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Decoding the Biomimetic Mineralization of Metal-Organic Frameworks in Water

ACS Nano 2024年第36期18卷 25170-25182页

作者： Lin, Shang-Wei Lam, Phuc Khanh Wu, Chin-Teng Su, Kuan-Hsuan Sung, Chi-Fang Huang, Sen-Ruo Chang, Je-Wei Shih, Orion Yeh, Yi-Qi Vo, Trung Hieu Tsao, Heng-Kwong Hsieh, Haw-Ting Jeng, U-Ser Shieh, Fa-Kuen Yang, Hsiao-Ching Department of Chemistry Fu Jen Catholic University New Taipei City24205 Taiwan Department of Chemistry National Central University Taoyuan32001 Taiwan National Synchrotron Radiation Research Center Hsinchu Science Park Hsinchu300092 Taiwan Department of Chemical and Materials Engineering National Central University Taoyuan32001 Taiwan Department of Electrical Engineering and Computer Sciences University of California at Berkeley CA94720 United States Department of Chemical Engineering College of Semiconductor Research National Tsing Hua University Hsinchu300044 Taiwan

This study unveils the "green" metal-organic framework (MOF) structuring mechanism by decoding proton transfer in water during ZIF-8 synthesis. Combining in situ small- to wide-angle X-ray scattering, multiscale simulations, and quantum calculations, we reveal that the ZIF-8 early-stage nucleation and crystallization process in aqueous solution unfolds in three distinct stages. In stage I, imidazole ligands replace water in zinc-water cages, triggering an "acidity flip" that promotes proton transfer. This leads to the assembly of structures from single zinc ions to 3D amorphous cluster nuclei. In stage II, amorphous nuclei undergo a critical transformation, evolving into crystalline nuclei and subsequently forming mesoscale-ordered structures and crystallites. The process proceeds until the amorphous precursors are completely consumed, with the transformation kinetics governed by an energy barrier that determines the rate-limiting step. In stage III, stable crystallite nanoparticles form in solution, characterized by a temperature-dependent thermal equilibrium of molecular interactions at the crystal-solution interface. Beyond these core advancements, we explore the influence of encapsulated pepsin and nonencapsulated lysozyme on ZIF-8 formation, finding that their amino acid proton transfer capacity and concentration influence the resulting biomolecule-MOF composite’s shape and encapsulation efficiency. The findings contribute to understanding the molecular mechanisms behind biomimetic mineralization and have potential implications for engineering proteins within amorphous MOF nuclei as protein embryo growth sites. © 2024 American Chemical Society.

关键词： X ray scattering

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Bandwidth-Engineered Ultra-Fast Time-Variant Metasurfaces

Bandwidth-Engineered Ultra-Fast Time-Variant Metasurfaces

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Yang, Ziwei Liu, Mingkai Smirnova, Daria Komar, Andrei Shcherbakov, Maxim Neshev, Dragomir Dept. of Electronics Materials Engineering Research School of Physics Australian National University ACT2601 Australia Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States

ISBN: (纸本)9798350345995

Time-variant metasurfaces [1] have recently provided an ultra-fast approach to manipulating the optical response compared with conventional tunable approaches, like thermal and electric tuning. They allow for femtosecond scale switching by tuning under high-energy pumping sources. However, despite the recent advances in this field, the ability to precisely tailor the metasurface bandwidth with ultra-fast time response has not been carefully explored. © 2023 IEEE.

关键词： Bandwidth

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Investigation on Structures of Axial Gap Type Magnetic Multiple Spur Gear for In-Wheel Motor System of Electric Vehicle

Investigation on Structures of Axial Gap Type Magnetic Multi...

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2023 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2023

作者： Kamijo, Taiga Aiso, Kohei Akatsu, Kan Aoyama, Yasuaki Shibaura Institute of Technology Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Electrical Engineering Tokyo Japan Yokohama National University Division of Intelligent Systems Engineering Department of Faculty of Engineering Yokohama Japan Center for Technology Innovation - Materials Hitachi Ltd. Research & Development Group Ibaraki Japan

ISBN: (纸本)9798350314274

A novel drive system using Magnetic Multiple Spur Gear (MMSG) and multiple high-speed motors is characterized by small size, lightweight, and high efficiency even at high-speed region, it is expected to apply to in-wheel motor systems. However, there is a risk of contact between the rotors when applied to in-wheel motor system. In this study, a novel axial-gap MMSG is proposed to solve this problem, stabilize the air gap between the high-speed and low-speed rotors, and effectively utilize the magnets, and its performance is evaluated by finite element analysis. © 2023 IEEE.

