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Liquid-cell Transmission Electron Microscopy for Tracking Self-assembly of Nanoparticles

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Phase Separation Kinetics of Dynamically Self-Assembling Nanoparticles with Toggled Interactions.

Zachary M Sherman¹, Helen Rosenthal¹, James W Swan¹

¹Department of Chemical Engineering, Massachusetts Institute of Technology , Cambridge, Massachusetts 02139, United States.

Langmuir : the ACS Journal of Surfaces and Colloids

|September 20, 2017

Summary

Dynamically controlling nanoparticle interactions accelerates the formation of ordered materials, overcoming limitations of traditional self-assembly. This new method enhances both speed and quality in nanomaterial fabrication.

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Area of Science:

Materials Science
Chemical Engineering
Physical Chemistry

Background:

Traditional nanoparticle self-assembly is constrained by thermodynamics, limiting the speed and quality of ordered material formation.
Existing models based on equilibrium thermodynamics cannot explain or predict out-of-equilibrium dynamic self-assembly processes.

Purpose of the Study:

To investigate the kinetics of nanoparticle self-assembly using dynamically controlled interactions.
To develop new theoretical frameworks for understanding and controlling dynamic self-assembly for reliable nanomaterial fabrication.

Main Methods:

Utilized dynamic simulation and theoretical analysis.
Studied monodisperse spherical nanoparticles with cyclically toggled short-ranged attractions.
Analyzed phase separation kinetics, structure quality, and parameter tunability.

Main Results:

Toggled attractions significantly enhanced phase separation rate, structure quality, and tunable parameter range compared to steady attractions.
Demonstrated precise control over kinetic mechanisms and assembly rates via temporal toggling parameters.
Developed phenomenological expressions for predicting self-assembly rates in gel network coarsening and dense phase nucleation/growth.

Conclusions:

Dynamic self-assembly with toggled interactions offers a pathway to overcome thermodynamic constraints in nanoparticle ordering.
The developed models provide predictive capabilities for controlling self-assembly kinetics and achieving high-quality nanomaterials.
This approach is crucial for the reliable fabrication of advanced nanomaterials through controlled dynamic processes.