Results 91 to 100 of about 7,911 (299)

Green and Low‐Carbon Synthesis of Lithium Sulfide (Li2S): From Fundamental Mechanisms to Scalable Manufacturing for Next‐Generation Energy Storage

Advanced Science, EarlyView.
This review focuses on green synthesis routes for lithium sulfide (Li2S)—a key material for lithium–sulfur batteries and sulfide solid electrolytes, whose traditional carbothermal reduction remains energy‐intensive and unsustainable. Specifically examined routes include low‐temperature solid‐state reactions, magnesiothermal reduction, and solution ...
Aiping Peng, Xingtao Qi, Lingfeng Zhu, Ningrui Zhan, Yaohui Qu, Hui Li, Hai Zhang, Fan Wang, Ze Zhang, Xuewen Wang, Zhenyu Yang, Tianyi Ma   +11 more
wiley   +1 more source

Power analysis dataset for QCA based multiplexer circuits

Data in Brief, 2017
Power consumption in irreversible QCA logic circuits is a vital and a major issue; however in the practical cases, this focus is mostly omitted.The complete power depletion dataset of different QCA multiplexers have been worked out in this paper. At −271.
Md. Abdullah-Al-Shafi, Ali Newaz Bahar, Peer Zahoor Ahmad, Firdous Ahmad, Mohammad Maksudur Rahman Bhuiyan, Kawsar Ahmed   +5 more
doaj   +1 more source

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

Advanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Enabling Quantum‐Compatible Nonvolatile Memory: Cryogenic Evaluation of 1T1R HfO2‐Based RRAM From 300 to 1.5 K

Advanced Electronic Materials, EarlyView.
Resistive memory devices are explored for operation at extremely low temperatures relevant to quantum computing. The study reveals how transistor behavior strongly influences memory performance under cryogenic conditions and introduces an optimized programming strategy.
Emilio Pérez‐Bosch Quesada, Alberto Mistroni, Ruolan Jia, Keerthi Dorai Swamy Reddy, Markus Fritscher, Felix Reichmann, Oliver Skibitzki, Juan Bautista Roldán, Helena Castán, Salvador Dueñas, Christian Wenger, Eduardo Pérez   +11 more
wiley   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Design of a Galois Field Multiplier Circuit using Quantum-dot Cellular Automata

Наука. Инновации. Технологии, 2022
The Quantum-dot Cellular Automata (QCA) is a possible future of nano-electronics computing technology, that promises small size, low power, and fast digital circuits compared to the existing transistor-based designs.
Maryam Jahantigh Akbarzadeh, Amir Sabbagh Molahosseini   +1 more
doaj  

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Maximum Stability Point Tracking Stabilizes Wide‐Bandgap Mixed‐Halide Perovskite Solar Cells

Advanced Energy Materials, EarlyView.
A maximum stability point tracking (MSPT) strategy suppresses charge trapping‐induced halide segregation, enabling wide‐bandgap perovskite solar cells to achieve significantly enhanced operational stability compared to conventional maximum power point tracking (MPPT).
Seongheon Kim, Geon Kim, Taeeon Kim, Kwang Seop Shin, Jongchan Kim, Mansoo Choi, Yun Seog Lee, Namyoung Ahn   +7 more
wiley   +1 more source

Fundamental 1:2 demultiplexer design in quantum-dot cellular automata nanotechnology

e-Prime: Advances in Electrical Engineering, Electronics and Energy
There are many issues with complementary metal oxide semiconductor (CMOS) technology in the ultra-nanoscale regime. A quantum-dot cellular automaton (QCA) is a promising innovation for crafting logic circuits in nanoscale dimensions.
Nandan Vaid, Vijay Kumar Sharma, Prashant Kumar   +2 more
doaj   +1 more source