Nontargeted Analysis of Face Masks: Comparison of Manual Curation to Automated GCxGC Processing Tools.

Masks constructed of a variety of materials are in widespread use due to the COVID-19 pandemic, and people are exposed to chemicals inherent in the masks through inhalation. This work aims to survey commonly available mask materials to provide an overview of potential exposure. A total of 19 mask materials were analyzed using a nontargeted analysis two-dimensional gas chromatography (GCxGC)-mass spectrometric (MS) workflow. Traditionally, there has been a lack of GCxGC-MS automated high-throughput screening methods, resulting in trade-offs with throughput and thoroughness. This work addresses the gap by introducing new machine learning software tools for high-throughput screening (Floodlight) and subsequent pattern analysis (Searchlight). A recursive workflow for chemical prioritization suitable for both manual curation and machine learning is introduced as a means of controlling the level of effort and equalizing sample loading while retaining key chemical signatures. Manual curation and machine learning were comparable with the mask materials clustering into three groups. The majority of the chemical signatures could be characterized by chemical class in seven categories: organophosphorus, long chain amides, polyethylene terephthalate oligomers, n-alkanes, olefins, branched alkanes and long-chain organic acids, alcohols, and aldehydes. The olefin, branched alkane, and organophosphorus components were primary contributors to clustering, with the other chemical classes having a significant degree of heterogeneity within the three clusters. Machine learning provided a means of rapidly extracting the key signatures of interest in agreement with the more traditional time-consuming and tedious manual curation process. Some identified signatures associated with plastics and flame retardants are potential toxins, warranting future study to understand the mask exposure route and potential health effects.

Microchannel plate detector detection efficiency to monoenergetic electrons between 3 and 28 keV.

An unshielded microchannel plate (MCP) detector with an ultrafine pore diameter of 2 µm was irradiated by an electron beam to determine the detection efficiency of electrons for creating detector signals, or counts. Tested electron energies spanned a range of 3 kiloelectron volts (keV) to 28 keV. Higher detection efficiencies were measured at the lower end of this energy range, 0.376 counts per incident electron at 3 keV down to 0.155 at 15 keV with an increase to 0.217 at 18 keV and then another decrease down to 0.15 counts per incident electron at 28 keV. The increase at 18 keV is attributed to primary electron interaction with the L shell electrons of lead (Pb), leading to an increase in secondary electron and X-ray generation within the MCP and thus an increase in detection efficiency. For the electron beam directed normal to the MCP surface, the lowest efficiency of 0.15 counts per incident electron was observed at 28 keV. Detection efficiency was also tested as a function of incident angle with angular steps of 5°. Detection efficiency was more sensitive to the angle of incidence as the incident electron energy decreased. The detection efficiency at 3 keV decreased from 0.376 counts per electron at the zero degree angle (normal incidence to MCP surface) to 0.027 counts per electron at an incident angle of 50° (average in both orientations). At 28 keV, the decrease in detection efficiency as a function of increasing angle was less pronounced, ranging from 0.15 counts per electron at zero degrees to 0.08 counts per electron at 50° (average in both orientations). Experimental data showed lower detection efficiencies compared with previously published data.