Metal-Free Reversible Double Cyclization of Cyanuric Diazide to an Asymmetric Bitetrazolate via Cleavage of the Six-Membered Aromatic Ring

Crystallization of the reaction mixture of 2-amino-4,6-diazido-1,3,5-triazine and excess tert-butylamine results in the isolation of tert-butylammonium N,N-[1'H-(1,5'-bitetrazol)-5-yl]cyanamidate, suggesting a complex decyclization/cyclization rearrangement involving breakage of the six-membered aromatic ring and the formation of two new five-membered azole rings mediated by deprotonation of the precursor by the amine. The addition of tert-butylamine to 2-amino-4,6-diazido-1,3,5-triazine gives spectroscopic indication of thermodynamically unfavorable reactivity in low-dielectric solvents, and high-level quantum chemical computations also suggest its formation to be unfavorable. A computed interconversion pathway describes the likely reaction mechanism and supports the general thermodynamic unfavorability of the reaction and the requirement for a high-dielectric environment to template formation of the ionic product and its trapping by crystallization.

High-Dimensional Neural Network Potentials for Accurate Prediction of Equation of State: A Case Study of Methane

Machine learning-based interatomic potentials, such as those provided by neural networks, are increasingly important in molecular dynamics simulations. In the present work, we consider the applicability and robustness of machine learning molecular dynamics to predict the equation of state properties of methane by using high-dimensional neural network potentials (HDNNPs). We investigate two different strategies for generating training data: one strategy based upon bulk representations using periodic cells and another strategy based upon clusters of molecules. We assess the accuracy of the trained potentials by predicting the equilibrium mass density for a wide range of thermodynamic conditions to characterize the liquid phase, supercritical fluid, and gas phase, as well as the liquid-vapor coexistence curve. Our results show an excellent agreement with reference phase diagrams, with an average error below ∼2% for all studied phases. Moreover, we confirm the applicability of models trained on cluster data sets for producing accurate and reliable results.

Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration

Predicting the rate constants of elementary reaction steps is key for the computational modeling of catalytic processes. Within transition state theory (TST), this requires an accurate estimation of the corresponding free energy barriers. While sophisticated methods for estimating free energy differences exist, these typically require extensive (biased) molecular dynamics simulations that are computationally prohibitive with the first-principles electronic structure methods that are typically used in catalysis research. In this contribution, we show that machine-learning (ML) interatomic potentials can be trained in an automated iterative workflow to perform such free energy calculations at a much reduced computational cost as compared to a direct density functional theory (DFT) based evaluation. For the decomposition of CHO on Rh(111), we find that thermal effects are substantial and lead to a decrease in the free energy barrier, which can be vanishingly small, depending on the DFT functional used. This is in stark contrast to previously reported estimates based on a harmonic TST approximation, which predicted an increase in the barrier at elevated temperatures. Since CHO is the reactant of the putative rate limiting reaction step in syngas conversion on Rh(111) and essential for the selectivity toward oxygenates containing multiple carbon atoms (C2+ oxygenates), our results call into question the reported mechanism established by microkinetic models.

Automated Generation of Microkinetics for Heterogeneously Catalyzed Reactions Considering Correlated Uncertainties

The study presents an ab-initio based framework for the automated construction of microkinetic mechanisms considering correlated uncertainties in all energetic parameters and estimation routines. 2000 unique microkinetic models were generated within the uncertainty space of the BEEF-vdW functional for the oxidation reactions of representative exhaust gas emissions from stoichiometric combustion engines over Pt(111) and compared to experiments through multiscale modeling. The ensemble of simulations stresses the importance of considering uncertainties. Within this set of first-principles-based models, it is possible to identify a microkinetic mechanism that agrees with experimental data. This mechanism can be traced back to a single exchange-correlation functional, and it suggests that Pt(111) could be the active site for the oxidation of light hydrocarbons. The study provides a universal framework for the automated construction of reaction mechanisms with correlated uncertainty quantification, enabling a DFT-constrained microkinetic model optimization for other heterogeneously catalyzed systems.

