Results 71 to 80 of about 6,886 (258)

Neuromorphic Devices and Computing for Sensing, Memory, and Control

Advanced Science, EarlyView.
This review introduces neuromorphic devices made from diverse materials. These devices mimic neuronal functions and architectures and, when integrated with artificial or biological computing, can form closed loops with neurons for pressure, optical, acoustic, and biochemical sensing and modulation.
Zhengguang Zhu, Nicholas Schaffer, Xiao Yang   +2 more
wiley   +1 more source

Electronic Chip‐Mimetic Medical Implants for Controllable Sonothermal Treatments

Advanced Science, EarlyView.
Inspired by the thermal effect of working electronic chips, which consist of various heterostructures, an sonothermal‐responsive platform has been developed through constructing metal‐semiconductor interface on implants. Biofilm infection and deep vein thrombosis can be successfully treated by controllable ultrasound treatments.
Zhengdong Zhang, Pan Liu, Jie Lei, Shuchang Peng, Qiu Chen, Wei Guan, Yizhou Zhu, Gang Liu, Xiaobo Feng, Cao Yang, Lei Tan   +10 more
wiley   +1 more source

From Flexible to Conformable Pressure Sensors: Mechanisms, Materials, and Biomedical Applications

Advanced Electronic Materials, EarlyView.
This review highlights recent progress, challenges and future opportunities in pressure sensing for advanced biomedical applications. We summarize key transduction mechanisms and emerging material strategies, discuss representative wearable and implantable applications for continuous physiological monitoring and provide a focused perspective on barrier
Rishabh B. Mishra, Andrea Ottomaniello, Virgilio Mattoli   +2 more
wiley   +1 more source

Atomic Scale Control of Thermal Conductivity in LaMnO3/SrMnO3 Superlattices

Advanced Electronic Materials, EarlyView.
Interface‐dominated cross‐plane thermal transport in [(LaMnO3)m/(SrMnO3)n]10 superlattices can be controlled by the octahedral rotation/tilt angle φOOR of the MnO6 octahedra, which as well is controlled by the “m/n” ratio. ABSTRACT We report atomic scale structure and phonon thermal transport in (LaMnO3)m/(SrMnO3)n/SrTiO3(100) superlattices (LMO/SMO ...
H. Ulrichs, V. Roddatis, U. Roß, J. P. Bange, D. Meyer, S. Lopatin, V. Radisch, D. Metternich, I. V. Maznichenko, S. Ostanin, R. Egoavil, I. Lazić, V. Moshnyaga   +12 more
wiley   +1 more source

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

Structure and Spectroscopic Characterisation of Phenanthroline‐Based Iodobismuthate(III) Complexes Utilised for Raw Acoustic Signal Classification

Advanced Intelligent Discovery, EarlyView.
Memristors based on trimethylsulfonium (phenanthroline)tetraiodobismuthate have been utilised as a nonlinear node in a delayed feedback reservoir. This system allowed an efficient classification of acoustic signals, namely differentiation of vocalisation of the brushtail possum (Trichosurus vulpecula).
Ewelina Cechosz, Gisya Abdi, Andrzej Sławek, Marlena Gryl, Konrad Szaciłowski   +4 more
wiley   +1 more source

Machine Learning Driven Inverse Design of Broadband Acoustic Superscattering

Advanced Intelligent Discovery, EarlyView.
Multilayer acoustic superscatterers are designed using machine learning to achieve broadband superscattering and strong sound insulation. By incorporating a weighted mean absolute error into the loss function, the forward and inverse neural networks accurately map structural parameters to spectral responses.
Lijuan Fan, Xiangliang Zhang, Ying Wu
wiley   +1 more source

Cellular Material Network: A General Machine Learning Architecture for Predicting Mechanical Properties of Cellular Materials

Advanced Intelligent Systems, EarlyView.
This study introduces Cellular Material Network (CM‐Net), a pioneering machine learning architecture integrating physical information, to predict the mechanical properties of cellular materials. Comprehensive validation through simulations and experiments demonstrates its accuracy in predicting nonlinear behaviors, including initial peak compression ...
Sicong Zhou, Qiang Gao, Liuchao Jin, Guoquan Xie, Kang Zhang, Wei‐Hsin Liao   +5 more
wiley   +1 more source

Thermally Drawn Bioelectric Catheters: Enabling Proprioceptive Endovascular Navigation

Advanced Intelligent Systems, EarlyView.
This work introduces a novel bioelectric navigation system eliminates the need for harmful fluoroscopy during endovascular surgeries. A bespoke 16‐electrode catheter is fabricated using rapid thermal drawing and laser micro‐machining. Paired with a real‐time tracking algorithm fusing vascular geometry detection and distance estimation, this technology ...
Alex Ranne, Heiko Maier, Jinshi Zhao, Songli Moey, Ayhan Aktas, Manik Chana, Burak Temelkuran, Nassir Navab, Ferdinando Rodriguez y Baena   +8 more
wiley   +1 more source

Experimental Characterization of Thermal, Acoustic, Magnetic, and Electrical Properties of Additive Manufactured Composites Reinforced With Ceramic Powders for Robotic Arms

Journal of Applied Polymer Science, Volume 142, Issue 16, April 20, 2025.
ABSTRACT An extensive experimental analysis was conducted to assess the thermal, acoustic, magnetic, and electrical properties of composite materials reinforced with varying ratios of silicon carbide (SiC) and alumina (Al₂O₃) powders, with a focus on enhancing the functionality of robotic arms.
Merdan Özkahraman, Mevlüt Yunus Kayacan, Ali Nadi Kaplan   +2 more
wiley   +1 more source