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Molecular Models

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

Noncovalent Attractions in Biomolecules

Noncovalent Attractions in Biomolecules

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.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
Hydrogen bonding results from the electrostatic attraction of a hydrogen atom covalently bonded to a strong-electronegative atom like oxygen,...

Two-Dimensional (2D) NMR: Overview

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The 1D NMR spectrum of large and complex molecules like natural products has complicated splitting patterns and overlapping signals, which can be easily interpreted using 2-dimensional (2D) NMR. Unlike 1D NMR, 2D NMR has two frequency axes that provide the coupling information between the nucleus A and nucleus B in a molecule. The process from which 2D spectra are obtained has four steps.
The first step is the preparation period, during which nucleus A is excited with a radiofrequency pulse.

Chemical Synapses

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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
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Noncovalent Attractions in Biomolecules

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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.
Four types of noncovalent interactions are hydrogen bonds, van der Waals forces, ionic bonds, and hydrophobic interactions.
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Chemical Synapses

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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
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Related Experiment Video

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Scalable Quantum Integrated Circuits on Superconducting Two-Dimensional Electron Gas Platform

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Published on: August 2, 2019

2D Atomic-Molecular Heterojunctions toward Brainoid Applications.

Fan Shu¹, Weilin Chen¹, Yu Chen²

¹Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.

Macromolecular Rapid Communications

|August 5, 2024

Summary

This summary is machine-generated.

Novel 2D atomic-molecular heterojunctions (2DAMH) offer stable, tunable brain-like computing. These advanced materials show promise for energy-efficient, high-speed brain-inspired intelligent systems.

Keywords:

2D materials artificial synapses covalent modification heterojunctions memristors

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Area of Science:

Materials Science
Neuroscience
Computer Engineering

Background:

Brainoid computing aims to mimic the brain's efficiency, overcoming limitations of traditional von Neumann architecture.
Current 2D material heterostructures face challenges in uniformity, fabrication, and adhesion, hindering brainoid device development.

Purpose of the Study:

To review advancements in 2D atomic-molecular heterojunctions (2DAMH) for brain-inspired computing.
To explore the electronic properties and synaptic potential of polymer-2D material 2DAMH in memristive devices.

Main Methods:

Covalent functionalization of 2D materials with functional molecules to create 2DAMH.
Analysis of electronic attributes and memristive device applications of these novel heterojunctions.

Main Results:

2DAMH provide enhanced stability and tunable functionalities compared to conventional heterostructures.
Demonstrated potential of 2DAMH in replicating biological synapse functionality within memristive devices.

Conclusions:

2DAMH represent a significant advancement for stable and functional brain-like devices.
Despite challenges in precision and scalability, 2DAMH hold vast potential for energy-efficient, brain-inspired intelligent systems.