Rate coefficients for interstellar gas-phase chemistry

1993 ◽  
Vol 89 (13) ◽  
pp. 2193 ◽  
Author(s):  
Bertrand R. Rowe ◽  
Andr� Canosa ◽  
Ian R. Sims
Keyword(s):  
Gas Phase ◽  
Rate Coefficients ◽  
Interstellar Gas ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

scholarly journals Chemical Effects of Interstellar Grains

1989 ◽  
Vol 8 ◽  
pp. 383-386
Author(s):  
David A. Williams
Keyword(s):  
Gas Phase ◽  
Solid State Chemistry ◽  
Cloud Chemistry ◽  
Interstellar Gas ◽  
Gas Phase Chemistry ◽  
Chemical Models ◽  
Chemical Effects ◽  
Interstellar Grains ◽  
Cyclic Dynamics ◽  
Phase Chemistry

ABSTRACTThe chemical effects of interstellar grains are briefly reviewed. Their dominant chemical role is to catalyze the formation of H2 which is the seminal molecule for efficient gas phase chemistry. In regions of at least moderate extinction grains accumulate molecular mantles of CO, H2O, etc. Solid state chemistry in such mantles may produce molecules of a type or in an abundance not achievable in the interstellar gas. Return of mantle material to the gas can – at least transiently – dominate gas phase chemistry. It is argued that the freeze-out of heavy atomic and molecular species on to grain surfaces limits the time available for chemistry, restricts molecular cloud chemistry to a “young” character, and suggests that chemical models of molecular clouds must have cyclic dynamics. Such models are briefly described.

scholarly journals The interstellar gas-phase chemistry of HCN and HNC

2014 ◽  
Vol 443 (1) ◽  
pp. 398-410 ◽  
Author(s):  
Jean-Christophe Loison ◽  
Valentine Wakelam ◽  
Kevin M. Hickson
Keyword(s):  
Gas Phase ◽  
Interstellar Gas ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

scholarly journals Bistability in Interstellar Gas‐Phase Chemistry

2006 ◽  
Vol 645 (1) ◽  
pp. 314-323 ◽  
Author(s):  
Gai I. Boger ◽  
Amiel Sternberg
Keyword(s):  
Gas Phase ◽  
Interstellar Gas ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

scholarly journals Discovery of Interstellar Propylene (CH 2 CHCH 3 ): Missing Links in Interstellar Gas-Phase Chemistry

2007 ◽  
Vol 665 (2) ◽  
pp. L127-L130 ◽  
Author(s):  
N. Marcelino ◽  
J. Cernicharo ◽  
M. Agúndez ◽  
E. Roueff ◽  
M. Gerin ◽  
...  
Keyword(s):  
Gas Phase ◽  
Interstellar Gas ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

scholarly journals On the nucleation of dust in oxygen-rich stellar outflows

2013 ◽  
Vol 371 (1994) ◽  
pp. 20120335 ◽  
Author(s):  
John M. C. Plane
Keyword(s):  
Gas Phase ◽  
Potential Energy Surfaces ◽  
Particle Growth ◽  
Asymptotic Giant Branch ◽  
Rate Coefficients ◽  
Stationary Points ◽  
Asymptotic Giant Branch Stars ◽  
Gas Phase Chemistry ◽  
Energy Surfaces ◽  
Phase Chemistry

Understanding the nature of dust condensation in the outflow from oxygen-rich asymptotic giant branch stars is a continuing problem. A kinetic model has been developed to describe the formation of gas-phase precursors from Ca, Mg, Fe, SiO and TiO in an outflow cooling from 1500 to 1000 K. Electronic structure calculations are used to identify efficient reaction pathways that lead to the formation of metal titanates and silicates. The molecular properties of the stationary points on the relevant potential energy surfaces are then used in a multi-well master equation solver to calculate pertinent rate coefficients. The outflow model couples an explicit treatment of gas-phase chemistry to a volume-conserving particle growth model. CaTiO 3 is shown to be the overwhelming contributor to the formation of condensation nuclei (CN), with less than 0.01 per cent provided by CaSiO 3 , (TiO 2 ) 2 and FeTiO 3 . Magnesium species make a negligible contribution. Defining CN as particles with radii greater than 2 nm, the model shows that for stellar mass loss rates above 3×10 −5  M ⊙  yr −1 , more than 10 −13  CN per H nucleus will be produced when the outflow temperature is still well above 1000 K. This is sufficient to explain the observed number density of grains in circumstellar dust shells.

