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Colloids

Colloids

Children at play often make suspensions such as mixtures of mud and water, flour and water, or a suspension of solid pigments in water known as tempera paint. These suspensions are heterogeneous mixtures composed of relatively large particles that are visible to the naked eye or can be seen with a magnifying glass. They are cloudy, and the suspended particles settle out after mixing. On the other hand, a solution is a homogeneous mixture in which no settling occurs and in which the dissolved...

Colloidal precipitates

Colloidal precipitates

The high insolubility of some precipitates can result in an unfavorable relative supersaturation. This can lead to colloidal particles with a large surface-to-mass ratio, where adsorption is promoted. For instance, in the precipitation of silver chloride, silver ions are adsorbed on the surface of the colloidal particles, forming a primary layer. This layer attracts ions of opposite charge (such as nitrate ions), forming a diffuse secondary layer of adsorbed ions. This electric double layer...

Colloids and Suspensions

Colloids and Suspensions

Children at play often make suspensions such as mixtures of mud and water, flour and water, or a suspension of solid pigments in water known as tempera paint. These suspensions are heterogeneous mixtures composed of relatively large particles visible to the naked eye or seen with a magnifying glass. They are cloudy, and the suspended particles settle out after mixing. The suspended particles in a suspension settle out after some time of mixing. The separation of particles from a suspension is...

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Updated: Jan 5, 2026

Synthesis and Characterization of Supramolecular Colloids

Synthesis and Characterization of Supramolecular Colloids

Published on: April 22, 2016

Unsupervised learning for local structure detection in colloidal systems.

Emanuele Boattini¹, Marjolein Dijkstra¹, Laura Filion¹

¹Soft Condensed Matter, Debye Institute for Nanomaterials Science, Utrecht University, Utrecht, The Netherlands.

The Journal of Chemical Physics

|October 24, 2019

Summary

This summary is machine-generated.

We developed a fast unsupervised learning algorithm using bond-orientational order parameters and neural networks to detect local environments in colloidal systems, matching standard methods with high precision.

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Area of Science:

Colloidal science
Computational physics
Materials science

Background:

Identifying local particle environments is crucial for understanding colloidal system behavior.
Traditional methods often require manual tuning and system-specific parameters.

Purpose of the Study:

To introduce a simple, fast, and unsupervised algorithm for detecting local environments in colloidal systems.
To autonomously group similar local environments without prior system knowledge.

Main Methods:

Utilized standard bond-orientational order parameters to characterize local particle environments.
Employed a neural network-based autoencoder combined with Gaussian mixture models for environment classification.
Tested the algorithm on diverse simulated colloidal systems, including mixtures and anisotropic particles.

Main Results:

Successfully identified relevant local environments across various colloidal systems with high precision.
Demonstrated effectiveness in analyzing self-assembled structures like fluid-crystal interfaces and grain boundaries.
The autoencoder identified key bond orientational order parameters relevant to system analysis.

Conclusions:

The developed unsupervised learning algorithm offers a robust and efficient alternative for local environment detection in colloidal systems.
This method simplifies analysis and reduces the need for system-specific parameterization.
Provides insights into the most influential order parameters for characterizing colloidal structures.