Raman Spectrum
Chart overview
Raman spectra display inelastic scattering intensity as a function of wavenumber shift, providing a molecular fingerprint for chemical identification and structural analysis.
Key points
- Materials scientists and chemists annotate characteristic vibrational modes such as G-band and D-band in carbon nanomaterials, or C-H and C=C stretches in organic compounds.
- Baseline-corrected, normalized spectra are essential for comparing samples across different experimental conditions.
Example Visualization
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"Create a Raman spectrum from my data. Plot Raman shift (cm-1) on the x-axis and intensity on the y-axis. Subtract a polynomial baseline and normalize the spectrum to its maximum peak. Annotate the top characteristic peaks with their wavenumber positions. Use journal formatting with Arial font and no top or right spines."
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Python Code Example
Console Output
Figure saved: plotivy-raman-spectrum.png
Common Use Cases
- 1Identifying carbon allotropes (graphene, CNT, diamond) via D-band and G-band ratio analysis
- 2Pharmaceutical polymorphism screening and solid-state form identification
- 3In situ monitoring of chemical reactions and intermediates under operando conditions
- 4Quality control and authentication of geological minerals and gemstones
Pro Tips
Perform asymmetric least-squares baseline subtraction before plotting to remove fluorescence background
Normalize spectra to the most intense peak when comparing across different samples or instruments
Annotate D/G band ratios directly on the figure for graphitic carbon material characterizations
Use scipy.signal.find_peaks with a minimum prominence threshold to automate peak detection
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.