Fillers play a central role in the design and optimization of composite polymer materials. In plastics, particulate additives are used to improve properties such as mechanical strength, thermal conductivity, heat resistance, electrical conductivity, and electromagnetic shielding. However, filler performance is not determined by chemistry alone. Particle size and particle shape are also critical variables because they influence surface area, dispersibility, handling behavior, and reinforcement effects in the final material. Smaller particles can increase specific surface area and enhance filler performance, while also increasing the risk of poor dispersibility and handling challenges. Anisotropic particles, including needle-like and plate-like forms, can be especially useful when mechanical reinforcement is required.
Despite the importance of morphology, obtaining robust and statistically meaningful particle size and particle shape data has historically been difficult. Conventional optical microscopy and electron microscopy can reveal particle shape, but quantitative evaluation of large numbers of particles is time-consuming and labor-intensive. Limited particle counts reduce statistical confidence, while manual classification can introduce operator bias and variability. In many traditional approaches, particle size and particle shape are treated as separate outputs, making it difficult to understand how these attributes interact within the same particle population. This analytical gap can restrict efforts in formulation development, quality control, and product differentiation for advanced plastic materials.
The full gated asset explores a comprehensive approach to particle morphology analysis using the Morphologi 4, an automated particle image analysis platform aligned with ISO 13322-1 principles. In the study, calcium carbonate (CaCO3), a widely used plastic filler, was selected as the model material. Two calcium carbonate powder samples with needle-like characteristics, referred to as Sample A and Sample B, were evaluated to demonstrate how automated image analysis can distinguish subtle yet meaningful differences in particle populations.
The samples were dry dispersed onto a glass plate using a Sample Dispersion Unit and analyzed under transmitted light with a 10× objective. Automated image analysis was then used to measure particle morphology. A solidity-based morphology filter was applied to exclude contacting particles and dust so that primary particles could be isolated for analysis. One measurement was performed per sample, with more than 15,000 particles analyzed in each case. This high particle count supports more statistically robust conclusions compared to manual methods that rely on small sample sizes.
One major topic is the comparison of volume-based and number-based particle size distributions. The study highlights how differences between these approaches can reveal important characteristics of particle populations. For example, one sample may appear finer when evaluated by volume-based metrics, while number-based analysis can reveal variations in fine particle content that directly impact dispersibility, processing behavior, and end-use performance.
Another focus is the relationship between particle size and particle shape. By combining circular equivalent (CE) diameter with aspect ratio in a two-dimensional analysis, it becomes possible to visualize how particles of different shapes are distributed within similar size ranges. This enables a more detailed understanding of how morphological differences influence functionality. Variations in aspect ratio distributions, particularly among larger particles, may influence reinforcement performance and mechanical strength when fillers are incorporated into polymer matrices.
For scientists and engineers working in plastics, polymer compounding, and materials development, the ability to evaluate particle size distribution, fine particle content, and aspect ratio in a unified workflow enables more informed material selection and formulation optimization. Automated particle image analysis allows these attributes to be measured simultaneously with high statistical confidence, providing deeper insight into filler behavior. This supports improved product performance, more consistent quality control, and clearer differentiation of advanced materials in competitive markets.
Register to access the full asset for a deeper exploration of the experimental design, particle morphology workflows, and analytical techniques used to characterize calcium carbonate fillers. The complete document expands on particle size and shape relationships, data interpretation, and practical implications for composite polymer systems, providing valuable technical guidance for researchers, formulators, and quality specialists.
The selection and control of fillers are critical factors in enhancing the performance and optimizing the cost of plastic materials. Fillers are particulate or powdered materials added to achieve a variety of functions, such as improving mechanical strength, imparting thermal conductivity and heat resistance, and providing electrical conductivity and electromagnetic shielding. It is well known that the performance of these fillers depends not only on material composition but also significantly on particle size and particle shape. For example, while a smaller particle size increases the specific surface area and enhances the filler effect, it also tends to lead to issues such as reduced dispersibility and poorer handling properties. Furthermore, when the aim is mechanical reinforcement, fillers with anisotropic shapes, such as needle-like or plate-like forms, are effective (Fig. 1).
![[AN260617-figure1.png] AN260617-figure1.png](https://dam.malvernpanalytical.com/3ee5fccd-3d13-4a94-ad36-b46c00654165/AN260617-figure1_Original%20file.png)
Thus, the simultaneous and quantitative evaluation of filler particle size and particle shape is indispensable for the design, quality control, and product differentiation of advanced plastic materials, for which demand has been growing in recent years [1].
Although the particle size and particle shape of fillers are important factors in the functional performance of plastic materials, it has been difficult to obtain sufficient information using conventional evaluation methods. In particular, while observation using optical or electron microscopes is common for evaluating particle shape, quantitative evaluation of a large number of particles requires considerable time and effort. Generally, as the number of particles measured is limited, ensuring statistical reliability is challenging, while, as the classification and quantification of shapes involves bias due to human judgment, there is a tendency for variation in the evaluation results. Moreover, it is difficult to evaluate the relationship between particle size and particle shape for the same particle, meaning that the two had to be treated as separate results. Consequently, the relationship between filler characteristics and material properties could not be fully visualized, limiting their application in product design and differentiation.
