Sampling of microplastics at a materials recovery facility.

Detecting, separating, and characterizing airborne microplastics from other airborne particulates is currently challenging due to the various instrumental constraints and related sample preparation hurdles that must be overcome. The ability to measure these real-world environments is needed to better assess the risks associated with microplastics. To that end, the current study focused on developing a methodology for sampling and characterizing airborne microplastics. Particulate sampling was carried out at a municipal materials recovery facility near a conveyer belt containing sorted plastic materials to collect airborne environmental particles on filters. Nucleopore filters were mounted on Teflon support rings, coated with 100 nm aluminum to reduce the background signal for micro-Raman spectroscopy, and marked with a fiducial pattern using a laser engraver. The fiducial pattern was crucial in identifying samples, relocating particles, and efficiently enabling orthogonal measurements on the same samples. Optimum sampling conditions of 2 h at 25 L/min were determined using light microscopy to evaluate the particle loadings. The filters were then cut into slices which were attached to sections of thin beryllium-copper sheeting for easy transfer of the filter between microscopy platforms. Scanning electron microscopy was used to identify carbon-rich particles. Light microscopy was used to identify colored particles which were also carbon-rich which were then analyzed using micro-Raman spectroscopy to identify specific polymers.

Registering Particle Data Sets Using a Rotation and Translation Invariant Nearest-Neighbor Algorithm.

It can be useful to register (or align) two sets of particle data measured from the same physical sample. However, if the two data sets were collected at different translational or rotational offsets, finding the optimal registration can be a challenge. We will present an algorithm that efficiently determines the rotation and translational offset that best registers (in a least-squares sense) the corresponding particles in two or more data sets measured from the same sample. This algorithm can be used to merge two data sets that have been collected on overlapping but otherwise distinct regions on the sample. Alternatively, it can be used to overlay data sets that have been collected on the same sample area to compare replicate data for quality control and measurement efficiency purposes.

Compton scattering artifacts in electron excited X-ray spectra measured with a silicon drift detector.

Artifacts are the nemesis of trace element analysis in electron-excited energy dispersive X-ray spectrometry. Peaks that result from nonideal behavior in the detector or sample can fool even an experienced microanalyst into believing that they have trace amounts of an element that is not present. Many artifacts, such as the Si escape peak, absorption edges, and coincidence peaks, can be traced to the detector. Others, such as secondary fluorescence peaks and scatter peaks, can be traced to the sample. We have identified a new sample-dependent artifact that we attribute to Compton scattering of energetic X-rays generated in a small feature and subsequently scattered from a low atomic number matrix. It seems likely that this artifact has not previously been reported because it only occurs under specific conditions and represents a relatively small signal. However, with the advent of silicon drift detectors and their utility for trace element analysis, we anticipate that more people will observe it and possibly misidentify it. Though small, the artifact is not inconsequential. Under some conditions, it is possible to mistakenly identify the Compton scatter artifact as approximately 1% of an element that is not present.