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IEEE Conference Photovoltaic Specialists (PVSC)

作者： Brandon Hagar Evyn Routh Mostafa Abdelhamid Peter Colter Salah Bedair Department of Electrical and Computer Engineering North Carolina State University Raleigh NC United States Department of Materials Science and Engineering North Carolina State University Raleigh NC United States

ISBN: (数字)9781665464260

ISBN: (纸本)9781665475822

We present Metal Organic Chemical Vapor Deposition (MOCVD) grown III-Nitride tunneling junctions (TJs) based on 2 significant technologies: 1) Strained Semibulk (SSB) templates improving material quality and 2) Co-Doping of n-type material. The additional use of a high indium content well at the TJ interface displayed improved results. These results are of interest to multijunction solar cell technology (MJSC) which requires an ohmic forward bias when utilizing TJ connected layers. GaN/InGaN structures grown on a previously published SSB layer have produced several very interesting results. Improved material quality in InGaN SSB structures were achieved using high temperature inter-layers to reduce defect density and lattice strain which concurrently reduced piezoelectric effects, causing a red shift in emission as compared to similar structures without an SSB layer. Co-doping of GaN/InGaN materials is particularly significant due to the large difference in activation energy between GaN n and p-type dopants, Si and Mg respectively. Co-doping is utilized to minimize common Mg p-type issues of diffusion and delay in GaN based structures when using MOCVD growth. Finally, device characterization results are presented which show several key improvements: 1) longer wavelength emission from InGaN material when using the same In composition 2) Forward bias ohmic resistance TJs previously only achievable using growth such as MBE, and 3) the surprising appearance of negative differential resistance (NDR) also previously published using MBE rather than MOCVD.

关键词： Resistance Photovoltaic systems Photovoltaic cells Piezoelectric effect Tunneling Amplitude modulation Silicon MOCVD Junctions Strain

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Twenty years of e-learning in health science: A bibliometric 7

Twenty years of e-learning in health science: A bibliometric

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Maulana, Fairuz Iqbal Febriantono, M. Aldiki Raharja, Dwi Risza Budi Khaeruddin Herasmara, Ridho Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conventionally. Supposedly with the advancement of technology and the use of the internet everywhere, learning such as e-learning can be important, especially in the health sector. Until this research was conducted, only 514 academic documents about e-learning in health sciences were found for 20 years from 2001 to 2020, obtained in searching on the Scopus database. This study presents a comprehensive overview of studies related to E-learning in the Health sciences sector. This study uses bibliometric analysis and indexed digital methods to map scientific publications throughout the world. This research employs the Scopus database to gather information, as well as the Scopus online analysis tool and Vosviewer to show the bibliometric network. The method consists with five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the most published and indexed articles by Scopus, papers published by researchers in the United States have the highest number of publications (80), followed by United Kingdom (63) and Australia with 45 academic publications. The processed data shows the pattern and trend of increasing the number of international publications in E-learning in Health sciences field, which Scopus index. © 2022 Elsevier B.V.. All rights reserved.

关键词： Publishing

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Advancing Semiconductor Manufacturing through DNA- Templated Lithography and Molecular-Scale Patterning of 2D materials

Advancing Semiconductor Manufacturing through DNA- Templated...

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IEEE Workshop on Microelectronics and Electron Devices, WMED

作者： Anumita Kumari Michael Curtis Arpan De Haitao Liu David Estrada M.P. Anantram Department of chemistry Univeristy of Pittsburgh Pittsburgh USA Micron School of Materials Science and Engineering Boise State University Boise USA Department of Electrical and Computer Engineering University of Washington Seattle USA

ISBN: (数字)9798350395945

ISBN: (纸本)9798350395952

This paper explores the potential of using DNA nanostructure to pattern molybdenum disulfide (MoS 2 ) for potential applications in nanoelectronics. We developed a gas phase synthesis of MoS 2 with minimal out-of-plane growth and low defect density. We used density functional theory (DFT) calculation to examine the bandgap modulation of MoS 2 due to counterion diffusion from DNA, highlighting the electron transfer mechanism during lithium intercalation. Lastly, we demonstrate deposition of DNA triangles and nanotubes on various MoS 2 surfaces. Contrary to previous results, we found that these DNA nanostructures maintained their structural stability. These findings collectively contribute valuable insights into using DNA nanotechnology to advance 2D electronics.

关键词： Photonic band gap DNA Modulation Nanotubes Semiconductor device manufacture Structural engineering Sulfur

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Irreversible Demagnetization Reduced Order Modeling in MnBi Interior Permanent Magnet Synchronous Motor Multi-Objective Optimization

Irreversible Demagnetization Reduced Order Modeling in MnBi ...

