Quantitative 3D Real-Space Analysis of Photonic Supraparticles

Area of Science:

• Colloidal science
• Materials science
• Soft matter physics

Background:

• Supraparticles (SPs) are assemblies of smaller particles with unique collective properties.
• Self-assembly (SA) in spherical confinement can form SPs, including those with icosahedral symmetry, not found in bulk systems.
• Previous experimental analysis of SP structures, particularly icosahedral ones, is limited, hindering statistically relevant conclusions.

Purpose of the Study:

• To develop and apply an advanced 3D microscopy technique for detailed structural analysis of supraparticles.
• To overcome the limitations of existing methods like scanning electron microscopy (SEM) for quantitative SP analysis.
• To gain experimental insights into the self-assembly pathways and defect formation mechanisms in SPs.

Main Methods:

• Utilized a combination of 3D confocal and stimulated emission depletion (STED) microscopy.
• Employed well-index-matched fluorescent core-shell colloidal silica spheres (442-478 nm diameter, <1% polydispersity).
• Applied unsupervised machine learning based on bond order parameters and local centrosymmetry deviations for structure classification.

Main Results:

• Obtained full 3D real-space datasets of multiple SPs within hours.
• Successfully classified SP structures, correcting misidentifications common with SEM.
• Provided quantitative, real-space insights into SA pathways and defect formation, especially for icosahedral SPs.

Conclusions:

• The new 3D microscopy approach enables accurate, statistically relevant analysis of SP structures.
• This technique offers a significant advancement over SEM for understanding SP formation and properties.
• The findings contribute to a deeper understanding of self-assembly processes and defect dynamics in colloidal systems.