Authors
Maryem Sabri, Loïc Wingert, Maximilien Debia, Isabelle Valois, Stéphane Hallé, Ludwig Vinches
Published in
Journal of occupational and environmental hygiene. Pages 1-12. Sep 15, 2026. Epub Sep 15, 2026.
Abstract
Additive manufacturing (3D printing) processes emit ultrafine particles (UFPs), raising concerns regarding occupational exposures and worker health. Portable diffusion-charging instruments may facilitate personal exposure monitoring; however, their performance under occupational 3D printing conditions remains insufficiently characterized. This study evaluated the Partector 2 Pro (Naneos GmbH, Windisch, Switzerland) against reference instruments during controlled printing of four commonly used thermoplastic filaments: polylactic acid (PLA), polyethylene terephthalate glycol (PETG), thermoplastic polyurethane (TPU/S-Flex), and nylon. Experiments were conducted in a wind tunnel to ensure stable emission conditions. Total particle number concentrations measured by the Partector 2 Pro were compared with those obtained from a scanning mobility particle sizer (SMPS) and a condensation particle counter (CPC), while particle size percentiles (D10 to D95) from the Partector 2 Pro were compared with those from the SMPS. For comparisons of concentrations, strong agreement was observed across all materials, with regression slopes ranging from 0.88 to 1.05 and Pearson's correlation coefficients higher than 0.96. Mean deviations ranged from -9.6% to +6.2%, remaining well within the manufacturer's stated ±30% accuracy specification. For comparisons of particle size metrics, the Partector 2 Pro systematically underestimated mobility diameters (slopes 0.82 to 0.90; r = 0.98 to 0.99). The magnitude of deviations was size-dependent, ranging from -27.0% to +7.5% for the smallest particles (D10), -28.3% to -7.3% for intermediate particles (D25 through D75), and -17.6% to -7.9% for the largest particles (D95). Overall, the Partector 2 Pro demonstrated reliable performance for real-time UFP number concentration monitoring during 3D printing. These results support its applicability for occupational exposure assessment and workplace surveillance in additive manufacturing settings.
PMID:
42742398
Bibliographic data and abstract were imported from PubMed on 15 Sep 2026.
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