Particle Size Distribution
Chart overview
Particle size distributions (PSDs) characterize the size dispersity of powders, nanoparticles, colloids, and aerosols, critically affecting reactivity, optical properties, bioavailability, and processing behavior.
Key points
- Obtained from dynamic light scattering (DLS), laser diffraction, electron microscopy image analysis, or sedimentation, the distribution is plotted as frequency or cumulative percentage versus particle diameter.
- Log-normal fitting is standard for most natural and synthesized distributions, and the mode, mean, and polydispersity index (PDI) summarize the distribution for reporting.
Python Tutorial
How to create a particle size distribution in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
How to Plot a Histogram in PythonExample Visualization
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Generate publication-ready particle size distributions with AI in seconds. No coding required – just describe your data and let AI do the work.
View example prompt
"Create a publication-quality particle size distribution plot from my DLS or sizing data. Plot intensity (or number or volume) percentage on the y-axis versus particle diameter (nm or um) on the x-axis using a bar histogram or smooth curve. Fit and overlay a log-normal distribution curve. Annotate the mean diameter (Z-average), mode, and PDI as text on the plot. Use a logarithmic x-axis if the size range spans more than one order of magnitude. Add axis labels with units, a legend, and a descriptive title. White background, professional styling."
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Python Code Example
Console Output
Figure saved: plotivy-particle-size-distribution.png
Common Use Cases
- 1Characterizing nanoparticle synthesis quality and batch-to-batch reproducibility
- 2Monitoring emulsion droplet size during formulation development in pharmaceutics
- 3Evaluating milling efficiency and powder classification in ceramic processing
- 4Correlating nanoparticle size with optical (LSPR) or catalytic properties
Pro Tips
Use a logarithmic x-axis for size distributions spanning more than one decade
Report the Z-average, number-weighted mean, and PDI directly on the plot as a text box
Overlay intensity-weighted and number-weighted distributions to reveal skewness
Show replicate measurements as semi-transparent overlapping curves with a mean bold line
Long-tail keyword opportunities
High-intent chart variations
Library comparison for this chart
matplotlib
Best when you need full control over axis formatting, annotation placement, and journal-specific styling for particle-size-distribution.
numpy
Useful in specialized workflows that complement core Python plotting libraries for particle-size-distribution analysis tasks.
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.