温石棉
材料科学
纤维
石棉
石棉纤维
生物医学工程
复合材料
扫描电子显微镜
冶金
材料试验
作者
Olivier Sioui-Latulippe,Gilles L’Éspérance,Jean‐Philippe Masse,Olivier Girard,Philippe Plamondon,Julien Trépanier,Joannie Martin
标识
DOI:10.1016/j.rechem.2026.103696
摘要
Asbestos, a hazardous workplace contaminant, requires reliable analytical methods to ensure effective worker protection. While phase-contrast optical microscopy (PCM) and transmission electron microscopy (TEM) are widely used, scanning electron microscopy (SEM) offers a cost-effective, faster alternative with high resolution. SEM is increasingly relevant for fine asbestos fiber analysis due to many regulatory changes. SEM imaging conditions were optimized through a quantitative comparison of contrast and sharpness with reference TEM images. The objective was to determine robust SEM operating conditions suitable for routine asbestos analysis while maintaining the capability for chemical characterization. Key parameters, including accelerating voltage, beam current, working distance, and detector type (USED, LABE, and LSED), were systematically evaluated. Although several parameter combinations produced sharp images, the USED detector yielded the highest sharpness at high accelerating voltages, whereas the LABE detector provided more stable contrast. A design of experiments approach enabled a quantitative assessment of image quality while accounting for EDS signal performance, operational efficiency, and the impact on fiber morphology measurements. The study further demonstrates that fiber size measurements derived from optimized intensity profiles extracted from SEM images are more accurate and reproducible than manual measurements. Using optimized experimental conditions, SEM was shown to reliably measure asbestos fibers as small as 20 nm. Most importantly, this study introduces a methodology applicable to any SEM, independent of instrument configuration for the characterization of asbestos fibers. This methodology can also be extended to other applications requiring accurate quantitative image-based measurements, improving measurement reliability and reducing overestimations across diverse experimental contexts.
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