• 목록
• 답변
• 글쓰기
• 게시판 리스트 옵션
  • 수정
  • 삭제

When analyzing particulate materials, understanding particle size distribution is essential for predicting performance in applications ranging from pharmaceuticals to industrial powders and environmental science. The two fundamental approaches to particle size quantification are number-based and volume-based, and each provides distinct insights that can lead to very different conclusions. Advanced imaging systems are now critical for uncovering hidden complexities in particle populations, offering visual and quantitative data that traditional sizing techniques often miss.

Measuring particle size by number means counting individual particles and determining how many fall into each size class. This method is ideal when particle count, not total mass, drives performance,—for example, in aerosol science where inhalation exposure depends on particle count, or in nanomaterials where biological interactions are often governed by surface area and concentration of individual entities. Digital imaging tools allow precise counting of particles at the micro- and nanoscale, providing a clear picture of how many particles exist at each size. The number-based approach brings attention to micron-scale or nano-scale entities that dominate exposure or reactivity, leading to more accurate risk and efficacy assessments.

In contrast, measuring by volume assigns weight to each particle based on its three-dimensional size, skewing results toward the most voluminous individuals. One large particle may account for most of the total volume, masking numerous smaller ones. This is often the preferred method in industries where flow properties, settling rates, or mixing behavior are critical—such as in concrete production or paint formulation. Volume-based methods like laser diffraction are common in these fields because they correlate well with bulk material behavior. However, small particles with high surface activity may remain undetected.

Imaging bridges the gap between these two methods by allowing direct visualization of particle morphology and size. Unlike approaches that estimate dimensions from indirect physical signals, imaging reveals irregular shapes, agglomerations, and surface features that profoundly affect how particles behave. A seemingly uniform particle in bulk data may be a fractal-like agglomerate, 粒子径測定 leading to misinterpretation of its true nature. Imaging shows whether a volume-dominated peak is due to a few large, dense particles or a cluster of smaller ones bound together.

Moreover, imaging enables the calculation of both number and volume distributions from the same dataset. By analyzing thousands of individual particles in an image, software can assign each particle a volume based on its measured dimensions, and then generate corresponding number and volume distributions side by side. The contrast between number and volume profiles uncovers hidden heterogeneity. For instance, A bulk volume profile may appear homogeneous, while particle counts reveal a bimodal population. This indicates potential instability or contamination.

The practical implications of this distinction are significant. For pharmaceuticals, volume metrics may suggest optimal pulmonary delivery, while imaging shows most particles are sub-micron and non-depositional, potentially leading to wasted dosage or unintended systemic absorption. In environmental monitoring, a volume-based measurement of airborne dust might suggest low risk, but imaging could expose high concentrations of ultrafine particles capable of penetrating deep into the respiratory system.

Ultimately, imaging transforms particle size analysis from a statistical exercise into a visual science. It replaces assumptions with concrete, image-based evidence. Counting shows how many particles exist; volume shows how much space they occupy together. When used together with imaging, these approaches provide a complete picture—one that balances the microscopic reality of individual particles with the macroscopic behavior of bulk materials. Depending solely on volume or number leads to blind spots; imaging-integrated analysis eliminates ambiguity.