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Understanding the fate of nitrogen in food waste hydrothermal liquefaction.

Standard enthalpies, entropies, and heat capacities are calculated for 16,813 halocarbons using an automated high-fidelity thermochemistry workflow. This workflow generates conformers at density functional tight binding (DFTB) level, optimizes geometries, calculates harmonic frequencies, and performs 1D hindered rotor scans at DFT level, and computes electronic energies at G4 level. The computed enthalpies of formation for 400 molecules show good agreement with literature references, but the majority of the calculated species have no reference in the literature. Thus, this work presents the most accurate thermochemistry for many halocarbons to date. This new data set is used to train an extensive ensemble of group additivity values and hydrogen bond increment groups within the Reaction Mechanism Generator (RMG) framework. On average, the new group values estimate standard enthalpies for halogenated hydrocarbons within 3 kcal/mol of their G4 values. A significant contribution towards automated mechanism generation of halocarbon combustion, this research provides thermochemical data for thousands of novel halogenated species and presents a self-consistent set of halogen group additivity values.

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.

The RMG-database for chemical property prediction is presented. The RMG-database consists of curated datasets and estimators for accurately predicting parameters necessary for constructing a wide variety of chemical kinetic mechanisms, including thermodynamics, kinetics, solvation effects, and transport properties. For thermochemistry prediction, the RMG-database contains 45 libraries of thermochemical parameters with a combined 4564 entries, a group additivity scheme with nine types of corrections including radical, polycyclic and surface absorption corrections with 1580 total curated groups and parameters for a graph convolutional neural net 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 tabled 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 speed up and facilitate kinetic analysis by enabling easy hypothesis testing on pathways, by providing parameters for model construction and by providing information to check other kinetic parameters against.

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. One important change is that RMG v3.0 is now Python 3 compatible, which supports the most up-to-date versions of cheminformatics and machine learning packages that RMG depends on. Additionally, 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 also includes parallelization for reaction generation and on-the-fly quantum calculations, and a new molecule isomorphism approach to improve computational performance. 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.

Kinetic parameters for surface reactions can be predicted using a combination of DFT calculations, scaling relations, and machine learning algorithms; however, construction of microkinetic models still requires a knowledge of all the possible, or at least reasonable, reaction pathways. The recently developed Reaction Mechanism Generator (RMG) for heterogeneous catalysis, now included in RMG version 3.0, is built upon well-established, open-source software that can provide detailed reaction mechanisms from user-supplied initial conditions without making a priori assumptions. RMG is now able to estimate adsorbate thermochemistry and construct detailed microkinetic models on a range of hypothetical metal surfaces using linear scaling relationships. These relationships are a simple, computationally efficient way to estimate adsorption energies by scaling the energy of a calculated surface species on one metal to any other metal. By conducting simulations with sensitivity analyses, users can not only determine the rate limiting step on each surface by plotting a "volcano surface" for the degree of rate control of each reaction as a function of elemental binding energies, but also screen novel catalysts for desirable properties. We investigated the catalytic partial oxidation of methane to demonstrate the utility of this new tool and determined that an inlet gas C/O ratio of 0.8 on a catalyst with carbon and oxygen binding energies of -6.75 eV and -5.0 eV, respectively, yields the highest amount of synthesis gas. Sensitivity analyses show that while the dissociative adsorption of O2 has the highest degree of rate control, the interactions between individual reactions and reactor conditions are complex, which result in a dynamic rate-limiting step across differing metals.

The automatic microkinetic mechanism generator for heterogeneous catalysis, RMG-Cat, has been extensively updated. Density functional theory calculations were performed for 69 adsorbates on Pt(111), and the resulting thermodynamic properties were added to RMG-Cat. The thermo database is significantly more accurate; it includes nitrogen-containing adsorbates for the first time as well as better capabilities for predicting the thermochemistry of novel adsorbates. Additionally, RMG-Cat can now simultaneously pursue a mechanism expansion both on the surface and in the gas phase. This heterogeneous/homogeneously coupled capability is tested on the catalytic combustion of methane on platinum. The results confirm that under some conditions the catalyst is capable of inducing thermal ignition in the gas phase.

