Multiscale analysis of triglycerides using X-ray scattering: implementing a shape-dependent model for CNP...

Area of Science:

• Materials Science
• Food Science
• Biophysics

Background:

• Triglyceride self-assembly into crystalline nanoplatelets (CNPs) is crucial for fat functionality.
• Traditional microscopy methods for CNP analysis often require destructive sample preparation.
• X-ray scattering, particularly ultra-small-angle X-ray scattering (USAXS), offers a non-disruptive method for studying CNPs within their native structure.

Purpose of the Study:

• To characterize the hierarchical structures of triglyceride-based fats using a combination of microscopy and X-ray scattering techniques.
• To develop and validate a new shape-dependent model for interpreting USAXS data of CNPs.
• To gain deeper insights into CNP conformation, cross-section, and aggregation behavior.

Main Methods:

• Characterization of four different 30% fat dilutions of stearic acid-based fats in triolein using various purities and preparation protocols.
• Application of diverse microscopy techniques: cryo-SEM, TEM, polarized light microscopy, and phase contrast microscopy.
• Utilized synchrotron-radiation X-ray scattering, including wide-angle (WAXS), small-angle (SAXS), and ultra-small-angle (USAXS) scattering.

Main Results:

• A novel shape-dependent model was proposed to interpret USAXS data, treating CNPs as polydisperse parallelepipeds.
• The model successfully characterized CNP aggregates using fractal dimensionality, providing insights into their cross-section and aggregation.
• Combined microscopy and X-ray scattering data offered a comprehensive understanding of the fat systems' mesoscale structures.

Conclusions:

• The developed model enhances the interpretation of USAXS data for CNPs, overcoming limitations of previous models.
• This research provides significant insights into the conformation and interactions of CNPs within fat systems.
• The findings are expected to facilitate the rational design of novel fat systems with tailored mesoscale properties.