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Self-polarized RGB device realized by semipolar micro-LEDs and perovskite-in-polymer films for backlight applications

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Opto-Electronic Advances 2024年第3期7卷 35-49页

作者： Tingwei Lu Yue Lin Tianqi Zhang Yue Huang Xiaotong Fan Shouqiang Lai Yijun Lu Hao-Chung Kuo Zhong Chen Tingzhu Wu Rong Zhang School of Electronic Science and Engineering Fujian Engineering Research Center for Solid-State LightingXiamen UniversityXiamen 361005China Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(IKKEM) Xiamen 361005China Department of Photonics and Graduate Institute of Electro-Optical Engineering College of Electrical and Computer EngineeringYang Ming Chiao Tung UniversityHsinchu 30010China Semiconductor Research Center Hon Hai Research InstituteTaipei 11492China.

In backlighting systems for liquid crystal displays,conventional red,green,and blue(RGB)light sources that lack polarization properties can result in a significant optical loss of up to 50%when passing through a *** address this inefficiency and optimize energy utilization,this study presents a high-performance device designed for RGB polarized *** device employs an array of semipolar blueμLEDs with inherent polarization capabilities,coupled with mechanically stretched films of green-emitting CsPbBr3 nanorods and red-emitting CsPbI3-Cs4PbI6 hybrid *** CsPbBr3 nanorods in the polymer film offer intrinsic polarization emission,while the aligned-wire structures formed by the stable CsPbI3-Cs4PbI6 hybrid nanocrystals contribute to substantial anisotropic emissions,due to their high dielectric *** resulting device achieved RGB polarization degrees of 0.26,0.48,and 0.38,respectively,and exhibited a broad color gamut,reaching 137.2%of the NTSC standard and 102.5%of the Rec.2020 *** compared to a device utilizing c-plane LEDs for excitation,the current approach increased the intensity of light transmitted through the polarizer by 73.6%.This novel fabrication approach for polarized devices containing RGB components holds considerable promise for advancing next-generation display technologies.

关键词： halide perovskite light-emitting-diodes polarized emission nanocrystals stability

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Antiferromagnetic Spin Orientation and Magnetic Domain Structure in Epitaxially Grown MnN Studied Using Optical Second-Harmonic Generation

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Physical Review Applied 2022年第3期18卷 034020-034020页

作者： Joongwon Lee Zexuan Zhang Huili (Grace) Xing Debdeep Jena Farhan Rana School of Electrical and Computer Engineering Cornell University Ithaca New York 14853 USA Department of Materials Science and Engineering Cornell University Ithaca New York 14853 USA

MnN is a centrosymmetric collinear antiferromagnet belonging to the transition-metal-nitride family with a high Néel temperature (660K), a low anisotropy field, and a large magnetic moment (3.3μB per Mn atom). Despite several recent experimental and theoretical studies, the spin symmetry (magnetic point group) and magnetic domain structure of the material remain unknown. In this work, we use optical second-harmonic generation (SHG) to study the magnetic structure of thin epitaxially grown single-crystal (001) MnN films. Our work shows that spin moments in MnN are tilted away from the [001] direction and the components of the spin moments in the (001) plane are aligned along one of the two possible in-plane symmetry axes ([100] or [110]), resulting in a magnetic-point-group symmetry of 2/m1’. Our work rules out magnetic-point-group symmetries 4/mmm1’ and mmm1’ that have been previously discussed in the literature. Four different spin domains consistent with the 2/m1’ magnetic-point-group symmetry are possible in MnN. A statistical model based on the observed variations in the polarization-dependent intensity of the second-harmonic signal collected over large sample areas puts an upper bound of 0.65μm on the mean domain size. Our results show that SHG can be used to probe the magnetic order in metallic antiferromagnets. This work is expected to contribute to the recent efforts in using antiferromagnets for spintronic applications.

关键词： Antiferromagnetism Magnetic domains Magneto-optics Spintronics Antiferromagnets Nitrides Transition metal alloys Optical second-harmonic generation

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Coupling monolayer transition metal dichalcogenide to a strain-tunable photonic crystal cavity

Coupling monolayer transition metal dichalcogenide to a stra...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Arnab Manna Johannes E. Fröch Sinabu Pumulo John Cenker Arthur Barnard Xiaodong Xu Arka Majumdar Department of Physics University of Washington Seattle Washington USA Department of Materials Science and Engineering University of Washington Seattle Washington USA Department of Electrical & Computer Engineering University of Washington Seattle Washington USA

We demonstrate a large in-situ strain tuning of cavity coupled photoluminescence of nanofabricated optical cavities integrated with a monolayer WSe 2 using a piezo-strain device capable of operating at cryogenic temp... 详细信息

ISBN: (纸本)9781957171258

关键词： Confocal microscopy Light matter interactions Photoluminescence Photonic crystal cavities Refractive index Strain

