Using GPC/SEC for Compositional Analysis

With synthetic polymers becoming more prevalent over the last century, a desire arose to fine-tune the physical properties of these materials in order to develop the optimal end-product for a given application.

Researchers explored a method in which two or more types of monomers are mixed, so that a single chain of the resulting polymer would be a combination and potentially have a special set of physical properties.

Copolymers, the term used to describe these polymers comprising of two or more different constituent units, are capable of exhibiting a wide range of monomer patterns and structures (e.g. comb, random, block, alternating).

Although the monomer arrangement and overall shape of the copolymers can impact their physical properties, so can the relative concentration of each existing monomer. For instance, in styrene-butadiene rubber, when styrene concentration is high, the resulting rubbers are more rigid. The materials are softer and more elastic with low styrene content.

In addition, mixing two different polymers where the individual polymer chains are not covalently bonded is an alternate, and at times a more accessible method to develop materials with customized properties.

These non-covalently bonded samples are called polymer mixtures or blends. It is essential to know the relative concentration of each monomer present in a copolymer or mixture in order to understand the physical properties of the end-product.

Gel permeation chromatography (GPC) or, equivalently, size-exclusion chromatography (SEC), is one analytical technique that can give this information. The GPC/SEC technique is extensively used for the characterization of different macromolecules, from bulk manufactured materials to natural proteins and polymers. Using this technique, the hydrodynamic size (R_H), intrinsic viscosity (IV), molecular weight distribution (M_w/M_n) and molecular weight moments (M_w, M_n) of these macromolecules can be measured. Malvern Panalytical’s OMNISEC, a complete, all-inclusive GPC/SEC system, is shown in Figure 1.

A short overview of how GPC/SEC works: A solvated sample is carried by a liquid mobile phase via an analytical column full of porous gel particles, where diffusion-controlled separation of the macromolecular components takes place.  It is eventually observed by varied detectors as each slice of sample elutes. A common advanced detection GPC/SEC setup comprises of a viscometer, refractive index (RI) UV/photodiode array (PDA) detector and light scattering detectors. For reasons that will be explained below, a minimum configuration of RI and UV/PDA detectors is needed for copolymer/compositional analysis.

In this article, one polymer mixture and one copolymer, both comprising of polystyrene (PS) and poly(methyl methacrylate) (PMMA), are studied using GPC/SEC so as to establish the relative concentration of each monomer present. The article will also present the results and comparisons that will be made between the two.

Figure 1. Malvern Panalytical’s OMNISEC Tetra Detection GPC/SEC System.

Copolymer / Compositional Analysis

Malvern Panalytical’s copolymer or compositional analysis method is designed to determine concentration information for materials made up of two distinct monomers. A few conditions must be met in order for the copolymer/compositional analysis method to work well. Both of the dn/dc values (or refractive index increment values) must be known and both monomers must have measurable refractive indices in the mobile phase. The two monomers must possess different absorbance profiles; ideally one monomer will absorb at a given wavelength of light and the other one will not. If there is no specific wavelength where one monomer absorbs and the other does not, it is essential to know the relative absorption at a given wavelength (alternately, one can use the dA/dc values of the individual components). By using the data from two concentration detectors the software can set up two calculations (equations [1] and [2]) to solve for the two unknowns (concentrations of A (C_A) and B (C_B)). This is why it is very important to know the dn/dc and dA/dc values for each component of the copolymer; without them there is no other way to know how much of the total RI or UV signals to attribute to each monomer.

It should be noted that the copolymer/compositional analysis method does not reveal the chromatograms of individual components in a sample, but provides the concentration of each component in a sample. These concentration plots may appear similar to the UV or RI chromatograms as those detectors respond to sample concentration; however, it is not possible to further analyze them as a chromatogram. The analysis operates the same even if the components are simply mixed together, or covalently bonded, as in a copolymer.

Analysis of a Copolymer

Using 3 × Malvern Panalytical LT-3000L columns, the PS/PMMA copolymer was analyzed in a mobile phase of THF. The sample solution was prepared at a concentration of 2.8 mg/mL in THF and the injection volume was 100 µL. Figure 2 shows the tetra detector chromatogram for the copolymer sample. The RI signal is presented in red, the low and light scattering detector is black, the right angle light scattering detector is green, the viscometer signal is blue and the UV signal from the PDA is purple. Also shown are limits of integration and baseline points.

Figure 2. Tetra detector chromatogram of the copolymer sample.

Similar to the earlier example for the copolymer sample, the dn/dc value used for PS in THF was 0.185 and for PMMA in THF was 0.085. The dA/dc values used for PMMA and PS were 0 and 1, respectively. These are not the absolute dA/dc values for PS and PMMA, but as PS has an absorption at 254 nm and PMMA does not, then all of the UV absorption data will be attributed to PS making the all or nothing values of 1 and 0 appropriate.

Table 1 shows the molecular characterization data for the copolymer sample. Besides the M_n, M_w, M_w/M_n, R_H and IV, the weight fractions of PMMA and PS are also listed. Data for three injections are presented, together with relative standard deviations.

Table 1. Molecular characterization data for three injections of the copolymer sample; Mz, M_w, M_n in Da.

The data for the copolymer sample is found to be consistent with the chromatograms (Figure 2), representing a well-defined sample with a narrow distribution. It should be noted that this copolymer sample has a single distribution, meaning the peak is not bimodal and only one sample peak is present. This indicates that the two different monomers are present within this single distribution. This is a common characteristic of copolymers; however, this indeed is not absolutely true for all examples of copolymers.

