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Peer-reviewedCC BY 4.0ACS Omega

Cluster Incorporation and Critical Nucleus Size in Crystal Growth: Particle-Resolved Observations Using Colloidal Crystals

  • Hiroyasu Katsuno1
  • Masahide Sato1
  • Yoshihisa Suzuki2
  1. 1Emerging Media Initiative, Kanazawa University , Kakuma-machi, , ,
  2. 2Tokushima University , , 2-1 minamijosanjima , ,
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Abstract

We investigate cluster incorporation and dissolution events in two-dimensional colloidal crystals using particle-resolved imaging and machine learning. Particle positions were identified from microscopy images using an object-detection method. The obtained particle positions were used to track individual particles and identify changes in cluster size. We analyzed the statistics of cluster incorporation and dissolution events as a function of the size change associated with each event. The observed event frequency is well described by the cluster size distribution. These results demonstrate that particle-resolved, machine-learning analysis can provide a quantitative approach to investigating cluster dynamics in colloidal crystallization. Furthermore, the analysis provides an estimate of the kinetic critical nucleus size. Under quasi-equilibrium conditions, we show that the kinetic critical size coincides with the thermodynamic critical size, even in the presence of a cluster-mediated growth pathway. Our experimental estimate of the kinetic critical nucleus size is consistent in order of magnitude with the thermodynamic estimate.

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Cite

BibTeXRIS
BibTeX
@article{katsuno2026cluster,
  title = {{Cluster Incorporation and Critical Nucleus Size in Crystal Growth: Particle-Resolved Observations Using Colloidal Crystals}},
  author = {Hiroyasu Katsuno and Masahide Sato and Yoshihisa Suzuki},
  journal = {ACS Omega},
  year = {2026},
  month = nov,
  doi = {10.1021/acsomega.6c08281},
  url = {https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c08281/5464857/Cluster-Incorporation-and-Critical-Nucleus-Size-in},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Cluster Incorporation and Critical Nucleus Size in Crystal Growth: Particle-Resolved Observations Using Colloidal Crystals
AU  - Hiroyasu Katsuno
AU  - Masahide Sato
AU  - Yoshihisa Suzuki
PY  - 2026
DA  - 2026/11/05
JO  - ACS Omega
DO  - 10.1021/acsomega.6c08281
UR  - https://pubs.acs.org/acsodf/article/doi/10.1021/acsomega.6c08281/5464857/Cluster-Incorporation-and-Critical-Nucleus-Size-in
AB  - We investigate cluster incorporation and dissolution events in two-dimensional colloidal crystals using particle-resolved imaging and machine learning. Particle positions were identified from microscopy images using an object-detection method. The obtained particle positions were used to track individual particles and identify changes in cluster size. We analyzed the statistics of cluster incorporation and dissolution events as a function of the size change associated with each event. The observed event frequency is well described by the cluster size distribution. These results demonstrate that particle-resolved, machine-learning analysis can provide a quantitative approach to investigating cluster dynamics in colloidal crystallization. Furthermore, the analysis provides an estimate of the kinetic critical nucleus size. Under quasi-equilibrium conditions, we show that the kinetic critical size coincides with the thermodynamic critical size, even in the presence of a cluster-mediated growth pathway. Our experimental estimate of the kinetic critical nucleus size is consistent in order of magnitude with the thermodynamic estimate.
SN  - 2470-1343
ER  -