Uncertainty Quantification of Linear Scaling, Machine Learning, and Density Functional Theory Derived Thermodynamics for the Catalytic Partial Oxidation of Methane on Rhodium.

Accurate and complete microkinetic models (MKMs) are powerful for anticipating the behavior of complex chemical systems at different operating conditions. In heterogeneous catalysis, they can be further used for the rapid development and screening of new catalysts. Density functional theory (DFT) is often used to calculate the parameters used in MKMs with relatively high fidelity.

Implementing the Blowers-Masel Approximation to Scale Activation Energy Based on Reaction Enthalpy in Mean-Field Microkinetic Modeling for Catalytic Methane Partial Oxidation.

Mean-field microkinetic modeling is a powerful tool for catalyst design and the simulation of catalytic processes. The reaction enthalpies in a microkinetic model often need to be adjusted when changing species' binding energies to model different catalysts, when performing thermodynamic sensitivity analyses, and when fitting experimental data. When altering reaction enthalpies, the activation energies should also be reasonably altered to ensure realistic reaction rates.

RMG Database for Chemical Property Prediction.

The Reaction Mechanism Generator (RMG) database for chemical property prediction is presented. The RMG database consists of curated datasets and estimators for accurately predicting the parameters necessary for constructing a wide variety of chemical kinetic mechanisms. These datasets and estimators are mostly published and enable prediction of thermodynamics, kinetics, solvation effects, and transport properties.

Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO Hydrogenation on Ni(111).

Automatic mechanism generation is used to determine mechanisms for the CO hydrogenation on Ni(111) in a two-stage process while considering the correlated uncertainty in DFT-based energetic parameters systematically. In a coarse stage, all the possible chemistry is explored with gas-phase products down to the ppb level, while a refined stage discovers the core methanation submechanism. Five thousand unique mechanisms were generated, which contain minor perturbations in all parameters.

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation.

In chemical kinetics research, kinetic models containing hundreds of species and tens of thousands of elementary reactions are commonly used to understand and predict the behavior of reactive chemical systems. Reaction Mechanism Generator (RMG) is a software suite developed to automatically generate such models by incorporating and extrapolating from a database of known thermochemical and kinetic parameters. Here, we present the recent version 3 release of RMG and highlight improvements since the previously published description of RMG v1.

Automated Transition State Theory Calculations for High-Throughput Kinetics.

A scarcity of known chemical kinetic parameters leads to the use of many reaction rate estimates, which are not always sufficiently accurate, in the construction of detailed kinetic models. To reduce the reliance on these estimates and improve the accuracy of predictive kinetic models, we have developed a high-throughput, fully automated, reaction rate calculation method, AutoTST. The algorithm integrates automated saddle-point geometry search methods and a canonical transition state theory kinetics calculator.

Transition state geometry prediction using molecular group contributions.

Detailed kinetic models to aid the understanding of complex chemical systems require many thousands of reaction rate coefficients, most of which are estimated, some quite approximately and with unknown uncertainties. This motivates the development of high-throughput methods to determine rate coefficients via transition state theory calculations, which requires the automatic prediction of transition state (TS) geometries. We demonstrate a novel approach to predict TS geometries using a group-additive method.

Double Potential Pulse Chronocoulometry for Detection of Plasma Membrane Cholesterol Efflux at Disk Platinum Microelectrodes.

A double potential pulse scheme is reported for observation of cholesterol efflux from the plasma membrane of a single neuron cell. Capillary Pt disk microelectrodes having a thin glass insulator allow the 10 μm diameter electrode and cell to be viewed under optical magnification. The electrode, covalently functionalized with cholesterol oxidase, is positioned in contact with the cell surface resulting in enzyme catalyzed cholesterol oxidation and efflux of cholesterol from the plasma membrane at the electrode contact site.

An extensible framework for capturing solvent effects in computer generated kinetic models.

Detailed kinetic models provide useful mechanistic insight into a chemical system. Manual construction of such models is laborious and error-prone, which has led to the development of automated methods for exploring chemical pathways. These methods rely on fast, high-throughput estimation of species thermochemistry and kinetic parameters.

Increased plasma membrane cholesterol in cystic fibrosis cells correlates with CFTR genotype and depends on de novo cholesterol synthesis.

Background: Previous observations demonstrate that Cftr-null cells and tissues exhibit alterations in cholesterol processing including perinuclear cholesterol accumulation, increased de novo synthesis, and an increase in plasma membrane cholesterol accessibility compared to wild type controls. The hypothesis of this study is that membrane cholesterol accessibility correlates with CFTR genotype and is in part influenced by de novo cholesterol synthesis.

Methods: Electrochemical detection of cholesterol at the plasma membrane is achieved with capillary microelectrodes with a modified platinum coil that accepts covalent attachment of cholesterol oxidase.

First-principles thermochemistry for the combustion of a TiCl4 and AlCl3 mixture.

AlCl(3) is added in small quantities to TiCl(4) fed to industrial reactors during the combustion synthesis of titanium dioxide nanoparticles in order to promote the rutile crystal phase. Despite the importance of this process, a detailed mechanism including AlCl(3) is still not available. This work presents the thermochemistry of many of the intermediates in the early stages of the mechanism, computed using quantum chemistry.

First-principles thermochemistry for silicon species in the decomposition of tetraethoxysilane.

Tetraethoxysilane (TEOS) is used as a precursor in the industrial production of silica nanoparticles using thermal decomposition methods such as flame spray pyrolysis (FSP). Despite the industrial importance of this process, the current kinetic model of high-temperature decomposition of TEOS to produce intermediate silicon species and eventually form amorphous silica (R-SiO2) nanoparticles remains inadequate. This is partly due to the fact only a small proportion of the possible species is considered.

First-principles thermochemistry for the production of TiO2 from TiCl4.

Despite the industrial importance of the process, the detailed chemistry of the high-temperature oxidation of titanium tetrachloride (TiCl4) to produce titania (TiO2) nanoparticles remains unknown, partly due to a lack of thermochemical data. This work presents the thermochemistry of many of the intermediates in the early stages of the mechanism, computed using quantum chemistry. The enthalpies of formation and thermochemical data for TiOCl, TiOCl2, TiOCl3, TiO2Cl2, TiO2Cl3, Ti2O2Cl3, Ti2O2Cl4, Ti2O3Cl2, Ti2O3Cl3, Ti3O4Cl4, and Ti5O6Cl8 were calculated using density functional theory (DFT).