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. The Blowers-Masel approximation (BMA) relates the reaction barrier to the reaction enthalpy. Unlike the Brønsted-Evans-Polani relationship, the BMA requires less data because only one parameter, the intrinsic activation energy, needs to be determined. We validate this application of BMA relations to model surface reactions by comparing against density functional theory data taken from the literature. By incorporating the BMA rate description into the open-source Cantera software, we enable a new workflow, demonstrated herein, allowing rapid screening of catalysts using linear scaling relationships and BMA kinetics within the process simulation software. For demonstration purposes, a catalyst screening for catalytic methane partial oxidation on 81 hypothetical metals is conducted. We compared the results with and without BMA-corrected rates. The heat maps of various descriptors (e.g., CH4 conversion, syngas yield) show that using BMA rates instead of Arrhenius rates (with constant activation energies) changes which metals are most active. Heat maps of sensitivity analyses can help identify which reactions or species are the most influential in shaping the descriptor map patterns. Our findings indicate that while using BMA-adjusted rates did not markedly affect the most sensitive reactions, it did change the most influential species.

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. For thermochemistry prediction, the RMG database contains 45 libraries of thermochemical parameters with a combination of 4564 entries and a group additivity scheme with 9 types of corrections including radical, polycyclic, and surface absorption corrections with 1580 total curated groups and parameters for a graph convolutional neural network trained using transfer learning from a set of >130 000 DFT calculations to 10 000 high-quality values. Correction schemes for solvent-solute effects, important for thermochemistry in the liquid phase, are available. They include tabulated values for 195 pure solvents and 152 common solutes and a group additivity scheme for predicting the properties of arbitrary solutes. For kinetics estimation, the database contains 92 libraries of kinetic parameters containing a combined 21 000 reactions and contains rate rule schemes for 87 reaction classes trained on 8655 curated training reactions. Additional libraries and estimators are available for transport properties. All of this information is easily accessible through the graphical user interface at https://rmg.mit.edu. Bulk or on-the-fly use can be facilitated by interfacing directly with the RMG Python package which can be installed from Anaconda. The RMG database provides kineticists with easy access to estimates of the many parameters they need to model and analyze kinetic systems. This helps to speed up and facilitate kinetic analysis by enabling easy hypothesis testing on pathways, by providing parameters for model construction, and by providing checks on kinetic parameters from other sources.

Quantifying the Impact of Parametric Uncertainty on Automatic Mechanism Generation for CO2 Hydrogenation on Ni(111).

Automatic mechanism generation is used to determine mechanisms for the CO2 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. Global uncertainty assessment, global sensitivity analysis, and degree of rate control analysis are performed to study the effect of this parametric uncertainty on the microkinetic model predictions. Comparison of the model predictions with experimental data on a Ni/SiO2 catalyst find a feasible set of microkinetic mechanisms within the correlated uncertainty space that are in quantitative agreement with the measured data, without relying on explicit parameter optimization. Global uncertainty and sensitivity analyses provide tools to determine the pathways and key factors that control the methanation activity within the parameter space. Together, these methods reveal that the degree of rate control approach can be misleading if parametric uncertainty is not considered. The procedure of considering uncertainties in the automated mechanism generation is not unique to CO2 methanation and can be easily extended to other challenging heterogeneously catalyzed reactions.

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 v1.0. Most notably, RMG can now generate heterogeneous catalysis models in addition to the previously available gas- and liquid-phase capabilities. For model analysis, new methods for local and global uncertainty analysis have been implemented to supplement first-order sensitivity analysis. The RMG database of thermochemical and kinetic parameters has been significantly expanded to cover more types of chemistry. The present release includes parallelization for faster model generation and a new molecule isomorphism approach to improve computational performance. RMG has also been updated to use Python 3, ensuring compatibility with the latest cheminformatics and machine learning packages. Overall, RMG v3.0 includes many changes which improve the accuracy of the generated chemical mechanisms and allow for exploration of a wider range of chemical systems.

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. The automatically calculated reaction rates compare favorably to existing estimated rates. Comparison against high level theoretical calculations show the new automated method performs better than rate estimates when the estimate is made by a poor analogy. The method will improve by accounting for internal rotor contributions and by improving methods to determine molecular symmetry.

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. Distances between reactive atoms at the TS are estimated using molecular group values, with the 3D geometry of the TS being constructed by distance geometry. The estimate is then optimized using electronic structure theory and validated using intrinsic reaction coordinate calculations, completing the fully automatic algorithm to locate TS geometries. The methods were tested using a diisopropyl ketone combustion model containing 1393 hydrogen abstraction reactions, of which transition states were found for 907 over two iterations of the algorithm. With sufficient training data, molecular group contributions were shown to successfully predict the reaction center distances of transition states with root-mean-squared errors of only 0.04 Å.

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. Enzymatically generated hydrogen peroxide accumulates at the electrode/cell interface during a 5 s hold-time and is oxidized during application of a potential pulse. A second, replicate potential pulse is applied 0.5 s after the first potential pulse to gauge background charge prior to significant accumulation of hydrogen peroxide. The difference in charge passed between the first and second potential pulse provides a measure of hydrogen peroxide generated by the enzyme and is an indication of the cholesterol efflux. Control experiments for bare Pt microelectrodes in contact with the cell plasma membrane show difference charge signals in the range of about 7-10 pC. Enzyme-modified electrodes in contact with the plasma membrane show signals in the range of 16-26 pC.