scholarly journals Formation of Al containing molecular complexes in the gas phase in dense molecular clouds: quantum study of the radiative association of Al++H2 and Al++D2

2021 ◽  
Vol 503 (2) ◽  
pp. 3089-3094
Author(s):  
Daria Jones (nee Burdakova) ◽  
Gunnar Nyman ◽  
Thierry Stoecklin
Keyword(s):  
Gas Phase ◽  
Molecular Clouds ◽  
Transition State Theory ◽  
Molecular Complexes ◽  
State Theory ◽  
Rate Coefficients ◽  
Gas Phase Chemistry ◽  
Radiative Association ◽  
Quantum Approach ◽  
Phase Chemistry

ABSTRACT Radiative association (RA) of Al+ with H2 is the first step in the formation of AlH in gas phase and is here investigated theoretically. We use recent potential energy and dipole moment surfaces and a quantum approach based on the driven equations formalism for performing the dynamics for both the Al+-H2 and Al+-D2 systems. The obtained RA rate coefficients are compared with previous evaluations based on transition state theory and found to be orders of magnitude larger. They are also compared to those obtained recently for the similar systems Na+-H2/D2. The possible role played by RA of Al+ with H2 in the gas phase chemistry of dense molecular clouds is discussed.

Shock-tube study of the decomposition of octamethylcyclotetrasiloxane and hexamethylcyclotrisiloxane

2020 ◽  
Vol 234 (7-9) ◽  
pp. 1395-1426 ◽  
Author(s):  
Paul Sela ◽  
Sebastian Peukert ◽  
Jürgen Herzler ◽  
Christof Schulz ◽  
Mustapha Fikri
Keyword(s):  
Mass Spectrometry ◽  
Shock Tube ◽  
High Repetition Rate ◽  
Rate Coefficients ◽  
Gas Chromatography Mass Spectrometry ◽  
High Concentration ◽  
Gas Phase Chemistry ◽  
Reflected Shock ◽  
Flight Mass Spectrometry ◽  
Phase Chemistry

AbstractShock-tube experiments have been performed to investigate the thermal decomposition of octamethylcyclotetrasiloxane (D4, Si4O4C8H24) and hexamethylcyclotrisiloxane (D3, Si3O3C6H18) behind reflected shock waves by gas chromatography/mass spectrometry (GC/MS) and high-repetition-rate time-of-flight mass spectrometry (HRR-TOF-MS) in a temperature range of 1160–1600 K and a pressure range of 1.3–2.6 bar. The main observed stable products were methane (CH4), ethylene (C2H4), ethane (C2H6), acetylene (C2H2) and in the case of D4 pyrolysis, also D3 was measured as a product in high concentration. A kinetics sub-mechanism accounting for the D4 and D3 gas-phase chemistry was devised, which consists of 19 reactions and 15 Si-containing species. The D4/D3 submechanism was combined with the AramcoMech 2.0 (Li et al., Proc. Combust. Inst. 2017, 36, 403–411) to describe hydrocarbon chemistry. The unimolecular rate coefficients for D4 and D3 decomposition are represented by the Arrhenius expressions ktotal/D4(T) = 2.87 × 1013 exp(−273.2 kJ mol−1/RT) s−1 and ktotal/D3(T) = 9.19 × 1014 exp(−332.0 kJ mol−1/RT) s−1, respectively.

scholarly journals Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

2019 ◽  
Vol 12 (3) ◽  
pp. 1209-1225 ◽  
Author(s):  
Christoph A. Keller ◽  
Mat J. Evans
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gas Phase ◽  
Atmospheric Chemistry ◽  
Random Forest Regression ◽  
Data Set ◽  
Gas Phase Chemistry ◽  
Chemical Conditions ◽  
Phase Chemistry ◽  
The Impact

Abstract. Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry transport models. Our training data consist of 1 month (July 2013) of output of chemical conditions together with the model physical state, produced from the GEOS-Chem chemistry model v10. From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine-learning-driven GEOS-Chem model compares well to the standard simulation. For ozone (O3), errors from using the random forests (compared to the reference simulation) grow slowly and after 5 days the normalized mean bias (NMB), root mean square error (RMSE) and R2 are 4.2 %, 35 % and 0.9, respectively; after 30 days the errors increase to 13 %, 67 % and 0.75, respectively. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10 % and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short-lived nitrogen species, errors become large, with NMB, RMSE and R2 reaching >2100 % >400 % and <0.1, respectively. This proof-of-concept implementation takes 1.8 times more time than the direct integration of the differential equations, but optimization and software engineering should allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations, and its applicability to operational air quality activities.

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