-optimize the optical and digital systems resulting in a robust classifier that achieves 95% accurate classification of handwriting digits and 94% accuracy in classifying both the digit and its polarization state. This approach could enable compact, high-speed, and low-power image and information processing systems for a wide range of applications in machine-vision and artificial intelligence. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy utilization

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Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation

arXiv

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arXiv 2021年

作者： Jo, Jeonghee Kwak, Bumju Lee, Byunghan Yoon, Sungroh Interdisciplinary Program in Bioinformatics Seoul National University Recommendation Team Kakao Corporation Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Department of Electrical and Computer Engineering Seoul National University

A molecule is a complex of heterogeneous components, and the spatial arrangements of these components determine the whole molecular properties and characteristics. With the advent of deep learning in computational chemistry, several studies have focused on how to predict molecular properties based on molecular configurations. Message passing neural network provides an effective framework for capturing molecular geometric features with the perspective of a molecule as a graph. However, most of these studies assumed that all heterogeneous molecular features, such as atomic charge, bond length, or other geometric features always contribute equivalently to the target prediction, regardless of the task type. In this study, we propose a dual-branched neural network for molecular property prediction based on message-passing framework. Our model learns heterogeneous molecular features with different scales, which are trained flexibly according to each prediction target. In addition, we introduce a discrete branch to learn single atom features without local aggregation, apart from message-passing steps. We verify that this novel structure can improve the model performance with faster convergence in most targets. The proposed model outperforms other recent models with sparser representations. Our experimental results indicate that in the chemical property prediction tasks, the diverse chemical nature of targets should be carefully considered for both model performance and generalizability. © 2021, CC BY.

关键词： Deep learning

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Discrete frequency infrared vibrational circular dichroism spectroscopy & imaging

Discrete frequency infrared vibrational circular dichroism s...

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Novel Techniques in Microscopy, NTM 2021 - Part of Biophotonics Congress: Optics in the Life sciences 2021

作者： Phal, Yamuna Yeh, Kevin Bhargava, Rohit Department of Electrical and Computer Engineering University of Illinois at Urbana Champaign 306 N. Wright St UrbanaIL61801 United States Departments of Bioengineering Chemistry Chemical and Biomolecular Engineering Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Cancer Center at Illinois University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign 405 N. Mathews Ave UrbanaIL61801 United States

Vibrational Circular Dichroism (VCD) arises from chiroptical phenomena in biological samples. We demonstrate VCD imaging, a novel microscopy technique that can provide molecular- and spatially-specific chirality mappi... 详细信息

ISBN: (纸本)9781557528209

关键词： Dichroism

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Terahertz Time-Domain Spectroscopy for Probing DC Conductivity of Single-Layer Graphene

Terahertz Time-Domain Spectroscopy for Probing DC Conductivi...

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Western New York Image Processing Workshop (WNYIPW)

作者： Debamitra Chakraborty Declan Walden Jing Cheng Julia Wallace Kiryl A. Niherysh Andrei V. Felsharuk Donats Erts Ivan Komissarov Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York USA Laboratory for Laser Energetics Rochester New York USA Department of Physics and Astronomy University of Rochester Rochester New York USA Department of Biomedical Engineering University of Rochester Rochester New York USA Institute of Chemical Physics University of Latvia Riga Latvia Department of Microtechnology and Nanoscience Chalmers University of Technology Gothenburg Sweden Department of Electrical and Computer Engineering University of Rochester Rochester New York USA

We report preliminary results of extracting electrical parameters like DC conductivity, mobility, and carrier concentration of single-layer graphene using Terahertz time-domain spectroscopy (THz-TDS) in transmission geometry. We utilize a simple Drude model and its extension, the Drude-Smith model, capable of accommodating charge carrier scattering by optical and acoustic phonons to describe the complex conductivity in graphene. We observed that a significant area of the sample scanned followed the Drude-Smith behavior, while at a few locations, the simple Drude behavior prevailed. We identified possible reasons that might contribute to this observation, like defects, the presence of polymer residue on the sample, or the sample strain. This research sheds new light on our understanding of graphene's electrical properties and lays the foundation for future investigations addressing defects and contaminants in graphene samples.

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Multi-core synthesis and maximum satisfiability applied to optimal sizing of solar photovoltaic systems

TechRxiv

引用

TechRxiv 2021年

作者： Trindade, Alessandro Galvão, Edilson Cordeiro, Lucas The Post-graduate Program in Electrical Engineering Manaus Brazil Department of Electricity Federal University of Amazonas Manaus Brazil School of Computer Science The University of Manchester United Kingdom

Annual global energy consumption growth is around 1.3% with forecasts until 2040. Photovoltaic systems became a suitable alternative to nuclear and fossil energy generation. In order to support this technology’s dissemination, we develop and evaluate an automated formal synthesis approach that assists in decision-making for off-grid systems. Our proposed approach, called PVz, is based on a variant of the counterexample-guided inductive synthesis;it has a multi-core feature, which can obtain the optimal sizing of photovoltaic systems focusing on Life Cycle Cost analysis. Given the electrical needs of a home, we seek a set of electrical equipment with the best possible combination of devices that meet the specified requirements. We calculate all costs related to maintenance over 20 years. The results presented are based on seven case studies;some of them are real ones from the Amazon region in Brazil. The same case studies were solved by a commercial optimization tool. Our technique and the commercial tool results were validated with popular simulation software to perform a fair comparison. Furthermore, we analyze some topics such as run-time, optimal solution, and configuration of the resulting systems. We claim that our technique is advantageous compared to the existing approaches in the literature. © 2021, CC BY-NC-SA.