This is an experimental and numerical study aiming to assess the emissions of nitrogen oxides (NOx) from combustion of bituminous and lignite coals in O2/CO2 environments, simulating one-pass dry oxy-combustion conditions, and to investigate the influences of temperature and oxygen partial pressure on such emissions. Combustion of coal particles and, separately, coal char particles, both in the size range of 75–90 µm, were conducted under globally fuel-lean conditions in a laboratory electrically-heated drop-tube furnace (DTF). The emissions of nitrogen oxides were correlated with separately-observed single particle volatile flame and char temperatures, deduced with multi-color optical pyrometry. As atmospheric nitrogen was absent in these experiments, the entirety of the monitored NOx was attributed to the oxidation of fuel-bound nitrogen. Experiments were conducted by separately increasing the furnace temperature or the oxygen mole fraction in the gas. Results showed that nitrogen oxide fractions in the combustion effluents increased both with increasing partial pressure of oxygen in the background gas (from 21% to 40%) and with increasing furnace temperature (from 1300 to 1500 K). As increasing either of these two parameters increases the particle temperatures (both volatile flame and char) it was hypothesized that the NOx evolution is a function of these particle temperatures. To further investigate such hypothesis a published kinetic model was used, which assumes that the coal-bound nitrogen (a) converts mostly to hydrogen cyanide and then to NO during the volatile combustion phase, and (b) converts directly to NO during the char combustion phase. Flow tube and flame simulations were performed using Cantera to investigate the relative impacts of temperature and oxygen mole fraction, and to understand the causes of the observed trends. Results support the hypothesis that the evolution of NO from fuel-bound nitrogen is a strong function of the particle volatile matter temperature.

Kinetic modeling of combustion chemistry has made substantial progress in recent years with the development of increasingly detailed models. However, many of the chemical kinetic parameters utilized in detailed models are estimated, often inaccurately. To help replace rate estimates with more accurate calculations, we have developed AutoTST, an automated Transition State Theory rate calculator. This work describes improvements to AutoTST, including: a systematic conformer search to find an ensemble of low energy conformers, vibrational analysis to validate transition state geometries, more accurate symmetry number calculations, and a hindered rotor treatment when deriving kinetics. These improvements resulted in location of transition state geometry for 93% of cases and generation of kinetic parameters for 74% of cases. Newly calculated parameters agree well with benchmark calculations and perform well when used to replace estimated parameters in a detailed kinetic model of methanol combustion.

A detailed chemical kinetic mechanism is developed for the gas-phase combustion of a liquid monopropellant, which is a blend of propylene-glycol-dinitrate, dibutyl-sebacate, and 2-nitro-diphenylamine (Otto Fuel II). The combustor is modeled as a steady-state burner-stabilized flame. The simulations reveal that not all of the dibutyl-sebacate is consumed in the flame, with approximately 5% persisting in the post-flame region. A large class of combustion byproducts are formed that have boiling points above the post-flame temperature and thus would be expected to condense out along the length of the combustor. This post-flame, two-phase behavior is hypothesized to be the cause of empirically observed oily build-up within the engine. This work represents a novel advancement in predictive modeling for propellant design, as it provides mechanistic insight into the possible origins of engine fouling.

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., 29 pages, 8 figures

A novel approach is presented for generating microkinetic mechanisms in heterogeneous catalysis. The open-source software RMG-Cat automatically develops a detailed list of elementary surface reactions, including thermodynamic properties for the adsorbates and parametrized rate coefficients for the reactions. The software proposes numerous possible surface intermediates and reactions, but it only retains those species that have a sufficiently high rate of formation. RMG-Cat was tested on the dry reforming of methane on nickel. The software correctly found the same set of elementary reactions as in a previously compiled microkinetic mechanism, as well as a few missing reactions. These results demonstrate the potential of this approach for predicting the dominant pathways in heterogeneous catalysis.

Understanding the gas-phase chemistry of silicon hydrides is the first step to building a realistic kinetic model for chemical vapor deposition (CVD). Functionality for thermodynamic and kinetic data estimation of silicon hydrides was added to the open-source software Reaction Mechanism Generator (RMG). Using the updated RMG, a detailed kinetic model was built for SiH4 thermal decomposition. The generated model was used to perform reactor simulations at various process conditions for comparison to prior SiH4 decomposition experiments in a flow tube. Results show that the RMG-generated model can reasonably replicate experimental results for SiH4 concentration profiles at different temperatures and residence times. While the effect of changing initial SiH4 concentration is not captured, a first pass sensitivity analysis reveals that reasonable errors in reaction rates could contribute to the discrepancy.

