Results 161 to 170 of about 3,726,775 (282)

Fiber‐Format Flexible Linear Electrostatic Motors

open access: yesAdvanced Materials, EarlyView.
A flexible coaxial fibre‐format electrostatic motor enables backdrivable and reversible linear actuation with high force density and functionality under bending, offering a scalable actuation platform for soft and wearable robotics. ABSTRACT Linear motion is fundamental to robotics, yet existing solutions are either rigid electromagnetic motors or ...
Sylvain Schaller, Herbert Shea
wiley   +1 more source

Giant Anisotropic Spin Absorption in Chiral Hybrid Semiconductors

open access: yesAdvanced Materials, EarlyView.
Researchers uncover that chiral hybrid organic–inorganic semiconductors absorb spin differently depending on its polarization direction. This anisotropic spin absorption is primarily driven by the local symmetry breaking within the layered structure.
Cong Yang   +12 more
wiley   +1 more source

Layered Spin‐Crossover Metal‐Organic Frameworks for Light‐Induced Control of Two‐Dimensional Quantum Materials

open access: yesAdvanced Materials, EarlyView.
Spin‐crossover metal‐organic frameworks (SCO‐MOFs) provide a route to control the electronic and magnetic properties of two‐dimensional quantum materials. By integrating a layered SCO‐MOFs with graphene, CrSBr, and NbSe2 nanodevices, thermally and light induced spin transitions induce strain that reversibly modulates charge transport, magnetic memory ...
Carla Boix‐Constant   +4 more
wiley   +1 more source

Mechanical Response and Functional Performance of Heat-Treated LPBF NiTi Shape Memory Alloys. [PDF]

open access: yesMaterials (Basel)
Ratajski J   +6 more
europepmc   +1 more source

Influence of Dynamic Strain Sweep on the Degradation Behavior of FeMnSi-Ag Shape Memory Alloys. [PDF]

open access: yesJ Funct Biomater, 2023
Roman AM   +9 more
europepmc   +1 more source

Breaking Bottlenecks in Solid Electrolyte Discovery With Large Artificial Intelligence Models

open access: yesAdvanced Materials, EarlyView.
Large AI models are presented as a route to break key bottlenecks in solid electrolyte discovery by linking materials databases, machine‐learning interatomic potentials, large language models, and autonomous experiments. This perspective highlights how AI can accelerate the identification of solid electrolytes with fast ion transport, stable interfaces,
Eric Jianfeng Cheng   +24 more
wiley   +1 more source

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