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IEEE Transportation Electrification Conference and Expo (ITEC)

作者： Ryan Brody Paul Ohodnicki Brandon Grainger Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA USA Department of Mechanical Engineering and Materials Science University of Pittsburgh Pittsburgh PA USA

Permanent magnet synchronous machines (PMSM) with neodymium iron boron (NdFeB) permanent magnets (PM) are typically used in applications, such as electric transportation, that require high power/torque density and efficiency. NdFeB is popular because of its exceptionally high remanent flux density ( $B_{r}$ ) and high coercivity ( $H_{c}$ ) at room temperature, but supply chain limitations of rare-earth elements (REE), such as neodymium (N d) and dysprosium (Dy), have recently caused significant REE price fluctuations. As a result, researchers are searching for ways to reduce or eliminate using NdFeB in PMSM designs. In the past, ferrite PMs were the next best option, but recently, manganese bismuth (MnBi) has emerged as another possible alternative to ferrites, with magnetic properties superior to ferrites but inferior to NdFeB at room temperature. MnBi is relatively new compared to commercial PMs and shows a unique trend of significantly increasing coercivity with increasing temperature. This makes rotor PMs significantly more susceptible to irreversible demagnetization at low temperatures. Reduced order models (ROMs), or meta-models, can estimate nonlinear machine phenomenon well enough for multi-objective optimization, but no literature has used this tool to characterize MnBi low temperature demagnetization. Furthermore, few sources explain how to work through the challenges of multi-objective optimization when using ROMs. Therefore, this paper presents a case study for limiting low temperature irreversible demagnetization in MnBi IPMSMs using ROMs and multi-objective optimization. The choices made and the tools available to produce a sufficiently accurate demagnetization ROM can be applied to a wide variety of nonlinear multiphysics phenomena.

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Utilizing Machine Learning Algorithms in Conjunction with Terahertz Spectra for Enhanced Plastic Sorting Efficiency

Utilizing Machine Learning Algorithms in Conjunction with Te...

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International Conference on Infrared and Millimeter Waves

作者： Nan Zhang Lim Zi Xi Josie Phua Wei Ji Anor Technologies Pte Ltd Singapore Institute of Materials Research and Engineering Agency for Science Technology and Research Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798350370324

ISBN: (纸本)9798350370331

The imperative of addressing climate change is clear, compounded by inadequate waste management intensifying plastic pollution. Challenges in waste management, such as manual sorting and recycling contamination, contribute to ongoing plastic accumulation. To surmount these hurdles, advanced technologies like terahertz (THz) technology integrated with machine learning algorithms such as Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), and Support Vector Machine (SVM) are being investigated. This integration demonstrates considerable potential in improving plastic separation and sorting processes, achieving an impressive accuracy of 100%. Such advancements represent a significant stride towards more effective and sustainable solutions in combating plastic pollution and revolutionizing waste management practices.

关键词： Waste management Support vector machines Machine learning algorithms Manuals Recycling Linear discriminant analysis Contamination Sorting Principal component analysis Plastic waste

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Thermal stability and phase transformation of α-, κ(Ε)-, and γ-Ga2O3 thin films to β-Ga2O3 under various ambient conditions

arXiv

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arXiv 2024年

作者： Tang, Jingyu Jiang, Kunyao Tseng, Po-Sen Kurchin, Rachel C. Porter, Lisa M. Davis, Robert F. Department of Materials Science and Engineering Carnegie Mellon University PittsburghPA15213 United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States

Phase transitions in metastable α-, κ(Ε)-, and γ-Ga2O3 films to thermodynamically stable β-Ga2O3 during annealing in air, N2, and vacuum have been systematically investigated via in-situ high-temperature X-ray diffraction (HT-XRD) and scanning electron microscopy (SEM). These respective polymorphs exhibited thermal stability to ~471-525℃, ~773-825℃, and ~490-575℃ before transforming into β-Ga2O3, across all tested ambient conditions. Particular crystallographic orientation relationships were observed before and after the phase transitions, i.e. (0006) α-Ga2O3 → (4̅02) β-Ga2O3, (004) κ(Ε)-Ga2O3 → (310) and (4̅02) β-Ga2O3, and (400) γ-Ga2O3 → (400) βGa2O3. The phase transition of α-Ga2O3 to β-Ga2O3 resulted in catastrophic damage to the film and upheaval of the surface. The respective primary and possibly secondary causes of this damage are the +8.6% volume expansion and the dual displacive and reconstructive transformations that occur during this transition. The κ(Ε)- and γ-Ga2O3 films converted to β-Ga2O3 via singular reconstructive transformations with small changes in volume and unchanged surface microstructures. Copyright © 2024, The Authors. All rights reserved.

