Identification of Microplastics using the Nicolet RaptIR FTIR Microscope

Importance of the topic

Microplastics are persistent, widespread environmental contaminants detected in water, air and food chains. Accurate identification and characterization (size, polymer type, morphology) are essential for regulatory compliance, environmental monitoring, source attribution and risk assessment. Analytical protocols must address challenges unique to microplastics: broad size ranges (from nanometers to millimeters), heterogeneous compositions, and chemical alteration caused by environmental degradation (oxidation, UV exposure). Automated, robust analytical workflows that combine optical imaging with infrared spectroscopy facilitate high-throughput, reproducible microplastic screening and reporting.

Objectives and overview of the study / application note

This application note demonstrates use of the Thermo Scientific Nicolet RaptIR FTIR Microscope together with OMNIC Paradigm software for automated identification and characterization of microplastics on filters. Goals included: establishing practical workflows for particle-based and chemical-mapping analyses, describing sample preparation compatible with SCCWRP/California SWRCB protocols, presenting a reflectance-mode polymer reference library, and illustrating automated particle detection, spectral acquisition and reporting for routine laboratory use.

Methodology

Sample collection and preparation followed the SCCWRP/California SWRCB guidance for microplastics in water and wastewater. Final filtrates in the 1–50 µm range were deposited onto reflective or low-background substrates such as silicon, gold-coated polycarbonate, alumina (Al2O3) or stainless-steel filters. Airborne particulates were collected by passive exposure of filters or slides.

Two complementary FTIR acquisition workflows were implemented:

• Particle analysis (find-then-measure): visual or automated image analysis locates particles on the filter; OMNIC Paradigm then sets optimized apertures based on particle form factors (size/shape), collects background spectra and records reflectance spectra for each particle.
• Chemical mapping (measure-then-find): the entire region (or selected subregions) is mapped so each pixel contains a full IR spectrum; multivariate tools (correlation, MCR, PCA) are used to locate and identify particles in low-contrast or densely populated samples.

Spectral acquisition parameters used in the examples: reflectance mode, single-point LN2-cooled MCT detector, spectral resolution 8 cm^-1, coaddition of 8–16 scans per spectrum. A dedicated reflectance-mode microplastics reference library was compiled from the CMDR Polymer Kit (Hawaii Pacific University), Polysciences microbead standards and Sigma-Aldrich polymer standards.

Used Instrumentation

The workflow and data presented used the following instrumentation and software:

• Thermo Scientific Nicolet RaptIR FTIR Microscope (reflectance FTIR micro-spectroscopy)
• OMNIC Paradigm software for automated particle finding, aperture selection, spectral acquisition and reporting
• Single-point LN2-cooled MCT detector for mid-IR detection
• Reference substrates: silicon filters, gold-coated polycarbonate, Al2O3 and stainless-steel filters

Microplastics reference library (summary)

A reflectance-mode reference library was created containing approximately 30 common polymers and several common contaminants. Representative entries include multiple polyethylene grades (ULDPE, LDPE, LLDPE, MDPE, HDPE), polypropylene, PET (virgin and recycled), PE-based additives, polyesters, polystyrenes (PS, EPS), polyamides (PA6, PA66), PVC (with and without phthalates), ABS, PMMA, PC, PTFE, PVDF, silicone, polyurethane, epoxy resin, nitrile glove material, cellulose acetate, natural fibers (hair, skin cells), soil/silica and rubber crumb. The library emphasizes reflectance-mode spectra to optimize spectral matching for measurements collected in reflection rather than ATR or transmission.

Main results and discussion

Automated particle analysis using the Nicolet RaptIR and OMNIC Paradigm allows three-step processing: region selection, automated spectral acquisition with aperture optimization, and library-based identification. Example results show rapid acquisition across tens to thousands of particles with per-particle outputs including polymer identity, size, shape and particle image. Size distributions by polymer class are generated automatically, facilitating population-level interpretation.

Chemical mapping of whole filters permits identification of low-contrast particles and dense particle clusters; maps can be correlated to specific polymers (e.g., polyethylene) and processed with multivariate algorithms to extract components. Mapping is computationally and storage intensive because many pixels contain no particulate material, but it is well suited for fibers, films, degraded/laminated particles and regions where optical contrast is insufficient.

Practical observations highlighted in the note:

• Reflection mode is preferred for automated multi-particle workflows to avoid tip contact and cross-contamination inherent to automated micro-ATR approaches.
• Reflectance-mode spectral matching is improved by using reflectance-mode libraries rather than ATR/transmission libraries, because environmental degradation can shift spectral features and reflective sampling geometry alters band shapes/intensities.
• Automated aperture selection based on particle form factors improves spectral quality and throughput.

Benefits and practical applications of the method

Key advantages of the presented workflow include high throughput, reproducibility, and an integrated reporting capability that returns particle counts, polymer IDs, size distributions and morphological images. The system supports both novice and experienced operators, streamlines routine environmental monitoring and research workflows, and enables retrospective analysis of stored spectral datasets using updated libraries. Applications span QC/QA in polymer manufacturing, environmental monitoring (water, wastewater, air), source attribution studies, and laboratory intercomparisons for method standardization.

Future trends and potential applications

Anticipated developments and opportunities include:

• Expanded and curated reflectance-mode spectral libraries, including aged/degraded polymer spectra to improve identification of environmentally weathered microplastics.
• Improved automation and machine-learning approaches for particle detection and spectral classification to reduce false positives and accelerate large-scale studies.
• Integration of Raman and hyperspectral modalities for complementary chemical specificity and enhanced identification of additives or complex mixtures.
• Standardized, community-adopted protocols and interlaboratory datasets to harmonize regulatory monitoring (e.g., adoption by water quality agencies).
• Optimization of data storage and on-instrument preprocessing to manage mapping datasets more efficiently.

Conclusion

The Nicolet RaptIR FTIR Microscope combined with OMNIC Paradigm software provides an effective, automated platform for reflectance-mode identification and characterization of microplastics on filters. The dual approaches of particle-based measurement and full-area chemical mapping address complementary sample conditions (sparse vs. dense/low-contrast), and the provision of a reflectance-mode polymer library improves identification fidelity. This solution supports routine environmental monitoring and research needs while enabling insights into particle origins and environmental degradation.

Reference

1. Rochman C. and Hoellein T., 2020. Science, 368, 1184–1185.
2. World Health Organization news release, 22 August 2019. Call for more research into microplastics and actions to reduce plastic pollution.
3. California State Water Resources Control Board and Southern California Coastal Water Research Project. Protocol for microplastics analysis using FTIR and Raman (SCCWRP/California SWRCB protocol).
4. De Frond H. et al., 2021. Analytical Chemistry, 93(48), 15878–15885.
5. Cowger W. et al., 2020. Analytical Chemistry, 93(21), 7543–7548.
6. CMDR Polymer Kit information, Hawaii Pacific University.