Microplastic Analysis Using Raman Imaging

Aquatic environments are severely damaged by widespread plastic pollution. The enormous worldwide production of plastics, estimated at 299 million tons/year, is linked to the increasing use of disposable goods.¹ 

Combined with the low degradability of polymers, this production has contributed to the accumulation of plastic debris in natural environments.^1,2

Although synthetic polymers are durable, large plastic objects can experience fragmentation, primarily through mechanical breakdown caused by abrasion from sand and other materials, as well as wave action. This process is driven by photochemical processes induced by UV-B light.^3,4,5

Smaller plastic fragments, with diameters of 5 mm or less, have been categorized as microplastics.^6,7 This category has been further divided into large microplastic (L-MPP, ranging between 1 and 5 mm) and small microplastic particles (S-MPP, ≤1 mm).⁸

Microplastics can absorb both persistent organic pollutants (POPs) and heavy metals from water and sediments. The smallest particles may also enter the food web, posing a major health risk to wildlife and ultimately to humans.^9,10

Microplastic identification and quantitation are important analytical challenges, and the absence of official analytical techniques complicates comparisons between studies. Most research studies performed to date begin with visual particle sorting using a stereo-microscope to separate potential microplastics from other debris.¹¹

Particle size is the primary limitation of visual sorting, as smaller sizes mean greater difficulty in discriminating microplastics from interfering particles.^12,13

It is therefore highly recommended to evaluate sorted particles using methods that enable proper plastic identification, such as spectroscopic methods or pyrolysis-GC/MS (py-GC/MS), even if these strategies are less efficient and miss the microplastic particles discharged by visual sorting.

A recent investigation showed that spectroscopic methods, such as FTIR, NIR, and Raman, improve the accuracy and sensitivity of microplastic identification and quantification. However, few techniques can achieve rapid and dependable analysis.¹⁴

In general, spectroscopic strategies are single-point analyses and are not automated; only a small number of semi-automated filter analysis studies have been performed, using single MCT detector-μFTIR-chemical mapping to evaluate only a few subareas of the filter surface.

More recently, focal plane array (FPA)-based imaging μFTIR was employed to scan the entire surface of a small filter with diameters exceeding 10 mm.^15,16,17

Although this strategy produced excellent results, exhibiting elevated lateral resolution and enabling particle detection down to a size of 20 μm, smaller microparticles and sub-microparticles cannot be evaluated using μFTIR methods due to diffraction phenomena that occur under 10 μm in FTIR.

An additional limitation is the lengthy analysis time, potentially lasting tens of hours.¹⁸ To address these issues, Raman imaging microscopy is one of the most promising methods, combining elevated spatial resolution, typical of the Raman microscopy technique, and the speed of an imaging technique.

This article presents a simulated evaluation of microplastic particles by using reference materials to provide an ideal analytical model of potential environmental samples.

Experimental

Materials and Methods

Three different reference materials were used to mimic microplastic debris with varying particle sizes: irregularly shaped polyethylene particles sieved at 74 microns (Sigma- Aldrich S.r.l.); polystyrene-divinylbenzene PS-DVB (2% of DVB - Sigma-Aldrich) microspheres sieved between 37 and 74 microns; and polyethylene-titanium dioxide PE-TiO₂ microspheres ranging from 27–45 micron (white polyethylene, opaque polymer microspheres beads, density 1.25 g/cm³ - Cospheric LLC).

Several milligrams of each particle type were mixed with 0.5 L of water and filtered through a gold-coated polycarbonate membrane (Whatman^® Nuclepore^™ Track-Etched Membrane 800195) with a 13 mm diameter and 0.8 μm pore size.

Following filtration, the membrane was dried in a furnace at 60 °C for two hours prior to Raman microscopy measurements to prevent filter shrinkage under laser illumination.

The filter was subsequently blocked between two thin glass coverslips (180 microns thick) to enhance its flatness, and measured in confocal mode using the Thermo Scientific^™ DXR^™2xi Raman Imaging Microscope. The gold filter was selected due to its flexibility, as it is suitable for both Raman and infrared microscopy measurements.