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. In this paper, we present a methodology for extending automatic mechanism generation to solution phase systems which requires estimation of solvent effects on reaction rates and equilibria. The linear solvation energy relationship (LSER) method of Abraham and co-workers is combined with Mintz correlations to estimate ΔG(solv)°(T) in over 30 solvents using solute descriptors estimated from group additivity. Simple corrections are found to be adequate for the treatment of radical sites, as suggested by comparison with known experimental data. The performance of scaled particle theory expressions for enthalpic-entropic decomposition of ΔG(solv)°(T) is also presented along with the associated computational issues. Similar high-throughput methods for solvent effects on free-radical kinetics are only available for a handful of reactions due to lack of reliable experimental data, and continuum dielectric calculations offer an alternative method for their estimation. For illustration, we model liquid phase oxidation of tetralin in different solvents computing the solvent dependence for ROO• + ROO• and ROO• + solvent reactions using polarizable continuum quantum chemistry methods. The resulting kinetic models show an increase in oxidation rate with solvent polarity, consistent with experiment. Further work needed to make this approach more generally useful is outlined.

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. Modified electrodes absent cholesterol oxidase serves as a baseline control. Cholesterol synthesis is determined by deuterium incorporation into lipids over time. Incorporation into cholesterol specifically is determined by mass spectrometry analysis. All mice used in the study are on a C57Bl/6 background and are between 6 and 8 weeks of age. RESULTS: Membrane cholesterol measurements are elevated in both R117H and DeltaF508 mouse nasal epithelium compared to age-matched sibling wt controls demonstrating a genotype correlation to membrane cholesterol detection. Expression of wt CFTR in CF epithelial cells reverts membrane cholesterol to WT levels further demonstrating the impact of CFTR on these processes. In wt epithelial cell, the addition of the CFTR inhibitors, Gly H101 or CFTRinh-172, for 24 h surprisingly results in an initial drop in membrane cholesterol measurement followed by a rebound at 72 h suggesting a feedback mechanism may be driving the increase in membrane cholesterol. De novo cholesterol synthesis contributes to membrane cholesterol accessibility. CONCLUSIONS: The data in this study suggest that CFTR influences cholesterol trafficking to the plasma membrane, which when depleted, leads to an increase in de novo cholesterol synthesis to restore membrane content.

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. The enthalpies of formation and thermochemical data for AlCl, AlO, AlOCl, AlOCl(2), AlO(2), AlO(2)Cl, AlOCl(3), AlO(2)Cl(2), AlO(3)ClTi, AlO(2)Cl(2)Ti, AlO(2)Cl(4)Ti, AlOCl(5)Ti, AlO(2)Cl(3)Tia (isomer-a), AlO(3)Cl(2)Ti, AlO(2)Cl(5)Ti, AlOCl(4)Ti, AlO(2)Cl(3)Tib (isomer-b), AlCl(7)Ti, AlCl(6)Ti, Al(2)Cl(6), Al(2)O(2)Cl, Al(2)O(2)Cl(3), Al(2)O(3)Cl(2), Al(2)O(2)Cl(2), Al(2)OCl(4), Al(2)O(3), and Al(2)OCl(3) were calculated using density functional theory (DFT). A full comparison between a number of methods is made for one of the important species, AlOCl, to validate the use of DFT and gauge the magnitude of errors involved with this method. Finally, equilibrium calculations are performed to try to identify which intermediates are likely to be most prevalent in the high temperature industrial process and as a first attempt to characterize the nucleation process.

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. This work presents the thermochemistry of practically all of the species that can exist in the early stages of the reaction mechanism. In order to ensure that all possible species are considered, the process is automated by considering all species that can be formed from the reactions that are deemed reasonable in the standard ethanol combustion model in the literature. Thermochemical data for 180 species (over 160 of which have not appeared in the literature before) are calculated using density functional theory with two different hybrid functionals, B3LYP and B97-1. The standard enthalpy of formation (DeltafH(298.15K) degrees) values for these species are calculated using isodesmic reactions. It is observed that internal rotation may be important because the barriers to rotation are reasonably low. Comparisons are then made between the rigid rotor harmonic oscillator approximation (RRHO) and the RRHO with some of the vibrational modes treated as hindered rotors. It is found that full treatment of the hindered rotors makes a significant difference to the thermochemistry and thus has an impact on equilibrium concentrations and kinetics in this system. For this reason, all of the species are treated using the hindered rotor approximation where appropriate. Finally, equilibrium calculations are performed to identify the intermediates that are likely to be most prevalent in the high-temperature industrial process. Particularly, Si(OH)4, SiH(OH)3, SiH2(OH)2, SiH3(OH), Si(OH)3(OCH3), Si(OH)2(OCH3)2, the silicon dimers (CH3)3-SiOSi(CH3)3 and SiH3OSiH3, and the smaller hydrocarbon species CH4, CO2, C2H4, and C2H6 are highlighted as the important species.

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). The use of isodesmic and isogyric reactions was shown to be important for determining standard enthlapy of formation (Delta(f)H(degree)(298K)) values for these transition metal oxychloride species. TiOCl2, of particular importance in this mechanism, was also studied with CCSD(T) and found to have Delta(f)H(degree)(298K) = -598 +/- 20 kJ/mol. Finally, equilibrium calculations were performed to identify which intermediates are likely to be most prevalent in the high temperature industrial process, and as a first attempt to identify the size of the critical nucleus.