Analytical Solutions for Microplastics

Significance of the Topic

Marine microplastics (particles <5 mm, down to sub‑µm sizes) are now recognized as pervasive environmental pollutants with potential ecological and human‑health implications through trophic transfer and chemical adsorption. Analytical capability to detect, count, size, identify polymer types, quantify mass fractions, and measure adsorbed toxicants is essential for monitoring, risk assessment, standardization, and development of mitigation strategies.

Objectives and Overview of the Publication

This application note presents a comprehensive, workflow‑oriented suite of analytical solutions for microplastics research and monitoring. The document aims to: (1) describe automated sample preparation and measurement strategies for environmental waters and biological samples; (2) compare nondestructive (FTIR, Raman, microscopy) and destructive (Py‑GC‑MS) approaches; (3) demonstrate particle imaging/counting and size/shape characterization; and (4) illustrate analysis of additives and adsorbed contaminants (PAHs, PFAS) and determination of blend ratios and elemental content in debris.

Methodology and Workflow

General workflow described in the publication comprises:

• Sampling: surface water (neuston nets, bridge/shore grabs), biota dissections for stomach contents.
• Pretreatment: sieving, oxidative digestion (H2O2), density separation (NaI), and filtration. Emphasis is placed on reproducibility, safety, and contaminant removal.
• Screening and sizing: stereomicroscopy and dynamic particle image analysis for rapid quantification and shape metrics for particles 5–100 µm.
• Material identification: nondestructive FTIR (benchtop and infrared microscopes) and Raman microscopy for minute particles; destructive Py‑GC‑MS for mixed samples and mass‑based quantification.
• Target contaminant analysis: solvent extraction of particles followed by GC‑MS/MS for PAHs and LC‑MS/MS for PFAS.

Key practical notes and constraints:

• MAP‑100 automated preparation standardizes digestion/separation/filtration for 0.3–5 mm long‑diameter particles but is prone to clogging on very sandy/muddy samples.
• Filter type affects IR measurement windows (e.g., PTFE and Al2O3 have absorption bands that can overlap polymer features); choice should be matched to target polymers and measurement mode.
• Smallest particles (<10 µm) typically require Raman microscopy or combined IR/Raman microscopes to obtain reliable spectra.

Used Instrumentation

Instruments and accessories used or recommended in the workflows include (typical models used are listed):

• Automated preparation: MAP‑100 Microplastic Automatic Preparation Device.
• FTIR spectrometers: IRSpirit‑X series, IRSpirit‑TX, IRTracer‑100, IRXross (benchtop FTIRs) with QATR‑S single‑reflection ATR.
• Infrared microscopy / mapping: AIMsight / AIM‑9000 infrared microscope systems with AIMsight wide‑field camera, high‑speed mapping program, particle analysis program, Particle Filter (PF) holder.
• IR/Raman microscopy: AIRsight infrared / Raman microscope (co‑registered IR and Raman measurements on the same stage).
• Particle imaging/count: iSpect DIA‑10 Dynamic Particle Image Analysis System (ASTM D8489 compatibility for 5–100 µm).
• Pyrolysis GC‑MS: EGA/PY‑3030D (pyrolyzer) coupled with GCMS‑QP2020 NX (or GCMS‑QP2050) and F‑Search MPs 2.0 software.
• Cryogenic homogenization: IQ MILL‑2070 cryogenic mill for reproducible grinding prior to Py‑GC‑MS.
• Elemental analysis: EDX‑8000 / EDX‑8100 energy dispersive X‑ray fluorescence spectrometers for Cu and other metals; imaging elemental techniques referenced for broader material characterization.
• Thermal analysis: DSC‑60 Plus series differential scanning calorimeter for blend composition by heat‑of‑fusion methods.
• Target contaminant quantification: GC‑MS/MS (GCMS‑TQ8040 RX) for PAHs and LC‑MS/MS (LCMS‑8060RX) for PFAS.

