Growth driven phase transitions in zinc oxide nanoparticles through machine-learning assisted simulations.

Area of Science:

• Materials Science
• Nanotechnology
• Computational Modeling

Background:

• Zinc oxide (ZnO) nanoparticles are technologically important materials with intricate structures.
• Bottom-up synthesis methods, like atom-by-atom deposition, are crucial for nanoparticle formation.
• Understanding the structural evolution of nanoparticles is key to controlling their properties.

Purpose of the Study:

• To investigate the formation mechanism of zinc oxide nanoparticles using atom-by-atom deposition modeling.
• To elucidate the structural phase transitions occurring during nanoparticle synthesis.
• To understand the role of ion redistribution in stabilizing nanoparticle structures.

Main Methods:

• Atom-by-atom deposition modeling was employed to simulate nanoparticle formation.
• Thermodynamic stability of different crystal structures (BCT, WRZ) was analyzed.
• Ion redistribution dynamics during phase transitions were examined.

Main Results:

• The body-centered tetragonal (BCT) structure is stable for small ZnO nanoparticles at equilibrium.
• The deposition process drives a phase transition to the more stable wurtzite (WRZ) phase.
• A specific ion redistribution mechanism compensates polar facets during the transition.

Conclusions:

• The study provides insights into the formation pathways of oxide nanoparticles.
• Understanding these mechanisms aids in designing ZnO nanoparticles with desired structural features.
• The findings contribute to the rational design of nanomaterials for specific applications.