关键词： Decision making

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Graphene calorimetric single-photon detector

arXiv

引用

arXiv 2024年

作者： Huang, Bevin Arnault, Ethan G. Jung, Woochan Fried, Caleb Russell, B. Jordan Watanabe, Kenji Taniguchi, Takashi Henriksen, Erik A. Englund, Dirk Lee, Gil-Ho Fong, Kin Chung Intelligence Community Postdoctoral Research Fellowship Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics Pohang University of Science and Technology Pohang790-784 Korea Republic of Department of Physics Washington University in St. Louis St. LouisMO United States Research Center for Electronic and Optical Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan RTX BBN Technologies Quantum Engineering and Computing Group CambridgeMA02138 United States

Single photon detectors (SPDs) [1, 2] are essential technology in quantum science, quantum network, biology, and advanced imaging [3-5]. To detect the small quantum of energy carried in a photon, conventional SPDs rely on energy excitation across either a semiconductor bandgap or superconducting gap. While the energy gap suppresses the false-positive error, it also sets an energy scale that can limit the detection efficiency of lower energy photons and spectral bandwidth of the SPD [6-8]. Here, we demonstrate an orthogonal approach to detect single near-infrared photons using graphene calorimeters [9-13]. By exploiting the extremely low heat capacity of the pseudo-relativistic electrons in graphene near its charge neutrality point [14], we observe an electron temperature rise up to ∼2 K using a hybrid Josephson junction. In this proof-of-principle experiment, we achieve an intrinsic quantum efficiency of 87% (75%) with dark count © 2024, CC BY.

关键词： Particle beams

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Rational Design of Safer Inorganic Nanoparticles via Mechanistic Modeling-Informed Machine Learning

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ACS Nano 2025年第23期19卷 21538-21555页

作者： Cave, Joseph Christiono, Anne Schiavone, Carmine Pownall, Henry J. Cristini, Vittorio Staquicini, Daniela I. Pasqualini, Renata Arap, Wadih Brinker, C. Jeffrey Campen, Matthew Wang, Zhihui Van Nguyen, Hien Noureddine, Achraf Dogra, Prashant Mathematics in Medicine Program Department of Medicine Houston Methodist Research Institute Houston 77030 TX United States Physiology Biophysics and Systems Biology Program Graduate School of Medical Sciences Weill Cornell Medicine New York 10065 NY United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge 02139 MA United States Department of Chemical Materials and Industrial Production Engineering University of Naples Federico II Naples 80138 Italy Department of Medicine Houston Methodist Houston 77030 TX United States Department of Medicine Weill Cornell Medicine New York 10065 NY United States Neal Cancer Center Houston Methodist Research Institute Houston 77030 TX United States Department of Imaging Physics University of Texas M.D. Anderson Cancer Center Houston 77030 TX United States Rutgers Cancer Institute Newark 08901 NJ United States Division of Cancer Biology Department of Radiation Oncology Rutgers New Jersey Medical School Newark 08901 NJ United States Division of Hematology/Oncology Department of Medicine Rutgers New Jersey Medical School Newark 08901 NJ United States Department of Chemical and Biological Engineering University of New Mexico Albuquerque 87106 NM United States College of Pharmacy University of New Mexico Albuquerque 87106 NM United States Department of Physiology and Biophysics Weill Cornell Medical College New York 10065 NY United States Department of Electrical and Computer Engineering University of Houston Houston 77204 TX United States

The safety of inorganic nanoparticles (NPs) remains a critical challenge for their clinical translation. To address this, we developed a machine learning (ML) framework that predicts NP toxicity both in vitro and in vivo, leveraging physicochemical properties and experimental conditions. A curated in vitro cytotoxicity dataset was used to train and validate binary classification models, with top-performing models undergoing explainability analysis to identify key determinants of toxicity and establish structure-toxicity relationships. External testing with diverse inorganic NPs validated the predictive accuracy of the framework for in vitro settings. To enable organ-specific toxicity predictions in vivo, we integrated a physiologically based pharmacokinetic (PBPK) model into the ML pipeline to quantify NP exposure across organs. Retraining the ML models with PBPK-derived exposure metrics yielded robust predictions of organ-specific nanotoxicity, further validating the framework. This PBPK-informed ML approach can thus serve as a potential alternative approach to streamline NP safety assessment, enabling the rational design of safer NPs and expediting their clinical translation. © 2025 The Authors. Published by American Chemical Society.

