Structural flexibility of a copper-based metal–organic framework: sorption of C₄-hydrocarbons and in situ XRD

Abstract

Structural transitions of a flexible Cu-MOF are observed by pressure-dependentin situXRD with C4-hydrocarbons whereby only 1-butene sorption shows two transformations.

Exceptional adsorption-induced cluster and network deformation in the flexible metal–organic framework DUT-8(Ni) observed by in situ X-ray diffraction and EXAFS

“…[17][18][19][20][21] In general, single crystal data during adsorption are very rare and sophisticated and thus powder diffraction is used giving good insight into structural framework deformation. So far only few groups have realized the parallelized collection of diffraction and adsorption data.…”

A Cu(II)‐Inorganic Co−Crystal as a Versatile Catalyst Towards ‘Click’ Chemistry for Synthesis of 1,2,3‐triazoles and β‐hydroxy‐1,2,3‐triazoles

“…MOC directed heterogeneous catalysis has a special significance in synthetic chemistry . Cu‐based MOCs earn considerable attention in various fields including organic synthesis, materials science, chemical biology, medicinal chemistry . The copper based homogeneous catalysis in organic syntheses is also well established .…”

Experimental and Neural Network Modeling of Partial Uptake for a Carbon Dioxide/Methane/Water Ternary Mixture on 13X Zeolite

“…It allows the calculation of 25 thermodynamic properties. FLUIDCAL was utilized and endorsed in several studies that applied the software to identify thermodynamic and physical properties (i.e., density of single components in mixtures) with the aid of available system parameters …”

“…It allows the calculationo f2 5t hermodynamic properties.F LUIDCALw as utilized and endorsed in several studies that applied the software to identifyt hermodynamic and physical properties (i.e., density of single components in mixtures)with the aid of available system parameters. [19][20][21] However, reliable computational predictionm ethods such as an artificial neural network (ANN)h ave garnered excessive attraction. [22][23][24] Fori nstance, Golzar et al [25] adopted genetic function algorithm (GFA) and ANN models to predict the density,s urface tension, and viscosity of pure quaternary ammonium-based ionic liquids.F otoohi et al [11] studied the prediction of binary mixtures from data reported previously by utilizing 2D EoS,t hat is,R K, SRK, PR, and modified Mohsennia-Modarress-Mansoori( M4) and compared the results with the data predicted by utilizing ANN model predictions.H owever, they claimed that the binary mixtures predicted by utilizing the ANN model showed better agreement and accuracy compared to the EoS studied.…”