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Stress testing of active pharmaceutical ingredients (API) is an important tool used to gauge chemical stability and identify potential degradation products. While different flavors of API stress testing systems have been used in experimental investigations for decades, the detailed kinetics of such systems as well as the chemical composition of prominent reactive species, specifically reactive oxygen species, are unknown. As a first step toward understanding and modeling API oxidation in stress testing, we investigated a typical radical "soup" solution an API is subject to during stress testing. Here we applied ab initio electronic structure calculations to automatically generate and refine a detailed chemical kinetics model, taking a fresh look at API oxidation. We generated a detailed kinetic model for a representative azobis(isobutyronitrile) (AIBN)/H2O/CH3OH stress-testing system with a varied cosolvent ratio (50%/50%-99.5%/0.5% vol water/methanol) for 5.0 mM AIBN and representative pH values of 4-10 at 40 °C that was stirred and open to the atmosphere. At acidic conditions, hydroxymethyl alkoxyl is the dominant alkoxyl radical, and at basic conditions, for most studied initial methanol concentrations, cyanoisopropyl alkoxyl becomes the dominant alkoxyl radical, albeit at an overall lower concentration. At acidic conditions, the levels of cyanoisopropyl peroxyl, hydroxymethyl peroxyl, and hydroperoxyl radicals are relatively high and comparable, while, at both neutral and basic pH conditions, superoxide becomes the prominent radical in the system. The present work reveals the prominent species in a common model API stress testing system at various cosolvent and pH conditions, sets the stage for an in-depth quantitative API kinetic study, and demonstrates the usage of novel software tools for automated chemical kinetic model generation and ab initio refinement.

Exploration, facilitates computations of thermodynamic properties of chemical species, high-pressure limit reaction rate coefficients, and pressure-dependent rate coefficient over multi-well molecular potential energy surfaces (PES) including the effects of collisional energy transfer on phenomenological kinetics. Arkane is especially flexible, computing a variety of quantities using different methods, as determined by the users. For example, it can use estimates to fill in information for molecules or reactions where quantum chemistry information is missing. The software solves the internal energy master equation for complex unimolecular reactions systems. Inputs to the software include converged electronic structure computations performed by the user using a variety of supported software packages (Gaussian, Molpro, Orca, TeraChem, Q-Chem, Psi4). The software outputs high-pressure limit rate coefficients and pressure-dependent phenomenological rate coefficients, as well as computed thermodynamic properties (enthalpy, entropy, and constant pressure heat capacity) with added energy corrections. Some of the key features of Arkane include treatment of 1D, 2D or ND hindered internal rotation modes, treatment of free internal rotation modes, quantum tunneling effect consideration (implementing the Eckart or Wigner approach), transition state theory (TST) and Rice-Ramsperger-Kassel-Marcus (RRKM) rate coefficient computations, master equation solution with three implemented methods, inverse-Laplace transform of high-pressure limit rate coefficients into the energy domain, energy corrections based on bond-additivity or isodesmic reactions, automated and efficient PES exploration, and PES sensitivity analysis. The present work describes the design of Arkane, how it should be used, and refers to the theory that it employs. Arkane is distributed via the RMG-Py software suite (https://github.com/ReactionMechanismGenerator/RMG-Py).

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.

Alternative fuels are essential to enable the transition to a sustainable and environmentally friendly energy supply. Synthetic fuels derived from renewable energies can act as energy storage media, thus mitigating the effects of fossil fuels on environment and health. Their economic viability, environmental impact, and compatibility with current infrastructure and technologies are fuel and power source specific. Nitrogen-based fuels pose one possible synthetic fuel pathway. In this review, we discuss the progress and current research on utilization of nitrogen-based fuels in power applications, covering the complete fuel cycle. We cover the production, distribution, and storage of nitrogen-based fuels. We assess much of the existing literature on the reactions involved in the ammonia to nitrogen atom pathway in nitrogen-based fuel combustion. Furthermore, we discuss nitrogen-based fuel applications ranging from combustion engines to gas turbines, as well as their exploitation by suggested end-uses. Thereby, we evaluate the potential opportunities and challenges of expanding the role of nitrogen-based molecules in the energy sector, outlining their use as energy carriers in relevant fields.

Predictive chemical kinetic models often consider hundreds to thousands of intermediate chemical species. An even greater number of species, that might not participate in the model directly, are required to generate pressure-dependent reaction networks for gas-phase systems. As this immense chemical search space is being explored using automated software tools by applying reaction templates, it is probable that non-physical species will infiltrate the model without being recognized by the compute or a human as such. These nonphysical species might obey chemical intuition as well as requirements coded in the software, e.g., obeying electron valence constraints of chemical elements, and may consequently remain unnoticed. Nonphysical species become an acute problem when their presence affects the model observables. The present work discusses and analyzes two specific cases of such species, diazenyl hydroxy (·NNOH) and diazenyl peroxide (·NNOOH), both previously suggested as intermediates in nitrogen combustion systems. A comprehensive conformational search did not identify any non-fragmented energy well for either of the two species, and energy scans performed for diazenyl peroxide (·NNOOH), both at DFT and CCSD(T), show that they barrierlessly decompose. This work highlights a broad implication for future automated chemical kinetic model generation, and provides a significant motivation to standardize non-physical species identification in chemical kinetic models.