Raman Analysis

Raman data was acquired using the DXR2xi Raman Imaging Microscope together with Thermo Scientific^™ OMNIC^™xi Raman imaging software. The DXR2xi Raman microscope can collect up to 600 spectra/second, allowing analysis of the entire filter surface. The Thermo Scientific DXR3xi Raman Imaging Microscope, the latest generation of the DXR Raman imaging platform, is now available. The DXR3xi builds on the Raman imaging capabilities demonstrated in this study and provides a current solution for Raman microscopy and chemical imaging applications.

This strategy significantly broadens the analytical possibilities for microplastics and sub-microplastics down to the nanoplastics range, while also substantially reducing analysis time. The enhanced acquisition speed makes the collection of large-area Raman images not only practical but routine.

The OMNICxi software also represents an advancement in software particularly engineered for imaging, offering a convenient and intuitive graphical interface for managing all additional data. The software also includes robust analytical algorithms to convert the data into informative Raman images instantaneously.

During data collection, multivariate curve resolution (MCR) is applied to compute the number, relative concentration, and distribution of components in an unknown mixture without requiring prior information.

Raman Imaging Result

The 13 mm gold filter has an active 10 mm diameter area that can be readily imaged using the DXR2xi Raman microscope. As illustrated by the red frame in Figure 1A, an area of 10 mm x 10 mm was chosen, and a 10x objective was used to scan the complete active surface.

The acquisition speed was 400Hz (400 spectra/seconds), and 13 exposures were acquired at each point using a 532 nm laser, a laser power of 10 mW measured at the sample, and a 25 μm pinhole aperture.

The image pixel size was 10 μm, and more than 600,000 spectra were collected. Figure 1B, C, and D each display the microplastic particle distribution (PE, PS-DVB, and PE-TiO2, respectively) on the filter surface following the collection of the Raman spectral image. Particle identification was achieved by employing the library search tool within the OMNICxi software.

The collection mode of the OMNICxi software allows the acquisition of one exposure over the entire area of interest, enabling a rapid visualization of the chemical data all over the image. Image quality can be enhanced by co-adding exposures to the first one with the same collection mode.

This feature enables users to view different chemical data such as peak height, peak area, peak position, peak area ratio, peak height ratio, and MCR during collection and to halt the experiment once the required information has been obtained.

Figure 1. A, optical image of the gold filter; B, distribution of irregular-shaped PE particles, highlighted in green; C, distribution of PS-DVB, highlighted in blue; D, distribution of PE-TiO2 microspheres, highlighted in red. Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy

Figure 3 exhibits the spectra of the three reference microplastic particulate materials used to generate a library. The search capability of the OMNICxi Raman imaging software, which enables particle identification by using integrated libraries, is displayed in Figure 3. Analysis of the Raman spectra highlights the benefits of Raman microscopy compared with infrared microscopy.

In addition to offering superior spatial resolution compared to infrared microscopy, Raman microscopy enables the identification of the plastic additive, titanium dioxide (TiO₂) in this example, due to its ability to access low wavenumber ranges down to 50 cm^-1.

Recent studies on the potential toxicity of TiO₂ have highlighted the importance of identifying and quantitating this additive, as it is commonly employed in packaging materials as well as in cosmetics such as scrubbing cream, toothpaste, and other widely used goods.¹⁸

The FPA infrared detector, which is most often used for this application, has a cut-off at 900 cm^-1 and is therefore unsuitable for TiO₂ determination.

The map evaluation using the MCR algorithm determines the distribution and dimensions of the three standard particles and enables visualization of each reference material with a different color.

Figure 1 illustrates the complete picture of the filter with the particulate materials identified, while Figure 4B presents an enlargement of a specific region where the capability of Raman analysis to identify small particles can be appreciated.

