Analysis of TGA-IR Data Using OMNIC Mercury TGA

This technical paper describes the TGA-IR experiment and then targets on the analysis of infrared data. Beginning with manual tools, the paper explains the Thermo Scientific OMNIC Mercury TGA Software function. This algorithm offers speed, reliability and thoroughness while allowing any user level to extract information from TGA-IR data sets.

Operators who work with materials such as rubbers, resins, plastics, pharmaceuticals, packaging and adhesives need to be able to differentiate between competitive products, origin of odors, toxic off-gases and the reasons for failure. In order to expose the underlying nature of these materials, thermal gravimetric analysis (TGA) is used.

As the temperature of the sample is increased and the breakdown is tracked by weight loss as the components vaporize. Though it is possible to obtain quantitative information, it is not possible to provide insights into the chemical identity of the off-gassing materials. With TGA-IR, the off-gassing materials are led through a transfer line to a gas cell where the gases and infrared light interact.

Data Analysis – Information Overload

Figure 1 shows the TGA-IR results for a wood sample. The FT-IR data need to reveal the following:

• The off-gassing material identity
• Time dependence of gas evolution

Figure 1. TGA-IR results for wood sample. The top section includes the temperature ramp and the first derivative of the weight loss (from the TGA), along with the Gram-Schmidt profile for the FT-IR data. The bottom is a spectrum at a single time point. The inset shows the 3-dimensional nature of the data.

The profile also conveys significant information about the manufacture of the material. The challenges faced with the analysis are:

• A complete manual analysis is made impractical by the quantity of spectra
• While selecting specific regions it is possible to miss information that may or may not be critical to the analysis
• Most of the time, multiple gases are being simultaneously produced. Hence the resulting spectra are intertwined, and complicated not single components

The OMNIC Mercury TGA software overcomes these challenges and provides rapid, comprehensive and reliable results to operators of different skill levels.

Basic TGA-IR Profiles

A standard TGA test sample is calcium oxalate monohydrate that reveals three relatively sharp TGA weight losses and three parallel peaks in the Gram- Schmidt profile. FT-IR and GS data focused on the first two emission peaks is shown in Figure 2. The first peak is water and the second peask shows CO and CO₂.

Figure 2. TGA-IR analysis (first two peaks) of Calcium Oxalate Monohydrate.The first peak is water; the second peak (inset) shows both CO and CO₂.

Three options are available to analyze the data shown in Figure 2. They are:

• Classic workflow
• Multi-component enhanced workflow and
• Fully automated workflow

Multi-Component Analysis at One-Time Point

Figure 3 shows data from a TGA-IR experiment on an epoxy. The spectrum or the lower window looks quite complicated. The Thermo Scientific OMNIC was developed to enable easy and effective multi-component searching. Figure 4 shows the results of single point TGA gas analysis by the software. There is a striking similarity between the multi-component search result and the original spectrum even though there are four components discovered.

Figure 3. TGA-IR for an epoxy. The spectrum (lower window) shows the typical highly overlapped spectrum for the selected time point.

Figure 4. Multi-component search result from the OMNIC Specta software. The algorithm has identified four components and is showing the correlation between the actual spectrum and the synthetic spectrum consisting of the four components in appropriate percentages.

Complete Analysis – Mercury TGA

The requirements for TGA-IR data analysis are as follows:

• Identification of most or all of the components in the gases evolved from the TGA
• Usage of all collected spectra, not just a narrow time region
• Computing the time evolution of all species identified
• It can be visually checked whether the results are authentic
• Ensure repeatability irrespective of the user.
• The results are compiled in a complete report

The TGA-IR data from a wood sample, shown in Figure 1, will be used to show the results of the Mercury TGA software. The dialog in Figure 5 is used for setting up Mercury. First the search library is chosen. Next, estimation of the number of components present is entered and Mercury TGA offers a tool to determine this input value. Up to eight components are permitted. A region selection tool is also present that enables selection of a spectral range for analysis.

Figure 5. Set up screen for Mercury TGA. All that is required is for the user to select the library and the number of components. Pressing the “Compute”button initiates the estimation routine for number of components.

The analysis begins when TGA identify is pressed. The five-component analysis shown in Figure 6 involved processing 450 spectra in the Series file using the 460 spectra in the High Resolution Nicolet TGA Vapor Phase library.

Figure 6. The Mercury TGA result for the wood sample originally shown in Figure 1. See the text for explanation of the various panes of information.

The procedure was completed in 20 seconds. The GS profile is seen in the upper left corner of the display. Below that the calculated time profiles for each of the evolved gases is displayed. ‘Unknown’ is the last profile and spectrum that shows the residual of the analysis. The upper right window is critical and shows the composite spectrum shown in red from the library spectra of the identified species in comparison with the collected spectrum in blue at a particular point of time. When the bar is scrolled in the Gram Schmidt plane, the user can compare each spectrum in the series file to the calculated composite at that time point. With this set of result panes it is observed that Mercury TGA fulfils the outlined requirements.

Selecting the Number of Components

The Mercury TGA has three inputs:

• OMNIC series data file
• Choice of libraries and
• Selection for number of components

Mercury TGA helps selection by involving an approximation tool used during setup and visual review of the results combines with an Add One button.

The Mercury TGA result for a polymer is shown in Figure 7.

Figure 7. TGA-IR results for a polymer resin analyzed by Mercury TGA using only three components. The comparison in the upper right pane of the synthetic (red) and actual (blue) spectrum shows that several components have been missed.

Figure 8 shows the impact of pressing the Add One button repeatedly till six components were chosen.

Figure 8. Same data as in Figure 7, with the number of components being set to 4 (top), 5 (middle) and 6 (bottom).

TGA-IR, Mercury-TGA and Kinetics

Firstly, in a TGA-IR analysis it is important to ensure good quality data. Faster collection is normally not required and since carbon dioxide and water are very common, and a purge is recommended. While analysis is being done, the actual versus composite spectra in the upper right of the Mercury TGA output offers visual confirmation of the results. This is far more accurate when compared to the numerical metric. Including user libraries containing TGA-IR spectra significantly improve the quality of analysis.

An exciting extension of the Mercury TGA algorithm involves analyzing data from Kinetics experiments. In Kinetics, the Series file is an overlapped set of spectra from starting reagents, intermediates and products, plus solvents. Mathematically, this is exactly the same situation as the TGA-IR file.

Figure 9. Profiles showing evolution of reactants and products during a kinetics experiment, as extracted by the Mercury TGA algorithm.

Conclusion

TGA-IR is a very powerful analytical procedure for deformulation of rubbers, plastics and many types of compounded materials. Users at any skill level and working in any market can apply TGA-IR to their deformulation problems, confidently and reliably.

This information has been sourced, reviewed and adapted from materials provided by Thermo Fisher Scientific – Materials & Structural Analysis.

For more information on this source, please visit Thermo Fisher Scientific – Materials & Structural Analysis.