Results 241 to 250 of about 21,932 (307)

Fully 3D‐Printed Wave‐Wound Electromagnetic Motors

open access: yesAdvanced Materials Technologies, EarlyView.
This work presents the first fully 3D‐printed wave‐wound electromagnetic motors, which are created using conductive nanoparticle inks, carbon‐filled nylon polymers, and surface mount components. These motors can achieve a stall torque of 7.62N·mmA−1$7.62 \nobreakspace N{\cdot }mm A^{-1}$ and efficiency of 28.2 %, which approaches the performance of ...
Joseph Schwalbe   +4 more
wiley   +1 more source

Optimal Use of Computed Tomography in Diagnosing Internal Herniation After Roux-en-Y Gastric Bypass: A Proposition for the Application of a Radiological Prediction Score. [PDF]

open access: yesObes Surg
van Hogezand LL   +12 more
europepmc   +1 more source

At Home Detection of Ovarian Health Biomarker in Menstruation Blood

open access: yesAdvanced Materials Technologies, EarlyView.
A lateral flow assay enables the detection of anti‐Müllerian hormone directly in unprocessed menstrual blood using silica‐gold nanoshells and smartphone‐assisted machine learning analysis. The platform supports decentralized, user‐operated testing in wearable and dipstick formats, highlighting the potential of menstrual blood as a non‐invasive matrix ...
Lucas Dosnon   +3 more
wiley   +1 more source

Probing Near‐Field EM Fluctuations in Superparamagnetic CoFeB With NV Quantum Dephasometry

open access: yesAdvanced Optical Materials, EarlyView.
We non‐invasively investigate the superparamagnetic spin dynamics of a 1.1 nm CoFeB layer by probing its near‐field EM fluctuations using NV centers‐based quantum dephasometry. Our findings are further supported by theoretical modeling and SQUID‐based magnetization characterization. These results provide critical insight into the magnetization dynamics
Shoaib Mahmud   +5 more
wiley   +1 more source

Data‐Efficient Electromagnetic Surrogate Solver Through Dissipative Relaxation Transfer Learning

open access: yesAdvanced Optical Materials, EarlyView.
Dissipative relaxation transfer learning (DIRTL) enables data‐efficient training of electromagnetic surrogate solvers by pretraining data generated with artificial material loss before fine‐tuning on target lossless data. The framework suppresses resonant outlier effects during early training, allowing effective adaptation to high‐amplitude resonances ...
Sunghyun Nam   +2 more
wiley   +1 more source

Novel Insights into Solution Electrospinning for Nanofibers. [PDF]

open access: yesMacromolecules
Wang C   +6 more
europepmc   +1 more source

Closure of Mesenteric Defects during Roux-en-Y Gastric Bypass Fails to Reduce Internal Herniation. [PDF]

open access: yesObes Surg
Taselaar AE   +4 more
europepmc   +1 more source

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