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Multi-scale modeling of early-stage morphology in solution-processed polycrystalline thin films.

David L Patrick1, Cyrus Schaaf, Robell Morehouse

  • 1Department of Chemistry, Western Washington University, 516 High St., Bellingham, WA 98225, USA. david.patrick@wwu.edu.

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Summary

A new model accurately predicts film formation in solution-processed polycrystalline films. It captures nucleation, growth kinetics, and domain structure, matching experimental data for tetracene films.

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Physics

Background:

  • Submonolayer polycrystalline films are crucial in various applications.
  • Understanding nucleation and growth kinetics is key for controlling film properties.
  • Solution-phase processing offers versatile fabrication methods.

Purpose of the Study:

  • To develop a predictive model for early-stage nucleation, growth kinetics, and mesoscale domain structure in solution-processed submonolayer polycrystalline films.
  • To combine stochastic nucleation with deterministic diffusion dynamics.
  • To validate the model against experimental data.

Main Methods:

  • A hybrid model integrating classical nucleation theory with a 2D diffusion equation.
  • Numerical solution of the diffusion equation, treating nuclei as monomer sinks.
  • Comparison with experimental measurements of solution-processed submonolayer tetracene films using vapor-liquid-solid deposition.

Main Results:

  • Excellent agreement between model predictions and experimental observations.
  • Accurate prediction of distinct induction, nucleation, and growth regimes.
  • Precise matching of nucleation onset time, domain density, and domain spacing statistics.
  • Detailed insights into the evolving monomer concentration landscape and derived rates.

Conclusions:

  • The developed model provides a robust framework for understanding and predicting film formation in solution-processed submonolayer polycrystalline films.
  • The model successfully captures key kinetic and structural characteristics, validating its predictive power.
  • This work offers a valuable tool for optimizing fabrication processes and material design.