Researchers discover new mechanism of nanoparticle formation, overcoming century-old classical model
Nanoparticles are vital in technologies like quantum-dot displays, nanocatalysts, and drug delivery, with tunable properties based on size and shape. Yet, the mechanisms of their uniform formation and growth remain unclear.
The classical nucleation theory (CNT), based on the Gibbs-Thomson equation, has guided nanoparticle research for more than a century. However, CNT cannot explain why nanoparticle systems consistently converge to uniform sizes. Recent advances in liquid-phase transmission electron microscopy (TEM) have provided real-time visualization of individual nanoparticles, revealing complex growth dynamics, but a quantitative theoretical framework has remained elusive.
In a breakthrough study, published in Volume 122 of the Proceedings of The National Academy of Sciences (PNAS)on June 10, 2025, ateam led by Professor Jaeyoung Sung from Chung-Ang University in South Korea, in collaboration with Professor Jungwon Park from Seoul National University and Distinguished Professor Taeghwan Hyeon from IBS Center for Nanoparticle Research, developed a new model explaining the multiphasic growth dynamics of nanoparticle ensembles.
Using the liquid-phase TEM, the researchers directly observed the growth trajectories of hundreds of colloidal nanoparticles, a few nanometers in size, in real time. The results revealed that nanoparticles exhibited complex size-dependent growth dynamics with multiple kinetic phases. In each of these kinetic phases, the statistics of nanoparticle size and their size-dependent growth showed distinct variations. They also foundthat nanoparticles undergo coalescence only in a small localized time window. These observations are unexplainable by previously reportedtheories.
Based on these findings, the team developed a new model and theory for nanoparticle growth. This model accounts for six essential characteristics of nanoparticle growth, including nanoparticle's energy, shape, configurational degeneracy,monomer's diffusion coefficient, andthemonomer association rate on the nanoparticle surface. The new theory also accounts for translation, rotation and vibration of a nanoparticle, as well as its interaction with surrounding molecules, factorsthat wereoverlooked in theCNT.
As a result, this new theory provides fresh physical insights into the role of nanoparticle motion and configurational degeneracy on their nucleation and growth, along with an unprecedented quantitative explanation of experimental data for nanoparticle growth dynamics. It also has broad applicability, validated across diverse nanoparticles, including platinum nanoparticles synthesized using different precursors, as well as metal oxide and semiconductor nanoparticles, under varying experimental conditions. Interestingly, this theory predicts that smaller nanoparticles can grow while larger ones dissolve,which is in direct contradiction with the classical Ostwald ripening picture, a remarkable new insight that explains why nanoparticle systems exhibit uniform size distributionsand size focusing dynamics.
“This work makesit possible tounderstand time-dependent size distributions of nanoparticlesand their size-dependent growth dynamicsin terms of fundamental principles in physics and chemistry,” remarks ProfessorSung. “This general theory can also beused tounderstand biological condensate formation and aggregation, which occur in many neurodegenerative diseases, including Alzheimer's disease.”
“However, understanding is one thing, and prediction is another. Together with advances in artificial intelligenceand computational chemistry, our theory offers a new framework for predictable nanoparticle synthesis, representing an exciting new direction for nanoparticle research. Thisknowledgewill prove usefulfor developing tailored nanoparticles for industrial applications like catalyst design, semiconductor manufacturing,and drug delivery,” concludes an optimisticProfessor Sung.
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