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Related Concept Videos

Ionic Radii03:10

Ionic Radii

33.4K
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...
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Ionic Bonds00:42

Ionic Bonds

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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...
129.6K
Molecular and Ionic Solids02:54

Molecular and Ionic Solids

20.0K
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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Solubility of Ionic Compounds02:55

Solubility of Ionic Compounds

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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.
68.1K
Ionic Crystal Structures02:42

Ionic Crystal Structures

16.9K
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...
16.9K
Ionic Compounds: Formulas and Nomenclature03:34

Ionic Compounds: Formulas and Nomenclature

86.4K
An element composed of atoms that readily lose electrons (a metal) can react with an element composed of atoms that readily gain electrons (a nonmetal) to produce ions through complete electron transfer. The compound formed by this transfer is stabilized by the electrostatic attractions (ionic bonds) between the oppositely charged ions.
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A Fabrication and Measurement Method for a Flexible Ferroelectric Element Based on Van Der Waals Heteroepitaxy
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A Monolithic Ferroelectric-Ionic Duality for Stochastic-Neuromorphic Core Integration.

Changhyeon Han1, Ryun-Han Koo2, Minsuk Song1

  • 1Department of Electrical Engineering, Hanyang University, Seoul, Republic of Korea.

Advanced Materials (Deerfield Beach, Fla.)
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Summary
This summary is machine-generated.

Researchers developed a novel ferroelectric-ionic device using engineered hafnia to unify memory and randomness for artificial intelligence. This breakthrough enables efficient learning under uncertainty in compact hardware systems.

Keywords:
hafnia ferroelectricslow‐frequency noiseneuromorphic computingoxygen vacancystochastic computing

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Area of Science:

  • Materials Science
  • Artificial Intelligence Hardware
  • Device Physics

Background:

  • Learning under uncertainty is critical for data-intensive AI.
  • Integrating stable memory and tunable stochasticity in miniaturized hardware is challenging.
  • Existing hardware struggles with the conflict between memory precision and stochastic variability.

Purpose of the Study:

  • To develop a single device that integrates stochastic encoding and synaptic memory.
  • To overcome the challenges of combining precise memory retention and controllable stochastic variability.
  • To create hardware for AI systems that require unified deterministic and probabilistic functions.

Main Methods:

  • Demonstrated a hafnia-based ferroelectric-ionic duality.
  • Engineered ferroelectric interfaces to repurpose oxygen vacancies as functional ionic components.
  • Utilized dual-mode switching for voltage-tunable stochasticity and enhanced synaptic behavior.

Main Results:

  • Achieved integration of stochastic encoding and synaptic memory within a single device.
  • Demonstrated voltage-tunable stochasticity and enhanced synaptic behavior.
  • Confirmed complementary metal-oxide-semiconductor (CMOS) compatibility and scalability for very-large scale integration (VLSI).

Conclusions:

  • Established a novel device paradigm unifying memory, randomness, and learning capabilities.
  • Repurposed oxygen vacancies in hafnia to create a functional ferroelectric-ionic dual-mode device.
  • Enabled efficient learning under uncertainty in AI applications through a scalable hardware platform.