Results 181 to 190 of about 20,617,992 (358)

An Ultra‐Robust Memristor Based on Vertically Aligned Nanocomposite with Highly Defective Vertical Channels for Neuromorphic Computing

Advanced Functional Materials, EarlyView.
An ultra‐robust memristor based on SrTiO3‐CeO2 (S‐C) vertically aligned nanocomposite (VAN) achieving exceptional endurance of 1012 switching cycles via interface engineering. Artificial neural networks (ANNs) integrated with S‐C VAN memristors exhibit high training accuracy across multiple datasets.
Zedong Hu, Hongyi Dou, Jeremy B. Gan, Cun Li, Xuanyu Sheng, Juanjuan Lu, Abhijeet Choudhury, Jialong Huang, Yizhi Zhang, Zhongxia Shang, Ye Cao, Xinghang Zhang, Haiyan Wang   +12 more
wiley   +1 more source

Exploration of factors affecting Australian students' mathematics grades: a multiple regression analysis based on PISA 2022 data. [PDF]

Front Psychol
Wei Y, Zhang Y.
europepmc   +1 more source

Design and Applications of Multi‐Frequency Programmable Metamaterials for Adaptive Stealth

Advanced Functional Materials, EarlyView.
This article provides a comprehensive overview of metamaterials, including their fundamental principles, properties, synthesis techniques, and applications in stealth, as well as their challenges and future prospects. It covers topics that are more advanced than those typically discussed in existing review articles, while still being closely connected ...
Jonathan Tersur Orasugh, Ankita Mohanty, Amit Malakar, Suryasarathi Bose, Suprakas Sinha Ray   +4 more
wiley   +1 more source

Convergence analysis of Suzuki's generalized nonexpansive mappings using the Picard-Abbas iteration process. [PDF]

PLoS One
Nawaz B, Gdawiec K, Ullah K, Noorwali M, Aphane M.   +4 more
europepmc   +1 more source

Amorphous High Entropy Alloy Nanosheets Enabling Robust Li–S Batteries

Advanced Functional Materials, EarlyView.
Amorphous ultrathin FeCoNiMoW high entropy alloy nanosheets are incorporated into the polypropylene separator of lithium‐sulfur batteries, enhancing their capacity, rate performance, and cycling stability. Abstract High‐entropy alloys (HEAs) show great potential for catalyzing complex multi‐step reactions, but optimizing their parameters, i.e ...
Ren He, Seungho Lee, Yang Ding, Chen Huang, Xuan Lu, Lirong Zheng, Ao Yu, Chaoyue Zhang, Canhuang Li, Xiaoyu Bi, Yaqiang Li, Yaqi Liao, Junshan Li, Ahmad Ostovari Moghaddam, Salimov Yernar, Ying Xu, Maria Ibáñez, Chaoqi Zhang, Linlin Yang, Yingtang Zhou, Andreu Cabot   +20 more
wiley   +1 more source

An exploratory pilot study of mathematics performance patterns: visual art students vs. non-art students. [PDF]

Front Psychol
Cao Y.
europepmc   +1 more source

Universal In Situ Isotope Exchange Raman Spectroscopy (IERS) Methodology for Measuring Oxygen Surface Exchange Dynamics Using a Probe Layer

Advanced Functional Materials, EarlyView.
A bespoke multilayer thin film configuration has been designed, which overcomes the material dependency of conventional isotope exchange Raman spectroscopy (IERS). This universal IERS methodology is efficient, non‐destructive and provides additional structural information and time resolution, which can be further extended to various isotopic elements ...
Zonghao Shen, Francesco Chiabrera, Alexander Stangl, Laetitia Rapenne, Federico Baiutti, Carmen Jiménez, Albert Tarancón, Mónica Burriel   +7 more
wiley   +1 more source

The effects of school psychological capital and achievement emotions on behavioral engagement of Chinese secondary mathematics learners: a structural equation modeling approach. [PDF]

Front Psychol
Kang X, Zhang S, Wu Y.
europepmc   +1 more source

Math for All: Exploring the impact of a professional development program to improve mathematics learning for students with disabilities

, 2022
Babette Moeller, Nesta Marshall, Matt Mcleod, Karen Rothschild, Teresa T. Duncan, Jason Schoeneberger, John H. Hitchcock   +6 more
openalex   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source