Linking Experimental and Ab Initio Thermochemistry of Adsorbates with a Generalized Thermochemical Hierarchy

Enthalpies of formation of adsorbates are crucial parameters in the microkinetic modeling of heterogeneously catalyzed reactions since they quantify the stability of intermediates on the catalyst surface. This quantity is often computed using density functional theory (DFT), as more accurate methods are computationally still too expensive, which means that the derived enthalpies have a large uncertainty. In this study, we propose a new error cancellation method to compute the enthalpies of formation of adsorbates from DFT more accurately through a generalized connectivity-based hierarchy. The enthalpy of formation is determined through a hypothetical reaction that preserves atomistic and bonding environments. The method is applied to a data set of 60 adsorbates on Pt(111) with up to 4 heavy (non-hydrogen) atoms. Enthalpies of formation of the fragments required for the bond balancing reactions are based on experimental heats of adsorption for Pt(111). The comparison of enthalpies of formation derived from different DFT functionals using the isodesmic reactions shows that the effect of the functional is significantly reduced due to the error cancellation. Thus, the proposed methodology creates an interconnected thermochemical network of adsorbates that combines experimental with ab initio thermochemistry in a single and more accurate thermophysical database.

Theory-Based Mechanism for Fluoromethane Combustion I: Thermochemistry and Abstraction Reactions

A new detailed chemical kinetic mechanism is presented for small fluorinated hydrocarbons. Ab initio electronic structure theory is used to provide heats of formation with subchemical accuracy. The ANL0 method is extended to include fluorine. The resulting heats of formation at 0 K are in excellent agreement with 36 benchmark species in the Active Thermochemical Tables, with a mean error of μ = -0.02 kJ/mol and a standard deviation of σ = 0.91 kJ/mol. The thermophysical properties for 92 small-molecule H/C/O/F species are computed. The rate coefficients for 40+ H-abstraction reactions involving H, O, F, OH, OF, HO₂, and various methyl radicals with CH₄, CH₃F, CH₂F₂, CHF₃, CH₂O, and CHFO are discussed. The computed rate constants are in excellent agreement with the available literature. Additionally, 30+ rate constants are provided for F abstraction, which are several orders of magnitude smaller than H abstraction. The thermophysical properties and rate constants are provided in a mechanism. This mechanism is the first in a series of theory-based investigations into the thermal destruction of per- and polyfluorinated 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.

Non-oxidative methane conversion by Fe single site catalysts: quantifying temperature limitations imposed by gas-phase pyrolysis

A combined heterogeneous and homogeneous model of methane conversion reveals the temperature limitations of methane to olefins, aromatics, and hydrogen (MTOAH).

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.

Diastereomers and Low-Temperature Oxidation

Diastereomers have historically been ignored when building kinetic mechanisms for combustion. Low-temperature oxidation kinetics, which continues to gain interest in both combustion and atmospheric communities, may be affected by the inclusion of diastereomers in radical chain-branching pathways. In this work, key intermediates and transition states lacking stereochemical specification in an existing diethyl ether low-temperature oxidation mechanism were replaced with their diastereomeric counterparts. Rate coefficients for reactions involving diastereomers were computed with ab initio transition state theory master equation calculations. The presence of diastereomers increased rate coefficients by factors of 1.2-1.6 across various temperatures and pressures. Ignition delay simulations incorporating these revised rate coefficients indicate that the diastereomers enhanced the overall reactivity of the mechanism by almost 15% and increased the peak ketohydroperoxide concentration by 30% in the negative temperature coefficient region at combustion-relevant pressures. These results provide an illustrative indication of the important role of stereomeric effects in oxidation kinetics.

Heterogeneity in susceptibility dictates the order of epidemic models

The fundamental models of epidemiology describe the progression of an infectious disease through a population using compartmentalized differential equations, but typically do not incorporate population-level heterogeneity in infection susceptibility. Here we combine a generalized analytical framework of contagion with computational models of epidemic dynamics to show that variation strongly influences the rate of infection, while the infection process simultaneously sculpts the susceptibility distribution. These joint dynamics influence the force of infection and are, in turn, influenced by the shape of the initial variability. We find that certain susceptibility distributions (the exponential and the gamma) are unchanged through the course of the outbreak, and lead naturally to power-law behavior in the force of infection; other distributions are often sculpted towards these "eigen-distributions" through the process of contagion. The power-law behavior fundamentally alters predictions of the long-term infection rate, and suggests that first-order epidemic models that are parameterized in the exponential-like phase may systematically and significantly over-estimate the final severity of the outbreak. In summary, our study suggests the need to examine the shape of susceptibility in natural populations as part of efforts to improve prediction models and to prioritize interventions that leverage heterogeneity to mitigate against spread.

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.

