Microplastics are defined as pieces of plastic that are less than 5mm in size.
They can be released directly from the production or use of consumer products (primary microplastics), or from the fragmentation of larger plastic objects (secondary microplastics). Geographically microplastics are present everywhere on Earth1 and have pervaded our food chains2 and our drinking water3, becoming a global environmental and potential health issue. A 2020 study published in the journal4Environmental Pollution revealed that there may be far more microplastics floating in the world’s oceans than previously estimated. The researchers estimate that the oceans could be home to as many as 125 trillion microplastic particles!
“Our research shows that the microplastics sampled with a finer net were mostly fibers that were significantly smaller than those sampled with a coarser net,” Pennie Lindeque, lead author of from Plymouth Marine Laboratory
Annually, textiles make up roughly 14% to 16% of plastic production which amounts to around 60 million tons per year. Things like synthetic clothing material and synthetic rugs give off millions of fibers annually and many of those fibers stay concentrated indoors.
Visual inspection, either by eye or using manual microscopy, is still widely used to identify and quantify microplastics. This method is however subjective, time-consuming and very susceptible to user bias, with non-plastic material often being mis-classified as microplastics. To help overcome this, chemical identification using non-destructive techniques such as Raman spectroscopy is now largely used to aid microplastic analysis1.
In Alfatestlab we use Morphologically-Directed Raman Spectroscopy (MDRS®) to characterize microplastics samples. This method allows individual microplastic particles to be automatically and independently characterized within a mixture, providing complimentary particle size, shape and chemical ID information.
From this component specific information can be obtained i.e. the particle size and shape distributions for each different type of microplastic present in the sample, in a single automated measurement.
Here we report results obtained by the manufacturer of the instrument (Morphologi 4-ID from Malvern Panalytical) using MDRS to characterize and identify a mixture of six types of microplastic collected on a membrane filter.
Materials and Methods
A 2 mL aliquot of an aqueous suspension containing six different plastics (listed in Table 1 along with some of their common uses) was vacuum filtered using glassware to avoid plastic contamination. The microplastic particles were collected on a 47 mm mixed cellulose ester membrane filter, which was then transferred to the Filter Plate accessory (see Figure 1) and allowed to dry overnight.
Table 1: The six plastic types used in this study and their common uses
Figure 1: Photograph of the Filter Plate holding the membrane filter to be measured on the Morphologi 4-ID stage, and the composite scan area of the filter at 2.5x magnification
A reference library was created by acquiring spectra from discrete samples of the six plastic types. During the MDRS analysis every particle spectrum was correlated to the spectral reference library, giving a correlation score between 0 and 1 – the more similar a particle spectrum is to the library component, the closer the correlation score is to 1. Typically, a threshold value is defined where the user has confidence that chemical components can be differentiated.
Results and Discussion
Figures 2 and 3 show the number and volume weighted Circle Equivalent Diameter (CED) distributions, respectively, for the microplastic particles imaged on the membrane filter at 2.5x magnification. The distributions reveal that the sample was very polydisperse, with particle sizes ranging from smaller than 10 µm up to 1 mm. The number-based particle size distribution allows to precisely count the fine material as each particle is counted equally, A volume weighted distribution, such as laser diffraction analysis for instance (where a single 100 µm particle has the same contribution as one thousand 10 µm particles) would emphasize the large microplastic particles.
Figure 2: Number weighted Circle Equivalent Diameter (CED) distribution for the microplastic particles imaged on the membrane filter at 2.5x magnification with example particle images at the 50th and 90th percentiles of the distribution
As can be seen from the particle images shown in Figures 2 the sample contained particles with a wide range of shapes, which can be described and plotted using shape descriptors such as circularity and elongation. The plastic types could not be differentiated based on the particle images alone however; further information was needed which was provided by chemical identification using Raman spectroscopy.
Raman spectra acquired on a subset of the imaged particles with a size of 100 µm or greater were classified based on the library spectrum to which the spectra gave the best correlation while also meeting a threshold value (see Figure 3). Due to the nature of the sample analyzed the reference library only contained the Raman spectra of plastics, but the instrument can also be used to differentiate between plastic and non-plastic particles such as natural materials like sand or cellulose.
Figure 3: Raman spectra of PE, PP, PS, PC, PET and PVC acquired on the Morphologi 4-ID, with example images of chemically identified particles of each plastic in the filtered sample
From the chemically identified particles a chart showing the percentage count of total classified particles for each plastic type was plotted (Figure 4). This shows that the most abundant microplastic particle type above 100 µm in size on the filter was PET (39%), followed by similar numbers of PE (27%) and PS (26%) particles, with the remaining 8% comprised of the other three plastic types.
Figure 4: The percentage of total counted particles for each plastic type identified by Raman spectroscopy (CED ≥ 100 µm)
By using MDRS it has become possible to look at and compare the particle size and shape distributions for each type of plastic in the microplastic sample. Figure 5 shows the circularity distribution for the three most abundant plastic types; PET, PE and PS, along with example particle images. The plastic particles are typically irregular in shape, with the PE particles showing much lower circularity values. This reflects the PE particle images which appear to be mostly fibrous in nature.
Figure 5: Circularity undersize distributions for the microplastic particles identified as PE (red), PET (black) and PS (blue) using MDRS with example particle images with a CED ≈ 250 µm for each plastic type
The second MDRS measurement targeted particles sized between 20 and 100 µm. Figure 6 compares the percentage count of total classified particles for each plastic type for both size ranges analyzed and shows high similarity. Being able to easily compare classified data sets, whether they are measurements from many different samples or even of the same sample, as shown here, can aid the rapid assessment of real processes. For instance, it could be used to compare the type and morphology of microplastics collected from different locations over several size bands, or used to track erosion rates and behaviors of different polymer types when subjected to a particular environment or treatment.
Figure 6: The percentage of total counted particles for each plastic type identified by Raman spectroscopy in the 20 – 100 and ≥ 100 µm size ranges
If you are part of a laboratory or team all over the world currently carrying out varying research into the origins, impact and fate of microplastics in the environment, you may be interested in analysing your samples using Morphologically-Directed Raman Spectroscopy (MDRS)!
We can facilitate the analysis of your samples containing microplastics using the Morphologi 4-ID, providing sizes, shapes and proportions of the several types of microplastic particles. We can help you in the understanding of the likely behavior of these materials in a process or environment, comparing samples from different locations. Contact us to discuss your analytical needs!
1. J.C. Prata, J.P da Costa, A.C. Duarte and T. Rocha-Santos, Methods for sampling and detection of microplastics in water and sediment: a critical review, Trends in Analytical Chemistry, 2020, 110, 150.
2. L.W.D. van Raamsdonk, M. van der Zande, A.A. Koelmans, R.L.A.P. Hoogenboom, R.J.B. Peters, M.J. Groot, A.A.C.M. Peijnenburg and Y.J.A.Weesepoel, Current insights into monitoring, bioaccumulation and potential health effects of microplastics present in the food chain, Foods, 2020, 9(1), 72