Results 101 to 110 of about 1,189 (219)

Comparative Insights and Overlooked Factors of Interphase Chemistry in Alkali Metal‐Ion Batteries

Advanced Energy Materials, EarlyView.
This review presents a comparative analysis of Li‐, Na‐, and K‐ion batteries, focusing on the critical role of electrode–electrolyte interphases. It especially highlights overlooked aspects such as SEI/CEI misconceptions, binder effects, and self‐discharge relevance, emphasizing the limitations of current understanding and offering strategies for ...
Changhee Lee, Zachary T. Gossage, Shinichi Kumakura, Shinichi Komaba   +3 more
wiley   +1 more source

Accuracy of digitally fabricated partial coverage ceramic restorations using conventional impression and intraoral scanners. [PDF]

BMC Oral Health
Adly A, Ellayeh M, El-Farag SAA.
europepmc   +1 more source

Photothermally Enhanced Hydrogel‐Based Thermoelectrochemical Cells for Stable, Passive Harvesting of Low‐Grade Heat

Advanced Energy Materials, EarlyView.
A photothermally enhanced hydrogel‐based thermoelectrochemical cell enables passive conversion of low‐grade heat and solar energy into electricity. The quasi‐solid device achieves high thermopower and stable long‐term operation under passive conditions.
Matteo Bevione, Ziyan Pan, Giulia Tagliabue   +2 more
wiley   +1 more source

Flexible Electrospun PVDF/PAN/Graphene Nanofiber Piezoelectric Sensors for Passive Human Motion Monitoring. [PDF]

Sensors (Basel)
Cirik H, Kabakci YG, Basyooni-M Kabatas MA, Kiliç HŞ.   +3 more
europepmc   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Synthesis, characterization of hybrid nanocomposite material and fabrication of their advance membrane for removal of antibiotics from water. [PDF]

PLoS One
Gul FS, Ullah H, Umar A, Umar A, Sarfraz M, Khan MS, Khan MU, Khan MS, Shafique S, Gurbanova L, Wadood A, Ogaly HA, Al-Zahrani FAM.   +12 more
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Mechanistic Insights into Quorum Quenching-Mediated Control of EPS and Biofilm Formation in Submerged MBR. [PDF]

Molecules
Sohail N, Martienssen M.
europepmc   +1 more source

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

Advanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang, Yongqing Zhang, Xinyu Deng, Hyeon Jeong Lee, Delong Zhang   +4 more
wiley   +1 more source

Fabrication of Nitrogen-Doped Mesoporous Carbon With Tunable Pore Size via Self-Assembly of Poly(4-Vinylpyridine)-Block-Poly(2,2,2-Trifluoroethyl Methacrylate). [PDF]

Macromol Rapid Commun
Kong Y, Miyamori Y, Sasaki H, Kikuchi R, Hatakeyama-Sato K, Hayakawa T, Nabae Y.   +6 more
europepmc   +1 more source