Using Multivariate Curve Resolution to Identify the Evolved Gases Created During a TGA-IR Experiment - - Spectroscopy
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Using Multivariate Curve Resolution to Identify the Evolved Gases Created During a TGA-IR Experiment

Application Notebook
pp. 22, 23

Thermal decomposition provides valuable information about the chemical composition of a material. Although thermal gravimetric analysis (TGA) measures small weight losses during a heating ramp, it cannot identify the chemicals corresponding to the weight loss. The sensitivity and speed of FT-IR makes it an ideal detector for the TGA evolved gas, but the interpretation of the large data files resulting from this hyphenated technique can be formidable. In this article, we describe an integrated approach that makes analyzing these large data sets much simpler.

Operators working with materials like rubbers, plastics, resins, pharmaceuticals, adhesives, and packaging need to understand differences between competitive products, causes of failures, or the origin of potentially toxic off-gassing. Standard analytical procedures are often incapable of distinguishing subtle differences like the presence of low-concentration additives or contaminants. Further, the same components may be present in two different materials, but the process used to make the products — such as the processing temperature — may vary, causing the interactions between components in the material to differ. This can lead to poor performance, such as early material fatigue or material failure. A complete analysis requires these materials to be deformulated — essentially torn apart to expose their underlying nature. Thermal TGA is a common tool for doing this. A temperature ramp is applied to the sample and the breakdown tracked through weight loss as components vaporize. Attaching a heated transfer line from the TGA outlet to a heated gas cell in the FT-IR spectrometer enables spectra to be acquired in real time as the gases evolve. The resulting data set contains several hundred spectra creating a complex data analysis challenge. Commonly, thermal degradation results in numerous chemicals being evolved simultaneously — not one at a time — resulting in a series of mixture spectra. The Thermo Scientific™ OMNIC™ Mercury TGA algorithm deconvolutes these spectra and provides both identification and time evolution information, completing the deformulation analysis.


Figure 1: TGA-IR accessory mounted in the sample compartment of the Nicolet iS50 FT-IR spectrometer.
Figure 1 shows a Thermo Scientific Nicolet™ iS™50 FT-IR spectrometer and the TGA-IR accessory. The flexible transfer line, heatable to 300 C, couples the outlet of the TGA to the IR gas cell.

The TGA-IR data set is three dimensional — frequency versus intensity (the spectrum) versus time. Typical TGA experiments ramp and hold for 30–60 min. The FT-IR normally collects multiple scans resulting in one spectrum about every 5 s. For a 1 h run, this yields 720 spectra — a daunting analytical challenge.

Results and Discussion

Figure 2: 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.
Figure 2 shows the TGA-IR results for the thermal decomposition of a wood sample. The top of the figure contains the temperature ramp and first derivative weight loss from the TGA and a Gram-Schmidt (GS) profile. The GS profile essentially shows the total change in the IR signal (similar to a chromatogram). The bottom pane contains the spectrum collected at one time point. The analytical challenge involves the extraction of useful information from this data set.

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