Metal-organic frameworks (MOFs), particularly UiO-66-NH, are employed as catalysts in many industrial catalyst applications. As converting catalysts into thin film significantly increases their catalytic properties, we report a general approach to synthesizing MOF thin films (UiO-66-Pyca-CuO). First, functionalization of UiO-66-NH was done with 3-pyridine carboxaldehyde by the postsynthesis method, and then, UiO-66-Pyca was entangled on the surface of copper oxide nanoparticles with a modern strategy (MOF thin film). The morphology and structure of the synthesized UiO-66-Pyca-CuO were determined by using X-ray diffraction, Fourier transform infrared, field-emission scanning electron microscopy, energy-dispersive analysis of X-ray, inductively coupled plasma-mass spectrometry, elemental analyses of CHNOS, temperature-programmed desorption of ammonia, Brunauer-Emmett-Teller, and X-ray photoelectron spectroscopy. We studied the catalytic action of the UiO-66-Pyca-CuO thin film in the synthesis of α-aminonitriles via Strecker reaction. Our studies show that this catalysis can be a suitable catalyst in the synthesis of α-aminonitriles because of having advantages such as using the solvent being environmentally friendly, easy separation of the catalyst (only by picking up the MOF thin film from inside the solution), the reaction at room temperature, high yield, and reusability.
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http://dx.doi.org/10.1021/acsami.4c09484 | DOI Listing |
Molecular junctions (MJs) are celebrated nanoelectronic devices for mimicking conventional electronic functions, including rectifiers, sensors, wires, switches, transistors, negative differential resistance, and memory, following an understanding of charge transport mechanisms. However, capacitive nanoscale molecular junctions are rarely seen. The present work describes electrochemically (E-Chem) grown covalently attached molecular thin films of 10, 14.
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Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
Neutron-Transformer Reflectometry Advanced Computation Engine (), a neural network model using a transformer architecture, is introduced for neutron reflectometry data analysis. It offers fast, accurate initial parameter estimations and efficient refinements, improving efficiency and precision for real-time data analysis of lithium-mediated nitrogen reduction for electrochemical ammonia synthesis, with relevance to other chemical transformations and batteries. Despite limitations in generalizing across systems, it shows promises for the use of transformers as the basis for models that could accelerate traditional approaches to modeling reflectometry data.
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Key Laboratory of High Performance Ship Technology, Wuhan University of Technology, Ministry of Education, Wuhan 430063, China.
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Department of Physics, SUNY University at Buffalo, Buffalo, NY, 14220, USA.
Quasi-2D perovskite made with organic spacers co-crystallized with inorganic cesium lead bromide inorganics is demonstrated for near unity photoluminescence quantum yield at room temperature. However, light emitting diodes made with quasi-2D perovskites rapidly degrade which remains a major bottleneck in this field. In this work, It is shown that the bright emission originates from finely tuned multi-component 2D nano-crystalline phases that are thermodynamically unstable.
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Cell Therapy Center, The University of Jordan, Amman, Jordan.
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