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Plasmonic Sensing: Connecting the Dots.

Hirak Chatterjee1, Dorothy Bardhan1, Sudip Kumar Pal1

  • 1Department of Chemistry, Assam University, Silchar-788011, India.

The Journal of Physical Chemistry Letters
|May 12, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a generalized model for plasmonic sensing, simplifying complex multicomponent systems. The new formalism accurately predicts plasmonic shifts regardless of nanostructure shape or analyte type.

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Area of Science:

  • Nanotechnology
  • Materials Science
  • Analytical Chemistry

Background:

  • Plasmonic sensing relies on shifts in plasmon resonance due to changes in the local dielectric environment.
  • Sensitivity is influenced by nanostructure morphology and material dielectric properties.
  • Analyzing multicomponent systems with inhomogeneous adsorption presents significant challenges.

Purpose of the Study:

  • To develop a generalized model for plasmonic sensing that accounts for complex systems.
  • To provide a unified formalism applicable to various nanostructure morphologies and analytes.
  • To simplify the interpretation of plasmonic sensing data in intricate environments.

Main Methods:

  • A retrospective formalism was developed by integrating electromagnetic scattering theories.
  • Macroscopic mixing rules were applied to model multicomponent dielectric effects.
  • Micromechanics at the metal-analyte interface were incorporated to describe adsorption phenomena.

Main Results:

  • The developed model provides a generalized approach to plasmonic sensing.
  • It successfully accounts for microscopic contributions from individual components to macroscopic plasmonic shifts.
  • The formalism is independent of nanostructure morphology and analyte composition.

Conclusions:

  • The proposed generalized model offers a robust framework for understanding plasmonic sensing in complex media.
  • This approach simplifies the analysis of multicomponent systems, overcoming limitations of previous methods.
  • The formalism enables more accurate prediction and interpretation of plasmonic sensor responses.