Geometrical vs. statistical models for describing phase transition kinetics in thin films

Literature Information

Publication Date 2004-10-26
DOI 10.1039/B411047A
Impact Factor 3.676
Authors

Ellen H. G. Backus


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Abstract

We present calculations and simulations to investigate different theories describing phase transitions in thin films, with special emphasis on the growth of the new phase after nucleation. In particular, models with geometric and statistical growth rules are compared. It is demonstrated that the commonly employed geometrical approach, which assumes nucleation and subsequent radial growth of the newly formed phase, has distinct limitations for thin film systems. More realistic statistical Monte Carlo simulations that are governed by statistical growth rules, predict that a non-spherical (prolate) shape may develop after nucleation at or near a surface or interface. In addition, the predicted kinetics of the phase transformation is notably different for the geometric vs. the statistical model, for similar parameters. The simulation results are compared to recent experiments on the crystallization of thin amorphous solid water films.

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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
Self-citation Rate: 10.3%
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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