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Zirconium chloro fluoride as catalyst for C-F bond activation and HF transfer of fluoroalkanes
(2024)
In this work1, we have successfully synthesised amorphous zirconium chloro fluoride (ZCF), which exhibits medium lewis acidity. In addition to investigating the local coordination sphere around the Zr atoms and the material properties, we were able to establish a catalytic behavior of ZCF in C-F bond activation reactions. We present the first heterogeneous catalyst that performs dehydrofluorination of a fluoroalkane and consecutive hydrofluorination of an alkyne at room temperature.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
The LOBSTER (Deringer et al., 2011;Maintz et al., 2013 ,2016 ;Nelson et al., 2020 ) software aids in extracting quantum-chemical bonding information from materials by projecting the plane-wave based wave functions from density functional theory (DFT) onto an atomic orbital basis. LobsterEnv, a module implemented in pymatgen (Ong et al., 2013) by some of the authors of this package, facilitates the use of quantum-chemical bonding information obtained from LOBSTER calculations to identify neighbors and coordination environments. LobsterPy is a Python package that offers a set of convenient tools to further analyze and summarize the LobsterEnv outputs in the form of JSONs that are easy to interpret and process. These tools enable the estimation of (anti) bonding contributions, generation of textual descriptions, and visualization of LOBSTER computation results. Since its first release, both LobsterPy and LobsterEnv capabilities have been extended significantly. Unlike earlier versions, which could only automatically analyze Crystal Orbital Hamilton Populations (COHPs) (Dronskowski & Blöchl, 1993), both can now also analyze Crystal Orbital Overlap Populations (COOP) (Hughbanks & Hoffmann, 1983) and Crystal Orbital Bond Index (COBI) (Müller et al., 2021). Extracting the information about the most important orbitals contributing to the bonds is optional, and users can enable it as needed. Additionally, bonding-based features for machinelearning (ML) studies can be engineered via the sub-packages “featurize” and “structuregraphs”. Alongside its Python interface, it also provides an easy-to-use command line interface (CLI) that runs automatic analysis of the computations and generates a summary of results and publication-ready figures. LobsterPy has been used to produce the results in Ngo et al. (2023), Chen et al. (2024), Naik et al. (2023), and it is also part of Atomate2 (2023) bonding analysis workflow for generating bonding analysis data in a format compatible with the Materials Project (Jain et al., 2013) API.
Crystalline materials that exhibit reversible mechanical responses upon exposure to external stimuli have garnered significant attention owing to their potential applications in various fields. Herein, we report a crystal of (2Z,2′Z)-2,2′-(1,4-phenylene)bis(3-(4-bromophenyl)acrylonitrile) (DSBr), which displays simultaneous elasto-plastic behaviour and reversible thermosalient effects. While elasto-plastic behaviour is attributed to underlying packing features, reversible thermosalient expansion is attributed to uniaxial expansion mediated by heat. Exceptional length increase and contraction upon cooling is due to the restorative nature of weak interactions through a cooperative effect. The cooperative movement of molecules is reflected in the unidirectional expansion of the habit plane. Thermosalient reversible expansion–contraction in elasto-plastic crystals have not been discussed in the literature so far. Detailed analysis reported herein provides a comprehensive understanding of the underlying mechanism of flexibility and thermosalient responses. This crystal's unique blend of reversible thermal expansion with flexibility holds substantial promise for applications in flexible thermal actuators.
A cobalt(III) complex, [Co(L)3](DMF) (1) of Schiff base ligand HL, 2-((E)-(benzylimino)methyl)-4-bromophenol is prepared and single crystal X-ray structural analysis have also been performed. The structures of complex 1 showed hexa-coordinated mononuclear systems that adopt octahedral geometry. The complex has also exhibited the supramolecular networks through non-covalent interactions like H-bonding, C–Hπ stacking. Moreover, the complex 1 is very effective in the catalytic fixation of carbon dioxide in propergyl alcohols to produce α-alkylidene cyclic carbonates. The catalytic production of α-alkylidene cyclic carbonates have been carried out through carboxylative cyclization of propargyl alcohols using CO2 balloon of 1 atm pressure at 80 ◦C. Solvent free condition (green synthesis) made this catalytic protocol eco-friendly towards the environment. Utilizing various substrates of propargyl alcohols moderate to high percentage yield (62–95%) of respective α-alkylidene cyclic carbonates product have been isolated over this catalytic reaction. Besides, the theoretical calculations (DFT) was performed for the prediction of probable mechanism of the catalytic reaction
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
This study investigates the mechanochemical reactions between AgBr 3-picolylamine and 4-picolylamine. The use of different stoichiometry ratios of the reagents allows [(AgBr)(n-pica)]n and [(AgBr)2(n-pica)]n to be obtained, and we report the new structures of [(AgBr)2(3-pica)]n and [(AgBr)2(4-pica)]n which are characterized by the presence of the following: (a) infinite inorganic chains, (b) silver atom coordinated only by bromide atoms and (c) argentophilic interactions. Furthermore, we studied the interconversion of [(AgBr)(n-pica)]n/[(AgBr)2(n-pica)]n by mechanochemical and thermal properties. The in situ experiments suggest that [(AgBr)(3-pica)]n is kinetically favoured while [(AgBr)2(3-pica)]n is converted into [(AgBr)(3-pica)]n only with a high excess of the ligand. Finally, the liquid nature of the ligands is not sufficient to assist the grinding process, and the complete reaction is observed with the addition of a small quantity of acetonitrile.
We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch
other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of filesystem- and cloud-based databases without the data storage process being part of the underlying workflow logic itself. Jobflow has been intentionally designed to be fully independent of the choice of workflow manager used to dispatch the calculations on remote computing resources. At the time of writing, Jobflow
workflows can be executed either locally or across distributed compute environments via an adapter to the FireWorks package, and Jobflow fully supports the integration of additional workflow execution adapters in the future.
Jobflow is a free, open-source library for writing and executing workflows. Complex workflows can be defined using simple python functions and executed locally or on arbitrary computing resources using the FireWorks workflow manager.
Some features that distinguish jobflow are dynamic workflows, easy compositing and connecting of workflows, and the ability to store workflow outputs across multiple databases.