Recurrent neural networks excel at predicting and generating complex high-dimensional temporal patterns. Due to their inherent nonlinear dynamics and memory, they can learn unbounded temporal dependencies from data. In a machine learning setting, the network's parameters are adapted during a training phase to match the requirements of a given task/problem increasing its computational capabilities.
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October 2024
Two diphosphanes with variable-length ligands tested as nucleophiles to prepare isoporphyrin copolymers in the presence of ditolylporphyrin of zinc (ZnTP) prevented the oxidation of the diphosphine ligand. This paper demonstrates the power of this approach and describes the photoelectrocatalytic properties. The obtained copolymers were characterized by UV-vis spectroscopy, X-ray photoelectron spectroscopy, atomic force micrograph (AFM), EQCM (Electrochemical Quartz Cristal Microbalance) and electrochemistry.
View Article and Find Full Text PDFMany ionic surfactants, such as sodium dodecyl sulfate (SDS) crystallize out of solution if the temperature falls below the crystallization boundary. The crystallization temperature is impacted by solution properties and can be decreased with the addition of salt. We studied SDS crystallization at liquid/vapor interfaces from solutions at high ionic strength (sodium chloride).
View Article and Find Full Text PDFHypothesis: Semifluorinated alkanes amphiphiles spontaneously form highly monodispersed hemimicelles at the surface of water. The origin of the formation and complex structure of these surprising supramolecular aggregates were only recently clarified using molecular dynamics simulations (MD). The existence of a pit at the center of these aggregates made up of almost 3000 molecules was indeed reproduced by the MD simulations, but not understood.
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