Particle size analysis in colloids is a crucial process for understanding the distribution of particle sizes within a dispersed system. This technique provides insights into the stability, behavior, and properties of colloidal suspensions. Accurately measuring particle size helps in determining the quality and performance of products in industries such as pharmaceuticals, food, and cosmetics. Analytical methods like dynamic light scattering (DLS), laser diffraction, and electron microscopy are commonly used for this purpose. These techniques assess particle size distribution, influencing viscosity, reactivity, and sedimentation rates. Effective particle size analysis ensures optimal formulation and enhanced efficiency in various colloidal applications.

🗯️MATLAB snippet

Particle size analysis in colloids is a crucial task to characterize the properties of dispersed particles, which can influence the stability and behavior of colloidal systems. MATLAB is an effective tool for this type of analysis due to its strong capabilities in data visualization, image processing, and numerical computation. Below, I will outline a typical workflow for performing particle size analysis of colloids using MATLAB.

Workflow for Particle Size Analysis with MATLAB

1. Image Acquisition: Obtain high-resolution images of colloidal particles using a microscope or scanning device.
2. Image Preprocessing:
   • Grayscale Conversion: Convert the image to grayscale for simpler processing.
   • Noise Reduction: Apply filters (e.g., Gaussian, median) to reduce noise.
   • Enhancement: Improve contrast using histogram equalization or adaptive histogram techniques.
3. Thresholding:
   • Use automatic or manual thresholding methods to create a binary image that separates the particles from the background.
4. Segmentation:
   • Apply morphological operations (e.g., dilation, erosion) to separate connected particles.
   • Use bwlabel or regionprops functions to identify individual particles.
5. Measurement:
   • Use regionprops to measure particle properties like area, centroid, and equivalent diameter.
6. Size Distribution Analysis:
   • Extract particle diameters or radii and plot histograms or cumulative distribution functions to analyze size distribution.

Sample MATLAB Code

Here's a basic example to get you started:

 % Load the image
 image = imread('colloidal_sample.jpg');
 ​
 % Convert to grayscale
 grayImage = rgb2gray(image);
 ​
 % Apply median filter for noise reduction
 filteredImage = medfilt2(grayImage, [3 3]);
 ​
 % Thresholding
 binaryImage = imbinarize(filteredImage, 'adaptive', 'ForegroundPolarity', 'dark');
 ​
 % Remove small objects (noise)
 cleanedImage = bwareaopen(binaryImage, 50);
 ​
 % Label connected components
 labeledImage = bwlabel(cleanedImage);
 ​
 % Measure properties
 properties = regionprops(labeledImage, 'Area', 'EquivDiameter');
 ​
 % Extract equivalent diameters
 equivDiameters = [properties.EquivDiameter];
 ​
 % Plot histogram of particle sizes
 figure;
 histogram(equivDiameters, 'Normalization', 'probability');
 title('Particle Size Distribution');
 xlabel('Equivalent Diameter (pixels)');
 ylabel('Probability');

Advanced Considerations

• Calibration: Convert pixel units to real-world units (e.g., micrometers) by using a calibration factor obtained from a known reference scale.
• Shape Analysis: Include other properties like aspect ratio or roundness to understand the shape characteristics.
• Automation: Implement batch processing for multiple images for efficiency.

Applications

• Quality Control: Ensuring uniformity in pharmaceutical formulations.
• Research: Characterizing the behavior of colloids in suspensions or emulsions.