A Python implementation of the effective mass model treats experimental transport properties of semiconductors. This implementation uses the notation of S. Kang and G. J. Snyder Charge-transport model for conducting polymers (2017) and can treat both organic and inorganic semiconductors. When non-polar phonon scattering limits conduction, s=1 (linear).
Basic assumptions of this model:
- Carriers involved in conduction (electrons/holes) are described by the Fermi-Dirac distribution function.
- There is a transport edge (a band edge), above which carriers contribute to conduction.
- A powerlaw describes the diffusivity of particles v.s. particle energy (linear for inorganic semiconductors).
Summary reports are easily generated by saving data in a structured way. Each instance of experimental data should be labeled by a unique identifier for the sample and the measurement type. The contents of each data file should have temperature (K) in the first column and the property (V/K or S/m) in the second column. Generating reports for a series of samples (same compound with different doping) is as easy as placing a data series in its own directory.
Example directory structure: "seebeck" and "conductivity" enforced
DataSeries
sample1_seebeck.csv
sample1_conductivity.csv
sample2_seebeck.csv
sample2_conductivity.csv
Example file structure: lines with "#" are ignored
#K , S/m
300, 1000
325, 950
350, 925
..., ...