scholarly journals Development of a combined dilatometer and mass spectrometer system for studying gas phase chemistry and kinetics during sintering

2010 ◽  
Author(s):  
Matthew Schurwanz
Keyword(s):  
Gas Phase ◽  
Mass Spectrometer ◽  
Gas Phase Chemistry ◽  
Mass Spectrometer System ◽  
Spectrometer System ◽  
Phase Chemistry

Ventilation-perfusion ratio distributions by mass spectrometry with membrane catheters

1982 ◽  
Vol 53 (3) ◽  
pp. 770-778 ◽  
Author(s):  
S. M. Mastenbrook ◽  
J. A. Dempsey ◽  
T. A. Massaro
Keyword(s):  
Gas Phase ◽  
Mass Spectrometer ◽  
Sulfur Hexafluoride ◽  
Inert Gases ◽  
Inert Gas ◽  
Rubber Membrane ◽  
Mass Spectrometer System ◽  
Phase Calibration ◽  
Spectrometer System ◽  
Ventilation Perfusion Ratio

We previously developed a quadrupole mass spectrometer system for measuring gas phase concentrations of multiple inert gases at trace levels. A new inlet with two silicone rubber membrane catheters now allows quantitative analysis of the inert gas concentrations in both blood and gas phase samples. We have determined the sensitivity, linearity, and reproducibility of this system by measuring blood-to-gas phase calibration curves for the following inert gases: sulfur hexafluoride, krypton, Freon 12, enflurane, diethyl ether, and acetone. We have used our mass spectrometer system to obtain multiple inert gas elimination data from three anesthetized, spontaneously breathing dogs. We conclude that our dual-membrane mass spectrometer system provides useful measurements of trace multiple inert gas concentrations in both blood and gas phase samples. Furthermore, the inert gases in blood can be measured directly without having to extract them into a gas phase, and the multiple inert gas elimination data acquired with our system can be used to provide estimates of ventilation-perfusion ratio distributions. Our mass spectrometer technique provides an alternative to the gas chromatographic approach for these measurements.

Gas-Phase Chemistry in the GC Orbitrap Mass Spectrometer

2018 ◽  
Vol 30 (4) ◽  
pp. 573-580 ◽  
Author(s):  
Tim U. H. Baumeister ◽  
Nico Ueberschaar ◽  
Georg Pohnert
Keyword(s):  
Gas Phase ◽  
Mass Spectrometer ◽  
Gas Phase Chemistry ◽  
Orbitrap Mass Spectrometer ◽  
Phase Chemistry

The Influence of Doping on the Etching of Si(111)

1986 ◽  
Vol 75 ◽  
Author(s):  
Harold F. Winters ◽  
D. Haarer
Keyword(s):  
Mass Spectrometer ◽  
Doping Level ◽  
Etch Rate ◽  
Plasma Reactors ◽  
Doping Effects ◽  
Mass Spectrometer System ◽  
Spectrometer System

AbstractIt has been recognized for some time that the doping level in silicon influences etch rate in plasma environments[1–8]. We have now been able to reproduce and investigate these doping effects in a modulated-beam, mass spectrometer system described previously [9] using XeF2 as the etchant gas. The phenomena which have been observed in plasma reactors containing fluorine atoms are also observed in our experiments. The data has led to a model which explains the major trends.

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.

Sign in / Sign up

Export Citation Format

Share Document