COMBINED APPROACH FOR THE CHARACTERIZATION OF PLASTICS USING SPECTRAL LIBRARIES CREATED FROM BOTH PYROLYSIS-GC-MS AND PYROLYSIS-APGC-TOF-MS

Significance of the Topic

Sustainable polymers and biobased plastics are gaining attention due to environmental concerns and recycling targets. Accurate identification of polymer composition, additives, and contaminants is critical for quality control, regulatory compliance, and advancing material design. Traditional GC–MS with electron ionization often fragments molecular ions, hindering precise identification. Combining pyrolysis with soft ionization and high‐resolution mass spectrometry addresses these challenges.

Goals and Overview of the Study

This study aimed to develop in‐house spectral libraries for pyrolysis‐GC–EI‐MS and pyrolysis‐APGC‐QTof MS to enhance polymer characterization. It compares the performance of classical electron ionization and atmospheric pressure soft ionization for identifying polymer constituents and unknown additives in both standard materials and real‐world samples.

Methodology

Sample preparation involved loading ~0.1 mg polymer standards or sample pieces into quartz‐lined capillaries for pyrolysis. Triplicate analyses were performed under standardized conditions. Pyrolysis was carried out with an initial temperature of 0 °C, a heating rate of 20 °C/ms, and a final temperature of 750 °C. Chromatographic separation used an Rtx‐5MS column (30 m × 0.25 mm × 0.25 µm) with a ramp from 45 °C to 300 °C at 20 °C/min. Data acquisition captured full‐scan mass spectra over m/z 10–650 (GC–EI‐MS) or m/z 10–1500 (APGC‐QTof MS) with MSE alternating collision energies for structural information.

Used Instrumentation

• Pyrolyzer CDS 5000 (CDS Analytical) attached to GC–MS and GC–QTof systems.
• GC–EI‐MS: Xevo TQ‐GC, electron energy 70 eV, source at 250 °C, transfer line at 300 °C.
• APGC‐QTof MS: Xevo G2‐XS QTof, corona current 3 µA, source at 150 °C, transfer line at 280 °C, MSE mode with low/high collision energies.

Main Results and Discussion

In‐house libraries built from averaged spectra of polymer standards showed reliable matching scores. A biobased PLA straw matched with reversed scores above 830 on both platforms. Soft ionization in APGC retained molecular ions—for example, polystyrene tetramer at m/z 416 compared to fragmented spectra under EI. In a bioplastic bag sample, erucamide was tentatively identified via accurate mass (m/z 338.3423) and fragment matching using MassFragment software. MSE data provided concurrent precursor and fragment information, improving confidence in unknown assignments.

Benefits and Practical Applications

• Enhanced molecular ion detection aids precise polymer identification.
• High‐resolution accurate mass data supports elemental composition and structural elucidation.
• Custom spectral libraries enable rapid screening of recyclates, bioplastics, and complex formulations.
• MSE acquisition streamlines analysis by capturing both low‐ and high‐energy spectra in a single run.

Future Trends and Possibilities

Expanding spectral libraries to cover a wider range of polymers, blends, and additives will improve identification capabilities. Integration of machine learning tools could accelerate library matching and predict fragmentation patterns. Advances in ambient ionization and real‐time monitoring may allow non‐destructive, in situ polymer analysis. Regulatory drivers and circular economy initiatives will further drive adoption of high‐confidence analytical workflows.

Conclusion

The combined approach of pyrolysis‐GC–EI‐MS and pyrolysis‐APGC‐QTof MS with bespoke spectral libraries offers a robust strategy for polymer characterization. Utilizing soft ionization and MSE enhances molecular ion visibility and structural information, facilitating accurate identification of polymers and additives in research and quality control.

References

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