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Related Concept Videos

General Properties of Solutions02:12

General Properties of Solutions

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Many common substances around us exist as a solution, such as ocean water, air, and gasoline. All solutions are mixtures of substances that are composed of varying amounts of two or more types of atoms or molecules. A mixture with a non-uniform composition is a heterogeneous mixture, whereas a mixture with a uniform composition is a homogeneous mixture. The components that make the homogeneous mixture are evenly spread out and thoroughly mixed. 
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Enthalpy of Solution02:39

Enthalpy of Solution

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There are two criteria that favor, but do not guarantee, the spontaneous formation of a solution:
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Network Covalent Solids02:18

Network Covalent Solids

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
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Solution Formation02:16

Solution Formation

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There is no one solvent that can dissolve every type of solute. Some substances that readily dissolve in a certain solvent might be insoluble in a different solvent. A simple way to predict which substances dissolve in which solvent is the phrase "like dissolves like". This means that polar substances, such as salt and sugar, dissolve in a polar substance like water. In contrast, non-polar substances are more soluble in non-polar solvents such as carbon tetrachloride.
This selective...
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Ideal Solutions02:24

Ideal Solutions

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According to Raoult’s law, the partial vapor pressure of a solvent in a solution is equal or identical to the vapor pressure of the pure solvent multiplied by its mole fraction in the solution. However, Raoult's Law is only valid for ideal solutions. For a solution to be ideal, the solvent-solute interaction must be just as strong as a solvent-solvent or solute-solute interaction. This suggests that both the solute and the solvent would use the same amount of energy to escape to the...
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Related Experiment Video

Updated: Jan 26, 2026

High-resolution Patterning Using Two Modes of Electrohydrodynamic Jet: Drop on Demand and Near-field Electrospinning
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High-resolution Patterning Using Two Modes of Electrohydrodynamic Jet: Drop on Demand and Near-field Electrospinning

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Fully Solution-Processed Transparent Artificial Neural Network Using Drop-On-Demand Electrohydrodynamic Printing.

Jason Yong1,2, You Liang1, Yang Yu1

  • 1Centre for Neural Engineering , The University of Melbourne , Carlton , Victoria 3053 , Australia.

ACS Applied Materials & Interfaces
|April 23, 2019
PubMed
Summary
This summary is machine-generated.

Researchers developed a low-cost, solution-processed transparent synaptic transistor for artificial neural networks (ANNs). This breakthrough enables rapid prototyping of neuromorphic systems, accelerating advancements in AI hardware and intelligent systems.

Keywords:
electrohydrodynamic printingneuromorphic devicesol−gel ITOsol−gel In2O3synaptic plasticitysynaptic transistorsthin film transistor

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Generation and Control of Electrohydrodynamic Flows in Aqueous Electrolyte Solutions
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Generation and Control of Electrohydrodynamic Flows in Aqueous Electrolyte Solutions
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Scalable Solution-processed Fabrication Strategy for High-performance, Flexible, Transparent Electrodes with Embedded Metal Mesh
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Scalable Solution-processed Fabrication Strategy for High-performance, Flexible, Transparent Electrodes with Embedded Metal Mesh

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Area of Science:

  • Neuromorphic Engineering
  • Artificial Intelligence Hardware
  • Solution-Processed Electronics

Background:

  • Artificial neural networks (ANNs), deep learning, and neuromorphic systems are advanced processing architectures for intelligent systems.
  • Current fabrication methods using complementary metal-oxide-semiconductor (CMOS) are costly, hindering broader research and development.
  • Solution-processed electronics present a viable alternative for cost-effective, rapid prototyping of neuromorphic devices.

Purpose of the Study:

  • To propose and demonstrate a novel, wholly solution-based process for fabricating low-cost, transparent synaptic transistors.
  • To enable emulation of biological synaptic functions for constructing artificial neural networks (ANNs).
  • To reduce the financial barriers for scientific community's application of new ideas in neuromorphic computing.

Main Methods:

  • Development of a novel, entirely solution-based fabrication process.
  • Creation of transparent synaptic transistors capable of mimicking biological synaptic behavior.
  • Construction of an artificial neural network (ANN) using these fabricated synaptic transistors.

Main Results:

  • Successful fabrication of low-cost, transparent synaptic transistors.
  • Demonstration of the transistors' ability to emulate biological synaptic functions.
  • Implementation of an ANN capable of encoding and decoding a 100 × 100 pixel image by configuring synaptic weights.

Conclusions:

  • The proposed solution-based process offers a low-cost and efficient method for producing synaptic transistors.
  • This technology facilitates the construction of artificial neural networks for image processing tasks.
  • The findings pave the way for accelerated research and broader accessibility in neuromorphic systems and AI hardware.