Gas-Phase Chemistry of the Sulfur Hexafluoride Fragment Ions SFn+(n= 0−5) and SFn2+(n= 2, 4). Ab Initio Thermochemistry of Novel Reactions of S+•and SF+

1998 ◽  
Vol 102 (27) ◽  
pp. 5189-5195 ◽  
Author(s):  
Regina Sparrapan ◽  
Maria Anita Mendes ◽  
Isabel P. P. Ferreira ◽  
Marcos N. Eberlin ◽  
Cristiano Santos ◽  
...  
Keyword(s):  
Ab Initio ◽  
Gas Phase ◽  
Sulfur Hexafluoride ◽  
Gas Phase Chemistry ◽  
Fragment Ions ◽  
Phase Chemistry

Thionyl Fluoride from Sulfur Hexafluoride Corona Discharge Decomposition:  Gas-Phase Chemistry of [SOF2]H+Ions

2002 ◽  
Vol 106 (40) ◽  
pp. 9261-9266 ◽  
Author(s):  
Federico Pepi ◽  
Andreina Ricci ◽  
Marco Di Stefano ◽  
Marzio Rosi ◽  
Giuseppe D'Arcangelo
Keyword(s):  
Corona Discharge ◽  
Gas Phase ◽  
Sulfur Hexafluoride ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

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.

Gas Phase Chemistry in Cellulose Fast Pyrolysis

2009 ◽  
Vol 48 (3) ◽  
pp. 1391-1399 ◽  
Author(s):  
R. Lanza ◽  
D. Dalle Nogare ◽  
P. Canu
Keyword(s):  
Gas Phase ◽  
Fast Pyrolysis ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

Mass Spectrometry and Gas-Phase Chemistry of Anilines

ChemInform ◽  
2007 ◽  
Vol 38 (30) ◽  
Author(s):  
Marcos N. Eberlin ◽  
Daniella Vasconcellos Augusti ◽  
Rodinei Augusti
Keyword(s):  
Mass Spectrometry ◽  
Gas Phase ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

Kinetic Analysis of C4 Alkane and Alkene Pyrolysis: Implications for SOFC Operation

2010 ◽  
Vol 7 (4) ◽  
Author(s):  
Ahmed Al Shoaibi ◽  
Anthony M. Dean
Keyword(s):  
Gas Phase ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Pyrolysis Kinetics ◽  
Fuel Conversion ◽  
Substantial Impact ◽  
Gas Phase Chemistry ◽  
Gas Phase Kinetics ◽  
Hydrocarbon Gas ◽  
Phase Chemistry

Pyrolysis experiments of isobutane, isobutylene, and 1-butene were performed over a temperature range of 550–750°C and a pressure of ∼0.8 atm. The residence time was ∼5 s. The fuel conversion and product selectivity were analyzed at these temperatures. The pyrolysis experiments were performed to simulate the gas-phase chemistry that occurs in the anode channel of a solid-oxide fuel cell (SOFC). The experimental results confirm that molecular structure has a substantial impact on pyrolysis kinetics. The experimental data show considerable amounts of C5 and higher species (∼2.8 mole % with isobutane at 750°C, ∼7.5 mole % with isobutylene at 737.5°C, and ∼7.4 mole % with 1-butene at 700°C). The C5+ species are likely deposit precursors. The results confirm that hydrocarbon gas-phase kinetics have substantial impact on a SOFC operation.

Gas-phase chemistry of the negative ions derived from azo- and hydrazobenzene

1991 ◽  
Vol 56 (2) ◽  
pp. 607-612 ◽  
Author(s):  
Steen Ingemann ◽  
Roel H. Fokkens ◽  
Nico M. M. Nibbering
Keyword(s):  
Gas Phase ◽  
Negative Ions ◽  
Gas Phase Chemistry ◽  
Phase Chemistry
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