Author: Alexander (Aleks) E. Siemenn <[email protected]>
If you use this package for your research, please cite the following paper:
Citation:
@article{,
author = {},
title = {},
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This package automatically extracts the direct band gap from an array of multiple measured reflectance spectra samples.
Steps for Extracting Band Gap:
- Compute Tauc plots from reflectance for each spectra.
- Each Tauc plot is smoothed using a Savitzky–Golay filter to reduce signal noise.
- The smoothed curves are processed into line segmented using a recursive segmentation process. This process segmements the smoothed curve in half recursively into smaller line segments until each line segment has a fit of
$R^2 \geq 0.990$ with its respective curve segment. - The peak locations of the Tauc plot are extracted after an extreme Savitzky–Golay smoothing filter is applied to locate the upper-bound for linear regression fitting in the next step.
- A linear regression across the entire Tauc plot range is fit for every n and n+1 pair of line segments. The regression lines that have a positive slope, intersect with the x-axis, and have the lowest RMSE with the Tauc plot between the x-intercept and the next Tauc plot peak are the regression lines used to compute band gap.
- Band gaps are extracted from the x-intercepts of the regression lines computed in the previous step.
Package installation requirements can be found in the requirements.txt file.
A demonstration of using the automatic band gap extractor package can be found in the example.ipynb file. The automatic band gap extractor code itself can be found in the extractor.py file under the autoextract() definition.
Input data should take the form of an (n x m) pandas array with n reflectance data points and (m - 1) measured spectra, where m = 0 is the wavelength. Below is an example of the input data format with 12 measured reflectance spectra and the wavelength values in the m = 0 column:
Our reflectance spectra are measured using a Resonon Pika L hyperspectral camera that has a 10,000 point scaling factor for reflectance intensity. Hence, to convert these reflectance spectra from 10,000 percentage points to a decimal autoextract(intensity_scale=10000).
Spectra with only a single peak will output a single band gap value based on the linear regression fit:
However, spectra with more than one clear peak will output multiple band gap values, one for each peak based on the linear regression fit:
| Band gap #1 | Band gap #2 |
|---|---|
 |
 |
If there are multiple band gap values extracted for a single spectra, the band gap associated with the highest intensity peak signal will be output first as bandgap0 and the following band gaps will follow this naming convention as bandgap1, bandgap2, . . . Images illustrating the Tauc plot (black curve), regression fit (dashed blue line), and band gap (red line) will be saved to the savepath location specified by the variable in the autoextract(savepath=) definition. In this path, a csv file of extracted band gaps will also be saved with each column header representing one of the input spectra from the (n x m) input pandas array and the rows representing each of the extracted band gap values.