A continuous flow liquid propellant strand burner for high pressure monopropellant and bipropellant combustion studies

A combustion assembly capable of continuously burning monopropellant and bipropellant liquid fuels at pressures up to 80 bars (1145 psig) was designed and constructed. The assembly is based on a liquid propellant strand burner where a manifold maintains small positive differential pressures on the fuel to maintain a steady supply into the reaction vessel. Optical ports enable direct visualization of the flame and will allow for future spectroscopic and imaging studies of the flame. The strand burner design was tested using nitromethane with both air and inert environments in the reaction vessel. Continuous combustion was sustained for almost 8 min in air (34 bars/500 psig) and more than 6 min in N₂ (70 bars/1000 psig). A unique outcome from the initial testing of this device is the ability to ignite liquid nitromethane in an inert environment without the use of a pilot flame started in air.

The impact of NOx addition on the ignition behaviour of n-pentane

Modern engine concepts present several opportunities for nitrogen combustion chemistry, particularly the interaction of NOx (NO + NO2) with fuel fragments and products of partial combustion.

Transition-metal-mediated reduction and reversible double-cyclization of cyanuric triazide to an asymmetric bitetrazolate involving cleavage of the six-membered aromatic ring

Cyanuric triazide reacts with several transition metal precursors, extruding one equivalent of N₂ and reducing the putative diazidotriazeneylnitrene species by two electrons, which rearranges to N-(1'H-[1,5'-bitetrazol]-5-yl)methanediiminate (biTzI^2-) dianionic ligand, which ligates the metal and dimerizes, and is isolated from pyridine as [M(biTzI)]₂Py₆ (M = Mn, Fe, Zn, Cu, Ni). Reagent scope, product analysis, and quantum chemical calculations were combined to elucidate the mechanism of formation as a two-electron reduction preceding ligand rearrangement.

Shock Tube Laser Schlieren Study of the Pyrolysis of Isopropyl Nitrate

The decomposition of isopropyl nitrate was measured behind incident shock waves using laser schlieren densitometry in a diaphragmless shock tube. Experiments were conducted over the temperature range of 700-1000 K and at pressures of 71, 126, and 240 Torr. Electronic structure theory and RRKM Master Equation methods were used to predict the decomposition kinetics. RRKM/ME parameters were optimized against the experimental data to provide an accurate prediction over a broader range of conditions. The initial decomposition i-C₃H₇ONO₂ ⇌ i-C₃H₇O + NO₂ has a high-pressure limit rate coefficient of 5.70 × 10²²T^-1.80 exp[-21287.5/T] s^-1. A new chemical kinetic mechanism was developed to model the chemistry after the initial dissociation. A new shock tube module was developed for Cantera, which allows for arbitrarily large mechanisms in the simulation of laser schlieren experiments. The present work is in good agreement with previous experimental studies.

A modular, multi-diagnostic, automated shock tube for gas-phase chemistry

A new shock tube has been constructed for investigations of high-temperature chemical kinetics with an emphasis on combustion chemistry. This instrument includes a diaphragmless driver and electrical control of valving. A diaphragmless design significantly improves repeatability of experimental conditions vs the use of diaphragms and leads to an approximate order of magnitude reduction in turnaround time between experiments. Electrical control of valves, combined with diaphragmless operation, also enables remote and automated operation of the shock tube. The design allows for both incident and reflected shock experiments with multiple diagnostics. The performance of the shock tube is demonstrated by reproducing previous literature measurements on the unimolecular decomposition of isobutyl nitrite and cyclohexene.

Decomposition kinetics for HONO and HNO2

This work presents a detailed investigation into the isomerization and decomposition of HONO and HNO₂.

Toward reliable characterization of energetic materials: interplay of theory and thermal analysis in the study of the thermal stability of tetranitroacetimidic acid (TNAA)

