Gas-Phase Chemistry of Bare V+Cation with Oxygen and Water at Room Temperature:  Formation and Hydration of Vanadium Oxide Cations

2001 ◽  
Vol 105 (17) ◽  
pp. 4259-4271 ◽  
Author(s):  
Gregory K. Koyanagi ◽  
Diethard K. Bohme ◽  
Ilona Kretzschmar ◽  
Detlef Schröder ◽  
Helmut Schwarz
Keyword(s):  
Vanadium Oxide ◽  
Gas Phase ◽  
Room Temperature ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

ChemInform Abstract: Gas-Phase Chemistry of Bare V+ Cation with Oxygen and Water at Room Temperature: Formation and Hydration of Vanadium Oxide Cations.

ChemInform ◽  
2010 ◽  
Vol 32 (33) ◽  
pp. no-no
Author(s):  
Gregory K. Koyanagi ◽  
Diethard K. Bohme ◽  
Ilona Kretzschmar ◽  
Detlef Schroeder ◽  
Helmut Schwarz
Keyword(s):  
Vanadium Oxide ◽  
Gas Phase ◽  
Room Temperature ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

Gas-Phase Chemistry of Vanadium Oxide Cluster Cations. 2. Reactions with CH2F2

1999 ◽  
Vol 103 (16) ◽  
pp. 2992-2998 ◽  
Author(s):  
R. C. Bell ◽  
K. A. Zemski ◽  
A. W. Castleman
Keyword(s):  
Vanadium Oxide ◽  
Gas Phase ◽  
Oxide Cluster ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

Gas-Phase Chemistry of Vanadium Oxide Cluster Cations. 1. Reactions with C2F6and CH3CF3

1998 ◽  
Vol 102 (43) ◽  
pp. 8293-8299 ◽  
Author(s):  
R. C. Bell ◽  
K. A. Zemski ◽  
A. W. Castleman
Keyword(s):  
Vanadium Oxide ◽  
Gas Phase ◽  
Oxide Cluster ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

Gas-Phase Chemistry of Vanadium Oxide Cluster Cations 3. Reactions with CCl4

1999 ◽  
Vol 103 (11) ◽  
pp. 1585-1591 ◽  
Author(s):  
R. C. Bell ◽  
K. A. Zemski ◽  
A. W. Castleman
Keyword(s):  
Vanadium Oxide ◽  
Gas Phase ◽  
Oxide Cluster ◽  
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
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