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March 29, 2026The Journal of Physical Chemistry Letters0 citations

Universality Encoded in Fano Lineshapes: A Decoupled Phonon Framework for Rapid Raman Parameter Extraction

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DDDejan M. DjokićDSDimitrije StepanenkoMOM. Opačić

Key Points

  • The study aims to simplify the extraction of microscopic parameters from Fano resonances in Raman spectra by utilizing a new geometric framework.
  • Developed an angular parametrization of renormalized Fano lineshapes.
  • Analyzed extremal separations and nodal points related to physical variables.
  • Interpreted the Fano profile using a many-body framework focusing on phonon dynamics.
  • Compared findings with existing Raman data on doped silicon.
  • Revealed intrinsic symmetries and spectral invariants from Fano lineshapes.
  • Achieved a significant reduction in computational complexity for parameter extraction.
  • Enabled efficient characterization of materials without intense computational resources.

Abstract

Fano resonances frequently emerge in Raman spectra of metals and doped semiconductors; however, extracting the relevant microscopic parameters typically requires nonlinear fitting procedures that obscure physical insight and increase the computational cost. Here, we demonstrate that a universal geometric feature is encoded in renormalized Fano lineshapes and can be revealed through an angular parametrization that exposes intrinsic symmetries and spectral invariants. Within this representation, extremal separations and nodal points have analytically been related to key underlying variables with a direct physical significance. We have successfully interpreted the dimensionless Fano profile within a many-body framework in terms of isolated dressed single-phonon dynamics associated with corrected Raman vertices, effectively decoupled from purely electronic contributions. A comparison with reported Raman data on doped silicon demonstrates a substantial reduction in computational complexity, thereby enabling efficient and physically transparent material characterization without reliance on resource-intensive modeling platforms.

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Cite This Study

Djokić et al. (2026) studied this question.

synapsesocial.com/papers/69c8c2b8de0f0f753b39d18bhttps://doi.org/10.1021/acs.jpclett.6c00592
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