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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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Linking machine learning with multiscale numerics: Data-driven discovery of homogenized equations

arXiv

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arXiv 2020年

作者： Arbabi, Hassan Bunder, Judith E. Samaey, Giovanni Roberts, Anthony J. Kevrekidis, Ioannis G. Department of Mechanical Engineering Massachusetts Institute of Technology Department of Chemical and Biomolecular Engineering Johns Hopkins University United States School of Mathematical Sciences University of Adelaide Australia Computer Science Department KU Leuven Belgium Departments of Chemical and Biomolecular Engineering & Applied Mathematics and Statistics Johns Hopkins University United States

The data-driven discovery of partial differential equations (PDEs) consistent with spatiotemporal data is experiencing a rebirth in machine learning research. Training deep neural networks to learn such data-driven partial differential operators requires extensive spatiotemporal data. For learning coarse-scale PDEs from computational fine-scale simulation data, the training data collection process can be prohibitively expensive. We propose to transformatively facilitate this training data collection process by linking machine learning (here, neural networks) with modern multiscale scientific computation (here, equation-free numerics). These equation-free techniques operate over sparse collections of small, appropriately coupled, space-time subdomains ("patches"), parsimoniously producing the required macro-scale training data. Our illustrative example involves the discovery of effective homogenized equations in one and two dimensions, for problems with fine-scale material property variations. The approach holds promise towards making the discovery of accurate, macro-scale effective materials PDE models possible by efficiently summarizing the physics embodied in "the best" fine-scale simulation models *** Codes 35B27 Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity

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Light(science & Applications) 2021年第9期10卷 1797-1808页

作者： Neha Goswami Yuchen R.He Yu-Heng Deng Chamteut Oh Nahil Sobh Enrique Valera Rashid Bashir Nahed Ismail Hyunjoon Kong Thanh H.Nguyen Catherine Best-Popescu Gabriel Popescu Department of Bioengineering University of Illinois Urbana-ChampaignUrbanaIllinois61801USA Beckman Institute of Advanced Science and Technology University of Illinois Urbana-ChampaignUrbanaIllinois61801USA Department of Electrical and Computer Engineering University of Illinois Urbana-ChampaignUrbanaIllinois61801USA Department of Chemical and Biomolecular Engineering University of Illinois at Urbana-ChampaignUrbanaIL61801USA Department of Civil and Environmental Engineering University of Illinois at Urbana-ChampaignUrbanaIL61801USA NCSA Center for Artificial Intelligence Innovation University of Illinois at Urbana-ChampaignUrbanaIL61801USA Holonyak Micro and Nanotechnology Laboratory University of Illinois at Urbana-ChampaignUrbanaIllinois61801USA Biomedical Research Center Carle Foundation Hospital509W University Ave.UrbanaIllinois61801USA Carle Illinois College of Medicine 807 South Wright St.UrbanaIllinois61801USA Mayo-Illinois Alliance for Technology Based Healthcare UrbanaIllinois61801USA Department of Pathology College of MedicineUniversity of Illinois at ChicagoChicagoILUSA

Efforts to mitigate the COVID-19 crisis revealed that fast,accurate,and scalable testing is crucial for curbing the current impact and that of future *** propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and *** ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length *** these data with fluorescence images for ground truth,we trained semantic segmentation models based on U-Net,a particular type of convolutional neural *** trained network was applied to classify the viruses from the interferometric images only,containing simultaneously SARS-CoV-2,H1N1(influenza-A virus),HAdV(adenovirus),and ZIKV(Zika virus).Remarkably,due to the nanoscale sensitivity in the input data,the neural network was able to identify SARS-CoV-2 *** other viruses with 96%*** inference time for each image is 60 ms,on a common graphic-processing *** approach of directly imaging unlabeled viral particles may provide an extremely fast test,of less than a minute per *** the imaging instrument operates on regular glass slides,we envision this method as potentially testing on patient breath *** necessary high throughput can be achieved by translating concepts from digital pathology,where a microscope can scan hundreds of slides automatically.