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Creating semi-Quanta multi-layer synthetic CNT images using CycleGAN

Creating semi-Quanta multi-layer synthetic CNT images using ...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Kaveh Safavigerdini Ramakrishna Surya Andrew Reinhard Zach Quinlan Filiz Bunyak Matthew R. Maschmann Kannappan Palaniappan Department of Electrical Engineering and Computer Science University of Missouri Columbia Mo USA Department of Mechanical & Aerospace Engineering University of Missouri Columbia Mo USA MU Materials Science and Engineering Institute University of Missouri Columbia Mo USA

We address the challenge of creating semi-Quanta images of vertically-oriented carbon nanotubes (CNT). Our previous method, CNTNeXt, successfully predicted the mechanical properties for semi-Scanning Electron Microscopy (SEM) images of CNT forests. However, for Quanta images, a more sophisticated approach is required. We propose a novel pipeline that modifies multi-layer synthetic (MLS) CNT images, which are synthetic representations of CNT structures, to resemble Quanta images. This is achieved using CycleGAN, a deep learning model designed for image-to-image translation tasks, which can learn mappings between two different domains without paired training data. By training the CycleGAN on both Quanta and MLS images, we are able to transfer the texture of Quanta images to the MLS images. The resulting ’semi-Quanta’ images are ready to train CNTNeXt, which effectively predicts the mechanical properties, including buckling load and stiffness of synthetic MLS images. Our method represents an advancement towards our ultimate goal of creating an automated CNT growth process, and opens up possibilities for further improvements and applications.

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Retraction Note: An efficient quantum based D2D computation and communication approach for the internet of things

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Mekala, M. S. Srivastava, Gautam Chun-Wei Lin, Jerry Dhiman, Gaurav Park, Ju H. Jung, Ho-Youl Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea RLRC for Autonomous Vehicle Parts and Materials Innovation Yeungnam University Gyeongsan Korea Department of Math and Computer Science Brandon University Brandon Canada Western Norway University of Applied Sciences Gaurav Dhiman Bergen Norway Department of Computer Science Government Bikram College of Commerce Patiala India Department of Electrical Engineering Yeungnam University Gyeongsan Korea Research Centre for Interneural Computing China Medical University Taichung Taiwan University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

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Deep Style Transfer for Generation of Photo-realistic Synthetic Images of CNT Forests

Deep Style Transfer for Generation of Photo-realistic Synthe...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Prashanth Reddy Kotha Minasadat Attari Matthew Maschmann Filiz Bunyak Department of Electrical Engineering and Computer Science University of Missouri Columbia MO USA Department of Mechanical and Aerospace Engineering University of Missouri Columbia MO USA MU Materials Science and Engineering Institute University of Missouri Columbia MO USA

Carbon nanotubes (CNTs) are promising nano-materials with diverse applications in various fields, ranging from electronics and energy storage to biomedical applications. Characterization of CNT forest structures and prediction of material properties through image analytics are critical for new material design and discovery. Artificial intelligence and machine learning (AI/ML) driven approaches offer promising solutions towards these goals. These data-driven approaches rely on large amounts of annotated data for robust training. However, growing, imaging, and annotation of CNT forests are complex, time consuming, expensive processes limiting availability of such data. In this paper, we propose a novel neural style transfer pipeline to generate photo-realistic and structure-aware synthetic images of CNT forests by imposing scanning electron microscopy (SEM) like appearance to physics-based simulation results. The proposed pipeline relies on a novel wavelet-based style transfer network and a selective decision fusion mechanism that allows better preservation of the thin curvilinear structures of CNTs, particularly their intricate details and connectivity, without introducing unwanted artifacts. Experimental results and quantitative analysis demonstrate refined style transfer that generates photo-realistic images that can be used to train AI/ML- driven CNT forest image analysis systems.

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A Circuit for Evaluating GaN HEMT Dynamic Rds(on) at Cryogenic Temperatures

A Circuit for Evaluating GaN HEMT Dynamic Rds(on) at Cryogen...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Charley Shi Suzanne Lo Duleepa J. Thrimawithana Francesca Adams Aaron Wadsworth Matthew G.S. Pearce Rachel A. Oliver David J. Wallis Rodney A. Badcock Department of Electrical Computer and Software Engineering The University of Auckland New Zealand Department of Materials Science and Metallurgy University of Cambridge United Kingdom Department of Engineering Cardiff University United Kingdom Robinson Research Institute Victoria University of Wellington New Zealand

ISBN: (数字)9798350351156

ISBN: (纸本)9798350351163

GaN HEMTs are promising power switching devices for cryogenic power converters in superconducting motor drives and space applications. However, GaN HEMTs experience dynamic on-resistance R ds(on) which is not well understood at cryogenic temperatures (CT). This paper presents a circuit for evaluating the dynamic R ds(on) of GaN HEMTs at CT by using on-the-fly measurements to capture off-state trapping transients. The evaluation circuit utilises a measurement circuit with a blocking switch to accurately measure the V ds. Due to the much lower R ds(on) at CT than room temperature (RT), an amplifier was implemented to amplify the output of the measurement circuit, for which the performance is validated using a coaxial shunt resistor. Results demonstrate that the circuit can successfully measure the dynamic R ds(on) 2 μs after the device turns on at both RT and 77 K.

