Results 91 to 100 of about 20,190 (261)

Helix Alignment, Chevrons, and Edge Dislocations in Twist‐Bend Ferroelectric Nematics

Advanced Science, EarlyView.
The recently discovered twist‐bend ferroelectric nematic (NTBF) is the new member of the multiferroic family, representing a fluid with an oblique helicoidal (heliconical) periodic structure of spontaneous electric polarization. The work presents a thorough exploration of the material properties of this phase, how the periodic modulation of ...
Bijaya Basnet, Priyanka Kumari, Sathyanarayana Paladugu, Damian Pociecha, Jakub Karcz, Przemysław Kula, Nataša Vaupotič, Ewa Górecka, Oleg D Lavrentovich   +8 more
wiley   +1 more source

Hardware Realization of Generalized Time-Frequency Distribution with Complex-Lag Argument

EURASIP Journal on Advances in Signal Processing, 2009
A hardware implementation of the Nth order complex-lag time-frequency distribution is proposed. The considered distribution provides an arbitrary high concentration for multicomponent signals with fast varying instantaneous frequency.
Srdjan Stanković, Nikola Žarić, Irena Orović   +2 more
doaj   +1 more source

Phase Diagrams and Piezoelectric Properties of Wurtzite Al1−x−yScxGdyN Heterostructural Alloys

Advanced Science, EarlyView.
This study demonstrates ferroelectricity and piezoelectric properties improvement of quaternary wurtzite Al1−x−yScxGdyN${\rm Al}_{1-x-y}{\rm Sc}_x{\rm Gd}_y{\rm N}$ films, guided by density functional theory calculations. Wurtzite Al1−x−yScxGdyN${\rm Al}_{1-x-y}{\rm Sc}_x{\rm Gd}_y{\rm N}$ films have a high optical bandgap, enhanced piezoelectric ...
Julia L. Martin, Cheng‐Wei Lee, Nate S.P. Bernstein, Thi Nguyen, Ande Bryan, Eli Cooper, John S. Mangum, Sage R. Bauers, Andriy Zakutayev, Keisuke Yazawa, Prashun Gorai, Rebecca W. Smaha   +11 more
wiley   +1 more source

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

Advanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju, Adithya Umanath Rai, Jose Thomson, Marzie Ghorbani, Shujing Zhao, Nick Birbilis   +5 more
wiley   +1 more source

A Molecular Engineering Strategy to Fine‐Tune Phototoxicity of AIE Probes for Super‐Resolution Imaging of Mitochondrial Cristae Dynamics

Advanced Science, EarlyView.
Regulation of molecular packing, phototoxicity, and ROS output of AIE photosensitizers is achieved through carboxylation and alkyl‐chain‐length tuning. The probe designed via this strategy enables super‐resolution visualization of inner‐mitochondrial‐membrane cristae dynamics under controlled oxidative stress, and fluorescence‐lifetime imaging of ...
Kongqi Chen, Mengjie Wang, Chuen Kam, Zhiming Wang, Qinrong Zhang, Sijie Chen   +5 more
wiley   +1 more source

High‐Throughput Data Generation and Transfer Learning Enabled Microstructure‐Property Integrated Design of Nickel‐Based Powder Metallurgy Superalloy

Advanced Science, EarlyView.
An integrated transfer learning framework integrates CALPHAD simulations, diffusion‐multiple experiments, and literature data to predict long‐term microstructural stability and short‐term mechanical properties of Ni‐based powder metallurgy superalloys. Based on these model predictions, a high‐performance, low‐density alloy, USTB‐PM750, is designed from
Zixin Li, Hongtao Zhang, Zichao Peng, Yuheng Zhang, Qian Lv, Yihuan Cao, Xuqing Wang, Huadong Fu, Jianxin Xie   +8 more
wiley   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

Fundamentals of Thermal Transport and Energy Conversion in Ultra‐High Temperature Ceramics: From Microscale Mechanisms to Macroscopic Properties

Advanced Science, EarlyView.
This review critically examines thermal transport and radiative properties of ultra‐high temperature ceramics for hypersonic flight, advanced nuclear systems, and next‐generation energy conversion devices. It explores phonon–photon–electron interactions, microstructural engineering, thermoelectric conversion, and machine learning‐accelerated multiscale
Zhipeng Pei, Yue Li, Jiayu Xing, Yue Zhu, Ru Zhang, Huan Wu, Zihao Qin, Gang Pei, Man Li   +8 more
wiley   +1 more source

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source