Results 151 to 160 of about 1,147,666 (317)

Bridging Theory and Experiment: Machine Learning Potential‐Driven Insights into pH‐Dependent CO₂ Reduction on Sn‐Based Catalysts

Advanced Functional Materials, EarlyView.
Machine learning potential (MLP) enables large‐scale molecular dynamics (MD) simulations, uncovering dynamic surface reconstruction of SnO₂ and SnS₂ under CO₂ reduction reaction condition. The negative dipole moments upon *OCHO adsorption are the primary factors driving the leftward shift of the volcano plot.
Yuhang Wang, Zelin Wu, Yingfang Jiang, Di Zhang, Qiang Wang, Congwei Wang, Huihui Li, Xue Jia, Jun Fan, Hao Li   +9 more
wiley   +1 more source

Accelerated Discovery of High‐Performance PCFC Cathodes: Computational‐Experimental Optimization of Cobalt‐Substituted Ba0.95La0.05FeO3‐δ

Advanced Functional Materials, EarlyView.
An integrated computational–experimental strategy accelerates the discovery of high‐performance PCFC cathodes. Computational screening using machine learning interatomic potentials and targeted experiments identifies optimal cobalt substitution in Ba0.95La0.05FeO3‐δ, reducing area‐specific resistance by 58% at 500 °C.
Abdullah Tahir, Yuhao Wang, Jingwei Li, Yunfeng Tian, Francesco Ciucci   +4 more
wiley   +1 more source

Battery‐Free, Wireless Multi‐Sensing Platform for Comprehensive Management of Pressure Injury and Hygiene

Advanced Functional Materials, EarlyView.
A battery‐free, wireless device for real‐time monitoring of pressure injury and hygiene integrates pressure (≈10 kPa), temperature (≈40 °C), and NH3 gas sensing with antibacterial functionality. Enabled by near‐field communication, it ensures simultaneous, interference‐free mechanical and chemical monitoring, offering a practical solution for pressure ...
Myungwoo Choi, Sunwoo Lee, Sang Won Kim, Ji Yeon Lee, Younghan Kim, Jewook Kim, Gye Hyeon Lee, Saehan Choi, Seonggwang Yoo, Hak‐Young Ahn, Taehoon Kim, Je‐Sang Lee, Weon Gi Min, Inkyu Park, Young‐Seok Shim, Yoon Sung Nam, Seokwoo Jeon, Jeong‐O Lee, Yong Suk Oh, Donghwi Cho   +19 more
wiley   +1 more source

Educating Future Computer Science Teachers to use the DQN Machine Learning Algorithm with ROS

, 2023

openalex   +1 more source

Selective Benzene Capture by Metal‐Organic Frameworks

Advanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) hold significant potential for capturing benzene from air emissions and hydrocarbon mixtures in liquid phases. This capability stems from their precisely engineered structures, versatile chemistries, and diverse binding interactions.
Zongsu Han, Ankit Mondal, Jiatong Huo, Yihao Yang, Hong‐Cai Zhou   +4 more
wiley   +1 more source

Quantum Dots and Perovskites‐Based Physically Unclonable Functions for Binary and Ternary Keys via Optical‐to‐Electrical Conversion

Advanced Functional Materials, EarlyView.
This study investigates optoelectronic PUFs that improve on traditional optical and electrical PUFs. The absorber materials are randomly coated through spray coating, ligand exchange, and dynamic spin coating. Incident light generates wavelength‐dependent binary multikey and enhances security ternary keys, approaching near‐ideal inter‐ and intra ...
Hanseok Seo, Taesung Park, Awais Ali, Byung Ku Jung, Young Kyun Choi, Junhyeok Park, Soong Ju Oh   +6 more
wiley   +1 more source

Printing Nacre‐Mimetic MXene‐Based E‐Textile Devices for Sensing and Breathing‐Pattern Recognition Using Machine Learning

Advanced Functional Materials, EarlyView.
This study presents a Ti3C2Tx MXene/WPU nacre‐mimetic nanomaterial as a printable ink for direct‐write printing onto textiles‐based sensors. The resulting wearable device demonstrates high sensitivity, biocompatibility, and mechanical strength. Furthermore, NFC‐enabled humidity sensor produces time‐series data, which informs a machine learning ...
Lulu Xu, Wenqing Chen, Qian Li, Rohit Gupta, Biswajoy Bagchi, Laurence B. Lovat, Manish K. Tiwari   +6 more
wiley   +1 more source

Biases and Ethical Considerations for Machine Learning Pipelines in the Computational Social Sciences

, 2023
De, S, Jangra, S, Agarwal, V, Johnson, J, Sastry, N   +4 more
openaire   +2 more sources

AI and Machine Learning in Engineering: Transformative Applications and Case Studies in Computational Science

This paper explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in engineering, emphasizing their integration with computational science to address complex challenges and optimize infrastructure. By leveraging advanced neural network architectures, such as Physics-Informed Neural Networks (PINNs) and deep ...
openaire   +2 more sources

Prediction of the Computer Science Department's Educational Performance Through Machine Learning Model by Analyzing Students' Academic Statements

, 2023
Md. Alamgir Hossain, Ishtiaq Ahammad, Md. Kawsar Ahmed, Md Imtiaz Ahmed   +3 more
openalex   +2 more sources