The thermal stability of energetic materials, being of the utmost importance for safety issues, is often considered in terms of kinetics, e.g., the Arrhenius parameters of the decomposition rate constant. The latter, in turn, are commonly determined using conventional thermoanalytical procedures with the use of simple Kissinger or Ozawa methods for kinetic data processing. However, thermal decomposition of energetic materials typically occurs via numerous exo- and endothermal processes including fast parallel reactions, phase transitions, autocatalysis, etc. This leads to numerous drawbacks of simple approaches. In this paper, we proposed a new methodology for characterization of the thermochemistry and thermal stability of melt-cast energetic materials, which is comprised of a complementary set of experimental and theoretical techniques in conjunction with a suitable kinetic model. With the aid of the proposed methodology, we studied in detail a novel green oxidizer, tetranitroacetimidic acid (TNAA). The experimental mass loss kinetics in the melt was perfectly fitted with a model comprised of zero-order reaction (sublimation or evaporation) and first-order thermal decomposition of TNAA with the effective Arrhenius parameters Ea = 41.0 ± 0.2 kcal mol-1 and log(A/s-1) = 20.2 ± 0.1. We rationalized the experimental findings on the basis of highly accurate CCSD(T)-F12 quantum chemical calculations. Computations predict that thermolysis of TNAA involves an intricate interplay of multiple decomposition channels of the three tautomers, which are equilibrated via either monomolecular reactions or concerted double hydrogen atom transfer in the H-bonded dimers; the calculated Arrhenius parameters of the effective rate constant coincide well with experiment. Most importantly, calculations provide detailed mechanistic evidence missing in the thermoanalytical experiment and explain formation of the experimentally observed primary products N2O and NO2. Along with the kinetics and mechanism of decomposition, the proposed approach yields accurate thermochemistry and phase change data of TNAA.

A combined photoionization time-of-flight mass spectrometry and laser absorption spectrometry flash photolysis apparatus for simultaneous determination of reaction rates and product branching

In recent years, predictions of product branching for reactions of consequence to both combustion and atmospheric chemistry have outpaced validating experiments. An apparatus is described that aims to fill this void by combining several well-known experimental techniques into one: flash photolysis for radical generation, multiple-pass laser absorption spectrometry (LAS) for overall kinetics measurements, and time-resolved photoionization time-of-flight mass spectrometry (PI TOF-MS) for product branching quantification. The sensitivity of both the LAS and PI TOF-MS detection techniques is shown to be suitable for experiments with initial photolytically generated radical concentrations of ∼1 × 10¹² molecules cm^-3. As it is fast (μs time resolution) and non-intrusive, LAS is preferred for accurate kinetics (time-dependence) measurements. By contrast, PI TOF-MS is preferred for product quantification because it provides a near-complete picture of the reactor composition in a single mass spectrum. The value of simultaneous LAS and PI TOF-MS detection is demonstrated for the chemically interesting phenyl radical + propene system.

Weakly Bound Free Radicals in Combustion: "Prompt" Dissociation of Formyl Radicals and Its Effect on Laminar Flame Speeds

Weakly bound free radicals have low-dissociation thresholds such that at high temperatures, time scales for dissociation and collisional relaxation become comparable, leading to significant dissociation during the vibrational-rotational relaxation process. Here we characterize this "prompt" dissociation of formyl (HCO), an important combustion radical, using direct dynamics calculations for OH + CH2O and H + CH2O (key HCO-forming reactions). For all other HCO-forming reactions, presumption of a thermal incipient HCO distribution was used to derive prompt dissociation fractions. Inclusion of these theoretically derived HCO prompt dissociation fractions into combustion kinetics models provides an additional source for H-atoms that feeds chain-branching reactions. Simulations using these updated combustion models are therefore shown to enhance flame propagation in 1,3,5-trioxane and acetylene. The present results suggest that HCO prompt dissociation should be included when simulating flames of hydrocarbons and oxygenated molecules and that prompt dissociations of other weakly bound radicals may also impact combustion simulations.

Pressure and temperature dependence of the reaction of vinyl radical with ethylene

This work reports measurements of absolute rate coefficients and Rice-Ramsperger-Kassel-Marcus (RRKM) master equation simulations of the C2H3+C2H4 reaction. Direct kinetic studies were performed over a temperature range of 300-700 K and pressures of 20 and 133 mbar. Vinyl radicals (H2C=CH) were generated by laser photolysis of vinyl iodide (C2H3I) at 266 nm, and time-resolved absorption spectroscopy was used to probe vinyl radicals through absorption at 423.2 nm. Measurements at 20 mbar are in good agreement with previous determinations at higher temperature. A weighted three-parameter Arrhenius fit to the experimental rate constant at 133 mbar, with the temperature exponent fixed, gives k=(7+/-1)x10(-14) cm3 molecule(-1) s(-1) (T/298 K)2 exp[-(1430+/-70) K/T]. RRKM master equation simulations, based on G3 calculations of stationary points on the C4H7 potential energy surface, were carried out to predict rate coefficients and product branching fractions. The predicted branching to 1-methylallyl product is relatively small under the conditions of the present experiments but increases as the pressure is lowered. Analysis of end products of 248 nm photolysis of vinyl iodide/ethylene mixtures at total pressures between 27 and 933 mbar provides no direct evidence for participation of 1-methylallyl.