关键词： Gallium compounds

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Perovskite Nickelate Thin Film Devices as Adaptive Weights for Photonic Computing Networks

Perovskite Nickelate Thin Film Devices as Adaptive Weights f...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Tae Joon Park Mohamad Hossein Idjadi Farshid Ashtiani Chenghao Wan Mikhail A. Kats Shriram Ramanathan School of Materials Engineering Purdue University West Lafayette IN USA Nokia Bell Labs Murray Hill NJ USA Department of Electrical and Computer Engineering University of Wisconsin-Madison Madison Wisconsin USA Department of Materials Science and Engineering University of Wisconsin-Madison Madison Wisconsin USA Department of Electrical and Computer Engineering Rutgers The State University of New Jersey Piscataway NJ USA

Atomic layer deposition (ALD) and sputtering of strongly correlated perovskite nickelate thin films are emerging fields for wafer-scale demonstration of quantum technologies. Functional properties of such oxide quantu... 详细信息

ISBN: (纸本)9798350369311

关键词： Adaptive systems Atomic layer deposition Thin film devices Quantum materials Perovskites Sputtering Electro-optical waveguides Photonics

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Deep Reinforcement Learning-Based LSTM Model for Traffic Flow Forecasting in Internet of Vehicles

Deep Reinforcement Learning-Based LSTM Model for Traffic Flo...

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Chinese Intelligent Automation Conference, CIAC 2021

作者： Chen, Zekuan Luo, Xiong Wang, Ting Wang, Weiping Zhao, Wenbing School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China Shunde Graduate School University of Science and Technology Beijing Foshan528399 China Department of Electrical Engineering and Computer Science Cleveland State University ClevelandOH44115 United States

ISBN: (纸本)9789811663710

Nowadays many large cities often suffer from road congestion. Then, it is necessary to improve the working efficiency of urban road network with the help of computational intelligence technologies, within the Internet of vehicles (IoV) environment. We are committed to designing traffic flow prediction methods through the use of machine learning models. In this paper, we predict the traffic flow and traffic situation of the selected road segment. Specifically, we propose a deep reinforcement learning (DRL)-based long short-term memory (LSTM) model to predict the traffic flow. On the basis of it, we characterize the traffic situation with fuzzy comprehensive evaluation (FCE)-based model. The experimental results show that the constructed DRL-based LSTM model can accurately predict the traffic flow data. Meanwhile, the traffic situation characterization model can represent the road traffic situation well. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Tenacity prediction of polyester materials based on an ensemble soft measurement model

Tenacity prediction of polyester materials based on an ensem...

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2024 China Automation Congress, CAC 2024

作者： Duan, Jiaxu Wang, Jun Tao, Youji Ma, Zhiping Wu, Xin Hu, Zhou State Key Laboratory of Environmental Adaptability for Industrial Products China National Electric Apparatus Research Institute Co. Ltd Guangzhou China Kinte Materials Science and Technology Co. Ltd Guangzhou China Department of Computer Science Hunan University of Technology and Business Changsha China School of Electronic Information and Electrical Engineering Yangtze University Jingzhou China

ISBN: (纸本)9798350368604

In the development process of polyester powder coating materials, it has great guiding significance for the development of new materials as the polyester material properties can be predicted through the given monomer structure and formula. In order to meet the requirements of rapid development of polyester materials, this paper establishes a prediction method based on an ensemble soft measurement model (ESM) for one key property-tenacity in prompting the acceleration of development cycle of polyester products. The processed data employed to train and test the model were collected from a local material company, and it covers over 100 kinds of polymerized resins. Firstly, the molecular expressions of monomers are converted to SMILES codes and further translated to ECFP fingerprints. Then, the principal component analysis (PCA) method is adopted to extract the elements with higher importance in each fingerprint. Finally, the ensemble model that consists of 4 machine learning models was established to make the multi-classification decision in tenacity prediction. The effectiveness of the prediction model is evaluated by comparing the predicted and the practical tensile test results. The results demonstrate that this approach achieves better performance in the material property prediction as well as generalization capability. © 2024 IEEE.

关键词： Tensile testing

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Introduction to a Cloud Based Spectroscopy Data Processing Platform for Application in Material Analysis

Introduction to a Cloud Based Spectroscopy Data Processing P...

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International Conference on Infrared and Millimeter Waves

作者： Nan Zhang Li Yongjia Phua Wei Ji Lim Zi Xi Josie Anor Technologies Pte Ltd Singapore Institute of Materials Research and Engineering Agency for Science Technology and Research Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (数字)9798350370324

ISBN: (纸本)9798350370331

Over the past two decades, research on integrating spectroscopy and Artificial Intelligence (AI) for material analysis across various industrial sectors has been widespread. Despite this, challenges persist in effectively training and deploying models for industrial use. This paper introduces a cloud-based platform called Senstrio, aimed at mitigating these challenges encountered when applying spectroscopy and AI in industrial material analysis. The SenstrioAI platform is designed for data processing and model training, enabling automated material identification. Concurrently, SenstrioSwift serves as a centralized management tool, facilitating remote monitoring and control of multiple Senstrio-supported spectrometer devices. Through the utilization of these web applications, researchers and practitioners can anticipate a reduction in the difficulties typically faced when employing spectroscopy and AI for industrial applications.

关键词： Training Spectroscopy Millimeter wave devices Data processing Data models Artificial intelligence Remote monitoring

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