Results 161 to 170 of about 20,076 (262)

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Advanced Intelligent Discovery, EarlyView.
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley   +1 more source

Interfaces and Transport Phenomena in Materials Under Extreme Conditions. [PDF]

Materials (Basel)
Chen Y, Hu R, Wang H.
europepmc   +1 more source

Evolution of Physical Intelligence Across Scales

Advanced Intelligent Discovery, EarlyView.
By following the evolution of physical intelligence across scales, this article shows how intelligence arises from materials, structures, physical interactions, and collectives. It establishes physical intelligence as the evolutionary foundation upon which embodied intelligence is built.
Ke Liu, Tianyun Huang, Aming Li, Pengyu Lv, Tingting Qin, Wenhui Chen, Hui Wei, Huiling Duan   +7 more
wiley   +1 more source

Dataset for classification of forming tool types for aircraft parts based on neural network models using CAD. [PDF]

Data Brief
DongGyu L, HyunSup L, JaeHo S, MinSeok S, JiWon K, YoungSoon K.   +5 more
europepmc   +1 more source

Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy

Advanced Intelligent Discovery, EarlyView.
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho‐Jui Hsieh, Neil Y. C. Lin   +9 more
wiley   +1 more source

A Systematic Review of Hybrid Polymeric Woven Composites: Mechanical Performance, Numerical Simulation, and Future Perspectives. [PDF]

Materials (Basel)
Tefera CA, Duda S, Sławski S.
europepmc   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Printed Wearable Sweat Rate Sensor for Continuous In Situ Perspiration Measurement

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
A wireless wearable sweat rate sensor system is presented, featuring digital 3D direct‐write printing on a flexible substrate with microfluidic layers for continuous, real‐time monitoring. Printed encapsulated metal electrodes are used for capacitance measurements, achieving high sensitivity (0.01 μL min−1) while maintaining a compact and lightweight ...
Mohammad Shafiqul Islam, Sangwon Cha, Md Farhad Hassan, Wenxin Cai, Tahsin Sejat Saniat, Cedar Rose Leach, Yasser Khan   +6 more
wiley   +1 more source

Research on the Formability of 2A12 Aluminum Alloy Sheet During High-Speed Hot Gas Bulging. [PDF]

Materials (Basel)
Kang Z, Zhao Y, Zhao H, Wang Y, Tian G, Zhao C, Liang J, Qian X, Lin Y, He Z.   +9 more
europepmc   +1 more source

Deep Learning Approaches for Classifying Crack States With Overload and Predicting Fatigue Parameters in a Titanium Alloy

Advanced Intelligent Systems, EarlyView.
This study proposes a deep learning approach to evaluate the fatigue crack behavior in metals under overload conditions. Using digital image correlation to capture the strain near crack tips, convolutional neural networks classify crack states as normal, overload, or recovery, and accurately predict fatigue parameters.
Seon Du Choi, Suman Timilsina, Youn Woo Shin, Yong Nam Kwon, Kee‐Sun Sohn, Ji Sik Kim   +5 more
wiley   +1 more source