ISO 21873-3:2018 is an international standard that specifies the procedure for determining the particle size distribution of powders using dynamic image analysis (DIA).
The Importance of ISO 21873-3:2018
Accurate measurement of particle size distribution is crucial in various industries such as pharmaceuticals, minerals, and chemicals. ISO 21873-3:2018 provides a standardized method for analyzing particles using DIA technology.
By adhering to ISO 21873-3:2018, companies can ensure reliable and consistent results across different laboratories. This allows for better quality control and enables effective comparisons between different products and suppliers.
The Principle of Dynamic Image Analysis
DIA technology involves capturing images of particles in motion within a flowing stream. These images are then analyzed using advanced algorithms to determine the size and shape of each particle.
The principle behind DIA is based on the relationship between particle size and how it scatters light. Larger particles scatter more light, leading to a higher intensity of scattered light, whereas smaller particles scatter less light.
ISO 21873-3:2018 provides guidelines for instrument calibration, sample preparation, and data analysis in order to obtain accurate and reliable results. It also covers the verification of instrument performance to ensure that measurements are traceable to the international unit system.
Advantages and Limitations
One advantage of using DIA technology according to ISO 21873-3:2018 is its ability to measure particles in a wide size range, from nanometers to millimeters. It also offers high-resolution imaging and can provide detailed information about particle shape.
However, it's important to note that ISO 21873-3:2018 does not cover all aspects of particle characterization. It is specific to dynamic image analysis and may not be suitable for certain types of materials or samples that require other measurement techniques.
Additionally, implementing ISO 21873-3:2018 may require specialized equipment and expertise. Proper training and validation are necessary to ensure accurate and reliable results.
Nina She