Microplastics have become an environmental health and safety concern, though we do not completely understand their long-term impacts. Microplastic, defined as plastic litter less than 5 mm in size, is the most abundant form of marine debris [1,2]. Microplastics are categorized as primary or secondary. Primary microplastics include small, manufactured items such as fibers and beads [3]. Secondary microplastics include fragments formed by a combination of physical, chemical, and biological processes [3].
Research laboratories must expand their capabilities to routinely analyze candidate microplastics from environmental samples. Spectroscopic techniques are well suited to polymer identification. This aids the determination of origin and helps predict biological impacts. Laboratory Raman spectroscopy is an alternative to confocal Raman microscopes and Fourier transform infrared (FTIR) microscopes for quick identification of polymer materials. However, very small samples are poor candidates for traditional Raman analysis. Raman microscopy was used to identify very small microplastic particles in this Application Note.
Raman spectroscopy has many benefits and adaptations for different applications. Raman microscopy allows easier sampling of small particles (<100 μm) than FTIR, another technique frequently used for microplastics identification. Raman systems tend to be much more portable than most other techniques, so testing can occur directly on site.
Aside from some interference from dyes, polymers and plastics are good candidates for Raman analysis. Figure 1 shows the Raman spectra of bulk polyethylene and polypropylene materials measured with 1064 nm excitation. The plastics can be clearly distinguished by their spectral features.
This Application Note explores the use of portable Raman microscopy for the identification of microplastics recovered from surface estuary waters.
Water samples were collected from the surface water of the Delaware Bay (USA). They were then transferred to glass jars and fixed with 4% formaldehyde. The total sample was size-fractioned on stainless steel sieves (5000, 1000, and 300 μm).
The 300 and 1000 μm samples were dried overnight at 90 °C. After drying, wet peroxide oxidation and density separation processes isolated microplastics from digested organic material [4].
Microplastics were collected onto 200 μm nitex mesh and dried. These samples were examined under a stereomicroscope and each piece was assigned a plastic type (i.e., fragment, fiber, bead, film, foam, rubber). This was followed by plastic identification with Raman spectroscopy.
Table 1. Experimental parameters.
| Equipment | Acquisition settings | |
|---|---|---|
| i-Raman EX | Laser Power | <165 mW |
| BAC151 video microscope | Int. time | 30 s–3 min |
| BWID software | Average | 1 |
An i-Raman® EX portable Raman system with 1064 nm laser excitation was used for all measurements (see Table 1 for specifications). 1064 nm laser excitation mitigates the spectral fluorescence resulting from 785 nm laser excitation of colored microplastic samples.
A BAC151C video microscope with an objective lens of 50× magnification (9.15 mm working distance, 42 μm spot size) was used to image the microplastics. Laser power was kept below 50% of the maximum (<165 mW) to avoid sample burning. BWID® software was used for identification of the microplastics against a reference library of plastics spectra.
Secondary microplastics
Several microplastic samples were analyzed. Figure 2a shows a blue microplastic fragment at the larger end of the microplastic size range (diameter approximately 4.5 mm). The irregular shape of this particle indicates that it is likely a secondary microplastic. Figure 2b is the Raman spectrum collected from the blue plastic fragment.
BWID software compares the acquired spectrum of an unknown to a library of reference materials to generate a hit quality index (HQI), a correlation coefficient. A first derivative is applied to the spectrum for the calculation. Spectral library search results are ranked from an HQI of 100 to 0 (best to worst match). BWID can be used with a variety of commercial spectral libraries, and it supports custom library building.
BWID matched the blue fragment in Figure 2a to a reference spectrum of polyethylene (PE) with a calculated HQI of 95.7 (Figure 3), indicating a strong spectral correlation.
Primary microplastics
Figure 4a shows the Raman spectrum acquired from a small, spherical bead (Figure 4b). This bead is likely a primary microplastic. BWID matched the sample spectrum to a reference spectrum of polystyrene with an HQI of 98.2.
Fibers are an important and common subgroup of microplastic particles. Figure 5a shows the Raman spectrum collected from a thin colored fiber (Figure 5b). BWID matched the Raman spectrum of the sample to a reference spectrum of polypropylene, with a calculated HQI of 74.9.
This relatively low value prompted further investigation into peaks in the sample spectrum that cannot be attributed to polypropylene. The peak at approximately 1537 cm-1 and the set of weak peaks from 670–790 cm-1 are consistent with the Raman spectrum of chlorinated copper phthalocyanine green pigment [5]. This is useful information for determining the origin of a sample.
Microplastics summary
A summary of the microplastics measured in this study indicates that the samples were mainly composed of polyethylene, polypropylene, or polystyrene (Table 2). Inconclusive results tend to come from black microplastics, which are a historically challenging material for Raman.
Sample degradation is another observed limitation. Low laser powers should be used (~10% of maximum) to prevent distortion and burning of the sample.
Table 2. Summary of identification results.
| Match Result | Number of samples |
|---|---|
| Polyethylene | 11 |
| Polypropylene | 4 |
| Polystyrene | 2 |
| Inconclusive | 5 |
Microplastics represent a potential threat to human health and our environment. Their robust characterization will be an important research topic in the near future. Raman microscopy is an effective tool to unambiguously identify these microplastics.
1064 nm excitation mitigates fluorescence from the dyes used in the plastics. Software correlation coefficient algorithms are useful for the simple identification of plastic material.
Thanks to Jonathan H. Cohen and Taylor Hoffman from the University of Delaware School of Marine Science and Policy for co-authoring this Application Note and providing the microplastic samples.
- Law, K. L. Plastics in the Marine Environment. Ann Rev Mar Sci 2017, 9, 205–229. https://doi.org/10.1146/annurev-marine-010816-060409.
- Galloway, T. S.; Cole, M.; Lewis, C. Interactions of Microplastic Debris throughout the Marine Ecosystem. Nat Ecol Evol 2017, 1 (5), 116. https://doi.org/10.1038/s41559-017-0116.
- Jambeck, J. R.; Geyer, R.; Wilcox, C.; et al. Plastic Waste Inputs from Land into the Ocean. Science 2015, 347 (6223), 768–771.
https://doi.org/10.1126/science.1260352. - Masura, J.; Baker, J.; Foster, G.; et al. Laboratory Methods for the Analysis of Microplastics in the Marine Environment: Recommendations for Quantifying Synthetic Particles in Waters and Sediments.; NOAA Technical Memorandum; Report NOS-OR&R-48; NOAA Marine Debris Division: Silver Spring, MD, 2015. https://doi.org/10.25607/OBP-604.
- Duran, A.; Franquelo, M. L.; Centeno, M. A.; et al. Forgery Detection on an Arabic Illuminated Manuscript by Micro-Raman and X-Ray Fluorescence Spectroscopy. Journal of Raman Spectroscopy 2011, 42 (1), 48–55. https://doi.org/10.1002/jrs.2644.
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