This paper investigates emissions of NOx from pulverized coal burning in O2/CO2 environments.Such environments are pertinent to oxy-coal combustion, a promising “clean-coal” technology. The replacement of the inert nitrogen gas in air with carbon dioxide, which has different physical properties, alters the combustion conditions in the furnace. Hence, the purpose of thiswork is to theoretically examine the effects of (a) the oxygen concentration in the O2/CO2 gases,and (b) the resulting combustion temperatures, on the evolution of NOx. To achieve these goals apreviously published kinetic model was used, which assumes that fuel-bound nitrogen is releasedalong with the tars during coal devolatilization and converts mostly to hydrogen cyanide. A sizable fraction of hydrogen cyanide is then converted to NO. Flame simulations were performed using Cantera to investigate the relative impacts of temperature and oxygen mole fraction, and to understand the causes of the observed trends.

Reaction Mechanism Generator (RMG) constructs kinetic models composed of elementary chemical reaction steps using a general understanding of how molecules react. Species thermochemistry is estimated through Benson group additivity and reaction rate coefficients are estimated using a database of known rate rules and reaction templates. At its core, RMG relies on two fundamental data structures: graphs and trees. Graphs are used to represent chemical structures, and trees are used to represent thermodynamic and kinetic data. Models are generated using a rate-based algorithm which excludes species from the model based on reaction fluxes. RMG can generate reaction mechanisms for species involving carbon, hydrogen, oxygen, sulfur, and nitrogen. It also has capabilities for estimating transport and solvation properties, and it automatically computes pressure-dependent rate coefficients and identifies chemically-activated reaction paths. RMG is an object-oriented program written in Python, which provides a stable, robust programming architecture for developing an extensible and modular code base with a large suite of unit tests. Computationally intensive functions are cythonized for speed improvements. Program summary Program title: RMG Catalogue identifier: AEZW_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEZW_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: MIT/X11 License No. of lines in distributed program, including test data, etc.: 958681 No. of bytes in distributed program, including test data, etc.: 9495441 Distribution format: tar.gz Programming language: Python. Computer: Windows, Ubuntu, and Mac OS computers with relevant compilers. Operating system: Unix/Linux/Windows. RAM: 1 GB minimum, 16 GB or more for larger simulations Classification: 16.12. External routines: RDKit, Open Babel, DASSL, DASPK, DQED, NumPy, SciPy Nature of problem: Automatic generation of chemical kinetic mechanisms for molecules containing C, H, O, S, and N. Solution method: Rate-based algorithm adds most important species and reactions to a model, with rate constants derived from rate rules and other parameters estimated via group additivity methods. Additional comments: The RMG software package also includes CanTherm, a tool for computing the thermodynamic properties of chemical species and both high-pressure-limit and pressure-dependent rate coefficients for chemical reactions using results from quantum chemical calculations. CanTherm is compatible with a variety of ab initio quantum chemistry software programs, including but not limited to Gaussian, MOPAC, QChem, and MOLPRO. Running time: From 30 s for the simplest molecules, to up to several weeks, depending on the size of the molecule and the conditions of the reaction system chosen.

Detailed kinetic models for the conversion of bio-oil to syngas through gasification were developed automatically using the open source software package Reaction Mechanism Generator (RMG). The influences of process operating conditions and of RMG parameters on the performance of models were investigated. Both temperature and pressure alter the product yields, although including pressure-dependent (chemically activated and fall-off) kinetics have minimal impact on these predictions. The model size is important, although currently constrained by available RAM, motivating development of improved memory-management algorithms in RMG. To validate the RMG-built mechanisms, simulations performed with Cantera were compared with experimental data from the literature. Agreements and disagreements between RMG-built models and literature show that the automated mechanism generation approach is promising, but reveal some families of reactions involving heteroatomic cycles that require improved estimates for bio-mass derived fuels. Research in this area would be greatly helped by more quantitative experimental data, ideally showing intermediate species profiles. These findings motivate extra studies and guide further RMG development.

Completely automated mechanism generation of detailed kinetic models is within reach in the coming decade. The recent developments in this field of chemical reaction engineering are anticipated to lead to some groundbreaking discoveries in the future, extending our fundamental understanding and resolving many of today's society problems such as energy production and conversion, emission reduction, greener chemical production processes, etc. In the present review, the focus is on the core of these automated mechanism generation for gas-phase and solution-phase processes that is on how the reaction kinetics and thermodynamic and transport properties of species are estimated and calculated starting from the fundamental elements of the software. With tasks such as the definition of reaction rules and reaction families, the unambiguous representation of species, and the choice of different termination criteria, generating a good reaction mechanism is still not as simple as pressing a “run” button. One of the main challenges that still needs to be overcome is how to deal with data scarcity and the combination with affordable computational chemistry calculations seems the logical step forward. The best practices are illustrated in a butane pyrolysis case study, which also exposes the challenges in the field of automatic kinetic model generation.

