Results 201 to 210 of about 2,209,550 (335)
Bio‐Inspired Molecular Events in Poly(Ionic Liquids)
Originating from dipolar and polar inter‐ and intra‐chain interactions of the building blocks, the topologies and morphologies of poly(ionic liquids) (PIL) govern their nano‐ and micro‐processibility. Modulating the interactions of cation‐anion pairs with aliphatic dipolar components enables the tunability of properties, facilitated by “bottom‐up ...
Jiahui Liu, Marek W. Urban
wiley +1 more source
Machine learning for improved path loss prediction in urban vehicle-to-infrastructure communication systems. [PDF]
Ben Ameur M +3 more
europepmc +1 more source
Fluorine‐Free Soft Nanocomposites for High‐Speed Liquid Impact Repellence
Fluorine‐free soft nanocomposite coatings are developed using silicone oil‐mediated mechanical‐stiffness control, enabling ‘dry’ liquid‐repellent surfaces that resist high‐speed water jet impacts up to ∼60 m/s. By tuning nanoparticle loading and oil content, the coatings also achieve >90% optical transparency, amphiphobicity with impact resistance to ...
Priya Mandal +4 more
wiley +1 more source
Path Loss Modeling for RIS-Assisted Wireless Communication in Tunnel Scenarios. [PDF]
Yang Q +6 more
europepmc +1 more source
Deciphering Small Molecule Diffusion Parameters Across Light Responsive Polymersome Membranes
Light‐responsive polymersomes bearing donor–acceptor Stenhouse adducts (DASAs) enable programmable control over small‐molecule transport across synthetic membranes. By systematically varying DASA density, an optimal functionalization regime is identified that maximizes light‐gated permeability.
Farzina Matubbar +7 more
wiley +1 more source
MAGTWIST: A compact magnetic rotary actuator, enabling smooth, stepless rotation, and on‐demand locking. Inspired by peristalsis, a soft polymer belt generates a traveling‐wave, enabling 270° rotation when heated. Cooling stiffens the belt, locking it in position and enabling it to withstand high loads.
Simon Frieler +3 more
wiley +1 more source
A Machine Learning Approach for Path Loss Prediction Using Combination of Regression and Classification Models. [PDF]
Iliev I +5 more
europepmc +1 more source