关键词： testing neural hundreds

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Expert survey to understand and optimize part orientation in direct metal laser sintering 28

Expert survey to understand and optimize part orientation in...

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28th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference, SFF 2017

作者： Hoelzle, David Peng, Hao Ghasri-Khouzani, Morteza Gong, Shan Attardo, Ross Ostiguy, Pierre Gatrell, Bernice Aboud Budzinski, Joseph Tomonto, Charles Neidig, Joel Ravi Shankar, M. Billo, Richard Go, David B. Dept. of Mechanical and Aerospace Engineering Ohio State University United States Dept. of Aerospace and Mechanical Engineering University of Notre Dame United States Dept. of Industrial Engineering University of Pittsburgh United States Johnson and Johnson Co. ITAMCO United States Dept. of Computer Science University of Notre Dame United States Dept. of Chemical and Biomolecular Engineering University of Notre Dame United States

The additive manufacturing (AM) process Direct Metal Laser Sintering (DMLS) uses a layer-by-layer workflow to build complex architecture metal structures in low-volumes. The primary process failure mechanism is a thermal stress driven thermal distortion that yields out-of-tolerance manufacture or complete process failure. However, DMLS design experts have developed heuristic rules to optimize the part orientation and support structure to reduce the likelihood of failure. We believe that experts innately attempt to minimize the design metrics of support volume (V), support-to-part surface area (A), maximal cross-sectional area of the slicing planes (X), parallelism of part faces with the recoater blade (P), and part height (H);however, it is unclear what relative weighting of each metric the expert uses. This manuscript details an interactive expert survey, the statistical analysis of the survey responses, and the synthesis of an automatic algorithm for part orientation based on survey data. We received responses from 18 experts and 151 total part orientation responses. The median survey respondent had greater than four years of DMLS experience. Our analysis shows that the expert attempts to minimize metric V the most, metric X the second most, and metric H the third most;experts put essentially no weight on metrics A and P. The manuscript concludes with two orientation design studies where the expert survey responses are used in a least squares minimization algorithm to automatically orient the part for DMLS manufacture. As a comparison set, novice users were instructed to orient the parts for best DMLS printing success without using the tool and required multiple attempts to successfully print the test parts. The automatically oriented parts failed on our first iteration of the code. The manuscript concludes with our proposed modifications to the code to improve results. Copyright © SFF *** rights reserved.

关键词： Surveys

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Growth and site-specific organization of micron-scale biomolecular devices on living mammalian cells

arXiv

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arXiv 2021年

作者： Jia, Sisi Phua, Siew Cheng Nihongaki, Yuta Li, Yizeng Pacella, Michael Li, Yi Mohammed, Abdul M. Sun, Sean Inoue, Takanari Schulman, Rebecca Chemical and Biomolecular Engineering Johns Hopkins University BaltimoreMD21218 United States Cell Biology Johns Hopkins University School of Medicine BaltimoreMD21205 United States Mechanical Engineering Johns Hopkins University BaltimoreMD21218 United States Computer Science Johns Hopkins University BaltimoreMD21218 United States

Mesoscale molecular assemblies on the cell surface, such as cilia and filopodia, integrate information, control transport and amplify signals. Synthetic devices mimicking these structures could sensitively monitor these cellular functions and direct new ones. The challenges in creating such devices, however are that they must be integrated with cells in a precise kinetically controlled process and a device’s structure and its precisely structured cell interface must then be maintained during active cellular function. Here we report the ability to integrate synthetic micron-scale filaments, DNA nanotubes, into a cell’s architecture by anchoring them by their ends to specific receptors on the surfaces of mammalian cells. These filaments can act as shear stress meters: how anchored nanotubes bend at the cell surface quantitatively indicates the magnitude of shear stresses between 0-2 dyn/cm2, a regime important for cell signaling. Nanotubes can also grow while anchored to cells, thus acting as dynamic components of cells. This approach to cell surface engineering, in which synthetic biomolecular assemblies are organized within existing cellular architecture, could make it possible to build new types of sensors, machines and scaffolds that can interface with, control and measure properties of cells. © 2021, CC BY-NC-SA.