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.

A systematic investigation on the effects of roaming radical reactions on global combustion properties for transportation fuels is presented. New software was developed that can automatically discover all the possible roaming pathways within a given chemical kinetic mechanism. This novel approach was applied to two mechanisms taken from the literature, one for heptane and one for butanol. Ignition delay times and laminar flame speeds were computed over a broad range of conditions, while testing varying degrees of roaming. As the degree of roaming is increased, the ignition delays increased, consistent with the hypothesis that roaming decreases the reactivity of the system. The percent increase in the ignition delay is strongly temperature dependent, with the largest effect seen in the negative temperature coefficient regime. Outside of this temperature range, the effect of roaming on global combustion properties is small, on the order of a few percent for ignition delays and less than a percent for flame speeds. The software that was used to create the new mechanisms and test the effects of roaming on combustion properties are freely available, with detailed tutorials that will enable it to be applied to fuels other than heptane and butanol., Comment: 25 pages, 6 figures; added hydroperoxide decomposition pathways, repeated analysis, regenerated results, expanded discussion

Large reaction mechanisms are often used to describe the combustion behavior of transportation-relevant fuels like gasoline, where these are typically represented by surrogate blends, e.g., n-heptane/iso-octane/toluene. We describe efforts to quantify the uncertainty in the predictions of such mechanisms at realistic engine conditions, seeking to better understand the robustness of the model as well as the important reaction pathways and their impacts on combustion behavior. In this work, we examine the importance of taking into account correlations among reactions that utilize the same rate rules and those with multiple product channels on forward propagation of uncertainty by Monte Carlo simulations. Automated means are developed to generate the uncertainty factor assignment for a detailed chemical kinetic mechanism, by first uniquely identifying each reacting species, then sorting each of the reactions based on the rate rule utilized. Simulation results reveal that in the low temperature combustion regime for iso-octane, the majority of the uncertainty in the model predictions can be attributed to low temperature reactions of the fuel sub-mechanism. The foundational, or small-molecule chemistry (C 0 C 4 ) only contributes significantly to uncertainties in the predictions at the highest temperatures (Tc = 900 K). Accounting for correlations between important reactions is shown to produce non-negligible differences in the estimates of uncertainty. Including correlations among reactions that use the same rate rules increases uncertainty in the model predictions, while accounting for correlations among reactions with multiple branches decreases uncertainty in some cases. Significant non-linear response is observed in the model predictions depending on how the probability distributions of the uncertain rate constants are defined. It is concluded that care must be exercised in defining these probability distributions in order to reduce bias, and physically unrealistic estimates in the forward propagation of uncertainty for a range of UQ activities.

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.

The number of possible side reactions and byproduct species grows very rapidly with the size of a chemical mechanism. A memory-efficient algorithm for automated mechanism generation is presented for coping with this combinatorial complexity. The algorithm selects normalized flux as a metric to identify unimportant species during model generation and prunes them with their reactions, without any loss of accuracy. The new algorithm reduces memory requirements for building kinetic models with 200–300 species by about a factor of 4, or for fixed computer hardware makes it possible to create models including about twice as many species as was previously possible. The increased capability opens the possibility of discovering unexplored reaction networks and modeling more complicated reacting systems. Keyword: Memory reduction; Mechanism generation; Pruning, United States. Department of Energy ( DE-FG02-98ER14914)

The combustion of TiCl4 to synthesize TiO2 nanoparticles is a multimillion tonne per year industrial process. The objective of this paper is to further the understanding of this process. Work toward three aspects of this multiscale problem is presented herein: gas-phase chemistry, surface chemistry, and the solution of a multidimensional population balance problem coupled to detailed chemical mechanisms. Presented here is the first thermodynamically consistent mechanism with physically realistic elementary-step rate constants by which TiCl4 is oxidized to form a stable Ti2OxCly species that lies on the path to formation of TiO2 nanoparticles. Second, progress toward a surface chemistry mechanism based on density functional theory (DFT) calculations is described. Third, the extension of a stochastic two-dimensional (surface-volume) population balance solver is presented. For the first time, the number and size of primary particles within each agglomerate particle in the population is tracked. The particle model, which incorporates inception, coagulation, growth, and sintering, is coupled to the new gas-phase kinetic model using operator splitting, and is used to simulate a heated furnace laboratory reactor and an industrial reactor. Using the primary particle information, transmission electron microscopy (TEM)-style images of the particles are generated, demonstrating the potential utility of first-principles modeling for the prediction of particle morphology in complex industrial systems. © 2007 American Chemical Society.

