α-Diphenylphosphino-N-(pyrazin-2-yl)glycine as a ligand in Ni-catalyzed ethylene oligomerization

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Unexpected formation of dinaphthoaza-17-crown-5 ether containing γ-aminopiperidine subunit

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Predicting polycyclic aromatic hydrocarbon formation with an automatically generated mechanism for acetylene pyrolysis

Using Reaction Mechanism Generator (RMG), we have automatically constructed a detailed mechanism for acetylene pyrolysis, which predicts formation of polycyclic aromatic hydrocarbons (PAHs) up to pyrene. To improve the data available for formation pathways from naphthalene to pyrene, new high‐pressure limit reaction rate coefficients and species thermochemistry were calculated using a combination of electronic structure data from the literature and new quantum calculations. Pressure‐dependent kinetics for the CH potential energy surface calculated by Zádor et al. were incorporated to ensure accurate pathways for acetylene initiation reactions. After adding these new data into the RMG database, a pressure‐dependent mechanism was generated in a single RMG simulation which captures chemistry from C to C. In general, the RMG‐generated model accurately predicts major species profiles in comparison to plug‐flow reactor data from the literature. The primary shortcoming of the model is that formation of anthracene, phenanthrene, and pyrene are underpredicted, and PAHs beyond pyrene are not captured. Reaction path analysis was performed for the RMG model to identify key pathways. Notable conclusions include the importance of accounting for the acetone impurity in acetylene in accurately predicting formation of odd‐carbon species, the remarkably low contribution of acetylene dimerization to vinylacetylene or diacetylene, and the dominance of the hydrogen abstraction CH addition (HACA) mechanism in the formation pathways to all PAH species in the model. This work demonstrates the improved ability of RMG to model PAH formation, while highlighting the need for more kinetics data for elementary reaction pathways to larger PAHs.     

Deoxygenation of methyl laurate to hydrocarbons on silica-supported Ni-Mo phosphides: Effect of calcination temperatures of precursor

SiO2-supported Ni-Mo bimetallic phosphides were prepared by temperature-programmed reduction(TPR) method from the phosphate precursors calcined at different temperatures. Their properties were characterized by means of ultraviolet-visible diffuse reflectance spectroscopy(UV-Vis DRS), H2temperature-programmed reduction(H2-TPR), X-ray diffraction(XRD), transmission electron microscopy(TEM), CO chemisorption, H2 and NH3temperature-programmed desorptions(H2-TPD and NH3-TPD). Their catalytic performances for the deoxygenation of methyl laurate were tested in a fixed-bed reactor. When the precursors were calcined at 400 and 500?C, respectively, Ni Mo P2 phase could be formed apart from Ni2 P and Mo P phases in the prepared C400 and C500 catalysts. However, when the precursors were calcined at600, 700 and 800°C, respectively, only Ni2 P and Mo P phases could be detected in the prepared C600, C700 and C800 catalysts. Also, in C400, C500 and C600 catalysts, Mo atoms were found to be entered in the lattice of Ni2 P phase, but the entering extent became less with the increase of calcination temperature. As the calcination temperature of the precursor increased, the interaction between Ni and Mo in the prepared catalysts decreased, and the phosphide crystallite size tended to increase, subsequently leading to the decrease in the surface metal site density and the acid amount. C600 catalyst showed the highest activity among the tested ones for the deoxygenation of methyl laurate. As the calcination temperature of the precursor increased, the selectivity to C12 hydrocarbons decreased while the selectivity to C11 hydrocarbons tended to increase. This can be mainly attributed to the decreased Ni-Mo interaction and the increased phosphide particle size. In sum, the structure and performance of Ni-Mo bimetallic phosphide catalyst can be tuned by the calcination temperature of precursor.     

On the Statistical Calibration of Physical Models

We introduce a novel statistical calibration framework for physical models, relying on probabilistic embedding of model discrepancy error within the model. For clarity of illustration, we take the measurement errors out of consideration, calibrating a chemical model of interest with respect to a more detailed model, considered as “truth” for the present purpose. We employ Bayesian statistical methods for such model‐to‐model calibration and demonstrate their capabilities on simple synthetic models, leading to a well‐defined parameter estimation problem that employs approximate Bayesian computation. The method is then demonstrated on two case studies for calibration of kinetic rate parameters for methane air chemistry, where ignition time information from a detailed elementary‐step kinetic model is used to estimate rate coefficients of a simple chemical mechanism. We show that the calibrated model predictions fit the data and that uncertainty in these predictions is consistent in a mean‐square sense with the discrepancy from the detailed model data.     

A Simple Device for the Solute Elution from Thin-Layer Chromatographic Plates to High-Performance Liquid Chromatographic or Mass Spectrometric Instrument

JPC – Journal of Planar Chromatography – Modern TLC -     

病原微生物感染的蛋白质组学研究

基于质谱的蛋白质组学在近20年有巨大的发展。在其应用中,病原微生物和与感染相关的蛋白质组学具有重要科学意义,体系复杂度又相对较小,一直受到广泛关注并有较快发展。本文从感染中病原微生物和宿主的蛋白质组学两方面入手,简要综述应用蛋白质组学研究感染过程的相关工作,着重介绍该领域近几年的主要进展,并对其发展做出展望。