Automated Workflow for the Analysis of Microplastics

Analyzing Microplastics in the Environment: Striving to Better Assess Occurrence, Fate and Effects
Oral Presentation

Prepared by L. Tisinger
Agilent, 201 Hansen ct., suite 108, wood dale, il, 60191, United States


Contact Information: [email protected]; 630-306-7304

ABSTRACT

Microplastics are pervasive in the environment and are now recognized as a significant pollutant. Detection and identification of microplastics can be difficult, owing to their small size, on the order of micrometers. Detection of microplastics has been done using light microscopy, differential scanning calorimetry, chromatography, Raman spectroscopy, and IR spectroscopy to name a few. The latter technique is especially useful, owing to the fact that it is considered to be the “gold standard” for materials identification. To that end, IR spectra are molecular fingerprints, i.e., every material has its unique infrared spectrum. In addition, there exist spectral libraries containing hundreds of thousands of compounds. All of these factors make IR spectroscopy especially attractive for microplastics analysis. Additionally, the combination of a microscope and an IR spectrometer provides an accurate means to easily identify microplastics and can be used to detect microplastics down to 10 micrometers. However, the technology does has a few drawbacks: (i) some samples might have thousands of particles, and analyzing each particle, one at a time, can be extremely labor intensive and time-consuming; (ii) the use of an IR imaging systems, which take snapshots of large sample regions, provide fast analysis of large samples, but data sets are very large due to the large number of non-microplastic spectra being collected; and (iii) the technology generally requires significant expertise to operate a microscope and analyze data. This presentation will describe a new, simplified approach to microplastics analysis that features a quantum cascade laser (QCL)-based infrared microscope with automated workflow. It includes a simplified, turnkey approach to microplastics identification, requiring little or no previous operational experience. Details of the system and microplastics data from a real-world sample will be presented