Main Results and Discussion

Automated preparation (MAP‑100):

• Automating digestion (H2O2), density separation (NaI), and filtration reduced analyst labor, improved reproducibility, and improved safety handling corrosive reagents.
• Observed effective extraction of 0.3–5 mm particles and cleaner spectra for downstream FTIR matching using a UV‑Damaged Plastics Library.

FTIR & infrared microscopy mapping:

• High‑speed mapping and particle‑analysis software enabled rapid compositional mapping of MPs <100 µm collected on filters, with automated component color coding, counting and basic volumetric/mass estimates (using published geometric mass–area relationships).
• The PF holder permits measurement of a range of filter substrates (PTFE, Al2O3, Au/PC, stainless steel), but spectral interferences from filter materials (e.g., PTFE absorption ~1,200 cm‑1; Al2O3 absorption 1,200–700 cm‑1) must be managed.
• UV/heat‑weathered plastics can be identified reliably when using a dedicated UV‑Damaged Plastics spectral library; MAP‑100 pretreatment improved match scores by removing environmental matrix signals.

IR/Raman microscopy:

• AIRsight allowed consecutive IR and Raman measurements on the same stage; Raman spectra enabled identification of particles down to ~5 µm that were difficult for IR.

Particle imaging (iSpect DIA‑10) and standards:

• Dynamic particle image analysis provided rapid, statistically robust counts, size distributions and shape metrics for particles 5–100 µm in line with ASTM D8489; image galleries and scatterplots helped assess shredding/fragmentation patterns.

Py‑GC‑MS quantitative multi‑polymer analysis:

• Calibration using a mixed MPs reference provided linear calibration (R2 ≥ 0.995) for 12 common plastics. Py‑GC‑MS + F‑Search MPs 2.0 enabled simultaneous identification and mass quantification of mixed plastics from environmental road‑side debris and river samples without isolating particles from matrix.
• Example roadside debris analysis found PE and SBR (tire wear) as dominant contributors by mass in the measured sample.

Combined workflows and cross‑validation:

• Using MAP‑100 + FTIR gave number‑based composition (counts and types); combining freeze‑grinding and Py‑GC‑MS provided mass‑based composition. Results showed PP and PE as dominant components in river samples by both approaches, but percent mass and percent number differ as expected.

Additives and adsorbed contaminants:

• EDX detected Cu coatings on some fishing net samples (up to ~15 wt% in some recycled nets), raising ecological concerns about copper exposure from net reuse.
• Adsorption experiments followed by solvent extraction and GC‑MS/MS (PAHs) and LC‑MS/MS (PFAS) confirmed that PAHs adsorb strongly to PP and PE; PFAS adsorption varied markedly by compound and polymer type, indicating selective adsorption behavior dependent on polymer chemistry.

Thermal analysis and blend quantification:

• DSC methods based on heats of fusion (individual component or calibration curve approaches) provided approximate proportions for blended plastics (LDPE/PP, HDPE/PP). Overlapping melting peaks reduce accuracy; peak deconvolution or calibration using total heat of fusion can improve estimates for certain blends.

Benefits and Practical Applications

• Integrated workflows cover sampling, automated pretreatment, imaging/counting, material identification and mass quantification — enabling laboratories to select appropriate toolsets for surveillance, source attribution, and regulatory monitoring.
• Automation (MAP‑100) and software (high‑speed mapping, particle analysis, F‑Search MPs) substantially reduce analyst time and increase inter‑laboratory reproducibility.
• Multi‑technique approaches (IR mapping + Raman + Py‑GC‑MS) allow cross‑validation: IR/Raman for spatially resolved, nondestructive identification and Py‑GC‑MS for sensitive mass quantification in complex mixtures.
• Particle image analysis aligned with ASTM D8489 yields standardized metrics (counts, size, shape) suitable for monitoring programs.

Limitations and Quality Considerations

• Risk of sample contamination (airborne fibers, operator contact) requires strict clean techniques and blanks.
• Filter substrate spectral absorption and sample matrix can obscure polymer features; filter choice and measurement mode must be optimized.
• Size detection limits differ by method: imaging ~5 µm lower limit; IR mapping ~10–20 µm practical lower limit; Raman can go down to a few µm but depends on fluorescence and sample properties.
• Py‑GC‑MS is destructive; it provides mass information but loses spatial and particle‑level context.

