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.     

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 -