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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Chemical Gardens as Flow-through Reactors Simulating Natural Hydrothermal Systems
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Pattern selection by material aging: Modeling chemical gardens in two and three dimensions.

Bruno C Batista1, Amari Z Morris1, Oliver Steinbock1

  • 1Department of Chemistry and Biochemistry, Florida State University, Tallahassee, FL 32306-4390.

Proceedings of the National Academy of Sciences of the United States of America
|July 3, 2023
PubMed
Summary

This study models chemical gardens, complex structures formed by precipitation. A cellular automaton model reveals how reactant injection and age bias in precipitate replacement create self-organizing filaments and branched shapes.

Keywords:
lattice modelpattern formationprecipitatesself-organization

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Area of Science:

  • Complex Systems
  • Materials Science
  • Chemical Engineering

Background:

  • Chemical gardens are intricate macroscopic structures formed via precipitation reactions.
  • Their thin, self-healing walls compartmentalize internal solutions and adapt to volume changes.
  • Spatial confinement influences pattern formation, leading to phenomena like self-extending filaments.

Purpose of the Study:

  • To develop a computational model simulating the self-organization of chemical garden structures.
  • To investigate the role of reactant injection and precipitate age bias in pattern formation.
  • To explore how buoyancy effects influence the morphology of chemical gardens in 2D and 3D.

Main Methods:

  • A cellular automaton model was employed, with lattice sites representing reactants or precipitate.
  • Simulations incorporated reactant injection, leading to expanding precipitate fronts.
  • An age bias favoring fresh precipitate replacement and a buoyancy effect were included.

Main Results:

  • The model successfully generated expanding, near-circular precipitate fronts driven by reactant injection.
  • Inclusion of an age bias resulted in the formation of thin-walled, self-extending filaments.
  • Incorporating buoyancy effects captured diverse branched and unbranched chemical garden morphologies.

Conclusions:

  • The cellular automaton model effectively replicates key features of chemical garden self-organization.
  • Temporal dynamics, specifically the age bias in precipitate replacement, are crucial for filament formation.
  • The model highlights the importance of material properties and physical forces in dictating complex structure formation.