Insight into the relative concentration of each monomer is provided by the weight fraction data. The weight fraction of PMMA and the weight fraction of PS were found to be 0.49 and 0.51, respectively. When the molecular mass of each monomer is considered (104.15 Da for styrene and 100.121 Da for methyl methacrylate), the weight fractions of PMMA and PS indicate that the monomers exist in a ratio of 1:1 (a 1:1 copolymer would have weight fractions of 0.51 for PS and 0.49 for PMMA from PS = 104.15 Da/204.271 Da; PMMA = 100.121 Da/204.271 Da).

While the weight fraction provides a better understanding of relative concentration, it does not provide insight into how that concentration exists over the MW range of the sample. In order to determine if one monomer is biased to the high or low MW material or if the two components exist uniformly throughout the material, a visual representation of the individual monomer concentrations, as illustrated in Figure 3, is examined.

Figure 3. Concentrations of PS (magenta), PMMA (dark blue), and total sample (aqua) plotted against retention volume for the copolymer sample.

In Figure 3, the concentration plots indicate that the concentrations of PMMA and PS are extremely similar at each point throughout the molecular weight distribution of the sample. The ratio of PS to PMMA will be about 1:1, irrespective of the molecular weight of a polymer chain within this sample. This type of information can help researchers in determining how effectively their copolymerization processes are working and whether the end-product displays the required characteristics.

Analysis of a Polymer Mixture

A polymer mixture sample was analyzed to complement the analysis of the copolymer sample. The analysis conditions were similar, using a mobile phase of THF and 2 × Malvern Panalytical T6000M columns. The sample solution was prepared at a concentration of 2.5 mg/mL in THF and the injection volume was 100 µL. Figure 4 shows the tetra detector chromatogram for the polymer mixture sample. As above, the RI signal is presented in red, the UV signal from the PDA is purple, the right angle light scattering detector is green, the viscometer signal is blue and the low and light scattering detector is black. Also shown are limits of integration and baseline points.

Figure 4. Tetra detector chromatogram of the polymer mixture sample.

As with the earlier example for the copolymer sample, the dn/dc value used for PMMA in THF was 0.085 and for PS in THF was 0.185. The dA/dc values used for PMMA and PS were 0 and 1, respectively.

Table 2 shows the molecular characterization data for the polymer mixture sample. Besides M_w, M_n, M_w/M_n, IV, and R_H, the weight fractions of PMMA and PS are also listed. Data for three injections are presented, together with relative standard deviations.

Table 2. Molecular characterization data for three injections of the copolymer sample; Mz, M_w, M_n in Da.

The data for the polymer mixture offers reproducible average values for the entire bimodal sample distribution. As opposed to the copolymer sample analyzed above, the polymer mixture clearly highlights two peaks in most of the detector responses shown in Figure 4. While this does not necessarily point out that they are composed of varied types/ratios of materials, the differences in the detector responses of each peak suggest that to be the case. A comparison of the UV and RI chromatograms for the polymer mixture particularly reveals that the narrower peak eluting at 18 mL is present in the RI signal but not the UV detector response. In other words, any material responsible for that peak does not possess a UV absorbance and is completely different than the material eluting earlier that provides the broad peak in the UV signal (14-20 mL).

As with the copolymer sample, the weight fraction data provides a detailed analysis of the relative concentration of each monomer. The weight fraction of PS in the polymer mixture and the weight fraction of PMMA were found to be 0.51 and 0.49, respectively. Similar to the copolymer example earlier explored, these weight fractions indicate a 1:1 ratio of PS to PMMA in the polymer mixture sample.

The weight fractions do not specify if the monomers are evenly distributed in a manner similar to the copolymer example even though these weight fractions indicate the PS:PMMA ratio in the polymer mixture is 1:1. By viewing the concentrations of each component as a function of elution volume, as depicted in Figure 5, it can be seen that the two components mostly comprise of different portions of the chromatograms. This makes sense for a sample that is just a mixture of two polymers, as each component would have its own distribution that may not or may overlap with the other.

Figure 5. Concentrations of PS (magenta), PMMA (dark blue), and total sample (aqua) plotted against retention volume for the polymer mixture sample.

In Figure 5, the concentration plots show that PMMA and PS in the sample comprise of separate distributions within the mixture. The earlier eluting, broader peak is pure PS, while the later eluting, narrower peak is mostly PMMA, with some low molecular weight material from the PS distribution contributing. This is in contrast to the equally distributed styrene and methyl methacrylate components in the copolymer sample discussed earlier. Both samples are comprised of PMMA and PS in a ratio of 1:1, but the way the two monomers are distributed throughout the samples are majorly different. Observation of this difference is only accessible when using a GPC/SEC setup that includes both an RI and UV detector.

Conclusions

Malvern Panalytical’s OMNISEC tetra detection GPC/SEC system offers exceptional chromatography data for the analysis of both the copolymer and polymer mixture samples. The analyses included the weight fractions of each component within the samples and revealed reproducible molecular characterization data. The MW profiles of the samples were found to be relatively different despite their chemical constituency being identical. The different MW profiles observed supported the view that the two samples were of different types; one a mixture of two polymers and one a copolymer with a single MW distribution, each with their own distribution. The copolymer/compositional analysis method provided accurate data, irrespective of the physical relationship between the PMMA and PS within each sample.

It must be noted that the same analysis techniques described here are not limited to synthetic polymers and can thus be applied to other application areas. The copolymer/compositional analysis method has been shown to be useful in the life-science arena, particularly to study protein-PEG conjugates or membrane proteins in detergent micelles.

The ability to test and measure the concentrations of components present a sample as part of advanced GPC/SEC analysis offers a valuable set of data and results. This type of advanced analysis can provide manufacturers and researchers the insight required to develop and produce unique products for particular applications.

This information has been sourced, reviewed and adapted from materials provided by Malvern Panalytical.

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