Residual Gas Analysis (SRS200)


Residual Gas Analysis (RGA) measures the partial pressures of the individual gases in a mixture.

RGA is a stand-alone vacuum system that is differentially pumped to accept atmospheric pressure sample gas input. A small quantity of sample gas is ionized. The ions are accelerated into a mass separation filter, resulting in a mass spectrum showing partial pressure vs. gas species mass.

The system can be used to measure:

  • Outgassing of a sample as a function of temperature (only volatiles)
  • Leak check vacuum system

Interpretation of RGA data is not always straightforward. Each channel represents a mass/charge ratio for atoms, molecules and molecular fragments that are detected at the RGA head after being ionized at a high voltage hot filament. Species volatilized by a sample may or may not get detected depending on a) whether they are condensable upon leaving the hot zone of the furnace and b) their room temperature vapor pressure. A channel may represent more than one species, e.g. 28 could be CO or N2. A large species may fragment to produce a variety of species that are detected at a variety of channels. The vertical axis is logarithmic, so signal intensities track with partial pressures in the vacuum system of the RGA and they increase by orders of magnitude as you go up the y-axis. By virtue of the scanning method, higher channels were measured after smaller channels and, in the case of controlled heating of the sample, were slightly hotter because of the ramp rate of the furnace.

If all samples are run under identical conditions, RGA results should be relatively comparable, even though detected gases cannot be absolutely quantified.

Reliability of comparisons follows these trends

  • Most reliable - Comparison of results for different samples under the same conditions.
  • Second most reliable - Comparison of results for the same sample under different conditions.
  • Least reliable -  Comparison of results for different samples under different conditions.

Confidence of individual results follow these trends

  • Most - Large relative change with large absolute change
  • More - Large relative change with small absolute change
  • Less - Small relative change with large absolute change
  • Least - Small relative change with small absolute change


David Wright
Research professional

Emmanuel Soignard
Operations Director

  • Materials processing and calorimetry
ASU Unit
Knowledge Enterprise
Cost for ASU Internal Cost for ASU Internal with Staff Assistance Cost for Other Academic/Non-Profit Cost for Other Academic/Non-Profit with Staff Assistance