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Related Concept Videos

Molecular and Ionic Solids02:54

Molecular and Ionic Solids

20.1K
Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...
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Ionic Bonds00:42

Ionic Bonds

131.1K
Overview
When atoms gain or lose electrons to achieve a more stable electron configuration they form ions. Ionic bonds are electrostatic attractions between ions with opposite charges. Ionic compounds are rigid and brittle when solid and may dissociate into their constituent ions in water. Covalent compounds, by contrast, remain intact unless a chemical reaction breaks them.
Opposing Charges Hold Ions Together in Ionic Compounds
Ionic bonds are reversible electrostatic interactions between ions...
131.1K
Solubility of Ionic Compounds02:55

Solubility of Ionic Compounds

68.3K
Solubility is the measure of the maximum amount of solute that can be dissolved in a given quantity of solvent at a given temperature and pressure. Solubility is usually measured in molarity (M) or moles per liter (mol/L). A compound is termed soluble if it dissolves in water.
68.3K
Ionic Crystal Structures02:42

Ionic Crystal Structures

17.1K
Ionic crystals consist of two or more different kinds of ions that usually have different sizes. The packing of these ions into a crystal structure is more complex than the packing of metal atoms that are the same size.
Most monatomic ions behave as charged spheres, and their attraction for ions of opposite charge is the same in every direction. Consequently, stable structures for ionic compounds result (1) when ions of one charge are surrounded by as many ions as possible of the opposite...
17.1K
Ionic Radii03:10

Ionic Radii

33.6K
Ionic radius is the measure used to describe the size of an ion. A cation always has fewer electrons and the same number of protons as the parent atom; it is smaller than the atom from which it is derived. For example, the covalent radius of an aluminum atom (1s22s22p63s23p1) is 118 pm, whereas the ionic radius of an Al3+ (1s22s22p6) is 68 pm. As electrons are removed from the outer valence shell, the remaining core electrons occupying smaller shells experience a greater effective nuclear...
33.6K
Metallic Solids02:37

Metallic Solids

20.7K
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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A Saline/Bipolar Radiofrequency Energy Device As an Adjunct for Hemostasis in Solid Organ Injury/Trauma
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Ionic decision-maker created as novel, solid-state devices.

Takashi Tsuchiya1, Tohru Tsuruoka1, Song-Ju Kim1

  • 1International Center for Materials Nanoarchitechtonics (WPI-MANA), National Institute for Materials Science, Tsukuba, Ibaraki 305-0044, Japan.

Science Advances
|September 12, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an ionic decision-maker that adapts to dynamic environments, outperforming conventional computers for complex tasks like multiarmed bandit problems (MBPs). This novel approach utilizes ion motion for adaptive decision-making in artificial intelligence applications.

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Area of Science:

  • Computational Science
  • Artificial Intelligence
  • Electrochemistry

Background:

  • Adaptive decision-making is crucial for intelligent systems but challenging with limited computational resources.
  • Conventional computers struggle with dynamic environmental changes impacting decision-making processes.
  • Dynamic competitive multiarmed bandit problems (MBPs) highlight issues like user collisions in communication networks.

Purpose of the Study:

  • To develop an adaptive decision-making system using electrochemical phenomena.
  • To demonstrate the system's capability in solving complex problems like multiarmed bandit problems (MBPs).
  • To address challenges in dynamic competitive environments, such as those in communication networks.

Main Methods:

  • Development of an 'ionic decision-maker' leveraging electrochemical phenomena.
  • Testing the device's performance on multiarmed bandit problems (MBPs).
  • Evaluation of its effectiveness in dynamic competitive multiarmed bandit problems.

Main Results:

  • The ionic decision-maker exhibits excellent dynamic adaptability.
  • Successfully solved multiarmed bandit problems (MBPs), maximizing rewards.
  • Effectively addressed dynamic competitive MBPs, mitigating issues like user collisions.

Conclusions:

  • Ionic decision-makers offer a novel approach to adaptive decision-making.
  • This ion-motion-based technique shows significant potential for computer science and artificial intelligence.
  • The technology could lead to more robust and adaptable intelligent systems.