Decoding the conductance of disordered nanostructures: a quantum inverse problem.

Area of Science:

• Condensed Matter Physics
• Materials Science
• Nanotechnology

Background:

• Characterizing nanoscale devices with impurities is crucial for sensing applications.
• Extracting impurity information solely from conductance spectra presents significant challenges.
• Advanced inversion techniques struggle with complex impurity distributions.

Purpose of the Study:

• To extend a methodology for extracting nanoscale device composition from conductance spectra.
• To develop an inversion tool capable of decoding conductance spectra to identify impurity concentration and nature.
• To demonstrate the method's applicability across various material systems and disorder distributions.

Main Methods:

• Applying a proposed inversion methodology to analyze conductance spectra.
• Testing the method on simple one-dimensional systems (electron gas, linear atomic chains).
• Validating the tool's robustness on complex materials (h-BN, graphene nanoribbons, carbon nanotubes).

Main Results:

• The inversion tool successfully decodes conductance spectra to determine impurity concentration and nature.
• The methodology is proven effective for both simple and complex electronic structures.
• The tool can probe disorder distribution within sublattice structures of materials.

Conclusions:

• The developed inversion tool provides a powerful method for characterizing nanoscale disorder from conductance spectra.
• This approach enhances understanding of material properties influenced by impurities.
• The technique offers a pathway to better design and control of nanoscale sensing devices.