Results 221 to 230 of about 13,636,242 (258)

Photonic‐Enabled Energy‐Efficient Transparent Neuromorphic Computing Devices: A Review

open access: yesAdvanced Science, EarlyView.
Transparent photonic neuromorphic computing devices merge optics and brain‐inspired computing to overcome von Neumann bottlenecks with ultrafast, low‐energy processing. By exploiting transparent oxides, 2D materials, phase‐change materials, and hybrid heterostructures, these platforms enable photonic synapses, memory, and logic for see‐through edge ...
Shuvaraj Ghosh   +8 more
wiley   +1 more source

Multiferroic‐Centric Materials and Systems Engineering for Battery Applications: An Insight Into Mechanisms, Strategies, and Characterizations

open access: yesAdvanced Science, EarlyView.
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su   +13 more
wiley   +1 more source

Sliding Ferroelectricity Driven Spin‐Layertronics in Altermagnetic Multilayers

open access: yesAdvanced Science, EarlyView.
Integrating sliding ferroelectricity with altermagnetism enables nonvolatile electrical control of spin and layer degrees of freedom. In bilayer CuF2, interlayer translation reverses layer‐locked spin‐split bands, establishing a multifunctional “spin‐layertronic” platform.
Rui Peng   +5 more
wiley   +1 more source

Boosting Ferroelectricity: 2D and Polymer Ferroelectric Hybrids Enabling Ambipolar Nonvolatile MoS2 Memory Transistor

open access: yesAdvanced Science, EarlyView.
Two‐dimensional CuInP2S6 nanosheets are incorporated into a P(VDF‐TrFE) matrix to induce polarization‐cooperative ferroelectric coupling. The resulting P(VDF‐TrFE)/CuInP2S6 hybrid film exhibits reinforced ferroelectric ordering and reduced coercive electric fields compared with pristine P(VDF‐TrFE).
Yeonsu Jeong   +10 more
wiley   +1 more source

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

open access: yesAdvanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai   +3 more
wiley   +1 more source

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