关键词： Shear stress

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Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon Coupling in Hybrid Organic-Inorganic Perovskites

Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Dasom Kim Jin Hou Geon Lee Ayush Agrawal Sunghwan Kim Hao Zhang Di Bao Andrey Baydin Wenjing WU Fuyang Tay Shengxi Huang Daivd Hagenmüller Elbert E. M. Chia Dai-Sik Kim Minah Seo Aditya D. Mohite Junichiro Kono Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Electrical and Computer Engineering Rice University Houston TX USA Department of Materials Science and NanoEngineering Rice University Houston TX USA Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Physics and Astronomy Seoul National University Seoul Republic of Korea Department of Chemical and Biomolecular Engineering Rice University Houston TX USA Department of Physics Long-wavelength Nanotechnology Laboratory Quantum Photonics Institute Ulsan National Institute of Science and Technology (UNIST) Ulsan Republic of Korea Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Smalley-Curl institute Rice University Houston TX USA CESQ-ISIS (UMR 7006) Universite de Strasbourg and CNRS Strasbourg France KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Department of Physics and Astronomy Rice University Houston TX USA

Methods for harnessing vibrational states are desired for phonon-based technologies. We realized ultrastrong coupling of two phonon modes in perovskite materials induced by ultrastrong coupling with a common photonic ... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Couplings Phonons Laser modes Perovskites Electro-optical waveguides Photonics

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Materials for aluminum batteries: Progress and challenges

引用

chemical engineering Journal 2025年 517卷

作者： Sunny Nandi Martin Pumera New Technologies – Research Centre University of West Bohemia Univerzitní 8 Plzeň 30614 Czech Republic Department of Chemical and Biomolecular Engineering Yonsei University 50 Yonsei-ro Seodaemun-gu Seoul 03722 Korea Faculty of Electrical Engineering and Computer Science VSB - Technical University of Ostrava 17. listopadu 2172/15 70800 Ostrava Czech Republic

Aluminum battery technologies, including Al-air, Al-ion, and Al-sulfur (Al-S), are considered promising energy storage systems because of their high theoretical capacity, abundance of resources, low cost, and environmental friendliness. Despite these benefits, there are still some critical challenges such as the formation of passivation layers, anodic corrosion, poor electrochemical performance, the polysulfide shuttle effect, and unwanted side reactions occurring between the electrolyte and the metal anode. In order to tackle these challenges, various strategies have been explored, including Al-alloys and composites, advanced cathode materials, and electrolytes. In this review, we will provide a comprehensive overview of the development of various Al-batteries, beginning with anodes, cathodes, and electrolytes. We have divided Al-batteries into three primary categories: Al-air batteries, Al-ion batteries, and Al-S batteries. The categorization was carried out on the basis of the electrochemical reactions that take place on the cathode side during the charge–discharge process. Furthermore, we provide insights into the remaining technical challenges that need to be addressed in order to get this technology from laboratory concept to practical application.

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Optimization of build orientation for minimum thermal distortion in DMLS metallic additive manufacturing 28

Optimization of build orientation for minimum thermal distor...

