The advent of π-stacked layered metal–organic frameworks (MOFs), which offer electrical conductivity on top of permanent porosity and high surface area, opened up new horizons for designing compact MOF-based devices such as battery electrodes, supercapacitors, and spintronics. Permutation of structural building blocks, including metal nodes and organic linkers, in these electrically conductive (EC) materials, results in new systems with unprecedented and unexplored physical and chemical properties. With the ultimate goal of providing a platform for accelerated material design and discovery, here we lay the foundations for the creation of the first comprehensive database of EC-MOFs with an experimentally guided approach. The first phase of this database, coined EC-MOF/Phase-I, is composed of 1,057 bulk and monolayer structures built by all possible combinations of experimentally reported organic linkers, functional groups, and metal nodes. A high-throughput screening (HTS) workflow is constructed to implement density functional theory calculations with periodic boundary conditions to optimize the structures and calculate some of their most relevant properties. Because research and development in the area of EC-MOFs has long been suffering from the lack of appropriate initial crystal structures, all of the geometries and property data have been made available for the use of the community through an online platform that was developed during the course of this work. This database provides comprehensive physical and chemical data of EC-MOFs as well as the convenience of selecting appropriate materials for specific applications, thus accelerating the design and discovery of EC-MOF-based compact devices.

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X-ray diffraction, Amorphous silicon, Multi-objective optimization, Monte Carlo methods. This paper addresses a difficult inverse problem that involves the reconstruction of a three-dimensional model of tetrahedral amorphous semiconductors via inversion of diffraction data. By posing the material-structure determination as a multiobjective optimization program, it has been shown that the problem can be solved accurately using a few structural constraints, but no total-energy functionals/forces, which describe the local chemistry of amorphous networks. The approach yields highly realistic models of amorphous silicon, with no or only a few coordination defects (≤1%), a narrow bond-angle distribution of width 9–11.5°, and an electronic gap of 0.8–1.4 eV. These data-driven information-based models have been found to produce electronic and vibrational properties of a-Si that match accurately with experimental data and rival that of the Wooten-Winer-Weaire models. The study confirms the effectiveness of a multiobjective optimization approach to the structural determination of complex materials, and resolves a long-standing dispute concerning the uniqueness of a model of tetrahedral amorphous semiconductors obtained via inversion of diffraction data.

This study was aimed to evaluate the quality of raw and pasteurized milk marketed in Dharan. Milk may be contaminated with pathogenic microorganisms and a mixture of several adulterants and such milk pose a risk to consumers. The study was carried out from September 2019 to January 2020. Collected samples were tested for adulterants (starch, formalin, neutralizer and table sugar) as well as microbial quality (Total Coliform count, Total Viable Count, Thermoduric Count, Escherichia. coli and Staphylococcus aureus) as per standard guideline. The adulterants starch, formalin and neutralizer were not detected in both raw and pasteurized milk. However, table sugar was present in 45% (9 out of 20) raw milk and 90% (18 out of 20) pasteurized milk. The average Total Viable Count, Total Coliform Count and Thermoduric Count of raw milk were, 59×105 CFU/ml, 14×104 CFU/ml and 5×103 CFU/ml respectively. Similarly, the average Total Viable Count, Total Coliform Count and Thermoduric Count of pasteurized milk were found to be 15×104 CFU/ml, 14×103CFU/ml and 4×103 CFU/ml respectively. E. coli was detected in 30% pasteurized milk whereas S. aureus was isolated from only 20%. Likewise, E.coli and S. aureus were found in 55% and 45% of raw milk respectively. The results of the study indicated that routine monitoring of dairy industries and raw milk vendors, awareness campaign and good hygienic practice should be promoted to upgrade the quality of raw and pasteurized milk.

We present a force-biased Monte Carlo (FMC) method for structural modeling of the transition-metal clusters of Fe, Ni, and Cu of size 13, 30, and 55 atoms. By employing the Finnis-Sinclair potential for Fe and the Sutton-Chen potential for Ni and Cu, the total energy of the clusters is minimized using the local gradient of the potentials in Monte Carlo simulations. The structural configurations of the clusters, obtained from the biased Monte Carlo approach, are analyzed and compared with the same from the Cambridge Cluster Database (CCD) upon relaxation of the clusters using the first-principles density-functional code NWChem. The results show that the total-energy value and the structure of the FMC clusters are essentially identical to the corresponding value and the structure of the CCD clusters. A comparison of the NWChem-relax FMC and CCD structures is presented by computing the pair-correlation function, the bond-angle distribution, the coordination number of the first-coordination shell, and the Steinhardt bond-orientational order parameter, which provide information about the two-and three-body correlation functions, the local bonding environment of the atoms, and the geometry of the clusters. An atom-by-atom comparison of the FMC and CCD clusters is also provided by superposing one set of clusters onto another, and the electronic properties of the clusters are addressed by computing the density of electronic states.