Tauc Plot
Chart overview
The Tauc plot is the standard method for extracting optical band gaps from UV-Vis transmission or reflectance data.
Key points
- By plotting (alpha*h*nu)^n against photon energy h*nu, where n = 2 for direct allowed transitions and n = 0.
- 5 for indirect transitions, a linear region emerges at the absorption onset whose extrapolation to the energy axis gives the band gap.
- This technique is essential for characterizing semiconductors, perovskites, metal oxides, and quantum dots intended for photovoltaics, photodetectors, and photocatalysis.
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Python Tutorial
How to create a tauc plot in Python
Use the full tutorial for implementation details, troubleshooting, and chart variations in matplotlib, seaborn, and plotly.
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"Create a publication-quality Tauc plot from my UV-Vis absorbance data. Calculate the absorption coefficient alpha from the absorbance, then plot (alpha*h*nu)^2 on the y-axis (for a direct band gap semiconductor) versus photon energy h*nu (eV) on the x-axis. Fit a straight line to the linear portion of the absorption edge and extrapolate it to the x-axis. Annotate the intersection point with the band gap value in eV. Add axis labels '(alpha*h*nu)^2 (eV/cm)^2' and 'Photon Energy (eV)', a legend, and a descriptive title. Use a white background."
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Python Code Example
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Common Use Cases
- 1Determining band gaps of perovskite thin films for solar cell efficiency prediction
- 2Characterizing quantum confinement effects in semiconductor nanocrystals
- 3Monitoring band gap engineering in alloyed semiconductors (e.g., CdZnS)
- 4Evaluating photocatalytic activity thresholds for visible-light-driven reactions
Pro Tips
Choose n = 2 for direct transitions and n = 0.5 for indirect allowed transitions
Perform a linear regression on the steeply rising linear region, not the noise floor
Annotate the extrapolated band gap value with an arrow and label at the x-intercept
Overlay the raw absorbance spectrum on a secondary y-axis for context
Frequently asked questions
When should you use a tauc plot?
The Tauc plot is the standard method for extracting optical band gaps from UV-Vis transmission or reflectance data. By plotting (alpha*h*nu)^n against photon energy h*nu, where n = 2 for direct allowed transitions and n = 0. Common applications include determining band gaps of perovskite thin films for solar cell efficiency prediction, characterizing quantum confinement effects in semiconductor nanocrystals, and monitoring band gap engineering in alloyed semiconductors (e.g., CdZnS).
Which Python libraries can create a tauc plot?
A tauc plot can be built in Python with matplotlib and numpy — matplotlib for precise control over axes, annotations, and journal styling and numpy. In Plotivy you describe the figure and it writes the matplotlib code for you.
Can I make a tauc plot without writing Python code?
Yes. Describe the tauc plot you need in plain language and upload your dataset — Plotivy's AI writes the Python code and renders a publication-ready figure. You still get the full, editable matplotlib source, so nothing is locked in a black box.
What are best practices for a clear tauc plot?
Choose n = 2 for direct transitions and n = 0.5 for indirect allowed transitions. Perform a linear regression on the steeply rising linear region, not the noise floor.
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 tauc-plot.
numpy
Useful in specialized workflows that complement core Python plotting libraries for tauc-plot analysis tasks.
Scientific Chart Selection Cheat Sheet
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