Researchers test the particle size using a laser particle size analyzer after a single classification of heavy calcium powder. Surprisingly, the fine particle size has a change, and the D100 value increased. After a second classification, the D100 value increased again, leading to further confusion.
Initially, there was a suspicion that other powders might have been mixing with the calcium powder. However, after ruling out this possibility, the issue persisted. So, let’s focus on the classification process and detection methods to understand why this happens.
Excluding Contamination: Focus on Classification and Detection
It’s unlikely to mix with other powders, so let’s look into the classification process and the detection methods. Here’s a simple analysis to provide clarity.
Grinding Equipment and Classifier Function
The grinding equipment blows powder particles into the classifier via an upward airflow. This classifier, or airflow classifier, uses mechanical centrifugal force to accelerate the powder particles in the airflow. Larger particles are thrown outward, hitting the iron wall and losing kinetic energy, which causes them to fall back down. Meanwhile, smaller particles remain in the center of the airflow and are carried to the dust collector or other subsequent processing stages.
Laser Particle Size Analyzer
The laser particle size analyzer tests particle sizes by placing dilute-phase powder particles in an aqueous solution. The analyzer uses single or dual light sources of specific wavelengths to induce interference or diffraction. It relies on models like the MIE or Fraunhofer to simulate the interaction of light with particles. Then it calculates statistical data on the volume diameter.
MIE Model: Primarily used for nanometer-level particle sizes.
Fraunhofer Model (F-Model): Better suited for larger particle sizes.
The principle behind these models is complex, but each model is tailored for different particle size ranges for our analysis.
Why Does D100 Keep Increasing?
When a powder undergoes multiple classifications, it can result in the D100 value (the particle size at which 100% of the material is smaller) increasing with each classification. This is often due to the following reasons:
- Particle Redistribution: After each classification, finer particles may be removed, leaving behind a greater proportion of coarser particles. The result is an overall shift in the particle size distribution, leading to an increase in the D100 value.
- Classifier Efficiency: The efficiency of the classifier can vary, especially when handling powders with a broad particle size distribution. Inconsistent classification may allow finer particles to re-enter the system, causing changes in the particle size results.
- Measurement Method Variability: The laser particle size analyzer’s methods, particularly the MIE and Fraunhofer models, may introduce discrepancies in measurement depending on the nature of the particles and the wavelength used for detection.
The Role of D97, D98, and D100 in Laser Particle Size Analysis
In the field of particle size analysis, much of the focus tends to be on D97 or D98 values, which represent the top cut particle sizes. While researchers rarely discusses D100. However, if you closely examine the test results from laser particle size analyzers—whether from Malvern, Bexter, or other instruments—you will notice that the D97 and D100 values can differ significantly. This difference, often in the range of 0.02% to 0.04%, highlights the complexities involved in particle size measurement.
Understanding the Value
The D100 value represents the particle size at which 100% of the sample is smaller. While this value may seem crucial, it is often overshadowed by the D97 and D98 measurements, which focus on the top cut-off point. The discrepancy between D97 and D100 values is not necessarily due to errors in the measurement process, but rather due to the indirect nature of laser particle size analysis.
Laser Particle Size Analyzer: Indirect Detection Method
Laser particle size analyzers, such as those from Malvern or Bexter, use interference or diffraction models to estimate the size of powder particles. These analyzers apply specific mathematical models, such as the MIE or Fraunhofer model, to calculate the volume diameter based on the interference waves created when light interacts with powder particles.
However, this process is indirect—the models rely on statistical calculations rather than direct measurement. As a result, inaccuracies can occur, especially in the extremes of the particle size range, such as the smallest (0%) and largest (100%) particle sizes. This statistical nature means the analysis may statistically exclude the certain large particles, which might be too rare or too large relative to the light wavelength.
Statistical Inaccuracies in Large and Small Particles
The accuracy of laser particle size analyzers is often limited by the statistical thresholds set by the models. When there is a small number of very fine particles, they may be overlooked due to statistical sampling limitations. Conversely, as the number of coarser particles increases, they enter the sample pool and contribute to the observed particle size distribution. This leads to D100 values being influenced by these statistical thresholds, which are inherently linked to the analyzer’s reproducibility and detection limits.
Conclusion
Laser particle size analyzers provide valuable statistical data through an indirect measurement method. They offer reproducibility within certain particle size ranges and are highly useful for guiding production and application processes. Powder consists of a wide range of particle sizes, and the analysis of these particles requires understanding both the fine and coarse extremes. Direct measurement methods, such as electron microscopy, offer a more accurate, visual approach to particle size observation but are often more resource-intensive.
Understanding the reasons behind the multiple classifications of heavy calcium powder and the changes in D100 involves considering both the classification process and the limitations of the detection methods. The multiple classification cycles and the behavior of the classifier, as well as the models used by the laser particle size analyzer, all play a role in the observed changes in particle size distribution. By optimizing the classification process and ensuring consistent detection methods, the issue of increasing D100 values can decrease.