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Exploring py-GC-MS

17th December 2019


Sample A and examples of NIST library search for targeted pyrolysis markers and an untargeted UV light stabiliser
2D pyrogram for sample B with illustrative identification of some markers for the target additives

Daniela Peroni discusses pyrolysis-GC×GC-MS for easier and more effective analysis of additives in polypropylene (PP)

Pyrolysis-gas chromatography-mass spectrometry (py-GC-MS) is a powerful technique for polymer characterisation. Pyrolysis thermally decomposes the material into smaller units (e.g. monomers, dimers) suitable for GC-MS. The degradation profiles, or pyrograms, elucidate polymeric composition and structure. On the other hand, due to the elevated complexity the profiling of minor yet key components such as additives (e.g. antioxidants, light stabilisers, plasticisers) is often challenging. Comprehensive 2D gas chromatography (GC×GC) combines two separation mechanisms to achieve superior separation power. Coupling pyrolysis to GC×GC can allow for easier and more confident additives profiling in polymers. Here, we present as case study the py-GC×GC-MS analysis of polypropylene (PP) materials with different additive content.

Experimental details

PP samples: material A (Irganox 1010: 0.025%, Irgafos 168: 0.11%) and B (Irganox 1010: 0.8%, Irgafos 168: 0.1%).

Measurements are performed with a CDS 5200 Pyroprobe coupled to an Agilent 7890B GC equipped with a Zoex ZX2 thermal modulator and an Agilent 5975C MSD with Triple-Axis Detector. Data is processed with GC Image.

When pyrolysed, Irganox 1010 and Irgafos 168 break down to several aromatic fragments that can be used as markers. The figure below shows an example of py-GC×GC analysis with pyrolysis at 750°C for 15 seconds.

The 2D pyrogram of sample B clearly shows the benefits of the enhanced chromatographic resolution. The aromatic markers are separated from the aliphatics generated by the PP matrix, so they can be found with a simple and quick workflow based on integration and library search. All target markers are successfully detected also in sample A, which has a lower content (especially for Irganox 1010, 250ppm or 0.025% w/w).

Confident identity confirmation is possible thanks to the clean spectra obtained as a result of the good separation (see figure on the left). In addition to the targets, other additives can be detected and identified in both samples with relative ease and satisfactory confidence. The peak capacity offered by GC×GC, in combination with the consequent spectral quality not compromised by co-elution, are in fact ideal features for general screening.

These results are a very significant improvement compared to standard py-GC-MS. The automated search for the markers by deconvolution in the 1D pyrogram is not successful. The reasons for this are the poor response and the co-elution with the much more abundant and complex aliphatic group, which make the task highly challenging at best. The additives can be found by searching manually for known selective mass fragments and assessing the presence of consistent MS pattern, which relies heavily on the operator, making it time-consuming and laborious.

In summary, Py-GC×GC-MS is a very powerful tool for the analysis of additives in polymeric materials. Targets can be found in an automated way. The aromatic fragments are efficiently separated and identified with good confidence. Finally, the number of peaks fully resolved makes it possible to find and identify potentially interesting unknowns.

Daniela Peroni is with JSB





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