Toward Accurate Quantum Mechanical Thermochemistry: (1) Extensible Implementation and Comparison of Bond Additivity Corrections and Isodesmic Reactions.

Obtaining accurate enthalpies of formation of chemical species, ΔHf, often requires empirical corrections that connect the results of quantum mechanical (QM) calculations with the experimental enthalpies of elements in their standard state. One approach is to use atomization energy corrections followed by bond additivity corrections (BACs), such as those defined by Petersson et al. or Anantharaman and Melius. Another approach is to utilize isodesmic reactions (IDRs) as shown by Buerger et al. We implement both approaches in Arkane, an open-source software that can calculate species thermochemistry using results from various QM software packages. In this work, we collect 421 reference species from the literature to derive ΔHf corrections and fit atomization energy corrections and BACs for 15 commonly used model chemistries. We find that both types of BACs yield similar accuracy, although Anantharaman- and Melius-type BACs appear to generalize better. Furthermore, BACs tend to achieve better accuracy than IDRs for commonly used model chemistries, and IDRs can be less robust because of the sensitivity to the chosen reference species and reactions. Overall, Anantharaman- and Melius-type BACs are our recommended approach for achieving accurate QM corrections for enthalpies.

Non-physical Species in Chemical Kinetic Models: A Case Study of Diazenyl Hydroxy and Diazenyl Peroxide.

Predictive chemical kinetic models often consider hundreds to thousands of intermediate species. An even greater number of species are required to generate pressure-dependent reaction networks for gas-phase systems. As this immense chemical search space is being explored using automated 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 non-physical species might obey chemical intuition as well as requirements coded in the software, e. g., obeying element electron valence constraints, and may consequently remain unnoticed. Non-physical species become an acute problem when their presence affects a model observable. Correcting a pressure-dependent network containing a non-physical species may significantly affect the computed rate coefficient. The present work discusses and analyzes two specific cases of such species, diazenyl hydroxy (⋅N=NOH) and diazenyl peroxide (⋅N=NOOH), both previously suggested as intermediates in nitrogen combustion systems. A comprehensive conformational search did not identify any non-fragmented energy well, and energy scans performed for diazenyl peroxide (⋅N=NOOH), at DFT and CCSD(T) show that it barrierlessly decomposes. 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.

Kinetic Modeling of API Oxidation: (2) Imipramine Stress Testing.

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., Mol. Pharm. 2021 18 (8), 3037-3049], we have generated the first ab initio predictive chemical kinetic model of free-radical oxidative degradation for API stress testing. We focused on imipramine oxidation in an azobis(isobutyronitrile) (AIBN)/H2O/CH3OH solution and compared the model's predictions with concurrent experimental observations. We analytically determined iminodibenzyl and desimipramine as imipramine's two major degradation products under industry-standard AIBN stress testing conditions, and our ab initio kinetic model successfully identified both of them in its prediction for the top three degradation products. This work shows the potential and utility of predictive chemical kinetic modeling and quantum chemical computations to elucidate API chemical stability issues. Further, we envision an automated digital workflow that integrates first-principle models with data-driven methods that, when actively and iteratively combined with high-throughput experiments, can substantially accelerate and transform future API chemical stability studies.

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.

Progress and Prospective of Nitrogen-Based Alternative Fuels.

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.

Kinetic Modeling of API Oxidation: (1) The AIBN/H2O/CH3OH Radical "Soup".

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.

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.

Large Intermediates in Hydrazine Decomposition: A Theoretical Study of the N3H5 and N4H6 Potential Energy Surfaces.

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 potential energy surfaces was performed at the CCSD(T)-F12a/aug-cc-pVTZ//ωB97x-D3/6-311++G(3df,3pd) level of theory, and pressure-dependent rate coefficients were determined. There are no low-barrier unimolecular decomposition pathways for triazane (n-N3H5), and its formation becomes more significant as the pressure increases; it is the primary product of N2H3 + NH2 below 550, 800, 1150, and 1600 K at 0.1, 1, 10, and 100 bar, respectively. The N4H6 surface has two important entry channels, N2H4 + H2NN and N2H3 + N2H3, each with different primary products. Interestingly, N2H4 + H2NN primarily forms N2H3 + N2H3, while disproportionation of N2H3 + N2H3 predominantly leads to the other N2H2 isomer, HNNH. Stabilized tetrazane (n-N4H6) formation from N2H3 + N2H3 becomes significant only at relatively high pressures and low temperatures because of fall-off back into N2H3 + N2H3. Pressure-dependent rate coefficients for all considered reactions as well as thermodynamic properties of triazane and tetrazane, which should be considered for kinetic modeling of chemical processes involving nitrogen- and hydrogen-containing species, are reported.

Nitrogen-Based Fuels: A Power-to-Fuel-to-Power Analysis.

What are the fuels of the future? Seven representative carbon- and nitrogen-based fuels are evaluated on an energy basis in a power-to-fuel-to-power analysis as possible future chemical hydrogen-storage media. It is intriguing to consider that a nitrogen economy, where hydrogen obtained from water splitting is chemically stored on abundant nitrogen in the form of a nontoxic and safe nitrogen-based alternative fuel, is energetically feasible.

Biodegradable, Biocompatible, and Implantable Multifunctional Sensing Platform for Cardiac Monitoring.

Cardiac monitoring after heart surgeries is crucial for health maintenance and detecting postoperative complications early. However, current methods like rigid implants have limitations, as they require performing second complex surgeries for removal, increasing infection and inflammation risks, thus prompting research for improved sensing monitoring technologies. Herein, we introduce a nanosensor platform that is biodegradable, biocompatible, and integrated with multifunctions, suitable for use as implants for cardiac monitoring. The device has two electrochemical biosensors for sensing lactic acid and pH as well as a pressure sensor and a chemiresistor array for detecting volatile organic compounds. Its biocompatibility with myocytes has been tested in vitro, and its biodegradability and sensing function have been proven with ex vivo experiments using a three-dimensional (3D)-printed heart model and 3D-printed cardiac tissue patches. Moreover, an artificial intelligence-based predictive model was designed to fuse sensor data for more precise health assessment, making it a suitable candidate for clinical use. This sensing platform promises impactful applications in the realm of cardiac patient care, laying the foundation for advanced life-saving developments.