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Related Concept Videos

Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

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Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
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Crystal Growth: Principles of Crystallization01:25

Crystal Growth: Principles of Crystallization

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Crystallization is a phase transformation process in which crystals are precipitated from a supersaturated solution or formed from other sources. During crystallization, atoms or molecules arrange themselves into a well-defined, rigid crystal lattice to minimize energy.
Initiating crystallization involves manipulating the concentration of the solute and the temperature of the solution. Since crystal growth occurs when the ratio of concentration and solubility of the solute in the solvent...
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Recrystallization: Solid–Solution Equilibria01:10

Recrystallization: Solid–Solution Equilibria

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Recrystallization is a purification technique used to separate impurities from solid compounds. In this technique, no chemical reactions occur. Instead, it exploits physical properties only, specifically, the solubility differences between the desired compound and impurities, either at a single temperature or at different temperatures, and under other selected conditions. The solid-solution equilibrium (solubility equilibrium) of each component in the solution represents a binary phase...
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Ziegler–Natta Chain-Growth Polymerization: Overview01:17

Ziegler–Natta Chain-Growth Polymerization: Overview

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Ziegler–Natta polymerization is another form of addition or chain‐growth polymerization used for synthesizing linear polymers over branched polymers. The catalyst used for polymerization is the Ziegler–Natta catalyst, named after Karl Ziegler and Giulio Natta, who developed it in 1953. This catalyst is an organometallic complex of titanium tetrachloride and triethyl aluminum, with the active form of the catalyst being an alkyl titanium compound. Using the Ziegler–Natta...
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Molecular Weight of Step-Growth Polymers01:08

Molecular Weight of Step-Growth Polymers

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Step growth polymerization involves bi or multifunctional monomers. Bifunctional monomers react to form linear step growth polymers, whereas multifunctional monomers react to form non-linear or branched polymers.
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...
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Generating Cocrystal Polymorphs with Information Entropy Driven by Molecular Dynamics-Based Enhanced Sampling.

Hongxing Song1, Leslie Vogt-Maranto1, Ren Wiscons2

  • 1Department of Chemistry, New York University, New York, New York 10003, United States.

The Journal of Physical Chemistry Letters
|November 3, 2020
PubMed
Summary
This summary is machine-generated.

Predicting organic cocrystal structures is complex. Using Shannon entropy with enhanced molecular dynamics efficiently maps potential polymorphs, aiding crystal structure searching.

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Area of Science:

  • Crystallography
  • Computational Chemistry
  • Materials Science

Background:

  • Predicting organic molecular cocrystal structures is challenging due to vast intermolecular orientation possibilities.
  • Exploring potential polymorphs requires efficient methods to navigate complex energy landscapes.

Purpose of the Study:

  • To develop an efficient method for predicting organic molecular cocrystal structures.
  • To utilize Shannon information entropy as a driving force for crystal structure searching.

Main Methods:

  • Constructed Shannon information entropy from an intermolecular orientational spatial distribution function.
  • Employed enhanced molecular dynamics, specifically driven adiabatic free energy dynamics.
  • Generated collective variables from Shannon entropy to differentiate polymorphs.

Main Results:

  • Successfully mapped a landscape of putative polymorphs for a resorcinol-urea cocrystal.
  • Demonstrated the transformation of the stable phase into alternate polymorphs using entropy-driven enhanced sampling.
  • Confirmed the stability of a novel structure at pressures above 1 GPa using density functional theory.

Conclusions:

  • Shannon entropy is an effective tool for driving enhanced molecular dynamics in crystal structure prediction.
  • Enhanced sampling methods, guided by entropy, are crucial for comprehensive crystal structure searching.
  • This approach is particularly valuable for systems with multiple independent molecules in the crystal lattice.