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.
Example Visualization
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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
Console Output
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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
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.