What value can Progenesis QI provide in the world of co-polymer characterization?

Polymers are critical to meeting key societal needs

The use of polymeric materials in our everyday lives is increasing rapidly driven by innovations in materials development and design. Examples of the scope of polymer uses include: structural materials for cars and airplanes, fabrics for clothing, packaging materials for food and medicines, medical devices like heart valves and joint replacements and as substrates for revolutionary 3D-printing applications. The latest innovations have delivered smart materials which can change their shape or properties based upon changes in their environment.  However, this wealth of new materials must be properly characterized in order to manufacture these polymers reproducibly and to achieve the required property characteristics, thus appropriate analytical technologies and comprehensive data are needed.

There are many advanced technologies available for polymer analysis. Today we will consider Pyrolysis-Gas Chromatography/Mass Spectrometry (Py-GC/MS) and how multivariate analysis of the data it produces provides novel insights into polymer structure.

Why use Py-GC/MS and what are the limitations?

Py-GC/MS is one of major analytical techniques for chemical structural elucidation of polymers. It involves identification of the gaseous products generated from degradation of a polymer heated to 600°C under inert gas providing data from which the detailed chemical structure of polymer can be estimated.

Typically, the GC/MS in these analyses uses a hard ionization technique; electron impact (EI). However, the data obtained by such ionization becomes increasingly complex, especially when there are increasing monomer numbers in the co-polymer. Many pyrolysis products are formed and each of them generates many fragment ions upon ionization. This can prove a limitation of the approach.

A new approach to Py-GC/MS

The experimental data can be simplified using a soft ionization technique like Atmospheric Pressure GC (APGC) ionization in place of EI as the high sensitivity and soft ionization allows observation of the molecular ion without fragmentation. (See this link for a  White Paper about APGC). Reduction in fragmentation enables the determination of larger fragments from the polymer backbone, enabling the connectivity of the monomer units to be inferred. Why does this matter? Well, different arrangements of the units in a polymer like a block copolymer vs. random copolymer would result in final material having different physical properties which can affect its end use. Therefore an understanding of what type of substructure exists in the polymer is very important.

Combining a high resolution mass spectrometry instrument such as quadrupole-time-of-flight (QToF) mass spectrometer with an APGC source (see Figure 1) enables the MS and MS/MS spectrum of each peak to be simultaneously collected. This data provides the elemental composition and fragment ion information needed for elucidation of chemical structures (see Figure 2).

Py-GC/MS setup Figure 1: Py-GC/MS setup in one of Waters laboratories using an EGA/PY-3030D pyrolysis unit attached to a GC equipped with an atmospheric pressure source for GC/MS (APGC source) and a Waters Xevo G2-XS QTof mass spectrometer.

Block co-polymer low and high energy spectra from MSE data acquisition Figure 2: Block co-polymer low and high energy spectra from MSE data acquisition. This is a data independent acquisition mode enabling simultaneous acquisition of low energy and high energy spectra. The low energy spectrum provides molecular ion related information from which elemental composition can be derived. High energy spectrum contains fragments from the molecular ion which help to confirm structure.

How is Progenesis QI applied to Py-APGC/MS data ?

Applying multivariate analysis to the Py-APGC-MS data enabled the characteristic pyrolysis products from the different co-polymer types to be automatically detected and identified as structural markers. The application of PG QI software removes the need to manually sift through the vast array of spectral data generated from each sample trying to detect and identify structurally significant pyrolysis products.

The data for two acrylic acid – styrene copolymers, one block and one random, were processed using Progenesis QI and following data alignment and peak picking the samples were analysed using an OPLS-DA model to compare the two groups. We can see the two polymer types are easily distinguished in the scores plot (Figure 3).

OPLS-DA model to compare the samples Figure 3: Following replicate analysis of the two co-polymer samples the data was aligned and peak picked using the workflow presented by Progenesis QI. The resulting data was analysed using an OPLS-DA model to compare the samples. The scores plot resulting from that analysis is shown here where it can be seen that the two co-polymer types are clearly discriminated.

From this model the block co-polymer marker components were extracted from an S-plot and confirmed on a trend plot (Figure 4).  The chemical structures of the marker components were determined from the MSe spectrum as described previously. Random co-polymer marker components were extracted and chemical structures elucidated using same procedure. Some of the structures determined are shown in Figure 5 where we can see how they are representative of block and random structures.

Plotting all the identified markers on an S-Plot Figure 4: Plotting all the identified markers on an S-Plot allows extraction of those which we are most confident provide significant discrimination between the samples. The intensity of these individual markers can then be plotted against the sample identities in a Trend Plot which, in this figure, shows the abundance of markers of the block co-polymer components extracted from data.

Examples of markers Figure 5: Here we show examples of markers that were identified for the block and random samples of styrene – acrylic acid co-polymers using the elucidation workflow described in the main text. Below the structures are some of the monomer sequences that they correspond to demonstrating how this approach can provide information about co-polymer backbone substructure.

Concluding thoughts

The analysis of Py-APGC-MS data by Progenesis QI enabled the discovery of markers which contributed towards the difference between co-polymers. These structural differences can be due to different polymerization methods used to produce the materials, or different monomer ratios used during production.

This study shows the utility of a pyrolyzer connected to a gas chromatograph and a mass spectrometer using soft, atmospheric pressure ionization for the characterisation of co-polymer structure. Analysing the information rich datasets using Progenesis QI software enabled markers to be identified that provide insight into the differences in monomer connectivity in block and random copolymers. Further details on this work can be found in the poster publication at the following link – Py_GCMS_Poster.

In addition to the use with pyrolysis GC/MS, multivariate analysis with Progenesis QI is also very useful in troubleshooting product failures like discoloration in a batch of polymeric material or mechanical or chemical failure of components. In some of the latest applications, polymer chemists have utilized this approach for marker analysis to understand different product performance of functional polymers such as photoresists and color-resists related to semiconductor and display manufacturing.

So, next time you look at your phone or tv, step into your car or take your seat on an airplane; remember the critical dependence you have on polymeric materials and that a lot of analytical testing has gone into the development process to provide you with such attractive, robust, safe and functional products!

Acknowledgements

Tim Jenkins, Waters, Wilmslow
Baiba Babovska, Waters, Milford
And our colleague in Japan, Tatsuya Ezaki of Nihon Waters K.K.

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