Results 191 to 200 of about 200,895 (293)

Study of Resistive Switching Dynamics and Memory States Equilibria in Analog Filamentary Conductive‐Metal‐Oxide/HfOx ReRAM via Compact Modeling

open access: yesAdvanced Electronic Materials, EarlyView.
A physics‐based compact model for Conductive‐Metal‐Oxide/HfOx ReRAM, accounting for ion dynamics, electronic conduction, and thermal effects, is presented. Accurate and versatile simulations of analog non‐volatile conductance modulation and memory state stabilization enable reliable circuit‐level studies, advancing the optimization of neuromorphic and ...
Matteo Galetta   +9 more
wiley   +1 more source

Sulfide‐Based Electrolytes for All‐Solid‐State Sodium Batteries

open access: yesAdvanced Energy Materials, EarlyView.
This review covers the structural features and synthesis strategies of sulfide‐based solid electrolytes, as well as critical challenges related to conductivity, interfacial and moisture stability, and scaling‐up for practical application in Sodium‐based All Solid‐State Batteries.
Han Yang   +6 more
wiley   +1 more source

Revealing structure and shaping priorities in plant and fungal cell wall architecture via solid-state NMR. [PDF]

open access: yesCell Surf
Xiao P   +8 more
europepmc   +1 more source

Pricing Dynamics in the US Hemp Market: A Vertical Price Transmission Analysis of the Hemp Value Chain

open access: yesAgribusiness, EarlyView.
ABSTRACT The US hemp market is a new and nascent industry that has been devoid of research for about half a century. This study examined the effects of exogenous shock on price at each phase of the value chain—Farm (hemp biomass), and its impact on prices at other phases of the value chain—Intermediary Processor (crude cannabidiol hemp) and Final ...
Solomon Odiase   +2 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

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