关键词： Current measurement Cryogenics Switches HEMTs Clamps MODFETs Transient analysis Voltage control Stress Switching circuits

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Linking Molecular Origin of Melt Fragility Index with Topological Phases of Network Glasses

SSRN

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SSRN 2024年

作者： Gogi, Vamshi Kiran Mandal, Avik Welton, Aaron Mohanty, Chandi Bhosle, Siddhesh Chakraborty, Shibalik Gunasekera, Kapila Chakravarty, Soumendu Almutairi, Badriah Boolchand, Punit Department of Electrical Engineering and Computer Science University of Cincinnati CincinnatiOH45221-0030 United States Department of Mechanical and Materials Engineering University of Cincinnati CincinnatiOH United States Department of Physics University of Cincinnati CincinnatiOH United States

Network glasses are composed of three topological phases: flexible, intermediate, and stressed-rigid. Intermediate Phase (IP) glasses form compact, rigid, and stress-free networks with no Open Degrees of Freedom (ODF). IP melts are observed to be super-strong with the lowest fragility index, m = 15 due to the absence of ODF in the parent glass. This feature is general and observed in multiple selenide, sulfide, and modified oxide glasses can be traced to Isostatically Rigid Local Structures (ISRLSs) formed in the IP. On the other hand, the flexible and stressed-rigid phase glasses form non-compacted and stressed networks with increasing residual pressure as the count of ODF increases. Their melts are found to be fragile and display fragility indices m > 20 as the count of the ODF in the glass increases and the count of ISRLSs dissipates. © 2024, The Authors. All rights reserved.

关键词： Glass

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DeepCat: A Deep Learning Approach to Understand Your Cat's Body Language

DeepCat: A Deep Learning Approach to Understand Your Cat's B...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Arwa Fawzy Ahmed Hassan Ahmed Gad Ahmed B. Zaky Haitham El-Hussieny Computer Science and Information Technology (CSIT) program Egypt-Japan University of Science and Technology Alexandria Egypt Department of Computer Science Engineering (CSE) Egypt-Japan University of Science and Technology Alexandria Egypt Electrical Engineering Department Faculty of Engineering at Shoubra Benha University Egypt Department of Mechatronics and Robotics Engineering Egypt-Japan University of Science and Technology Alexandria Egypt

Pet owners experience difficulty in understanding their pets' body language and its implications for animal welfare, given that animals cannot utilize human speech to communicate their emotions and health conditions. However, previous experiments for analyzing cat behavior have demonstrated that cats are precisely expressive. DeepCat, a deep-learning approach developed in this study, translates cats' body language signals, enabling owners to discern their feline companions' intended messages and emotional states. Our DeepCat model was trained on a dataset comprising 10,000 cat images, implementing automatic labeling to track key features, including the tail, eyes, and mouth. Presented as a Flutter application, DeepCat can function everywhere, allowing owners to easily monitor their cats and make informed decisions in situations that require caution. This paper discusses the potential benefits and limitations of DeepCat and provides suggestions for future research in this domain.

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Terahertz Time-Domain Imaging for Radiotherapy Treatment Monitoring in Pancreatic Ductal Adenocarcinoma

Terahertz Time-Domain Imaging for Radiotherapy Treatment Mon...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Debamitra Chakraborty Bradley N. Mills Jing Cheng Ivan Komissarov Scott Gerber Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York Laboratory for Laser Energetics University of Rochester Rochester New York Department of Surgery University of Rochester Medical Center Rochester New York Department of Electrical and Computer Engineering University of Rochester Rochester New York

ISBN: (纸本)9798350369311

Terahertz time-domain imaging was performed of stereotactic body radiotherapy-treated murine pancreatic ductal adenocarcinoma (PDAC) with a high spatial resolution. To generate 2D maps of the tissue samples, the refractive index and absorption coefficient were used as imaging markers extracted with the maximum a-posteriori probability (MAP) estimation algorithm. The results were then compared with the terahertz maps of untreated murine PDAC tissue samples and demonstrated that terahertz imaging can probe physical changes with high sensitivity in the tissue post treatment.

关键词： Maximum a posteriori estimation Terahertz wave imaging Laser theory Sensitivity Refractive index Radiation therapy Time-domain analysis Spatial resolution Probes Monitoring

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