Figure 2. Raman spectra of the microplastic standards: PE - polyethylene; PE-TiO₂ polyethylene-titanium dioxide; PS-DVB – polystyrene-divinylbenzene. Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy

Figure 3. Identification of unknown particles through library search of the microplastic standards. Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy

Figure 4. B, distribution of irregular-shaped PE particles; C, distribution of PS-DVB; D, distribution of PE-TiO₂ microspheres. Enlargement of specific region of Figure 1 to emphasize the chemical image resolution reachable with 10 micron pixel size. Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy

Figure 5 presents a map covering an area of about 1.5 mm x 2 mm obtained using a smaller image pixel size of three microns and a 50x long working distance objective. The same acquisition parameters were used; three exposures were gathered for each point, and more than 400,000 spectra were collected.

The reduced image pixel size enhances image quality and enables accurate identification of the circular shape of the smallest standard microspheres of PE-TiO₂, as demonstrated in Figure 5D.

These particles were not well defined in the previous map. OMNICxi software allows users to view the chemical image superimposed onto the optical image and to control its transparency, enabling a direct correlation between image and chemical data.

Figure 5. A, optical image of the gold filter; B, distribution of irregular-shaped PE particles, highlighted in green; C, distribution of PS-DVB, highlighted in blue; D, distribution of PE-TiO2 microspheres, highlighted in red. Superposition of the optical and chemical images for a direct correlation between the sample image and the chemical information. Image Credit: Thermo Fisher Scientific - Vibrational Spectroscopy

Conclusion

This article shows the application of Raman imaging microscopy for analyzing and identifying small microplastic particles. It demonstrates a valid alternative to, and in some cases, a more appealing method than, infrared imaging microscopy.

In fact, Raman microscopy enables evaluation of particles smaller than one micron and supports accurate identification of both plastic materials and inorganic materials such as titanium dioxide and other additives.

Acknowledgments

Produced from materials originally authored by M. Rocchia and I. Ruff from Thermo Fisher Scientific, and A. Vianello from the National Research Council – Institute for the Dynamics of Environmental Processes.