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.

In this study the coagulation, condensation, and sintering of nanoparticles is investigated using a stochastic particle model. Each stochastic particle consists of interacting polydisperse primary particles that are connected to each other. In the model sintering occurs between each individual pair of neighboring primary particles. This is important for particles in which the range of the size of the primary particles varies significantly. The sintering time is obtained from the viscous flow model. The model is solved using a stochastic particle algorithm. The particles are represented in a binary tree that contains the connectivity as well as the degree of sintering information. Particles are forme, coagulate, sinter, and experience condensation according to known rate laws. The particle binary tree, along with it the degree of sintering, is updated after each time step according to the rates of the different processes. The stochastic particle method uses the technique of fictitious jumps and linear proc...

The combustion of TiCl4 to synthesize TiO2 nanoparticles is a multimillion tonne per year industrial process, the fundamental details of which are still not known. The gas-phase kinetic model presented by West et al. [R.H. West, M.S. Celnik, O.R. Inderwildi, M. Kraft, G.J.O. Beran, W.H. Green, Ind. Eng. Chem. Res. 46 (19) (2007) 6147–6156] is improved upon using density functional theory (DFT) and variational transition state theory (VTST) calculations. The pressure-dependent rate expression for the reaction TiCl3 + O2 ⇌ TiO2Cl3 is found using VTST, a stable Ti 2 O 2 Cl 6 species is located on the minimum energy pathway for TiCl 3 + TiO 2 Cl 3 ⇋ 2 TiOCl 3 , and a number of new elementary reactions are added. Thermochemical data are provided for Ti 2 O 2 Cl 6 , Ti 2 O 2 Cl 5 and TiCl 2 OCl . The new kinetic model is used to simulate a rapid compression machine (RCM) and a plug flow reactor (PFR) described in the literature. Agreement with the RCM measurements is good, but simulations of the PFR are less satisfying, suggesting that surface deposition on the reactor walls may have dominated these measurements, which have been the basis of many theoretical models. Finally, the gas-phase kinetic model is coupled to a particle population balance model (PBM) incorporating inception, coagulation, growth, and sintering.

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.

A detailed PAH growth model is developed, which is solved using a kinetic Monte Carlo algorithm. The model describes the structure and growth of planar PAH molecules, and is referred to as the kinetic Monte Carlo–aromatic site (KMC-ARS) model. A detailed PAH growth mechanism based on reactions at radical sites available in the literature, and additional reactions obtained from quantum chemistry calculations are used to model the PAH growth processes. New rates for the reactions involved in the cyclodehydrogenation process for the formation of 6-member rings on PAHs are calculated in this work based on density functional theory simulations. The KMC-ARS model is validated by comparing experimentally observed ensembles on PAHs with the computed ensembles for a C2H2 and a C6H6 flame at different heights above the burner. The motivation for this model is the development of a detailed soot particle population balance model which describes the evolution of an ensemble of soot particles based on their PAH structure. However, at present incorporating such a detailed model into a population balance is computationally unfeasible. Therefore, a simpler model referred to as the site-counting model has been developed, which replaces the structural information of the PAH molecules by their functional groups augmented with statistical closure expressions. This closure is obtained from the KMC-ARS model, which is used to develop correlations and statistics in different flame environments which describe such PAH structural information. These correlations and statistics are implemented in the site-counting model, and results from the site-counting model and the KMC-ARS model are in good agreement. Additionally the effect of steric hindrance in large PAH structures is investigated and correlations for sites unavailable for reaction are presented.

An updated rate of O2 oxidation of one to four ring polyaromatic hydrocarbons in premixed flames is presented based on density function theory simulations of oxygen attack at different radical sites on various PAHs. The rate is in agreement with other rates found in the literature; however, it is several orders of magnitude lower than the currently accepted oxidation rate of multi-ring aromatic species, including soot. Simulations are presented of a premixed flame using this improved rate and a new advanced soot particle model, which is developed in this paper. This model includes unprecedented detail of the particles in the ensemble, including the aromatic content, C/H composition and primary-particle aggregate structure. The O2 oxidation rate calculated in this paper is shown to give a better prediction of particle number density and soot volume fraction for a premixed flame. The predicted particle size distributions are shown also to describe better the experimental data. Predicted C/H ratio and PAH size distributions are shown for the flame. Computed TEM-style images are compared to experimental TEM images, which show that the aggregate structure of the particles is well predicted.