The Morphologi 4 is a useful tool for addressing these challenges. In this application note, we demonstrate the utility of the Morphologi 4—a high-performance particle image analysis platform that follows the principles described within ISO 13322-1—by comprehensively evaluating the particle morphology of plastic fillers [2].
In this study, calcium carbonate, which is commonly used as a filler for plastics, was selected as the subject of evaluation. Two types of calcium carbonate (CaCO3) powder samples with needle-like shapes (Sample A and Sample B) were used as model samples. The Morphologi 4 particle image analysis system was used for particle morphology evaluation.
The samples were dry-dispersed using the Sample Dispersion Unit (SDU) onto a glass plate (SDU settings: Sample Volume: 13 mm³). Automated image analysis measurements were performed under transmitted light conditions at a 10× objective magnification. A morphology filter (Solidity < 0.98) was applied to exclude contacting particles and dust, thereby extracting primary particles. One measurement was performed on each sample, analyzing more than 15,000 particles in each sample.
From the volume-based particle size distribution by Circular Equivalent (CE) diameter (Fig. 2), Sample B was slightly finer than Sample A, with Dv50 values of 15.04 µm for A and 11.04 µm for B. However, focusing on the particle size distribution by number (Fig. 3), the proportion of fine powder components of 2 µm or less was 17% for A and 6% for B, indicating that Sample B had a lower fine powder content (Table 1).
![[AN260617-figure2.png] AN260617-figure2.png](https://dam.malvernpanalytical.com/50098ead-1e5e-4898-bf90-b46c006525fe/AN260617-figure2_Original%20file.png)
![[AN260617-figure3.png] AN260617-figure3.png](https://dam.malvernpanalytical.com/5bba221b-495b-49e6-8cd1-b46c0065333e/AN260617-figure3_Original%20file.png)
| Sample | Dv10 (μm) | Dv50 (μm) | Dv90 (μm) | Dn10 (μm) | Dn50 (μm) | Dn90 (μm) | Fine (<2 μm) % |
|---|---|---|---|---|---|---|---|
| CaCO₃: A | 8.02 | 15.04 | 26.31 | 1.54 | 5.61 | 12.75 | 17 |
| CaCO₃: B | 6.40 | 11.04 | 18.83 | 2.76 | 6.06 | 10.80 | 6 |
These results indicate that Sample B possesses the characteristic of being ‘fine-grained yet low in fine powder’, suggesting the potential to achieve high functionality, good dispersibility, and excellent handling properties when used as a filler.
Fig. 4 shows a two-dimensional scattergram combining particle size (volume-weighted CE diameter) and aspect ratio. This analysis allowed us to intuitively see how particles of different shapes were mixed together even within the same particle size range.
![[AN260617-figure4.png] AN260617-figure4.png](https://dam.malvernpanalytical.com/3770765a-9f5d-4129-a84f-b46c00648127/AN260617-figure4_Original%20file.png)
The obtained data were divided into large and small particle groups based on Dv10, and shape comparisons were performed for each group (Fig. 5 and Table 2). In the aspect ratio distribution for the particle group below Dv10 (Fig. 5(a)), the average aspect ratios were A: 0.675 and B: 0.683, indicating an extremely small difference between the two samples. On the other hand, for the particle group with Dv10 or greater, the average aspect ratios were A: 0.437 and B: 0.512, revealing that Sample A contained a greater proportion of elongated particles (Fig. 5(b)).
![[AN260617-figure5.png] AN260617-figure5.png](https://dam.malvernpanalytical.com/86c7c7db-880d-4f2e-9e14-b46c00648255/AN260617-figure5_Original%20file.png)
| Sample | Mean aspect ratio (<Dv10) | Mean aspect ratio (≧Dv10) |
|---|---|---|
| CaCO3: A | 0.675 | 0.437 |
| CaCO3: B | 0.683 | 0.512 |
Based on these results, it is considered that, for example, when used as a reinforcing filler to improve mechanical strength, Sample A may exhibit a higher reinforcing effect from the perspective of particle shape.
Furthermore, Fig. 6 shows representative particle images that are around the mode values of the volume-based particle size, and aspect ratio distributions. The ability to correlate numerical data with actual particle images is a major feature of particle image analysis using the Morphologi 4.
![[AN260617-figure6.png] AN260617-figure6.png](https://dam.malvernpanalytical.com/2b30b078-4718-42c0-81f1-b46c00648349/AN260617-figure6_Original%20file.png)
In this application note, we have comprehensively evaluated the particle size and particle shape of calcium carbonate samples, which is widely used as a filler for plastics, using the Morphologi 4.
The results demonstrated that the output size and shape results including particle size distribution, fine particle content, and aspect ratio can be evaluated simultaneously with high statistical accuracy. The Morphologi 4 provides an effective analytical solution that strongly supports material development, quality control, and product differentiation, enabling the ‘visualization’ of filler performance.