Results 31 to 40 of about 119 (119)

Photonic Nanomaterials for Wearable Health Solutions

Advanced Materials, EarlyView.
This review discusses the fundamentals and applications of photonic nanomaterials in wearable health technologies. It covers light‐matter interactions, synthesis, and functionalization strategies, device assembly, and sensing capabilities. Applications include skin patches and contact lenses for diagnostics and therapy. Future perspectives emphasize AI‐
Taewoong Park, Jung Woo Leem, Young L. Kim, Chi Hwan Lee   +3 more
wiley   +1 more source

Challenges and Opportunities of Upconversion Nanoparticles for Emerging NIR Optoelectronic Devices

Advanced Materials, EarlyView.
The special photo‐responsiveness of upconversion nanoparticles has opened up a new path for the advancement of near‐infrared (NIR)‐responsive optoelectronics. However, challenges such as low energy‐conversion efficiency and high nonradiative losses still persist.
Sunyingyue Geng, Hangfei Li, Ziyu Lv, Yongbiao Zhai, Bobo Tian, Ying Luo, Ye Zhou, Su‐Ting Han   +7 more
wiley   +1 more source

Characterization and Inverse Design of Stochastic Mechanical Metamaterials Using Neural Operators

Advanced Materials, EarlyView.
This study presents a DeepONet‐based machine learning framework for designing stochastic mechanical metamaterials with tailored nonlinear mechanical properties. By leveraging sparse but high‐quality experimental data from in situ micro‐mechanical tests, high predictive accuracy and enable efficient inverse design are achieved.
Hanxun Jin, Boyu Zhang, Qianying Cao, Enrui Zhang, Aniruddha Bora, Sridhar Krishnaswamy, George Em Karniadakis, Horacio D. Espinosa   +7 more
wiley   +1 more source

Pressure Induced Molecular‐Arrangement and Charge‐Density Perturbance in Doped Polymer for Intelligent Motion and Vocal Recognitions

Advanced Materials, EarlyView.
The sidechain doped polymer is investigated to have great potential for pressure detection due to the advantages including molecular rearrangement and charge‐density perturbance induced by micro‐mechanical deformation. This sensor displayed low‐pressure detection limit of 0.7 Pa as well as a rapid response time of 18.8 ms, enabling multi‐mode motion ...
Huimin Lu, Lei Zhang, Jingyan Jiang, Jian Song, Zhongchao Zhou, Wujian Wu, Ziqian Cheng, Tengfei Yan, Hong Hu, Tingting Zhao, Zhen Xu, Siyi Luo, Hui Li, Jianhua Zhang, Charles H. Lawrie   +14 more
wiley   +1 more source

Electrospinning and Nanofiber Technology: Fundamentals, Innovations, and Applications

Advanced Materials, EarlyView.
This review explores electrospinning fundamentals, methods for synthesizing polymer, metal oxide, carbon, and composite nanofibers, and advancements in fiber architectures like porous, core–shell, and aligned structures. It highlights applications in functional membranes, sensors, energy systems, and catalyst design while addressing future ...
Yujang Cho, Jong Won Beak, Mingyu Sagong, Seongcheol Ahn, Jong Seok Nam, Il‐Doo Kim   +5 more
wiley   +1 more source

Next‐Generation Image Sensors Based on Low‐Dimensional Semiconductor Materials

Advanced Materials, EarlyView.
Low‐dimensional semiconductor materials are promising candidates for photosensitive components in next‐generation image sensors. This review offers a thorough and timely examination of novel image sensors, covering their working principles, intriguing materials categorized into four main groups, and advanced imaging applications.
Yunxia Hu, Zhaoli Gao, Zhengtang Luo, Liang An   +3 more
wiley   +1 more source

Uni‐Axial Densification of Slurry‐Casted Li₆PS₅Cl Tapes: The Role of Particle Size Distribution and Densification Pressure

Advanced Materials, EarlyView.
Solid‐state batteries promise high energy density and safety for electric vehicles. This study examines the densification of slurry‐casted Li₆PS₅Cl separator tapes, highlighting the role of particle size distribution and uni‐axial pressure. Wet milling refines particles while preserving conductivity, and a critical compaction threshold leads to ...
Quoc‐Anh Tran, Meenal Agrawal, Michael Häusler, Johannes Hörmann, Mohsen Sadeqi Moqadam, Günther J. Redhammer, Ingeborg Sellæg Ellingsen, Mir Mehraj Ud Din, Per Erik Vullum, Roman Zettl, Timo Danner, Arnulf Latz, Volker Hennige, Roland Brunner, Daniel Rettenwander   +14 more
wiley   +1 more source

Learning and Controlling Silicon Dopant Transitions in Graphene Using Scanning Transmission Electron Microscopy

Advanced Materials Interfaces, EarlyView.
Atomic transition rates for manipulating single silicon atoms in graphene with an electron beam in the scanning transmission electron microscope are experimentally extracted. Automated experiments and deep learning methods provide a means to probe and collect vast amounts of data to determine transition probabilities as a function of beam coordinate. A
Max Schwarzer, Jesse Farebrother, Joshua Greaves, Ekin Dogus Cubuk, Rishabh Agarwal, Aaron Courville, Marc G. Bellemare, Sergei Kalinin, Igor Mordatch, Pablo Samuel Castro, Kevin M. Roccapriore   +10 more
wiley   +1 more source

Wide‐Field Bond Quality Evaluation Using Frequency Domain Thermoreflectance with Deep Neural Network Feature Reconstruction

Advanced Materials Interfaces, EarlyView.
In this work, wide‐field (>1 mm2) frequency‐domain thermoreflectance (FDTR) hyperspectral imaging is used to image subsurface indium bump bonds 50 µm below the surface. Thermal analysis with Monte Carlo uncertainty propagation is used to evaluate bump quality, while a trained deep neural network (can rapidly reconstruct bump geometry contact area maps.
Amun Jarzembski, Siddharth Nair, Wyatt Hodges, Matthew Jordan, Anthony McDonald, Logan Antiporda, Greg W. Pickrell, Timothy Walsh, Fabio Semperlotti, Jason Neely, Luke Yates   +10 more
wiley   +1 more source

Photoactive Monolayer MoS2 for Spiking Neural Networks Enabled Machine Vision Applications

Advanced Materials Technologies, EarlyView.
Molybdenum disulfide (MoS2) optoelectronic devices are implemented as Leaky Integrate‐and‐Fire (LIF) neurons in spiking neural networks (SNNs), where light‐induced photocurrent dynamics represent potentiation (τd) and depression (τd), emulating neuronal membrane potential.
Thiha Aung, Sindhu Priya Giridhar, Irfan H. Abidi, Taimur Ahmed, Akram AI‐Hourani, Sumeet Walia   +5 more
wiley   +1 more source