关键词： artificial intelligence cytotoxicity machine learning mathematical modeling nanoparticle nanotoxicity PBPK

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Mechanically reprogrammable Pancharatnam-Berry metasurface for microwaves

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Advanced Photonics 2022年第1期4卷 69-79页

作者： Quan Xu Xiaoqiang Su Xueqian Zhang Lijuan Dong Lifeng Liu Yunlong Shi Qiu Wang Ming Kang Andrea Alù Shuang Zhang Jiaguang Han Weili Zhang Tianjin University Center for Terahertz Waves and College of Precision Instrument and Optoelectronics EngineeringKey Laboratory of Optoelectronic Information Technology(Ministry of Education of China)TianjinChina Shanxi Datong University Institute of Solid State Physics and College of Physics and Electronic ScienceShanxi Province Key Laboratory of Microstructure Electromagnetic Functional MaterialsDatongChina Wuhan University of Technology School of Information EngineeringWuhanChina Tianjin Normal University College of Physics and Materials ScienceTianjinChina City University of New York Advanced Science Research CenterPhotonics InitiativeNew YorkUnited States City University of New York Graduate CenterPhysics ProgramNew YorkUnited States University of Hong Kong Faculty of ScienceDepartment of PhysicsHong KongChina University of Hong Kong Department of Electrical and Electronic EngineeringHong KongChina Guilin University of Electronic Technology Guangxi Key Laboratory of Optoelectronic Information ProcessingSchool of Optoelectronic EngineeringGuilinChina Oklahoma State University School of Electrical and Computer EngineeringStillwaterOklahomaUnited States

Metasurfaces have enabled the realization of several optical functionalities over an ultrathin platform,fostering the exciting field of flat *** metasurfaces are achieved by arranging a layout of static meta-atoms to imprint a desired operation on the impinging wavefront,but their functionality cannot be *** and programmability of metasurfaces are the next important step to broaden their impact,adding customized on-demand functionality in which each meta-atom can be individually *** demonstrate a mechanical metasurface platform with controllable rotation at the meta-atom level,which can implement continuous Pancharatnam–Berry phase control of circularly polarized *** the proof-of-concept experiments,we demonstrate metalensing,focused vortex beam generation,and holographic imaging in the same metasurface template,exhibiting versatility and superior *** dynamic control of electromagnetic waves using a single,low-cost metasurface paves an avenue towards practical applications,driving the field of reprogrammable intelligent metasurfaces for a variety of applications.

关键词： reprogrammable metasurfaces Pancharatnam-Berry phase mechanical metasurfaces microwaves

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Two-step electronic response to magnetic ordering in a van der Waals ferromagnet

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Physical Review B 2024年第4期109卷 045416-045416页

作者： Han Wu Jian-Xin Zhu Lebing Chen Matthew W. Butcher Ziqin Yue Dongsheng Yuan Yu He Ji Seop Oh Bin Gao Jianwei Huang Shan Wu Cheng Gong Yucheng Guo Sung-Kwan Mo Jonathan Denlinger Donghui Lu Makoto Hashimoto Matthew B. Stone Alexander I. Kolesnikov Songxue Chi Junichiro Kono Andriy H. Nevidomskyy Robert J. Birgeneau Pengcheng Dai Ming Yi Department of Physics and Astronomy and Rice Center for Quantum Materials Rice University Houston Texas 77005 USA Theoretical Division Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Center for Integrated Nanotechnologies Los Alamos National Laboratory Los Alamos New Mexico 87545 USA Department of Physics University of California at Berkeley Berkeley California 94720 USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston Texas 77005 USA Materials Sciences Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA National Institute for Materials Science 1-1 Namiki Tsukuba Ibaraki 305-0044 Japan Department of Applied Physics Yale University New Haven Connecticut 06511 USA Department of Electrical and Computer Engineering and Quantum Technology Center University of Maryland College Park Maryland 20742 USA Advanced Light Source Lawrence Berkeley National Laboratory Berkeley California 94720 USA Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo Park California 94025 USA Neutron Scattering Division Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Department of Electrical and Computer Engineering Rice University Houston Texas 77005 USA Smalley-Curl Institute Rice University Houston Texas 77005 USA Department of Material Science and Nanoengineering Rice University Houston Texas 77005 USA Department of Materials Science and Engineering University of California Berkeley California 94720 USA

The two-dimensional material Cr2Ge2Te6 is a member of the class of insulating van der Waals (vdW) magnets. Here, using high resolution angle-resolved photoemission spectroscopy in a detailed temperature dependence study, we identify a clear response of the electronic structure to a dimensional crossover in the form of two distinct temperature scales marking onsets of modifications in the electronic structure. Specifically, we observe Te p-orbital-dominated bands to undergo changes at the Curie transition temperature TC while the Cr d-orbital-dominated bands begin evolving at a higher temperature scale. Combined with neutron scattering, density functional theory calculations, and Monte Carlo simulations, we find that the electronic system can be consistently understood to respond sequentially to the distinct temperatures at which in-plane and out-of-plane spin correlations exceed a characteristic length scale. Our findings reveal the sensitivity of the orbital-selective electronic structure for probing the dynamical evolution of local moment correlations in vdW insulating magnets.

关键词： Electronic structure Magnetic order Magnetic phase transitions Van der Waals systems Angle-resolved photoemission spectroscopy

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