引用

28th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference, SFF 2017

作者： Peng, Hao Ghasri-Khouzani, Morteza Gong, Shan Attardo, Ross Ostiguy, Pierre Gatrell, Bernice Aboud Budzinski, Joseph Tomonto, Charles Neidig, Joel Ravi Shankar, M. Billo, Richard Go, David B. Hoelzle, David Dept. of Aerospace and Mechanical Engineering University of Notre Dame United States Dept. of Industrial Engineering University of Pittsburgh United States Johnson and Johnson Co. United States ITAMCO United States Dept. of Computer Science University of Notre Dame United States Dept. of Chemical and Biomolecular Engineering University of Notre Dame United States Dept. of Mechanical and Aerospace Engineering Ohio State University United States

The additive manufacturing (AM) process direct metal laser sintering (DMLS) can quickly produce complex parts. However, thermal stress in DMLS may induce thermal distortion and cause build failure. This manuscript presents an optimization algorithm for the build orientation in DMLS to minimize thermal distortion. The algorithm is built on the foundation of two coupled thermal and thermo-mechanical models developed in our previous work. The DIRECT search method and a universal objective function for thermal distortion were used. Constraints were included to rule out build orientations resulting in overheating or excessive oxidation. The optimization algorithm was tested on a rectangular bar and a complex, contoured part. Both parts were printed using an EOS M290 machine, and measured by a coordinate measuring machine. In comparison to build orientations chosen by two novice operators, the optimized build orientations gave significant reduction in the thermal distortion and number of print trials before print success. Copyright © SFF *** rights reserved.

关键词： 3D printers

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Democratizing advanced surgical guidance: decoupling the state of the art from tertiary centers and breaking trail for autonomous robotic surgery in austere environments 11

Democratizing advanced surgical guidance: decoupling the sta...

引用

Molecular-Guided Surgery: Molecules, Devices, and Applications XI 2025

作者： Henderson, Eric R. Gracias, David H. Ramakrishnan, Venkat M. Tiomkin, Stas Wachs, Juan P. Wollin, Daniel A. Thayer School of Engineering Dartmouth College HanoverNH United States Geisel School of Medicine at Dartmouth Dartmouth College HanoverNH United States Dartmouth Health LebanonNH United States Department of Chemical and Biomolecular Engineering Johns Hopkins University BaltimoreMD United States Department of Chemistry Johns Hopkins University BaltimoreMD United States Department of Materials Science and Engineering Johns Hopkins University BaltimoreMD United States Department of Oncology Johns Hopkins University School of Medicine BaltimoreMD United States Johns Hopkins University BaltimoreMD United States Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine BaltimoreMD United States Johns Hopkins School of Medicine BaltimoreMD United States Department of Urology Boston Children's Hospital Harvard Medical School BostonMA United States Department of Urology Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Computer Science Texas Tech University LubbockTX United States Edwardson School of Industrial Engineering Purdue University West LafayetteIN United States

ISBN: (纸本)9781510683501

Surgery involves iterative identification of anatomical structures and intervention upon them. In recent years, machine-based tissue recognition has advanced substantially, enhancing the safety and efficacy of medical procedures by reducing uncertainty about structure identity through quantitative evaluation (e.g., tissue density, optical properties, fluorescence contrast). However, while tissue-identifying tools have progressed rapidly, the development of intervention tools has lagged. It is worth considering the eventual convergence of these technologies at their mature stage, culminating in autonomous robotic surgery (ARS). Beyond technical feasibility, deploying such a groundbreaking technology requires careful consideration. Typically, expensive and novel medical advancements are introduced in tertiary academic medical centers, where state-of-the-art infrastructure and trained personnel are available. However, ARS holds the greatest potential for regions lacking access to surgeons, making it crucial to define the optimal scenarios for its implementation. The technical demands of ARS will vary significantly depending on the type of procedure. Decision-making should prioritize a focused set of surgery-requiring conditions and assess the cumulative risk profile of offering ARS in regions with no existing treatment options. Key factors in this evaluation include: 1) procedure commonness;2) ARS feasibility with current technology;3) risk of adverse events from a robotic intervention;4) procedure urgency (i.e., risk of no intervention);5) risk of abandoning procedure in the setting of technical failure;6) ability to have remote human oversight;and 7) current availability of resources in the target population/region. Based on these considerations, the initial stabilization of high-energy open skeletal trauma-particularly in active combat military settings-represents a highly feasible and valuable early application. Additionally, the future development of self-sufficie

关键词： Robotic surgery

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