Results 131 to 140 of about 347,710 (303)

Array‐Level Characterization of Cryogenic RRAM

Advanced Electronic Materials, EarlyView.
This paper reports the first array‐level comprehensive electrical characterization of a 1024‐device HfO2‐based RRAM array from 300, 77 to 4 K, covering forming, set/reset switching, endurance, retention, relaxation, and read disturb. The results manifest the high performance of RRAM array at cryogenic temperatures and huge application potential for ...
Yuyao Lu, Ruofei Hu, Zhixing Jiang, Feng Xu, Bin Gao, Jianshi Tang, He Qian, Huaqiang Wu   +7 more
wiley   +1 more source

Self‐Adhesive Conductive Elastomers for Gel‐Free Biopotential Recording

Advanced Electronic Materials, EarlyView.
σPOMaC, a self‐adhesive conductive citrate elastomer incorporating PEDOT:PSS and DBSA, enables gel‐free biopotential electrodes with stable conductivity and intrinsic skin adhesion. The composite exhibits low resistivity (∼ 0.02 Ω·cm), robust electrical performance during repeated use, and reliable on‐body ECG acquisition comparable to Ag/AgCl ...
Kirstie M. K. Queener, Alec Brewer, He Sun, Jack Twiddy, Vladimir A. Pozdin, Koji Sode, Alper Bozkurt, Michael Daniele   +7 more
wiley   +1 more source

Material Strategies for Stimulation and Recording in Neural Biocomputing Platforms

Advanced Electronic Materials, EarlyView.
Material strategies enabling stimulation and recording are central to neural biocomputing systems. This review examines how electronic materials govern the encoding of inputs and decoding of outputs in living neural networks. Advances in electrical, optical, and multimodal interfaces highlight emerging design principles for biocomputing platforms ...
Sehong Kang, Junggeon Park, Aryaman Joshi, Rakid Haque, Gyu Weon Hwang, Hyunjoon Kong   +5 more
wiley   +1 more source

Exploiting Temperature Effects for Robust Control and Reference Circuits Using Thin‐Film Contact‐Controlled Transistors

Advanced Electronic Materials, EarlyView.
Compact circuits based on contact‐controlled transistors are well‐suited to unsupervised thermal management, sensitive temperature measurement, or temperature‐stable current references. Demonstrated on flexible microcrystalline silicon and supported by simulation, the approach does not require supply voltage regulation, remains manufacturable across ...
Eva Bestelink, Jean‐Charles Fustec, Olivier de Sagazan, Rogier Fransen, George Bairaktatis, Emmanuel Jacques, Radu A. Sporea   +6 more
wiley   +1 more source

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

The Biofuels Blueprint: Understanding the U.S. Renewable Fuel Standard

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT We provide a comprehensive review of the U.S. Renewable Fuel Standard (RFS), synthesizing nearly two decades of program evolution, market outcomes, and economic analysis. The RFS mandates minimum volumes of renewable fuel blending through a nested structure based on life‐cycle greenhouse gas reductions, enforced via tradeable Renewable ...
Maria Gerveni, Todd Hubbs, Scott H. Irwin, James H. Stock   +3 more
wiley   +1 more source

From continuous to interruptible distillation: Flexible electric heating column architecture with fast start‐up

AIChE Journal, EarlyView.
Abstract Electrification of distillation offers a promising route to reducing scope‐1 emissions from one of the chemical industry's most energy‐intensive unit operations. However, conventional adiabatic columns are dynamically inflexible: Long, energy‐intensive start‐ups make shutdown and restart impractical under variable electricity prices and ...
Samuel Mercer, Michael Baldea
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

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source