Jun. 21, 2019
Applications

Find, Classify and Identify Microparticles with Raman Imaging

Comprehensive Tools for Automated Analysis

  • Fig. 1: Particles from a wastewater treatment plant sludge sample (50 g) distributed on 7 filters and a blank sample filter (Explanation and results in the text).Fig. 1: Particles from a wastewater treatment plant sludge sample (50 g) distributed on 7 filters and a blank sample filter (Explanation and results in the text).
  • Fig. 1: Particles from a wastewater treatment plant sludge sample (50 g) distributed on 7 filters and a blank sample filter (Explanation and results in the text).
  • Fig. 2: Size and polymer types of MP particles in a sludge sample from a wastewater treatment plant
  • Fig. 3: Particles in a cosmetic peeling cream. A: Optical bright field image overlaid with the confocal Raman image. B: Corresponding Raman spectra of the molecular components in the sample. C: Pie chart of the quantitative compound distribution in the sample. D: Graphical representation of the correlation between chemical characteristics and particle size.
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High-resolution measurements of particles are of great interest in many fields of application. Combining confocal Raman microscopy with particle analysis tools makes it possible to find, classify and identify particles almost entirely automatically.
 
Pollen, dust, flour, metal flakes and pigments in paints, titanium dioxide in sunscreen and toothpaste, fat crystals in food emulsions – these and many more substances in our daily lives contain or consist of microparticles. Recently, the public and scientific community have directed their attention toward microplastic particles in the environment [1, 2].
The definition of a microparticle is not universally agreed upon. According to the International Union of Pure and Applied Chemistry, microparticles are smaller than 100 micrometers and larger than 0.1 micrometer [3]. Other definitions range from 1 micrometer to 5 mm in size [4]. Confocal Raman microscopy is ideally suited to finding, classifying and identifying microparticles because not only does it yield images with a resolution down to 300 nm, but with Raman vibrational spectroscopy the chemical components of a sample can be identified [5]. It is a nondestructive method that requires little, if any, sample preparation. A Raman microscope can generate high-resolution images that show both the structural features and distribution of molecules within a sample. However, Raman spectroscopic imaging is not yet widely applied to microparticle analysis.
The challenge in Raman microparticle analysis lies in automating the detection of individual particles and classifying those of interest by size or shape before determining their chemical compositions [6, 7]. For such analyses, a comprehensive and integrated software solution is essential [8, 9, 10]. Here, using the examples of microplastics in an environmental sample and microparticles in a cosmetic peeling cream, we present the application of the open source program GEPARD [8] and the commercial solution ParticleScout [9].
 
Microplastics in Environmental Samples
Increasing plastic manufacturing, low recycling rates, the degradation of plastic during the utilization phase and the degradation of plastic waste inevitably produces millions of tons of microplastic particles (MP) every year that find their way into rivers, the sea, sediments and air.

MP originating from packaging and the food web are already found in food and drink. To assess possible effects of MP on the health of humans and animals, it is necessary to quickly and reliably monitor their abundance, size, size distribution and chemical composition. This can be done by a combination of optical particle recognition and Raman microscopy. The complex and difficult problem of determining these data from real environmental samples is shown with a sludge sample from a wastewater treatment plant. 50 grams of the sludge sample were pretreated and purified and then filtered in a two-stage filtration process using 7 silicon filters: 3 filters with 50 µm and 4 filters with 10 µm pore size (fig.1) [11]. An optical particle fragmentation was done with the GEPARD program [8]. We identified 143572 particles and fibers from all 7 filters. A blank sample from our laboratory is also included in figure 1.

After this optical localization of the particles on all filters, Raman analysis was performed with an alpha300 R microscope (WITec GmbH) and their chemical composition identified using the TrueMatch Raman database management software (WITec GmbH). 566 MP particles (118 from these are dyes) were identified, of which 371 were definitely from the sludge sample. All others (parafilm, PV23, PTFE) could have resulted from sample preparation and were therefore discarded. The extension of the 10 µm filter 2 in figure 1 shows a detailed view of the result, which displays the MP particles in color. The distribution by polymer type and particle size is shown in figure 2. The majority of the MP particles were between 10 and 100 micrometers in size. The bulk plastics PP, PE and PET and some dyes are most frequently represented as MP particles.
 
