Results 181 to 190 of about 21,023 (296)

Quantitative Insights into Atomic Scale Oil–Clay Adhesion Governing Shale Oil Retention and Mobility

open access: yesENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
Atomic force microscopy reveals a clear adhesion hierarchy between shale oil and clay minerals, governed by nanoscale surface chemistry. Strong interactions on chlorite and illite arise from abundant polar sites and cation bridging, whereas weaker coupling on montmorillonite limits interfacial binding.
Chen Lv, Guanwen Lu, Guandong Su
wiley   +1 more source

Harnessing Thin‐Film Solid‐State Electrolytes: Enabling Breakthroughs in All‐Solid‐State Batteries

open access: yesENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
Schematic illustration highlighting the advantages of transitioning from traditional thick solid‐state electrolytes (SSEs) to thin‐film SSEs. Thinning the electrolyte enables higher ionic conductivity, reduced interfacial polarization, improved flexibility, compact electrode contact, and enhanced energy density, offering a promising pathway toward high‐
Yitao He   +3 more
wiley   +1 more source

Concept, Tests, and Optimum Design of Novel Negative Stiffness Friction Dampers for Seismic Protection of Long‐Span Bridges

open access: yesEarthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Viscous dampers (VDs) are widely used to mitigate the excessive responses of long‐span bridges. However, VDs face some intricate challenges in engineering practices, including high damping coefficient requirement, potential base shear amplification and tremendous damping force under high velocity. To address these issues, this study develops a
Ruisheng Ma   +5 more
wiley   +1 more source

Learning Rocking Dynamics From Sparse Shake‐Table Data With Interpretable Physics‐Informed Neural Networks

open access: yesEarthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT We present a hybrid interpretable Physics‐Informed Neural Network Long‐Short Term Memory (Hybrid PINN LSTM) framework for predicting the seismic response of rocking blocks. Existing analytical models rely on uncertain idealizations, while purely data‐driven and machine‐learning approaches lack physical consistency and interpretability.
Shirley Shen   +1 more
wiley   +1 more source

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