Gauging the chemical stability of active pharmaceutical ingredients (APIs) is critical at various stages of pharmaceutical development to identify potential risks from drug degradation and ensure the quality and safety of the drug product. Stress testing has been the major experimental method to study API stability, but this analytical approach is time-consuming, resource-intensive, and limited by API availability, especially during the early stages of drug development. Novel computational chemistry methods may assist in screening for API chemical stability prior to synthesis and augment contemporary API stress testing studies, with the potential to significantly accelerate drug development and reduce costs. In this work, we leverage quantum chemical calculations and automated reaction mechanism generation to provide new insights into API degradation studies. In the continuation of part one in this series of studies [Grinberg Dana et al.

© 2019 Wiley Periodicals, Inc. This work discusses efficient and automated methods for constructing a set of representative resonance structures for arbitrary chemical species, including radicals and biradicals, consisting of the elements H, C, O, N, and S. Determining the representative reactive structures of chemical species is crucial for identification of reactive sites and consequently applying the correct reaction templates to generate the set of important reactions during automated chemical kinetic model generation. We describe a fundamental set of resonance pathway types, accounting for simple resonating structures, as well as global approaches for polycyclic aromatic species. Automatically discovering potential localized structures along with filtration to identify the representative structures was shown to be robust and relatively fast. The algorithms discussed here were recently implemented in the Reaction Mechanism Generator (RMG) software. The final structures proposed by this method were found to be in reasonable agreement with quantum chemical computation results of localized structure contributions to the resonance hybrid.

Large complex formation involved in the thermal decomposition of hydrazine (N2H4) is studied using transition state theory-based theoretical kinetics. A comprehensive analysis of the N3H5 and N4H6 ...

While different flavors of API stress testing systems have been used in experimental investigations for decades, the detailed kinetics of such systems as well as the chemical composition of prominent reactive species, specifically reactive oxygen species, are unknown.As a first step toward understanding and modeling API oxidation in stress testing, we investigated a typical radical "soup" solution an API is subject to during stress testing.Here we applied ab-initio electronic structure calculations to automatically generate and refine a detailed chemical kinetics model, taking a fresh look at API oxidation.We generated a detailed kinetic model for a representative azobisisobutyronitrile (AIBN)/H2O/CH3OH stress-testing system with varied co-solvent ratio (50%/50% -- 99.5%/0.5% vol. water/methanol) and for representative pH values (4--10) at 40oC stirred and open to the atmosphere.At acidic conditions hydroxymethyl alkoxyl is the dominant alkoxyl radical, and at basic conditions, for most studied initial methanol concentrations, cyanoisopropyl alkoxyl becomes the dominant alkoxyl radical, albeit at an overall lower concentration.At acidic conditions the levels of cyanoisopropyl peroxyl, hydroxymethyl peroxyl, and hydroperoxyl radicals are relatively high and comparable, while at both neutral and basic pH conditions, superoxide becomes the prominent radical in the system.The present work reveals the prominent species in a common model API stress testing system at various co-solvent and pH conditions, sets the stage for an in-depth quantitative API kinetic study, and demonstrates usage of novel software tools for automated chemical kinetic model generation and ab-initio refinement.

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.

The mutual oxidation of n-pentane and NO₂ at 500–1000 K has been studied at equivalence ratios of 0.5 and 1.33 by using an atmospheric-pressure jet stirred reactor (JSR). N-pentane, O₂, NO, NO₂, CO, CO₂, CH₂O, C₂H₄, and CH₃CHO are simultaneously quantified, in-situ by using an electron-impact molecular beam mass spectrometer (EI-MBMS), a micro-gas chromatograph (μ-GC), and a mid-IR dual-modulation faraday rotation spectrometer (DM-FRS). Both fuel lean and rich experiments show that, in 550–650 K, NO₂ addition inhibits low temperature oxidation. With an increase of temperature to the negative temperature coefficient (NTC) region (650–750 K), NO₂ addition weakens the NTC behavior. In 750–1000 K, high temperature oxidation is accelerated with NO₂ addition and shifted to lower temperature. Two kinetic models, a newly developed RMG n-pentane/NOx model and Zhao's n-pentane/NOx model (Zhao et al., 2018, Submitted) were validated against experimental data. Both models were able to capture the temperature-dependent NO₂ sensitization characteristics successfully. The results show that although NO₂ addition in n-pentane has similar effects to NO at many conditions due to fast NO and NO₂ interconversion at higher temperature, it affects low temperature oxidation somewhat differently. When NO2/NO interconversion is slow, NO₂ is relatively inert while NO can strongly promote or inhibit oxidation., National Science Foundation (Grant CBET-1507358)

Due to increasing global greenhouse gas emissions there is urgency to pursue all measures to develop carbon-neutral energy technologies. The most attractive approach for large-scale energy storage is perhaps in the form of synthetic fuels. In this work, a nitrogen-based fuel consisting of an aqueous solution of urea and ammonium nitrate is evaluated for chemical stability and handling safety. The steady combustion of the fuel was demonstrated at 10 MPa. Thermal analysis under an ambient pressure dry air flow in the range of 25–500 °C showed that the monofuel does not react with atmospheric oxygen in a ventilated open system, and only endothermic thermal processes were observed in the condensed phase. Under applied pressure the fuel combusts exothermically above 300 °C, after water vaporization. The fuel described herein is an excellent gas generator with a potential effluent composition of 73.0 % H2O, 21.6 % N2, and 5.4 % CO2, which is an environmentally friendly working fluid.