关键词： Electric vehicles

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Enhanced ferromagnetism in an artificially stretched lattice in quasi-two-dimensional Cr2Ge2Te6

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Physical Review B 2025年第2期111卷 L020402-L020402页

作者： Hiroshi Idzuchi Andres E. Llacsahuanga Allcca Anh Khoa Augustin Lu Mitsuhiro Saito Subhadip Das John F. Ribeiro Michel Houssa Ruishen Meng Kazutoshi Inoue WPI Advanced Institute for Materials Research (AIMR) Tohoku University Sendai 980-8577 Japan Center for Science and Innovation in Spintronics (CSIS) Tohoku University Sendai 980-8577 Japan Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Birck Nanotechnology Center and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Mathematics for Advanced Materials Open Innovation Laboratory National Institute of Advanced Industrial Science and Technology Sendai 980-8577 Japan Research Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba 305-0044 Japan Institute of Engineering Innovation The University of Tokyo Tokyo 113-8656 Japan Institute of Physics and Astronomy and Villum Centers for Dirac Materials and for Hybrid Quantum Materials and Devices Aarhus University 8000 Aarhus-C Denmark School of Electrical and Computer Engineering Purdue University West Lafayette Indiana 47907 USA Department of Physics and Astronomy Katholieke Universiteit Leuven 3001 Leuven Belgium

Crystal lattice is key in understanding magnetic interactions between atoms in solids. As the effective control of the lattice via tensile stress is limited, there are only a few demonstrations of controlling magnetic properties by expanding the lattice. This study reveals a clear correlation between enhanced magnetism, where Curie temperature (TC) is doubled from ∼60 to ∼120 K, and lattice expansion in prototypical van der Waals magnet Cr2Ge2Te6 with sputtered dielectric overlayers NiO and MgO. We ascribe the mechanism of this TC increase to a change in exchange interactions induced by strain, studied by several experimental probes and computational approaches.

关键词： Van der Waals forces

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AV Navigation in 3D Urban Environments with Curriculum-based Deep Reinforcement Learning

AV Navigation in 3D Urban Environments with Curriculum-based...

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2023 International Conference on Unmanned Aircraft Systems, ICUAS 2023

作者： Braathen De Carvalho, Kevin De Oliveira, Iure Rosa L. Brandao, Alexandre S. Universidade Federal de Viçosa Nucleus of Specialization in Robotics Department of Electrical Engineering Graduate Program in Computer Science MG Viçosa36570-900 Brazil Federal University of Viçosa Department of Electrical Engineering MG Viçosa Brazil

ISBN: (纸本)9798350310375

Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires robust and efficient decision-making algorithms. This paper presents a novel approach to UAV navigation in 3D environments using a Curriculum-based Deep Reinforcement Learning (DRL) approach. The proposed method utilizes a deep neural network to model the UAV's decision-making process and to learn a mapping from the state space to the action space. The learning process is guided by a reinforcement signal that reflects the performance of the UAV in terms of reaching its target while avoiding obstacles and with energy efficiency. Simulation results show that the proposed method has a positive trade off when compared to the baseline algorithm. The proposed method was able to perform well in environments with a state space size of 22 millions, allowing the usage in big environments or in maps with high resolution. The results demonstrate the potential of DRL for enabling UAVs to operate effectively in complex environments. © 2023 IEEE.

关键词： Reinforcement learning

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Cascaded multiparameter quantum metrology

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Physical Review A 2025年第1期111卷 012618-012618页

作者： Gregory Krueper Lior Cohen Juliet T. Gopinath Department of Physics University of Colorado Boulder Boulder Colorado 80309 USA Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder Colorado 80309 USA Materials Science and Engineering Program University of Colorado Boulder Boulder Colorado 80309 USA

We present an innovative, platform-independent concept for multiparameter sensing where the measurable parameters are in series, or cascaded, enabling measurements as a function of position. With temporally resolved detection, we show that squeezing can give a quantum enhancement in sensitivity over that of classical states by a factor of e2r, where r≈1 is the squeezing parameter. As an example, we have modeled an interferometer that senses multiple phase shifts along the same path, demonstrating a maximal quantum advantage by combining a coherent state with squeezed vacuum. Further classical modeling with up to 100 phases shows linear scaling potential for adding nodes to the sensor. The approach can be applied to remote sensing, geophysical surveying, and infrastructure monitoring.

关键词： Interferometers

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Effects of Encapsulant Properties on the Thermo-Mechanical Reliability of Double-Side Cooled Power Modules for Traction Inverters

Effects of Encapsulant Properties on the Thermo-Mechanical R...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Boshkovski, Filip Nicholas, Carl Zhang, Zichen Paret, Paul Ngo, Khai. D. T. Lu, Guo-Quan Virginia Tech Department of Materials Science and Engineering Blacksburg United States Virginia Tech Cpes Bradley Department of Electrical and Computer Engineering Blacksburg United States National Renewable Energy Laboratory GoldenCO United States

ISBN: (纸本)9798350316445

Double-side cooled power modules are being developed for next-generation traction inverters due to their better heat extraction, lower profile, and lower parasitic inductances. However, due to their rigid structure, they cause reliability concerns arising from high thermo-mechanical stresses at the interconnection joints in the module. In this work, a materials-based approach using rigid encapsulants is presented for reducing thermo-mechanical fatigue. Finite-element thermo-mechanical simulations were performed to examine the effects of the elastic modulus and coefficient of thermal expansion of epoxy-based encapsulants on the bond deformation inside a double-side cooled power module. It was found that a rigid encapsulant with a high modulus of 6.0 GPa or above and a coefficient of thermal expansion around 20 ppm/°C would improve the thermo-mechanical reliability of double-side cooled power modules by decreasing the permanent bond deformation inside the modules by 50-60%. © 2023 IEEE.