Particles in Cosmetics
The ParticleScout software (WITec GmbH) [9] enables highly automated, quick and straightforward analysis of microparticles on the basis of a confocal white light image of the sample and Raman microscopy. Localization, classification and identification of its chemical compounds are easily carried out with this analysis tool, as demonstrated here with a cosmetic peeling cream sample. For imaging, an alpha300 R microscope equipped with ParticleScout was used (fig. 3). First a survey of a large area was generated by the stitching together of optical bright field images by an automated routine. Focus stacking yielded more sharply defined particle outlines. 3941 particles were located and categorized according to their physical shape and size using Boolean filters. As conventional Raman imaging of large areas would also include much of the empty space surrounding the sparsely distributed particles, the software automatically records spectra of identified particles only, thus greatly accelerating the workflow of the measurement.
Using the seamlessly-integrated TrueMatch software, the Raman data were processed and the components could be identified by referencing the Raman database information. The chemical analysis revealed anatase (a mineral form of titanium dioxide) and boron nitride particles in an oil matrix (Raman spectra in fig. 3B). Titanium dioxide in cosmetics is currently under debate in the European Union with regard to its toxic effects [12].  Further evaluation of the results determined the quantitative prevalence of the molecular sample components in the particles (fig. 3C) and also the distribution of chemical compounds correlated to particle size (fig. 3D). In extended analyses, particles could also be linked to parameters such as area, perimeter, bounding box, Feret diameter, aspect ratio, equivalent diameter, spherical equivalent volume and others. As particle classification, image processing and analysis of Raman spectra are executed within one platform, ParticleScout offers an effective solution for automated, comprehensive investigations of particles.
 
Conclusion
The presented measurements of microparticles carried out using a confocal Raman microscope were largely automated and illustrate the potential of the featured analysis tools for comprehensive investigations in many fields of application. The particle fragmentation program and particle analysis software integrated with the Raman database management component have the speed and sensitivity necessary for both high sample throughput and precise particle characterization.
 

Authors
Dieter Fischer1, Franziska Fischer1, Josef Brandt1, Lars Bittrich1, Klaus-Jochen Eichhorn1, Harald Fischer2, Olaf Hollricher2, Karin Hollricher2,Miriam Böhmler2

Affiliations
1 Leibniz Institute of Polymer Research Dresden, Hohe Str. 6, Dresden, Germany
2 WITec GmbH, Ulm, Germany

 

Contact
Karin Hollricher
WITec GmbH
Ulm, Germany
Karin.Hollricher@witec.de
www.witec.de

Dieter Fischer
Leibniz Institute of Polymer Research Dresden
Dresden, Germany
fisch@ipfdd.de

References

[1] Rios Mendoza LM., Karapanagioti H.l, Ramírez Álvarez N, Micro(nanoplastics) in the marine environment: Current knowledge and gaps, Curr Opinion Environ Sci & Health 1, 47-51 (2018), doi 10.1016/j.coesh.2017.11.004
[2] Allen S. et al., Atmospheric transport and deposition of microplastics in a remote mountain catchment, Nature Geosci. 12, 339-344 (2019), doi 10.1038/s41561-019-0335-5
[3] Pure Appl. Terminology for biorelated polymers and applications (IUPAC Recommendations 2012), Chem. 84, No. 2, pp. 377–410 (2012), doi 10.1351/PAC-REC-10-12-04
[4] Imhof HK, Schmid J., Niessner R., Ivleva NP, Laforsch CA.,A novel, highly efficient method for the separation and quantification of plastic particles in sediments of aquatic environments, Limnol Oceangr Meth. 10, 524-537 (2012), doi 10.4319/lom.2012.10.524
[5] Toporski J., Dieing T, Hollricher O., Confocal Raman microscopy in life sciencesMicroscopie confocale Raman et sciences de la vie, Springer Series in Surface Sciences 66 (2018), Springer International Publishing
[6] Araujo CF, Nolasco MM, Ribeiro AMP., Ribeiro-Claro, PJA., Identification of microplastics using Raman spectroscopy: Latest developments and future prospects
,Water Res. 142, 426-440 (2016), doi 10.1016/j.watres.2018.05.060
[7] Anger PM., von der Esch E., Baumann T., Elsner M., Ivleva NP., Raman microspectroscopy as a tool for microplastic particle analysis, Trends Anal Chem. 109, 214-226 (2018), doi 10.1016/j.trac.2018.10.010
[8] GEPARD (Gepard Enabled PARticle Detection), OPEN SOURCE Software,       https://gitlab.ipfdd.de/GEPARD/gepard [last visited on: 18.06.19]
[9] ParticleScout, https://www.witec.de/products/accessories/particlescout [last visited on: 18.06.19]

[10] Anger PM., Prechtl L., Elsner M., Niessner R., Ivleva NP., submitted
[11] Käppler A., Windrich F., Löder M.G., Malanin M., Fischer D, Labrenz M., Eichhorn K-J., Voit B., Identification of microplastics by FTIR and Raman microscopy: a novel silicon filter substrate opens the important spectral range below 1300 cm−1 for FTIR transmission measurements, Anal Bioanal Chem 407, 6791-6801 (2015), doi: 10.1007/s00216-015-8850-8

[12] https://ec.europa.eu/info/law/better-regulation/initiatives/ares-2019-14... [last visited on: 18.06.19]
 

Contact

Leibniz Institute of Polymer Research Dresden


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