Arrhenius Plot
Chart overview
The Arrhenius plot is the standard graphical technique for determining activation energies of thermally activated processes, from chemical reaction kinetics to solid-state diffusion, creep, and dielectric relaxation.
Key points
- By linearizing the Arrhenius equation as ln(k) = ln(A) - Ea/(RT), a straight line fit yields the activation energy Ea from the slope and the pre-exponential factor A from the intercept.
- This analysis is ubiquitous in heterogeneous catalysis, battery electrode kinetics, semiconductor dopant diffusion, and protein denaturation studies.
Python Tutorial
How to create a arrhenius 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 Arrhenius plot from my kinetics data. Plot ln(k) or log(k) on the y-axis versus 1/T (K^-1) on the x-axis. Perform a linear regression and overlay the fit line. Calculate and annotate the activation energy Ea = -slope * R with its uncertainty, and the pre-exponential factor A from the intercept. Add axis labels 'ln(k)' (or 'log(k)') and '1/T (K^-1)', a legend with the fitted Ea value, and a descriptive title. Use data point markers with error bars if uncertainty data is available. White background, professional styling."
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Common Use Cases
- 1Determining activation barriers for heterogeneous catalytic reactions
- 2Measuring dopant diffusion activation energies in semiconductor processing
- 3Characterizing creep mechanisms and diffusion-controlled deformation in alloys
- 4Modeling lithium-ion battery charge transfer kinetics over temperature ranges
Pro Tips
Plot 1/T on the x-axis in units of 10^-3 K^-1 to avoid very small numbers
Include error bars on data points and report the 95% confidence interval on Ea
Check for curvature indicating a non-Arrhenius mechanism or multiple regimes
Annotate the slope value and convert to Ea in kJ/mol or eV using R = 8.314 J/mol/K
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 arrhenius-plot.
numpy
Useful in specialized workflows that complement core Python plotting libraries for arrhenius-plot analysis tasks.
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