References and Further Reading

1. PlasticsEurope. Plastics -the Facts 2014/2015 An analysis of European plastics production, demand and waste data. Available at: https://plasticseurope.org/wp-content/uploads/2021/10/2014-Plastics-the-facts.pdf.
2. Barnes, D.K.A., et al. (2009). Accumulation and Fragmentation of Plastic Debris in Global Environments. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1526), pp.1985–1998. DOI:10.1098/rstb.2008.0205. https://coastal.piratelab.org/wp-content/uploads/2018/08/Barnes-etal-2009-Pacific-Garbage-Patch.pdf.
3. Andrady, A.L. (2011). Microplastics in the Marine Environment. Marine Pollution Bulletin, 62(8), pp.1596–1605. DOI:10.1016/j.marpolbul.2011.05.030. https://www.sciencedirect.com/science/article/pii/S0025326X11003055.
4. Cooper, D.A. and Corcoran, P.L. (2010). Effects of mechanical and chemical processes on the degradation of plastic beach debris on the island of Kauai, Hawaii. Marine Pollution Bulletin, 60(5), pp.650–654. DOI:10.1016/j.marpolbul.2009.12.026. https://www.sciencedirect.com/science/article/abs/pii/S0025326X09005426.
5. Corcoran, P.L., Biesinger, M.C. and Grifi, M. (2009). Plastics and beaches: A degrading relationship. Marine Pollution Bulletin, 58(1), pp.80–84. DOI:10.1016/j.marpolbul.2008.08.022. https://www.sciencedirect.com/science/article/abs/pii/S0025326X0800430X.
6. Moore, C.J. (2008). Synthetic polymers in the marine environment: A rapidly increasing, long-term threat. Environmental Research, 108(2), pp.131–139. DOI:10.1016/j.envres.2008.07.025. https://www.sciencedirect.com/science/article/abs/pii/S001393510800159X.
7. Thompson, R.C. (2004). Lost at Sea: Where Is All the Plastic? Science, 304(5672), pp.838–838. DOI:10.1126/science.1094559. https://www.science.org/doi/abs/10.1126/science.1094559.
8. Imhof, H.K., et al. (2012). A novel, highly efficient method for the separation and quantification of plastic particles in sediments of aquatic environments. Limnology and Oceanography: Methods, 10(7), pp.524–537. DOI:10.4319/lom.2012.10.524. https://aslopubs.onlinelibrary.wiley.com/doi/abs/10.4319/lom.2012.10.524.
9. Farrell, P. and Nelson, K. (2013). Trophic level transfer of microplastic: Mytilus edulis (L.) to Carcinus maenas (L.). Environmental Pollution, 177, pp.1–3. DOI:10.1016/j.envpol.2013.01.046. https://www.sciencedirect.com/science/article/abs/pii/S0269749113000614.
10. Setälä, O., Fleming-Lehtinen, V. and Lehtiniemi, M. (2014). Ingestion and transfer of microplastics in the planktonic food web. Environmental Pollution, 185(185), pp.77–83. DOI:10.1016/j.envpol.2013.10.013. https://www.sciencedirect.com/science/article/abs/pii/S0269749113005411.
11. Hidalgo-Ruz, V., et al. (2012). Microplastics in the marine environment: A review of the methods used for identification and quantification. Environmental Science & Technology, 46(6), pp.3060–3075. DOI:10.1021/es2031505. https://pubs.acs.org/esthag/article-abstract/46/6/3060/1577896/Microplastics-in-the-Marine-Environment-A-Review?redirectedFrom=fulltext.
12. Dekiff, J.H., et al. (2014). Occurrence and spatial distribution of microplastics in sediments from Norderney. Environmental Pollution, 186, pp.248–256. DOI:10.1016/j.envpol.2013.11.019. https://www.sciencedirect.com/science/article/abs/pii/S0269749113006088.
13. Fries, E., et al. (2013). Identification of polymer types and additives in marine microplastic particles using pyrolysis-GC/MS and scanning electron microscopy. Environmental science. Processes & impacts, 15(10), pp.1949–56. DOI:10.1039/c3em00214d. https://pubs.rsc.org/em/article/15/10/1949/402248/Identification-of-polymer-types-and-additives-in.
14. Song, Y.K., et al. (2015). A comparison of microscopic and spectroscopic identification methods for analysis of microplastics in environmental samples. Marine Pollution Bulletin, 93(1–2), pp.202–209. DOI:10.1016/j.marpolbul.2015.01.015. https://www.sciencedirect.com/science/article/abs/pii/S0025326X15000314.
15. Harrison, J.P., Ojeda, J.J. and Romero-González, M.E. (2012). The applicability of reflectance micro-Fourier-transform infrared spectroscopy for the detection of synthetic microplastics in marine sediments. Science of The Total Environment, 416, pp.455–463. DOI:10.1016/j.scitotenv.2011.11.078. https://www.sciencedirect.com/science/article/abs/pii/S0048969711013969.
16. Vianello, A., et al. (2013). Microplastic particles in sediments of Lagoon of Venice, Italy: First observations on occurrence, spatial patterns and identification. Estuarine, Coastal and Shelf Science, 130, pp.54–61. DOI:10.1016/j.ecss.2013.03.022. https://www.sciencedirect.com/science/article/abs/pii/S0272771413001480.
17. Löder, M.G.J., et al. (2015). Focal plane array detector-based micro-Fourier-transform infrared imaging for the analysis of microplastics in environmental samples. Environmental Chemistry, 12(5), pp.563–581. DOI:10.1071/en14205. https://connectsci.au/en/article/12/5/563/190232/Focal-plane-array-detector-based-micro-Fourier.
18. Shi, H., et al. (2013). Titanium dioxide nanoparticles: a review of current toxicological data. Particle and Fibre Toxicology, 10(1), p.15. DOI:10.1186/1743-8977-10-15. https://link.springer.com/article/10.1186/1743-8977-10-15.

This information has been sourced, reviewed, and adapted from materials provided by Thermo Fisher Scientific - Vibrational Spectroscopy.

For more information on this source, please visit Thermo Fisher Scientific - Vibrational Spectroscopy.