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Register a new userA twist on the reaction of the CN radical with methylamine in the interstellar medium: new hints from a state-of-the-art quantum-chemical study
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Despite the fact that the majority of current models assume that interstellar complex organic molecules (iCOMs) are formed on dust-grain surfaces, there is some evidence that neutral gas-phase reactions play an important role. In this paper, we investigate the reaction occurring in the gas phase between methylamine (CH$_3$NH$_2$) and the cyano (CN) radical, for which only fragmentary and/or inaccurate results have been reported to date. This case study allows us to point out the pivotal importance of employing quantum-chemical calculations at the state of the art. Since the two major products of the CH$_3$NH$_2$ + CN reaction, namely the CH$_3$NH and CH$_2$NH$_2$ radicals, have not been spectroscopically characterized yet, some effort has been made for filling this gap.
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Interstellar complex organic molecules (iCOMs) are assumed to be mainly formed on dust-grain surfaces. However, neutral gas-phase reactions in the interstellar medium (ISM) can play an important role. In this paper, by investigating the reaction between aldehydes and the cyano radical, we show that both formaldehyde (CH$_2$O) and acetaldehyde (CH$_3$CHO) can lead to the formation of formyl cyanide (HCOCN). Owing to accurate quantum-chemical computations followed by rate constant evaluations, we have been able to suggest and validate an effective mechanism for the formation of HCOCN, one of the molecules observed in the ISM. Quite interestingly, the mechanism starting from CH$_2$O is very effective at low temperature, while that involving CH$_3$CHO becomes more efficient at temperatures above 200 K.
Recent astronomical observations of both isomers E and Z of imines such as cyanomethanimine, ethanimine and 2-propyn-1-imine, have revealed that the abundances in the ISM of these isomers differ by factors of ~3-10. Several theories have been proposed to explain the observed behavior, but none of them successfully explains the [E]/[Z] ratios. In this work we present a detailed study of the kinetics of the one-step E-Z isomerization reactions of cyanomethanimine, ethanimine and 2-propyn-1-imine under interstellar conditions (in the 10-400 K temperature range). This reaction was previously thought to be non-viable in the ISM due to its associated high-energy barrier (about 13,000 K). In this Letter, we show that considering the multidimensional small curvature tunneling approximation, the tunneling effect enables the isomerization even at low temperatures. This is due to the fact that the representative tunneling energy lies in the vibrational ground state of the least stable isomer up to approximately 150 K, making the reaction constants of the isomerization from the least stable to the most stable isomer basically constant. The predicted [E]/[Z] ratios are almost the same as those reported from the astronomical observations for all imines observed. This study demonstrates that the [E]/[Z] ratio of imines in the ISM strongly depends on their relative stability.
We report here the first detection in the interstellar medium of the cyanomidyl radical (HNCN). Using the Yebes 40m and the IRAM 30m telescopes, we have targeted the doublets of the $N$=2$-$1, 4$-$3, 5$-$4, 6$-$5, and 7$-$6 transitions of HNCN toward the molecular cloud G+0.693-0.027. We have detected three unblended lines of HNCN, these are the $N$=6$-$5 doublet and one line of the $N$=4$-$3 transition. Additionally we present one line of the $N$=5$-$4 transition partially blended with emission from other species. The Local Thermodynamic Equilibrium best fit to the data gives a molecular abundance of (0.91$pm$0.05)$times$10$^{-10}$ with respect to H$_2$. The relatively low abundance of this species in G+0.693-0.027, and its high reactivity, suggest that HNCN is possibly produced by gas-phase chemistry. Our work shows that this highly reactive molecule is present in interstellar space, and thus it represents a plausible precursor of larger prebiotic molecules with the NCN backbone such as cyanamide (NH$_2$CN), carbodiimide (HNCNH) and formamidine (NH$_2$CHNH).
Datasets in the Natural Sciences are often curated with the goal of aiding scientific understanding and hence may not always be in a form that facilitates the application of machine learning. In this paper, we identify three trends within the fields of chemical reaction prediction and synthesis design that require a change in direction. First, the manner in which reaction datasets are split into reactants and reagents encourages testing models in an unrealistically generous manner. Second, we highlight the prevalence of mislabelled data, and suggest that the focus should be on outlier removal rather than data fitting only. Lastly, we discuss the problem of reagent prediction, in addition to reactant prediction, in order to solve the full synthesis design problem, highlighting the mismatch between what machine learning solves and what a lab chemist would need. Our critiques are also relevant to the burgeoning field of using machine learning to accelerate progress in experimental Natural Sciences, where datasets are often split in a biased way, are highly noisy, and contextual variables that are not evident from the data strongly influence the outcome of experiments.
A wide variety of real life complex networks are prohibitively large for modeling, analysis and control. Understanding the structure and dynamics of such networks entails creating a smaller representative network that preserves its relevant topological and dynamical properties. While modern machine learning methods have enabled identification of governing laws for complex dynamical systems, their inability to produce white-box models with sufficient physical interpretation renders such methods undesirable to domain experts. In this paper, we introduce a hybrid black-box, white-box approach for the sparse identification of the governing laws for complex, highly coupled dynamical systems with particular emphasis on finding the influential reactions in chemical reaction networks for combustion applications, using a data-driven sparse-learning technique. The proposed approach identifies a set of influential reactions using species concentrations and reaction rates,with minimal computational cost without requiring additional data or simulations. The new approach is applied to analyze the combustion chemistry of H2 and C3H8 in a constant-volume homogeneous reactor. The influential reactions determined by the sparse-learning method are consistent with the current kinetics knowledge of chemical mechanisms. Additionally, we show that a reduced version of the parent mechanism can be generated as a combination of the significantly reduced influential reactions identified at different times and conditions and that for both H2 and C3H8 fuel, the reduced mechanisms perform closely to the parent mechanisms as a function of the ignition delay time over a wide range of conditions. Our results demonstrate the potential of the sparse-learning approach as an effective and efficient tool for dynamical system analysis and reduction. The uniqueness of this approach as applied to combustion systems lies in the ability to identify influential reactions in specified conditions and times during the evolution of the combustion process. This ability is of great interest to understand chemical reaction systems.
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