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Densities and viscosities for the binary mixtures of n-nonane/1-heptanol, n-nonane/1-octanol, n-nonane/1-nonanol, and n-nonane/1-decanol were presented in this work. The measurements were carried o...

Structural binary cleavage of 3,7-diamino-2,6-dinitro-1H,5H-pyrazolo-[1,2-a]pyrazole-1,5-dione 3, under nucleophilic conditions, leads to the formation of a monocyclic pyrazole unit of 5-amino-4-nitro-1,2-dihydro-3H-pyrazol-3-one, 4. Additionally, various salts of the pyrazole ring were synthesized and fully characterized. Detonation properties and mechanical sensitivities of 4 and other new compounds are remarkably improved compared to 3. This simple and efficient strategy is highly desirable for future studies on the development of insensitive and high performing materials.

To develop efficient ionic liquid (IL)-based separation processes like extractive distillation, the knowledge of IL-involved vapor–liquid equilibria (VLE) is necessary. Since experimental measureme...

The extension of the translated–consistent Peng–Robinson (tc-PR) equation of state (EoS) to mixtures has been investigated. For this purpose, advanced EoS/aresE,γ mixing rules are used to combine t...

This work reported the experimental data of densities, speed of sound, and refractive index of a binary mixture containing ethyl isobutyrate and cyclohexane at different temperatures (293.15, 298.1...

This work theoretically assessed the necessary and sufficient conditions for the formation of an adsorption azeotrope in a binary gas mixture when this mixture exhibits either intersecting or nonintersecting single gas isotherms. The thermodynamically consistent dual process Langmuir (DPL) model with equal component i saturation capacities qi,js on site j and the general DPL model with nonequal qi,js on site j were used for this purpose. Analytical expressions derived for both DPL models, in terms of the single gas isotherm DPL model parameters, were used to find examples or to determine theoretically when an adsorption azeotrope forms in a binary gas mixture for both intersecting and nonintersecting single gas isotherms. For the general DPL model, it was determined that neither necessary nor sufficient conditions exist for the formation of an adsorption azeotrope in a binary gas mixture. This means that an adsorption azeotrope can form irrespective of whether the corresponding single gas isotherms intersect or not. For the thermodynamically consistent DPL model, it was determined that the intersection of the single gas isotherms is a sufficient condition for the formation of an adsorption azeotrope in a binary gas mixture, but it is not a necessary condition. This means that intersecting single gas isotherms guarantee the formation of an adsorption azeotrope in the corresponding binary gas mixture, while nonintersecting single gas isotherms can also result in the formation of an adsorption azeotrope in the corresponding binary gas mixture. Overall, this analysis provides a well-posed resolution to the question of necessity and sufficiency for the formation of adsorption azeotropes in binary gas mixtures and the intersection of their corresponding single gas isotherms based on two physically sound formulations of the very popular DPL model.

With the increased demand for controlled deterministic growth of III–V semiconductors at the nanoscale, the impact and interest of understanding defect formation and crystal structure switching becomes increasingly important. Vapor–liquid–solid (VLS) growth of semiconductor nanocrystals is an important mechanism for controlling and studying the formation of individual crystal layers and stacking defects. Using in situ studies, combining atomic resolution of transmission electron microscopy and controlled VLS crystal growth using metal organic chemical vapor deposition, we investigate the simplest achievable change in atomic layer stacking–single twinned layers formed in GaAs. Using Au-assisted GaAs nanowires of various diameters, we study the formation of individual layers with atomic resolution to reveal the growth difference in forming a twin defect. We determine that the formation of a twinned layer occurs significantly more slowly than that of a normal crystal layer. To understand this, we conduct thermodynamic modeling and determine that the propagation of a twin is limited by the energy cost of forming the twin interface. Finally, we determine that the slower propagation of twinned layers increases the probability of additional layers nucleating, such that multiple layers grow simultaneously. This observation challenges the current understanding that continuous uniform epitaxial growth, especially in the case of liquid-metal assisted nanowires, proceeds one single layer at a time and that its progression is limited by the nucleation rate.