A new, advanced soot particle model is developed that describes soot particles by their aromatic structure, including functional site descriptions and a detailed surface chemistry mechanism. A methodology is presented for the description of polyaromatic hydrocarbon (PAH) structures by their functional sites. The model is based on statistics that describe aromatic structural information in the form of easily computed correlations, which were generated using a kinetic Monte Carlo algorithm to study the growth of single PAH molecules. A comprehensive surface reaction mechanism is presented to describe the growth and desorption of aromatic rings on PAHs. The model is capable of simulating whole particle ensembles which allows bulk properties such as soot volume fraction and number density to be found, as well as joint particle size and surface area distributions. The model is compared to the literature-standard soot model [J. Appel, H. Bockhorn, M. Frenklach, Combust. Flame 121 (2000) 122–136] in a plug-flow reactor and is shown to predict well the experimental results of soot mass, average particle size, and particle size distributions at different flow times. Finally, the carbon/hydrogen ratio and the distribution of average PAH sizes in the ensemble, as predicted by the model, are discussed.

A simple model for the gas-phase synthesis of carbon nanotubes on iron catalyst particles has been developed. It includes a growth model for the catalyst particles and describes nanotube growth processes through carbon monoxide disproportionation and hydrogenation. Models for particle–particle interactions and sintering are also included. When carbon arrives at a catalyst particle it can either dissolve in the particle until a saturation limit is reached, or form a graphene layer on the particle, or go on to form a nanotube. Two models for incipient nanotube growth are considered. The first allows nanotubes to form once a catalyst particle reaches the saturation condition. The second only allows nanotubes to form on the collision of two saturated particles. The particle system is solved using a multivariate stochastic solver coupled to the gas-phase iron chemistry using an operator splitting algorithm. Comparison with experimental data gives a good prediction of the nanotube length, and reasonable values of catalyst particle diameter and nanotube diameter. A parametric study is presented in which the carbon monoxide reaction rate constants are varied, as is the fraction of carbon allowed to form nanotubes relative to surface layers. The assumptions of the coagulation and sintering models are also discussed.

Catalytic chemical vapor deposition (CVD) is a popular method to synthesize carbon nanotubes (CNTs). At the presence of catalysts (usually trasition metals), the hydrocarbon feedstock decomposes controllably at elevated temperatures and can form tubular structures. It has been suggested that trace amounts of weak gas-phase oxidants, such as CO2, can enhance the CNT synthesis by extending the catatlyst life. It is not clear, however, how such additives affect the CVD reaction environment. In this study, ethylene gas was introduced to a preheated furnace/CVD reactor where meshes of stainless steel were placed. Therein ethylene was thermally decomposed in nitrogen mixed with different amounts of carbon dioxide. The meshes served as catalytic substrates for the CNT growth. The compositions of the ethylene pyrolyzates were analysed both with and without the presence of catalysts, to explore the possible contributions of CO2 addition to the CNT formation. The latter compositions were compared with kinetic model predictions of the thermal decomposition of ethylene. Both experimental and simulation results indicated that 1,3-butadiene (C4H6) was the most abundant hydrocarbon species of ethylene decomposition (at 800 °C) and that decomposition was inhibitted at the presence of CO2. A commesurate effect on CNT formation was observed experimentally, whereas the quality of CNTs got improved.

