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Polymers

Polymers

The word polymer is derived from the Greek words “poly” which means “many” and “mer” which means “parts”. Polymers are long chains of molecules composed of repeating units of smaller molecules, known as monomers. They either occur naturally, such as DNA and proteins, or can be constructed synthetically, like plastics. They have varied structural characteristics, such as linear chains, branched chains, or complex networks, that contribute to the properties that they exhibit. Additionally,...

Polymers

Polymers

The word polymer is derived from the Greek words “poly” which means “many” and “mer” which means “parts”. Polymers are long chains of molecules composed of repeating units of smaller molecules, known as monomers. They either occur naturally, such as DNA and proteins, or can be constructed synthetically, like plastics. They have varied structural characteristics, such as linear chains, branched chains, or complex networks, that contribute to the properties that they exhibit. Additionally,...

Polymers

Polymers

The word polymer is derived from the Greek words “poly” which means “many” and “mer” which means “parts”. Polymers are long chains of molecules composed of repeating units of smaller molecules, known as monomers. They either occur naturally, such as DNA and proteins, or can be constructed synthetically, like plastics. They have varied structural characteristics, such as linear chains, branched chains, or complex networks, that contribute to the properties that they exhibit. Additionally,...

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Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

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Published on: April 15, 2015

Neuromorphic Computing Primitives Using Polymer-Networked Nanoparticles.

Yinong Zhao¹, Xingfei Wei², Rigoberto Hernandez^2,1,3

¹Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States.

The Journal of Physical Chemistry. C, Nanomaterials and Interfaces

|December 18, 2024

Summary

This summary is machine-generated.

Engineered nanoparticle networks show potential for brain-like computing. Controlling polymer attachment and block copolymer properties enables complex networks with multiple states for neuromorphic applications.

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Area of Science:

Materials Science
Computational Chemistry
Nanotechnology

Background:

Nanoparticle networks are promising for brain-like computing.
Understanding polymer attachment dynamics is crucial for network design.
The complexity of engineered nanoparticle networks (ENPNs) is limited by current methods.

Purpose of the Study:

To investigate polyelectrolyte attachment dynamics onto gold nanoparticles (AuNPs).
To explore the potential of engineered nanoparticle networks for neuromorphic computing.
To establish rules for creating complex and functional nanoparticle networks.

Main Methods:

Utilized a two-bead-monomer dissipative particle dynamics (TBM-DPD) model.
Simulated polyelectrolyte (poly(allylamine hydrochloride) - PAH) attachment to AuNPs.
Employed engineered block copolymers with conductive middle blocks.

Main Results:

Identified heterogeneity in polymer coverage on AuNPs.
Established commensurability rules between AuNPs and PAHs, defining AuNP valency.
Demonstrated that engineered block copolymers can mediate AuNP spacing.

Conclusions:

Single polyelectrolyte homopolymers limit ENPN complexity.
Controlling conductive block length and network topology allows for multiple network states.
These findings pave the way for ENPNs in primitive neuromorphic computing.