Results 191 to 200 of about 143,606 (219)

Resolving the Cu(bdc) Conundrum: Identifying Non‐Porous Packing of Prototypical Coordination‐Network Thin Films Combining Advanced Diffraction Techniques and Computational Modelling

Advanced Functional Materials, EarlyView.
Solution‐processed Cu(bdc) forms prototypical MOF thin films for which a multitude of not fully satisfactory structural models have been suggested. Combining rotating grazing‐incidence diffraction and X‐ray reflectivity on two complementary samples with density‐functional theory, we first discard the previously suggested models and then identify a non ...
Narges Taghizade, Mario Fratschko, Robbin Steentjes, Mercedes Linares‐Moreau, Paolo Falcaro, Christof Wöll, Roland Resel, Egbert Zojer   +7 more
wiley   +1 more source

Design of Reliable, Resilient, and Robust Architecture and Control for Next-Generation Optical-Wireless Networks. [PDF]

Sensors (Basel)
Nleya B, Pule B.
europepmc   +1 more source

Nanoengineered Binetworks in Multifunctional Anionic Block Polymer Mesoblends for Improved Mechanical Properties

Advanced Functional Materials, EarlyView.
Mesoblends consist of a nanostructured polymer into which a second polymer is imbibed with a selective solvent. In this work, a solvent‐templatable pentablock terpolymer possessing a sulfonated midblock is modified with a hydrophilic polymer that is photocrosslinked in situ, yielding a hierarchical binetwork.
Kacie M. Wells, Ivan Kuzmenko, Reza A. Ghiladi, Richard J. Spontak   +3 more
wiley   +1 more source

Machine Learning for Intelligent and Adaptive Communication Systems: From Optimization to Emerging Paradigms. [PDF]

Sensors (Basel)
Lee H, Sun Y, Simpson O.
europepmc   +1 more source

Optical in-memory computing using laser array. [PDF]

Light Sci Appl
Alkhazragi O.
europepmc   +1 more source

Proactive soft-failure prediction in optical transport networks via physics-inspired features and Infrastructure-as-Code orchestration. [PDF]

Sci Rep
Ali OM, Radwan AMA, Radwan OMA, Elsherbini MM.   +3 more
europepmc   +1 more source

Towards a physics-informed network paradigm with data generation and background noise removal for different distributed acoustic sensing applications. [PDF]

Light Sci Appl
Wan Y, Wang H, Yu X, Chen J, Fan X, He Z.   +5 more
europepmc   +1 more source

A Hybrid Deep Learning Approach for Performance Prediction in Optical Communication Systems Based on PON Scenarios. [PDF]

Sensors (Basel)
Muslim A, Gündoğan E, Kaya M, Alhajj R.   +3 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Survey on Machine Learning for Traffic-Driven Service Provisioning in Optical Networks

IEEE Communications Surveys and Tutorials, 2023
Tania Panayiotou, Maria Michalopoulou, Georgios Ellinas   +2 more
exaly