Results 111 to 120 of about 103,708 (231)

Decoding High‐voltage LiCoO2: From Degradation to Stabilization Toward Durable Li‐ion Batteries

Advanced Materials, EarlyView.
This review systematically addresses the degradation mechanisms and stabilization strategies for high‐voltage LiCoO2 cathodes. Key enhancement approaches including foreign‐ion doping, surface modifications, structural design, and electrolyte optimization are critically assessed.
Zezhou Lin, Yiran Ying, Huangxu Li, Yanhao Ren, Tiancheng Liu, Peiyu Hou, Haitao Huang   +6 more
wiley   +1 more source

Generative Models for Crystalline Materials

Advanced Materials, EarlyView.
Generative machine learning models are increasingly used in crystalline materials design. This review outlines major generative approaches and assesses their strengths and limitations. It also examines how generative models can be adapted to practical applications, discusses key experimental considerations for evaluating generated structures, and ...
Houssam Metni, Laura Ruple, Lauren N. Walters, Luca Torresi, Jonas Teufel, Henrik Schopmans, Jona Östreicher, Yumeng Zhang, Marlen Neubert, Yuri Koide, Kevin Steiner, Paul Link, Lukas Bär, Mariana Petrova, Gerbrand Ceder, Pascal Friederich   +15 more
wiley   +1 more source

Oxygen Evolution Reaction Catalysts for Acidic‐Media CO2 Electrolyzers

Advanced Materials, EarlyView.
Acidic‐media CO2 electroreduction (CO2R) could decarbonize chemical production, despite relying on rare‐earth elements for anodic oxygen evolution reaction (OER). Transferring the learnings from mature sister technologies (water electrolysis) could accelerate technological development.
Mingcheng Huang, Adnan Ozden
wiley   +1 more source

Two‐Way Shape Memory Polymer Composite Gripper for Adaptive Robotic Applications

Advanced Materials Technologies, EarlyView.
A two‐way shape memory polymer (SMP) composite is developed with intrinsic shape‐changing capability driven solely by temperature, eliminating external actuation loads. Embedding the SMP in a low‐stiffness elastomeric matrix enabled reversible transformations during heating and cooling cycles.
Aamna Hameed, Kamran Ahmed Khan
wiley   +1 more source

Accelerating Discovery to Deployment: Argonne's Materials Engineering Research Facility (MERF) and Its Role in Scaling Materials Technologies for Water and Resource Solutions

Advanced Materials Technologies, EarlyView.
In this Perspective, we highlight the processing science and scale‐up capabilities of the Materials Engineering Research Facility (MERF) at the U.S. Department of Energy's Argonne National Laboratory, with an emphasis on practical solutions for sustainable water and critical resource recovery. We demonstrate how national laboratories bridge fundamental
Yuepeng Zhang, Bratin Sengupta, Xiaoao Shi, Haihui Pu, Jeffrey W. Elam, Gregory K. Krumdick, Jessica Durham Macholz, Allison Bennett Irion, Junhong Chen, Seth B. Darling   +9 more
wiley   +1 more source

Competitive swarm reinforcement learning improves stability and performance of deep reinforcement learning. [PDF]

Sci Rep
Huang X, Luo W, Li L, Zhang H, Jiang H, Gao X.   +5 more
europepmc   +1 more source

Transducers Across Scales and Frequencies: A System‐Level Framework for Multiphysics Integration and Co‐Design

Advanced Materials Technologies, EarlyView.
Transducers convert physical signals into electrical and optical representations, yet each mechanism is bounded by intrinsic trade‐offs across bandwidth, sensitivity, speed, and energy. This review maps transduction mechanisms across physical scale and frequency, showing how heterogeneous integration and multiphysics co‐design transform isolated ...
Aolei Xu, Mengyao Xiao, Zhouli Sui, Yinpeng Wang, Daoye Zheng, Yuxin Liu, Luwei Wang, Huajun Liu, Chengkuo Lee   +8 more
wiley   +1 more source

Mechanistic interpretability of reinforcement learning in Medicaid care coordination. [PDF]

BMJ Health Care Inform
Basu S, Patel S, Sheth P, Muralidharan B, Elamaran N, Kinra A, Batniji R.   +6 more
europepmc   +1 more source

Characterization of Droplet Formation in Ultrasonic Spray Coating: Influence of Ink Formulation Using Phase Doppler Anemometry and Machine Learning

Advanced Materials Technologies, EarlyView.
This study explores how machine learning models, trained on small experimental datasets obtained via Phase Doppler Anemometry (PDA), can accurately predict droplet size (D32) in ultrasonic spray coating (USSC). By capturing the influence of ink complexity (solvent, polymer, nanoparticles), power, and flow rate, the model enables precise droplet control
Pieter Verding, Danny E. P. Vanpoucke, Yunus T. Aksoy, Tobias Corthouts, Maria R. Vetrano, Wim Deferme   +5 more
wiley   +1 more source

NeuroAction: a neuroevolutionary approach to reinforcement learning for autonomous vehicles. [PDF]

Sci Rep
Aboyeji E, Ajani OS, Fenyom I, Mallipeddi R.   +3 more
europepmc   +1 more source