We report a deep learning (DL) model that predicts various material properties while accepting directly accessible inputs from routine experimental platforms: chemical compositions and diffraction data, which can be obtained from the X-ray or electron-beam diffraction and energy-dispersive spectroscopy, respectively. These heterogeneous forms of inputs are treated simultaneously in our DL model, where the novel chemical composition vector is proposed by developing element embedding with the normalized composition matrix. With 1524 binary samples available in the Materials Project database, the model predicts formation energies and band gaps with mean absolute errors of 0.29 eV/atom and 0.66 eV, respectively. According to the weighing test between these two inputs, the properties tend to be more influenced by the chemical composition than the crystal structure. This work intentionally avoids using inputs that are not directly accessible (e.g., atomic coordinates) in experimental platforms, and thus is expected to substantially improve the practical use of DL models.

Understanding the molecular-level solubility of CO2 and its mixtures is essential to the progress of gas-treating technologies. Herein, we use grand canonical Monte Carlo simulations to study the single-component gas absorption of SO2, N2, CH4, and H2 and binary mixtures of CO2/SO2, CO2/N2, CO2/CH4, and CO2/H2 of varying mole fractions within multivalent ionic liquids (ILs). Our results highlight the importance of the free volume effect and the anion effect when interpreting the absorption behavior of these mixtures, similar to the behavior of CO2 found in our previous study (Phys. Chem. Chem. Phys.2020,22, 20618-20633). The deviation of gas solubility between the pure component absorption versus the binary absorption, as well as the solubility selectivity, highlights the importance of the relative affinity of gas species within a mixture to the different anions. The absorption selectivity within a specific IL system can be predicted based on the relative gas affinity to the anion.

Metal-insulator transition (MIT) compounds are materials that may exhibit insulating or metallic behavior, depending on the physical conditions, and are of immense fundamental interest owing to their potential applications in emerging microelectronics. There is a dearth of thermally-driven MIT materials, however, which makes delineating these compounds from those that are exclusively insulating or metallic challenging. Here we report a material database comprising temperature-controlled MITs (and metals and insulators with similar chemical composition and stoichiometries to the MIT compounds) from high quality experimental literature, built through a combination of materials-domain knowledge and natural language processing. We featurize the dataset using compositional, structural, and energetic descriptors, including two MIT relevant energy scales, an estimated Hubbard interaction and the charge transfer energy, as well as the structure-bond-stress metric referred to as the global-instability index (GII). We then perform supervised classification, constructing three electronic-state classifiers: metal vs non-metal (M), insulator vs non-insulator (I), and MIT vs non-MIT (T). We identify two important descriptors that separate metals, insulators, and MIT materials in a 2D feature space: the average deviation of the covalent radius and the range of the Mendeleev number. We further elaborate on other important features (GII and Ewald energy), and examine how they affect classification of binary vanadium and titanium oxides. We discuss the relationship of these atomic features to the physical interactions underlying MITs in the rare-earth nickelate family. Last, we implement an online version of the classifiers, enabling quick probabilistic class predictions by uploading a crystallographic structure file.

Here, we report density functional theory calculations combined with the k-means clustering algorithm and the Spearman rank correlation analysis to investigate the stability mechanisms of eight-atom binary metal AB clusters, where A and B are Fe, Co, Ni, Cu, Ga, Al, and Zn (7 unary and 21 binary clusters). Based on the excess energy analysis, the six most stable binary clusters are NiAl, NiGa, CoAl, FeNi, NiZn, and FeAl, and except for FeNi, their highest energetic stabilities can be explained by the hybridization of the d- and sp-states, which is maximized at the 50% composition, i.e., A4B4. Based on the Spearman correlation analysis, the energetic stability of the binary clusters increases with an increase in the highest occupied molecule orbital-lowest unoccupied molecular orbital (HOMO-LUMO) energy separation, which can be considered as a global descriptor. Furthermore, reducing the total magnetic moment values increases the stability for binary clusters without the Fe, Co, and Ni species, while the binary FeB, CoB, and NiB clusters increase their energetic stability with a decrease in the cluster radius, respectively, i.e., an energetic preference for compact structures.