Aims The diagnosis of intraductal papillary lesions of the breast on core biopsy remains challenging in pathology, with most patients requiring formal surgical excision for a definitive diagnosis. The aim of this study was to determine whether a representative panel of proliferative cell cycle immunohistochemical markers (cyclin A2, cyclin B1 and cyclin D1) could improve the specificity of pathological diagnosis of these lesions. Methods A series of 68 surgically excised intraductal papillary lesion cases were retrospectively selected, and immunohistochemistry for cyclin A2, cyclin B1 and cyclin D1 was performed. Results Cyclin B1 (OR 1.80, 95% CI 1.01 to 3.2, p=0.046) and cyclin D1 (OR 1.13, 95% CI 1.05 to 1.22, p=0.002) expression was independently associated with a diagnosis of malignancy in papillary lesions, although expression was frequently heterogeneous and only focal. Cyclin A2 expression (OR 0.76, 95% CI 0.41 to 1.4, p=0.38) was not associated with a malignant diagnosis in multivariable logistic regression models. All three cyclins displayed high sensitivity (80%–95%) for a diagnosis of malignancy, although cyclin B1 showed a superior specificity of 72.7% compared with the low specificity of cyclins A2 and D1. Conclusions Our study has identified for the first time that the expression of key cell cycle markers differs between benign and malignant papillary breast lesions and identified changes to the mitotic marker, cyclin B1, as particularly significant. However, given the low level and heterogeneous nature of expression of these markers, there remains a significant risk of undersampling in core biopsies and thus they are unlikely to be useful in routine clinical practice.

The development of kinetic models to describe the time evolution of chemically reacting systems is a fundamental objective of chemical kinetics. Ideally, the most accurate approach to this problem is to include all possible species and reactions of any importance to predict, within the specified uncertainty, a wide variety of experimental data. The complexity of these detailed models grows with an increase in the number of fuel components. When the detailed kinetic model is to be coupled to transport equations, the computational tasks often become formidable due to the intrinsic presence of a wide range of time and length scales, which result in the well-known stiffness problem. Such difficulties have motivated the development of numerous model order reduction techniques during the past three decades. In this paper, the most used reduction techniques for detailed kinetic models are presented and the advantages and disadvantages of each are explained.Copyright © 2014 by ASME

Background: The purpose of the present study was to evaluate the changing role of radiation therapy in rectal cancer and to determine the patterns of referral of patients during a 15-year period. Methods: From 1982 to 1997, 464 patients with carcinoma of the rectum were referred to the Department of Radiation Oncology, Royal Prince Alfred Hospital; 79% of patients had locoregional disease alone and 21% had distant metastasis. Radiation therapy consisted of irradiation (definitive or palliative) alone to the primary tumour in 9.7% of cases; preoperative radiation in 7.3% of cases; preoperative chemoradiation in 7.5% of patients; postoperative radiation in 15.3% of patients; postoperative chemoradiation in 12.3% of patients; treatment of pelvic recurrence in 23.5% of patients and treatment of metastases in 9.1% of patients. The remainder were treated elsewhere (1.9%) or not treated (13.4%). Results: There was an average annual 14% increase in referrals over the accrual period. Recurrent rectal cancer decreased from approximately 30% of referrals during 1982–91 to approximately 10% in 1995–7. The use of postoperative adjuvant radiation reached a peak of 50% in 1993. The use of preoperative radiation increased suddenly in 1994 from < 10% to a sustained rate of approximately 30% of referrals. The use of chemoradiation commenced in 1990 for postoperative adjuvant treatment and in 1994 for preoperative treatment. The median survival time from initial diagnosis was 35 months, with 2- and 5-year survival rates of 62 and 28%, respectively. Survivals at 5 years for patients treated with preoperative and postoperative radiation (with or without chemotherapy) and with recurrent disease were 56, 44 and 21%, respectively. Conclusions: The present study illustrates the changing role of radiation therapy in the management of rectal cancer. The increase in referrals over the observation period was due to increased multidisciplinary input into the initial management of these patients, based on reported clinical trials. The steady increase in the use of adjuvant therapy has paralleled a decrease in referrals for treatment of recurrence and reflects current clinical results. The sequencing of adjuvant therapy is changing currently, with greater emphasis on preoperative adjuvant treatment. Currently most adjuvant therapy includes chemotherapy.

Over a 30-month period from July 1995 to December 1997, new detections of methicillin-resistant Staphylococcus aureus (MRSA) were prospectively studied in a tertiary referral hospital. The aims of the study were to determine the incidence of colonization of patients admitted to each of the hospital's 39 clinical units and ascertain where each patient had become colonized. Epidemiological information (time to detection, ward movement, admission to other hospitals, data on MRSA isolations in hospital wards) and phage typing were used by the hospital's infection control unit to make this determination. Routine containment procedures included cohorting, flagging and triclosan body washes. Surveillance cultures were collected infrequently. Patients known to be colonized with MRSA were excluded from orthopaedic and haematology wards. During the study period, 995 patients were found to be newly colonized. The incidence of colonization varied from nil to 72 per 1000 admissions, being highest in the main intensive care unit and in services which frequently used that unit. The incidence of colonization in elective orthopaedic surgery (< 1 per 1000) and haematology (3 per 1000) was very low. Determining the place where patients acquired MRSA was made difficult by the high frequency of endemic phage types and frequent patient transfer between wards. Epidemiological data suggested that the main intensive care unit and surgical wards nursing patients with colorectal, urological and vascular diseases were the places where most patients became colonized. MRSA was never acquired by patients nursed in wards which practised an exclusion policy towards patients known to be colonized with MRSA. Our data suggest that in tertiary referral hospitals, where MRSA is not only endemic but frequently imported from other hospitals, it is possible to establish areas where MRSA is never acquired.

