Extending the analysis of EELS spectrum-imaging data, from elemental to bond mapping in complex nanostructures

Area of Science:

• Materials Science
• Spectroscopy
• Chemistry

Background:

• Elemental electron energy-loss spectroscopy (EELS) commonly uses least squares fitting for analysis.
• Existing methods often assume stable core-loss signal shapes.
• Complex materials like boron nitride present challenges for traditional EELS analysis.

Purpose of the Study:

• To explore the application of least squares fitting for identifying variations in fine structures of core-loss signals.
• To map different boron bonding types within complex boron nitride samples.
• To refine EELS analysis for advanced materials characterization.

Main Methods:

• Adaptation of multiple least squares fitting for EELS data.
• Application to spectrum-imaging mode data acquisition.
• Analysis of fine structure variations in core-loss edges.

Main Results:

• Successfully identified variations in the fine structures of core-loss signals in boron nitride.
• Enabled mapping of diverse boron bonding types within the samples.
• Demonstrated an improved procedure for detailed EELS analysis.

Conclusions:

• Least squares fitting can be effectively adapted to analyze fine structure variations in EELS.
• This method provides a powerful tool for mapping bonding types in complex materials.
• The enhanced procedure offers improved capabilities for materials characterization using EELS.