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Related Concept Videos

Structures of Solids02:22

Structures of Solids

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Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
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Metallic Solids02:37

Metallic Solids

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Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
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¹H NMR: Complex Splitting01:13

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A proton M that is coupled to a proton X results in doublet signals for M. However, NMR-active nuclei can be simultaneously coupled to more than one nonequivalent nucleus. When M is coupled to a second proton A, such as in styrene oxide, each peak in the doublet is split into another doublet.
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Molecular Orbital Energy Diagrams
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The molecular orbital theory describes the distribution of electrons in molecules in a manner similar to the distribution of electrons in atomic orbitals. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Mathematically, the linear combination of atomic orbitals (LCAO) generates molecular orbitals. Combinations of in-phase atomic orbital wave functions result in regions with a high probability of electron density, while...
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In the AX proton spin system, proton A can sense the two spin states of a coupled proton X, resulting in a doublet NMR signal with two peaks of equal (1:1) intensity. When proton A is coupled to two equivalent protons (AX2 spin system), the spin states of each X can be aligned with or against the external field, creating three possible scenarios. This results in a 1:2:1  triplet signal, where the central peak corresponds to the chemical shift of A and is twice as large or intense as the...
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Construction and Systematical Symmetric Studies of a Series of Supramolecular Clusters with Binary or Ternary Ammonium Triphenylacetates
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Multi-atom pattern analysis for binary superlattices.

Wesley F Reinhart1, Athanassios Z Panagiotopoulos

  • 1Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA. azp@princeton.edu.

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|September 27, 2017
PubMed
Summary
This summary is machine-generated.

We developed a new method to analyze binary superlattices without templates. This approach uses machine learning and considers more structural information, enabling the characterization of imperfect crystal structures.

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Area of Science:

  • Materials Science
  • Crystallography
  • Machine Learning

Background:

  • Characterizing complex materials like binary superlattices is challenging.
  • Existing methods often require templates or struggle with defects.
  • The Neighborhood Graph Analysis (NGA) method analyzes structures based on the first coordination shell.

Purpose of the Study:

  • To present an extended, template-free method for characterizing binary superlattices.
  • To incorporate information from the second coordination shell for a more comprehensive analysis.
  • To enable the characterization of partial or defective superlattice structures.

Main Methods:

  • Extension of the Neighborhood Graph Analysis (NGA) method.
  • Development of a framework for analyzing multi-atom patterns, including second coordination shell information.
  • Leveraging machine learning techniques for pattern classification and discovery of collective variables.
  • Construction of an efficient metric for quantitative pattern comparison.

Main Results:

  • A unified signature for constituent particles in superlattices.
  • The first algorithm capable of characterizing partial or defective superlattice structures.
  • Discovery of emergent collective variables mapping patterns to a global phase space.
  • Successful classification of configurations from simulated binary colloidal self-assembly.

Conclusions:

  • The developed method offers a robust, template-free approach to superlattice characterization.
  • It advances the analysis of complex, potentially defective crystalline structures.
  • The method provides new insights into self-assembly processes through machine learning-driven phase space mapping.