Results 81 to 90 of about 278 (222)

Modeling the limiting performance of resistive superconductor fault current limiters for 2G HTS tape

Firat University Journal of Experimental and Computational Engineering, 2022
Fault currents in power systems force valuable power system elements thermally, electro-dynamically and electromagnetically. Due to the increase in fault current levels, the installation of components resistant to fault currents and the damage of these ...
Muhsin Tunay Gençoğlu, Buğra Yılmaz
doaj  

SPICE‐Compatible Compact Modeling of Cuprate‐Based Memristors Across a Wide Temperature Range

Advanced Electronic Materials, EarlyView.
A physics‐guided compact model for YBCO memristors is introduced, incorporating carrier trapping, field‐induced detrapping, and a differential balance equation to describe their switching dynamics. The model is compared with experiments and implemented in LTspice, allowing realistic circuit‐level simulations.
Thomas Günkel, Aleix Barrera, Lluís Balcells, Narcís Mestres, Enrique Miranda, Anna Palau, Jordi Suñé   +6 more
wiley   +1 more source

An Optimal Design Approach for Resistive and Inductive Superconducting Fault Current Limiters via MCDM Techniques

Iranian Journal of Electrical and Electronic Engineering, 2011
The design process of a superconducting current limiter (SFCL) requires simulation and definition of its electrical, magnetic and thermal properties in form of equivalent circuits and mathematical models. However, any change in SFCL parameters: dimension,
H. Heydari, R. Sharifi
doaj  

Advancing Energy Materials by In Situ Atomic Scale Methods

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
Progress in in situ atomic scale methods leads to an improved understanding of new and advanced energy materials, where a local understanding of complex, inhomogeneous systems or interfaces down to the atomic scale and quantum level is required. Topics from photovoltaics, dissipation losses, phase transitions, and chemical energy conversion are ...
Christian Jooss, Michael Seibt, Martin Wenderoth, Oliver Bünermann, Ole Bunjes, Till Domröse, Christian Eckel, Francesca Falorsi, Christoph Flathmann, Monica Kolek Martinez de Azagra, Matthias Krüger, Jonas Lindner, Tobias Meyer, Claus Ropers, Ulrich Ross, Kai Rossnagel, Sreeju Sreekantan Nair Lalithambika, Simone Techert, Georg A. Traeger, Cynthia Volkert, R. Thomas Weitz, Alec M. Wodtke   +21 more
wiley   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

A Critical Assessment of Bonding Descriptors for Predicting Materials Properties

Advanced Intelligent Discovery, EarlyView.
The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik, Nidal Dhamrait, Katharina Ueltzen, Christina Ertural, Philipp Benner, Gian‐Marco Rignanese, Janine George   +6 more
wiley   +1 more source

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

Advanced Intelligent Discovery, EarlyView.
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley   +1 more source

Geometry‐Based Neural‐Network Prediction of Electron Localization Function Topology in Dense Hydrogen

Chemistry – A European Journal, EarlyView.
We present a machine‐learning framework that predicts the electron localization function (ELF) of dense hydrogen directly from atomic geometry, bypassing explicit electronic‐structure calculations. Trained on ab initio data for fluid hydrogen across multiple pressures, the model achieves high accuracy and reveals pressure‐dependent nonlocal ...
Xiaoyu Wang, Miriam Marqués, Sergio Gómez, Francesc Serratosa, Eva Zurek, Julia Contreras‐García   +5 more
wiley   +1 more source

Experimental methods in chemical engineering: Magnetometry

The Canadian Journal of Chemical Engineering, EarlyView.
Abstract Magnetometry is a non‐invasive technique to characterize the behaviour of magnetic materials of which the most common contain Fe, Co, and Ni. Vibrating sample magnetometers (VSMs) vibrate samples at a fixed frequency in a static, homogeneous magnetic field to induce a voltage in pick up coils according to Faraday's law of induction.
Michael Claeys, Dominic De Oliveira, Gregory S. Patience, M. Olga Guerrero‐Pérez   +3 more
wiley   +1 more source

Magnetic Supraparticles as Identifiers in Single‐Layer Lithium‐Ion Battery Pouch Cells

ChemSusChem, Volume 18, Issue 6, March 15, 2025.
As an alternative to externally applied optical identifiers, magnetic supraparticles (SPs) can be used for contactless identification of lithium‐ion battery pouch cells via magnetic particle spectroscopy. This study validated the integration and detection of magnetic markers in three model scenarios.
Sara Li Deuso, Simon Ziegler, Daniel Weber, Felix Breuer, Daniel Haddad, Stephan Müssig, Andreas Flegler, Guinevere A. Giffin, Karl Mandel   +8 more
wiley   +1 more source