In the rapidly advancing world of battery technology, quality control and performance optimization are paramount. Historically, the particle size of powders used in batteries, especially lithium and graphite, has been the primary focus. However, recent research and industry trends have illuminated the significance of another parameter: particle shape.
Hydraulic fracturing, or fracking, has revolutionized the energy industry, unlocking vast reserves of previously inaccessible natural gas and oil. This innovative technique involves injecting a high-pressure fluid into a wellbore to create small fractures in deep rock formations, allowing natural gas or oil to flow more freely. However, maintaining the integrity of these fractures is a significant challenge facing the industry.
Particle analysis is an important field with broad applications in numerous industries. The demand for reliable and accurate particle analysis techniques has led to the development of various automated tools for particle size analysis. These tools report the particle size in terms of equivalent spherical diameter, a necessary representation for irregularly shaped particles.
Sieve correlation has been a well-established method in particle sizing for many years, but the development of automated dynamic image analysis is beginning to revolutionize the process. In this blog post, we will discuss how automated dynamic image analysis is becoming a complementary method to sieve correlation and how it provides more accurate and detailed results.
Dynamic image analysis (DIA) is a digital method of particle characterization that enables a researcher to identify the shape, size, and number of particles in a sample. Due to its high accuracy and reproducibility, DIA is a widely used image analysis method across most industries to support quality control, compliance, and research applications.
The behavior of raw materials can typically be predicted by properly studying a sample and identifying physical characteristics such as particle size. Features most commonly analyzed include distribution, size, and shape. Particle size measurement is a crucial process in various industries, as understanding specific particles and their properties can enhance productivity, reduce waste and ensure product quality.
Particle size is a crucial property to measure when dealing with particulate raw materials. It underlies key manufacturing parameters like flowability, packing, and wetting–each of which can have a demonstrable impact on the end quality of a part or product.
Creating parts using metal powders requires exceptional high-quality raw materials. Many analytical tools are used to pre-screen metal powders to ensure proper powder flowability, prevent clogging, and ensure adequate particle deposition to guarantee well-made parts.
Particle size reduction is a routine method of increasing surface area and making downstream processing more efficient. It is well documented that particle size distribution (PSD) affects powder flowability. However, studies have demonstrated the limitations of particle size analysis – particularly in applications with irregular particles. Ignoring particle shape can lead to significant under-estimation of the mean particle size which leads to errors in PSD results.
Particle size is a critical factor in qualifying abrasive powders. Larger particles typically produce an aggressive cutting action with fast material removal rates, albeit with a poor surface finish. Smaller particles, by contrast, offer a smoother finish with significantly reduced rates of removal.
