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Van der Waals Interactions01:24

Van der Waals Interactions

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Atoms and molecules interact with each other through intermolecular forces. These electrostatic forces arise from attractive or repulsive interactions between particles with permanent, partial, or temporary charges. The intermolecular forces between neutral atoms and molecules are ion–dipole, dipole–dipole, and dispersion forces, collectively known as van der Waals forces.
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The ideal gas law is an approximation that works well at high temperatures and low pressures. The van der Waals equation of state (named after the Dutch physicist Johannes van der Waals, 1837−1923) improves it by considering two factors.
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Thus far, the ideal gas law, PV = nRT, has been applied to a variety of different types of problems, ranging from reaction stoichiometry and empirical and molecular formula problems to determining the density and molar mass of a gas. However, the behavior of a gas is often non-ideal, meaning that the observed relationships between its pressure, volume, and temperature are not accurately described by the gas laws.
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Noncovalent attractions are associations within and between molecules that influence the shape and structural stability of complexes. These interactions differ from covalent bonding in that they do not involve sharing of electrons.
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Van de Graaff generators (or Van de Graaffs) are devices used to demonstrate high voltage due to static electricity that can also be used for research. Robert Van de Graaff first built one in 1931 (based on original suggestions by Lord Kelvin) for use in nuclear physics research.
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Self-selective van der Waals heterostructures for large scale memory array.

Linfeng Sun1, Yishu Zhang2, Gyeongtak Han1

  • 1Department of Energy Science, Sungkyunkwan University, Suwon, 16419, Korea.

Nature Communications
|July 20, 2019
PubMed
Summary
This summary is machine-generated.

Researchers developed a novel self-selective memory cell using hexagonal boron nitride and graphene. This innovation minimizes sneak currents, enabling efficient terabit-scale 3D memory and neuromorphic computing.

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Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Science

Background:

  • Large-scale crossbar arrays are key for 3D memory and neuromorphic computing.
  • Sneak currents and integration challenges hinder current crossbar memory cell designs.

Purpose of the Study:

  • To introduce a novel self-selective memory cell for large-scale crossbar arrays.
  • To overcome limitations of existing memory cells, such as process integration and destructive read operations.

Main Methods:

  • Fabrication of a vertical heterostructure memory cell using hexagonal boron nitride (hBN) and graphene.
  • Integration of non-volatile and volatile memory operations within hBN layers.
  • Utilizing a graphene layer to control volatile filament diffusion.

Main Results:

  • Demonstrated self-selectivity of 10^10.
  • Achieved an on/off resistance ratio exceeding 10^3.
  • Minimized sneak currents for practical large-scale memory operation.

Conclusions:

  • The developed self-selective memory cell offers a viable solution for terabit-scale, energy-efficient 3D memory.
  • This approach addresses fundamental challenges in crossbar array architectures.
  • Enables practical readout margins crucial for advanced computing systems.