关键词： double-side cooled modules rigid encapsulants thermo-mechanical reliability

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SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARNING

SODIUM-ION BATTERY CYCLE LIFE PREDICTION USING MACHINE LEARN...

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International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Liu, Linfeng Juwono, Filbert H. Wong, W.K. Purwanto, Erick Jin, Tingyan Zhen, Yuyang Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing China School of Instrument Science and Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781837242689

In this study, we explore the classification and prediction capabilities of three models-Genetic programming (GP), Logistic Regression (LR), and the Kolmogorov-Arnold Network (KAN)-on the task of sodium-ion battery life prediction. By leveraging a dataset composed of multiple battery characteristics, we aim to determine the remaining power of sodium-ion batteries using these machine learning models. The KAN model, being a novel approach, demonstrates superior performance across various metrics, including accuracy, precision, recall, and F1 score, when compared to the other two models. This highlights the potential of KAN as a robust model for complex classification tasks in the field of battery life prediction. © The Institution of engineering & Technology 2024.

关键词： Genetic programming

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The Implementation of TOGAF for Enterprise System Planning on XYZ Campus

The Implementation of TOGAF for Enterprise System Planning o...

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2023 International Conference on Networking, electrical engineering, computer science, and Technology, IConNECT 2023

作者： Sirait, Tamsir Hasudungan Lumban Gaol, Ford Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Binus Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University Computer Science Department Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia Universitas Indonesia Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350331172

Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information management for decision-making. However, don't forget that the campus must consider what technology is appropriate to assist them achieve their goals, particularly in the current industrial era 4.0 where technology is available with many different choices. The campus requires an enterprise architecture in order to design, manage, and coordinate information technology infrastructure, applications, and processes strategically and thoroughly. The adoption of enterprise information system architecture (EA) is also intended to improve the quality of services provided to internal and external stakeholders. In this case, Enterprise Architecture can help an organization to match its information technology resources with business processes and strategies to achieve their goals. This research was conducted using TOGAF ADM, also known as the Open Group Architecture Framework Architecture Development Method. This method offers best practices for creating enterprise architecture and emphasizes several steps that include creating an architectural vision, information systems, business architecture modeling to help XYZ campus manage all their information technology. © 2023 IEEE.

关键词： Information management

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Radiation-Resilient Amorphous Indium Oxide FEFETs for Embedded Nonvolatile Memory

Radiation-Resilient Amorphous Indium Oxide FEFETs for Embedd...

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2025 IEEE International Reliability Physics Symposium, IRPS 2025

作者： Kirtania, Sharadindu Gopal Waqar, Faaiq G. Chakraborty, Dyutimoy Shin, Jaewon Sarkar, E. Reiss, Justin Yeager, Jason Dean Wolfe, Douglas E. Yu, Shimeng Datta, Suman School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta 30332 GA United States Pennsylvania State University Department of Materials Science and Engineering State College 16802 PA United States

ISBN: (纸本)9798331504779

We report the first-ever study on the radiation resilience of a back-end-of-line (BEOL) compatible W-doped Indium Oxide (IWO) ferroelectric field-effect transistor (FEFET) under neutron irradiation. The findings demonstrate remarkable resilience of both Hf_0.5Zr_0.5O₂(HZO) ferroelectric capacitors (FECAPs) and IWO FEFETs to neutron irradiation. Subjected to neutron irradiation at fluences up to 10¹⁵ n/cm², HZO FECAPs maintain stable remnant polarization (2P_r) and coercive voltage (2V_c) up to 10¹¹ cycles without any breakdown. The IWO FEFETs show robust switching performance and exhibit minimal degradation in memory window (MW) and read current window (CW). Importantly, the retention and endurance performance remain stable after neutron radiation exposure, without significant failure. Destiny simulations were performed to assess the impact of radiation on the system-level performance of an 8 MB 1T-1FEFET-based embedded RAM at the 32 nm node. The results showed negligible effects on read latency and only a minor 0.36% increase in dynamic read energy at a neutron fluence of 10y15 n/cm². The combination of high performance, non-volatility, and radiation hardness makes IWO FEFETs a promising candidate for high-density, persistent memory applications in radiation-rich environments. © 2025 IEEE.

关键词： 1T-1FEFET RAM back-end-of-line (BEOL) Defect mitigation embedded memory Endurance FEFET HZO Indium oxide (IWO) Neutron irradiation NVM Radiation-hardened memory Retention

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