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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.