Results 101 to 110 of about 44,201 (267)

Investigation on the Impact of Memristive Device Variability on Uncoupled Memristive Cellular Nonlinear Networks

Advanced Electronic Materials, EarlyView.
Memristors, nanoscale devices that compute and store data in the same place, promise compact, energy‐efficient image‐processing hardware, yet no two devices behave exactly alike. Simulations based on real device measurements show that a 256‐cell memristive cellular nonlinear network (M‐CellNN) detects image edges perfectly despite this variability and ...
Salwa Yasmeen Neyaz, Kristoffer Schnieders, Yongmin Wang, Alon Ascoli, Ioannis Messaris, Ronald Tetzlaff, Vasileios Ntinas, Ricardo Carmona Galán, Stefan Wiefels, Vikas Rana, Stephan Menzel   +10 more
wiley   +1 more source

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Modulation of Local Environment for Selective Bicarbonate Conversion to Multi‐Carbon Products

Advanced Energy Materials, EarlyView.
Highly porous Cu electrode partially coated with an ionomer/carbon composite enables efficient conversion of bicarbonate to multi‐carbon products. By regulating the local reaction microenvironment, this strategy overcomes selectivity and reaction‐rate limitations, achieving Faradaic efficiencies of 43% for ethylene and 60% for total multi‐carbon ...
Tai Nguyen, Anh N. Nguyen, Guorui Gao, Jackson Crane, Thành Trần‐Phú, Gelson Tavares da Silva, Aldo Gonzalez‐Lopez, Hung Lai, Khac Huy Dinh, Duc‐Minh Phan‐Pham, Martin Guay, Carlos Escobedo, Rosalie K. Hocking, Jonathan Raisin, Lucia H. Mascaro, Cao‐Thang Dinh   +15 more
wiley   +1 more source

Operando Electro‐Rheology: Unraveling Decoupled Ion Transport‐Mechanical‐Morphological Properties in Nanostructured Block Copolymer Electrolytes

Advanced Energy Materials, EarlyView.
A novel operando methodology, which combines simultaneous rheological and electrochemical measurements under dc polarization in a LiTFSI‐doped polystyrene‐block‐poly(ethylene oxide) block copolymer electrolyte, is developed. Time‐resolved monitoring of the viscoelastic and electrochemical responses to galvanostatic stimulus enables to elucidate the ...
Martino Airoldi, Mercedes Fernandez, Irune Villaluenga   +2 more
wiley   +1 more source

Vulnerability analysis of power grid structure based on complex network theory

Frontiers in Energy Research
IntroductionThe key nodes of an intelligent distribution network significantly impact the reliability and stability of the distribution network’s operation. The failure of these key nodes can severely affect the safe operation of the distribution network.
Zhichun Yang, Huaidong Min, Jie Zhao, Xuzhu Dong, Fan Yang, Chenhao Wang, Nan Zhang   +6 more
doaj   +1 more source

Mechanochemically Disordered Li2Ta2OCl10 Oxyhalide Unlocks Superionic Conductivity (>10 mS.cm−1)

Advanced Energy Materials, EarlyView.
This work presents a new crystalline oxyhalide, Li2Ta2OCl10, built from [Ta2OCl10]2− dimers. The disorder induced by ball‐milling drives the dramatic 5000‐fold increase in ionic conductivity to reach 11 mS.cm−1. The material shows promise as a solid electrolyte for batteries and opens new prospects for the discovery of new dimer‐based compounds ...
Clément Morel, Anshuman Chaupatnaik, Jie Liu, Mingxue Tang, Horyung Ji, Mouna Ben Yahia, Marie‐Liesse Doublet, Tatiana N. Kobernik, Dmitry A. Aksyonov, Artem M. Abakumov, Gwenaëlle Rousse, Jean‐Marie Tarascon   +11 more
wiley   +1 more source

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Advanced Intelligent Discovery, EarlyView.
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley   +1 more source

Dual lanthanides synergistically boost stability and kinetics for spinel LiMn2O4 cathodes

Communications Chemistry
Mn-site doping in spinel LiMn2O4 (LMO) mitigates Mn3+-induced Jahn-Teller distortion. However, this strategy faces inherent trade-offs. Specifically, low-valent doping weakens oxygen bonding, while high-valent doping increases Mn3+ content.
Zhushun Zhang, Jun Du, Tenghao Li, Hengchao Sun, Shuai bing Li, Huakun Peng, Peng Liu, Dapeng Du, Tianyi Wang, Chengyin Wang, Likun Pan, Jiabao Li   +11 more
doaj   +1 more source

Autonomous X‐Ray Fluorescence Mapping for Nanoscale Chemical Speciation of Fine Particulate Matter

Advanced Intelligent Discovery, EarlyView.
We present X‐AutoMap, an autonomous X‐ray fluorescence mapping framework that integrates real‐time analysis with rule‐based computer vision to selectively target chemically relevant regions. By avoiding background‐dominated areas, the method reduces acquisition time by fourfold while enabling accurate particle‐level speciation.
Carlos Deleon, Dmitri Gavrilov, Peggy O’Day, Ajith Pattammattel   +3 more
wiley   +1 more source

Spatially Informed Feature Selection and Machine Learning in Matrix‐Assisted Laser Desorption/Ionization Imaging for Cohort‐Scale Molecular Tissue Phenomics in Glioblastoma

Advanced Intelligent Discovery, EarlyView.
Matrix‐assisted laser desorption/ionization imaging‐based identification of reliable small molecule markers across heterogeneous glioblastoma cohorts is challenging with intensity‐only methods. We present spatially informed feature selection (SIFS), a spatially informed framework that prioritizes molecules consistently colocalizing with histopathology.
Shad A. Mohammed, Denis Abu Sammour, Dirk C. Hoffmann, Philipp Sievers, Tobias Bausbacher, Melissa Hahn, Yasemin Ucal, Iris Mildenberger, Christiane A. Opitz, Stefan Schmidt, Michael Platten, Oliver Wasenmüller, Wolfgang Wick, Andreas von Deimling, Tobias Kessler, Carsten Hopf   +15 more
wiley   +1 more source