ramansep

Separate strain from carrier density in Raman maps of 2D materials, using two phonon modes whose lever arms differ.

A single Raman frequency responds to strain and to carrier density at once, so one mode cannot tell the two apart. Two modes with sufficiently different responses form a linear, invertible probe: measure both shift maps, invert a 2x2 matrix per pixel, and obtain a strain map and a carrier-density map with propagated uncertainties.

Status

v0.2.0 (alpha). The inversion core, uncertainty propagation, conditioning diagnostics, a synthetic end-to-end example, and two cited coefficient sets for monolayer MoS2 are implemented and tested. The API may change before v1.0.

Cited coefficient sets (new in v0.2)

Two example sets for monolayer 1H-MoS2 ship with full provenance, and the test-suite reproduces the published separation results of the source paper from them (edge charge of 2.3e12 cm^-2 from a 0.5 cm^-1 A'1 redshift with 2LA(M) unmoved; 0.134% interior tension carrying no significant charge):

• mos2_a1_2la(): the A'1 + 2LA(M) pair of the source paper. Strain lever arms -5.1 and -20.9 cm^-1 per percent of biaxial strain (frozen-phonon DFT, ratio 4.1); A'1 doping coefficient -2.2 cm^-1 per 1e13 cm^-2 of electrons (measured, Chakraborty et al., Phys. Rev. B 85, 161403(R) (2012)). The 2LA(M) doping coefficient is unmeasured and set to zero; the source paper bounds the consequence of that choice at 16% of a recovered edge charge.
• mos2_eprime_a1(): the historically used all-optical E' + A'1 pair, built from the measured biaxial Grueneisen parameters of Michail et al., ACS Appl. Mater. Interfaces 16, 49602 (2024). Both optical modes respond weakly to strain, so for equal shift noise this pair returns about five times the strain uncertainty of the A'1 + 2LA(M) pair; it is included for comparison and for workflows where the overtone is not available.

Each function's docstring states which number comes from which source and the conditions of applicability (1H monolayer, SiO2-supported, 300 K; 532 nm for the disorder-activated calibration). Check that your sample matches before use.

What this package deliberately does not include

No constants beyond the two documented MoS2 sets are shipped. Lever arms depend on material, mode pair, excitation wavelength and substrate; a measurement tool that ships unverified constants propagates wrong results. For any other system you provide a ModeCoefficients from the literature or your own calibration, and the reference field is mandatory so the provenance of every number travels with the analysis.

Install

pip install -e .

Use

import numpy as np
from ramansep import SeparationModel, ModeCoefficients

coeffs = ModeCoefficients(
    mode1_name="A'1", mode2_name="2LA(M)",
    k1_strain=...,   # cm^-1 per unit strain, from your calibration
    k1_density=...,  # cm^-1 per unit carrier density
    k2_strain=...,
    k2_density=...,
    reference="cite the source of these numbers",
)
model = SeparationModel(coeffs)
result = model.invert(dw_mode1, dw_mode2, sigma1=0.1, sigma2=0.1)
# result.strain, result.density, result.strain_sigma, result.density_sigma

SeparationModel warns when the mode pair is poorly conditioned, i.e. when the two modes respond too similarly for a reliable separation.

Method

The method of using the first-order A'1 mode together with the disorder-activated 2LA(M) overtone, whose strain lever arms differ severalfold while only A'1 responds appreciably to carrier density, is developed in:

T. M. Mahim and M. M. Rahman, "Two Raman phonons quantify the fixed edge charge left by patterning monolayer transition metal dichalcogenides" (under review). Code for the paper itself: https://github.com/Tanvir-Mahmud-Mahim/Width-scaling-in-monolayer-semiconductor-nanoribbon-transistors

This package is the general-purpose, material-agnostic inversion tool; the paper repository reproduces the specific published study.

Roadmap

• v0.2 (done): documented example coefficient sets with citations
• v0.3: peak-fitting front end (load spectra, fit the two modes, feed the inversion)
• v0.4: joint Bayesian inversion with spatial priors

License

Apache-2.0