Outcomes have improved significantly for many women diagnosed with breast cancer. For the heterogeneous group of tumours lacking expression of the oestrogen, progesterone and HER2 receptors, 'triple negative' breast cancers (TNBC), the prognosis overall has remained quite poor. When TNBC recurs, there is often little response to chemotherapy, and there are a few treatment options in this setting. Thus, there is an urgent clinical need to identify new therapeutic targets in order to improve the outlook for these patients. This review highlights the most promising therapeutic targets identified through new sequencing technologies, as well as through studies of apoptosis. We also present mounting evidence that the developmental signalling pathways Wnt/β-catenin, NOTCH and Hedgehog play an important role in the pathogenesis and progression of TNBC with new therapeutic approaches inhibiting these pathways in advanced preclinical studies or early clinical trials.

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.

Nosocomial transmission of hepatitis B virus (HBV), associated with interventional procedures, has been attributed to its survival on improperly decontaminated instruments. To date, guidelines for chemical disinfection of potentially contaminated heat-sensitive instruments have been based largely on extrapolation of data from in-vitro disinfectant testing. Direct infectivity testing has not been possible for HBV because of the lack of a practical culture assay or susceptible experimental animal model. In this study the related duck hepadnavirus was used to simulate in-vivo transmission of a HBV during surgery, and to evaluate the effectiveness of 2% glutaraldehyde disinfection of surgical laparoscopes. Multiple laparoscopic liver biopsies were performed on 'biohazardous' duck hepatitis B (DHBV) positive ducks. Laparoscopes were then subjected to different disinfection regimes using 2% glutaraldehyde, and residual infectivity tested by placing their tips into the peritoneal cavities of uninfected four-day-old ducklings. Direct transmission of DHBV occurred in all ducks when laparoscopes were not washed. Rinsing with water lowered the transmission rate to 64% and no infection transmission occurred after 5 min of contact time with the disinfectant. In contrast, previous in-vitro studies had shown complete viral inactivation after a shorter period of disinfection. It is postulated that the longer inactivation time observed in our study may be a result of surface interactions of virus and instrument, interfering with disinfectant access or activity. Tests of instrument surface samples for viral DNA by the polymerase chain reaction (PCR) did not correlate with transmission of virus infection in vivo. PCR is an inappropriate test for evaluating the efficacy of disinfectant action despite its sensitivity. This in use method will allow testing of other decontamination procedures and their effectiveness on more complex surgical instruments.

OBJECTIVE To review the breast cancers detected in the first three years of the Central Sydney Area Health Service Breast X-ray Programme, their histopathology and their surgical management within the program. DESIGN Between March 1988 and March 1991, women screened in the program who had a suspicious lesion were referred for surgical assessment at the program assessment centre at Rachel Forster Hospital. These women were seen by staff of the assessment centre, including program surgeons, and were then treated at either Rachel Forster Hospital or Royal Prince Alfred Hospital. Features examined include cancer detection, clinical findings, diagnostic techniques, histopathological diagnosis of the lesions and surgical management. RESULTS One hundred and eight cancers were detected in 105 women, with 59% of the cancers impalpable. The benign to malignant ratio was 1.0:1.5. Twenty-four cancers (22%) were ductal carcinoma-in-situ with or without microinvasion, and 84 (78%) were frankly invasive. Of the 86 axillary dissections, 63 (73%) showed no node involvement on histological examination. At the time of diagnosis, 27% of the women had axillary node involvement proven by axillary dissection. The overall mastectomy rate was 58%. Radiotherapy, chemotherapy and tamoxifen were used in both stage I and stage II disease. CONCLUSION The surgical management of cancers reflects similar findings reported in other screening programs. There is an increasing trend towards breast conservation surgery and up to 90% of the women in this study present with favourable prognostic factors for long term survival.