Future Trends and Opportunities

• Standardization: Continued development and harmonization of international standards (ASTM, ISO, national guidelines) for sampling, pretreatment, and reporting will improve data comparability.
• Automation and high‑throughput mapping: Faster spectral mapping and improved particle recognition will support large‑scale monitoring programs.
• Enhanced spectral libraries: Expanded libraries for UV/thermally weathered plastics and additives will increase identification confidence for environmental samples.
• Hybrid analytical chains: On‑filter imaging + automated spectral classification with machine learning and mass‑based validation by Py‑GC‑MS will produce robust number‑to‑mass conversions for exposure assessment.
• Target contaminant fate studies: Coupling adsorbate quantification (PAHs, PFAS, flame retardants) with bioavailability and trophic transfer experiments will refine human‑health risk assessments.
• Miniaturized/field‑portable instrumentation and in‑situ sensors may enable distributed monitoring and rapid screening at source sites.

Conclusion

The document describes an end‑to‑end analytical strategy for microplastics, combining automated pretreatment (MAP‑100), high‑speed imaging and particle analysis (iSpect DIA‑10), infrared and Raman microspectroscopy, Py‑GC‑MS mass quantification, and targeted chemical analyses for adsorbed toxicants. The recommended multi‑technique approach balances nondestructive identification and spatial resolution with destructive, mass‑based quantification to provide complementary datasets that support monitoring, source attribution, and environmental risk assessment. Continued method harmonization, spectral database expansion, and automation will improve throughput, comparability, and the value of microplastics monitoring programs.

References

• Kataoka T., Iga Y., Baihaqi R. A., et al. Geometric relationship between the projected surface area and mass of a plastic particle. Water Research. 2024;261:122061.
• ASTM D8489. Test Method for Determination of Microplastics Particle and Fiber Size, Distribution, Shape, and Concentration in Waters with High to Low Suspended Solids Using a Dynamic Image Particle Size and Shape Analyzer. ASTM International; 2023.
• ASTM D8402. Standard Practice for Development of Microplastic Reference Samples for Calibration and Proficiency Evaluation in All Types of Water Matrices with High to Low Levels of Suspended Solids. ASTM International.
• ASTM D8333. Standard Practice for Preparation of Water Samples with High, Medium, or Low Suspended Solids for Identification and Quantification of Microplastic Particles and Fibers Using Raman Spectroscopy, IR Spectroscopy, or Pyrolysis‑GC/MS. ASTM International.
• wk87463 (ASTM). New Test Method for Spectroscopic Identification and Quantification of Microplastic Particles in Water Using Infrared (IR) Spectroscopy (under investigation).
• Kühn S., Jamieson A., Keighley R., Egelkraut‑Holtus M. In every ocean, at every depth — microfibers and microplastics: Micro‑FTIR analysis of smallest particles from deep sea to polar ice. SHIMADZU NEWS. 2018;2.
• Hartl M. G. J., Watson D., Davenport J. Biofouling in the Marine Aquaculture Industry, with Particular Reference to Finfish – Current Status and Future Challenges. Marine Estate Research Report (AQU/06/03). The Crown Estate; 2006.
• Nikolaou M., Neofitou N., Skordas K., Castritsi‑Catharios I., Tziantziou L. Fish farming and anti‑fouling paints: a potential source of Cu and Zn in farmed fish. Aquaculture Environment Interactions. 2014;5:163–171.
• Yasojima M., Mizuka H., Mine T., Takemori H., Takeuchi S., Yasui Y. Adsorption Characteristics of Chemical Substances on Microplastics; Proceedings of the 22nd Symposium of the Japan Society on Water Environment. Sapporo; 2019.
• Yasojima M., Mizuka H., Mine T., Takemori H. Existence of Unknown Chemical Substances Adsorbed on Microplastics Immersed in Rivers and Adsorption Characteristics of Chemical Substances on Microplastics; Proceedings of the 56th Environmental Engineering Research Forum. Okayama; 2019.