Results 221 to 230 of about 1,013,892 (284)

Quantifying Subsurface Weak in‐Plane Magnetization of Mixed Phase BiFeO3 by Scanning Nitrogen Vacancy Magnetometry

open access: yesAdvanced Functional Materials, EarlyView.
We use scanning nitrogen vacancy magnetometry to directly image the weak in‐plane magnetic moments in mixed phase BiFeO3 at the nanoscale and quantify the local magnetic moments to be 18.8±2.0 μB/nm2 in the rhombohedral‐like phase and 1.5±0.6 μB/nm2 in the well‐known non‐magnetic tetragonal‐like phase.
Lei Wang   +14 more
wiley   +1 more source

Development of a Diagnostic Tool for Elbow Instability Combining 4D-CT Imaging and Mechanical Load Analysis. [PDF]

open access: yesBiomed Eng Comput Biol
Gehring J   +10 more
europepmc   +1 more source

Glissile Interphase Boundaries Enable Collective Phase Switching in Epitaxial Polar Oxides

open access: yesAdvanced Functional Materials, EarlyView.
A triple point is identified in the phase diagram of low‐symmetry epitaxial BiFeO3 thin film along with an extended regime of phase competition associated with a flattened energy landscape. The electromechanical response is shown to be governed by correlated interphase‐boundary motion, including scale‐free avalanche dynamics characteristic of systems ...
Mohammad Moein Seyfouri   +11 more
wiley   +1 more source

Advances in Sustainable and Wearable Textile Based Soft Robotics

open access: yesAdvanced Functional Materials, EarlyView.
This Review examines advances in wearable textile‐based soft robotics, focusing on sustainable materials, integrated sensing, and scalable actuation. It discusses manufacturing and system integration across healthcare, assistive robotics, prosthetics, and human–machine interfaces, and highlights key challenges in circular design, including life‐cycle ...
Zahir Abbas   +6 more
wiley   +1 more source

Implantable Ionic Memristors Based on Natural Polymer Heterojunctions

open access: yesAdvanced Functional Materials, EarlyView.
We report an implantable natural polymer‐based ionic memristor composed of hyaluronic acid, chitosan, and PDMS. The device achieved 98.94% accuracy in MNIST classification while reducing training time by 36.8% compared with a conventional artificial neural network (ANN).
Dong‐yup Lee   +6 more
wiley   +1 more source

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