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Analysis of chrysotile asbestos in air filters using ARL X’TRA Companion X-ray Diffractometer

Applications | 2025 | Thermo Fisher ScientificInstrumentation
X-ray, XRD
Industries
Environmental
Manufacturer
Thermo Fisher Scientific

Summary

Importance of the Topic


Asbestos monitoring in air samples is critical for protecting public health and ensuring regulatory compliance. Chrysotile, the most common form of asbestos, requires sensitive and reliable analytical methods to detect low fiber concentrations in environmental and occupational settings. X-ray diffraction (XRD) stands out for its ability to identify and quantify crystalline asbestos fibers, differentiating them from other fibrous materials within complex matrices.

Objectives and Overview


The application note outlines the development and validation of a rapid XRD-based protocol using the ARL X’TRA Companion diffractometer to quantify chrysotile asbestos on air filters. The study evaluates method linearity, precision, and limit of detection (LoD) across a concentration range representative of real-world sampling.

Methodology


Air filter samples composed of quartz fiber membranes were spiked with known amounts of chrysotile asbestos (10 to 1000 µg). Each filter was loaded into specialized sample cups and analyzed in reflection mode, focusing on the 002 diffraction peak (11.0 to 13.5°2θ) using Cu Kα radiation at 1.5418 Å. Data acquisition lasted 15 minutes per sample with continuous sample spinning. Integral peak intensities were extracted via single peak fitting, and calibration curves were constructed using linear regression for the 10–100 µg range, while higher concentrations were assessed through an extended regression model. Statistical parameters, including R2, standard error of estimate (SEE), and LoD (calculated as three times the standard deviation of the regression residuals divided by the slope), were determined.

Instrumentation Used

  • Thermo Scientific ARL X’TRA Companion X-ray Diffractometer featuring a θ/θ goniometer and 600 W Cu X-ray source for routine phase analysis.
  • Solid-state pixel detector for rapid data collection and one-click Rietveld quantification.
  • Profex software for peak fitting and regression analysis.

Results and Discussion


The calibration within the 10–100 µg range demonstrated excellent linearity (R2 = 0.992) with a SEE of 3.9 µg and an LoD of 11.9 µg. These values satisfy regulatory detection requirements for typical air sampling volumes (1–2 m3), yielding detection limits between approximately 7 and 60 µg of chrysotile per filter depending on the standard. The method’s precision and sensitivity position XRD as a complementary or alternative approach to microscopy-based techniques, particularly when low detection thresholds are needed.

Practical Benefits and Applications


The ARL X’TRA Companion workflow enables rapid screening of air filter samples with minimal operator training. Automated one-click quantification and direct integration with laboratory information management systems (LIMS) streamline data handling and ensure traceability. This approach supports routine environmental monitoring, occupational safety assessments, and compliance with standards such as OSHA and Chinese norm GBT 23263.

Future Trends and Potential Applications


Ongoing advancements in detector technology and software automation are expected to further lower detection limits and reduce analysis times. Combining XRD with complementary techniques, such as electron microscopy or Raman spectroscopy, could enhance fiber characterization in mixed matrices. Expanded applications may include indoor air quality monitoring, industrial process control, and forensic investigations of asbestos-containing materials.

Conclusion


The XRD-based protocol using the ARL X’TRA Companion offers a robust, sensitive, and efficient solution for quantifying chrysotile asbestos on air filters. With demonstrated linearity, low detection limit, and streamlined operations, this method supports rigorous environmental and occupational health monitoring.

Content was automatically generated from an orignal PDF document using AI and may contain inaccuracies.

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