Results 221 to 230 of about 148,969 (298)

Shear strengthening of masonry panels with GFRP: preliminary experimental results [PDF]

, 2000
Bonfiglioli, Barbara, Manfroni, Odine, Pascale, Giovanni, Stratford, Tim   +3 more
core  

Photoswitching Conduction in Framework Materials

Advanced Functional Materials, EarlyView.
This mini‐review summarizes recent advances in state‐of‐the‐art proton and electron conduction in framework materials that can be remotely and reversibly switched on and off by light. It discusses the various photoswitching conduction mechanisms and the strategies employed to enhance photoswitched conductivity.
Helmy Pacheco Hernandez, Stefan Hecht, Wolfgang Wenzel, Lars Heinke, Mariana Kozlowska   +4 more
wiley   +1 more source

Numerical investigation of ultimate capacity enhancement in corrugated web steel beams with different stiffening configurations. [PDF]

Sci Rep
Fouda A, Dabaon M, Abdrabou SE, Ghannam M.   +3 more
europepmc   +1 more source

MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance

Advanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
wiley   +1 more source

Detecting and Evaluating Fatigue Damage Mechanisms in Concrete with Embedded Aggregate Sensors. [PDF]

Materials (Basel)
Song Z, Cheng S, He H, Li W, Liu Y.
europepmc   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Strengthening strategies for unreinforced stone masonry walls using FRP and CFM composites. [PDF]

Sci Rep
Celik T.
europepmc   +1 more source

PEDOT‐Based Eutectogel Electrode Arrays for Enhanced High‐Resolution Electrogastrography: Fabrication, Stability, and Wearable Performance

Advanced Functional Materials, EarlyView.
Here, we present a high‐density PEDOT eutectogel electrode array for enhanced body surface gastric mapping. Silver electrodes are blade‐coated onto flexible substrates, followed by electrogelation of PEDOT:PSS and the deposition of a PEDOT:LS eutectogel.
Christopher Slaughter, Luke Gatecliff, Xudong Tao, Ruben Ruiz‐Mateos Serrano, Amy T. Jin, Matias L. Picchio, Daniele Mantione, Antonio Dominguez‐Alfaro, George G. Malliaras   +8 more
wiley   +1 more source

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

Advanced Functional Materials, EarlyView.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi, Sumukh Shankar Sharadaprasad, Rochus Schmid   +2 more
wiley   +1 more source