Tools for the quantification of the dynamic assembly of colloidal chains of ellipsoidal particles

Veronica Grebe; Mingzhu Liu; Marcus Weck

doi:10.1016/j.colcom.2022.100661

Tools for the quantification of the dynamic assembly of colloidal chains of ellipsoidal particles

Veronica Grebe, Mingzhu Liu, Marcus Weck

Chemistry

Research output: Contribution to journal › Article › peer-review

Abstract

The assemblies of colloidal particles and their characterization are becoming more complex as new particle shapes, materials, and assembly methods are being introduced. Consequentially, there is a need for tools that can analyze videos and extract quantitative data from qualitative media to elucidate the dynamic processes of assembly formations. This contribution presents an algorithm that analyzes videos of the 2D assembly process of ellipsoidal particles. Particles are both classified using abstracted interparticle properties and tracked over time. Our algorithm is customized to quantiatively analyze the dynamic assembly of the ellipsoidal colloidal particles into chains, which break and reform over time. By analyzing the yielded data from our algorithm, we better elucidate the dynamic nature of particle chain formation. We foresee our algorithm being capable of analyzing videos in real time to study the dynamic assembly behavior of diverse matter such as biological samples and nanoparticles of various shapes.

Original language	English (US)
Article number	100661
Journal	Colloids and Interface Science Communications
Volume	50
DOIs	https://doi.org/10.1016/j.colcom.2022.100661
State	Published - Sep 2022

Keywords

Colloidal chains
Dynamic assembly
Image processing
Particle classification
Particle detection
Particle tracking

ASJC Scopus subject areas

Biotechnology
Physical and Theoretical Chemistry
Surfaces, Coatings and Films
Colloid and Surface Chemistry
Materials Chemistry

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10.1016/j.colcom.2022.100661

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title = "Tools for the quantification of the dynamic assembly of colloidal chains of ellipsoidal particles",

abstract = "The assemblies of colloidal particles and their characterization are becoming more complex as new particle shapes, materials, and assembly methods are being introduced. Consequentially, there is a need for tools that can analyze videos and extract quantitative data from qualitative media to elucidate the dynamic processes of assembly formations. This contribution presents an algorithm that analyzes videos of the 2D assembly process of ellipsoidal particles. Particles are both classified using abstracted interparticle properties and tracked over time. Our algorithm is customized to quantiatively analyze the dynamic assembly of the ellipsoidal colloidal particles into chains, which break and reform over time. By analyzing the yielded data from our algorithm, we better elucidate the dynamic nature of particle chain formation. We foresee our algorithm being capable of analyzing videos in real time to study the dynamic assembly behavior of diverse matter such as biological samples and nanoparticles of various shapes.",

keywords = "Colloidal chains, Dynamic assembly, Image processing, Particle classification, Particle detection, Particle tracking",

author = "Veronica Grebe and Mingzhu Liu and Marcus Weck",

note = "Funding Information: Marcus Weck reports financial support was provided by US Department of Energy. Funding Information: This work was supported through funding from Department of Energy under Grant Award No. DE-SC0007991 . The authors acknowledge the use of shared facilities provided through the Materials Research Science and Engineering Center (MRSEC) program of the National Science Foundation under Award Number DMR-1420072. VG acknowledges support from The Margaret Strauss Kramer Fellowship. Publisher Copyright: {\textcopyright} 2022 The Authors",

year = "2022",

month = sep,

doi = "10.1016/j.colcom.2022.100661",

language = "English (US)",

volume = "50",

journal = "Colloids and Interface Science Communications",

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AU - Grebe, Veronica

AU - Liu, Mingzhu

AU - Weck, Marcus

N1 - Funding Information: Marcus Weck reports financial support was provided by US Department of Energy. Funding Information: This work was supported through funding from Department of Energy under Grant Award No. DE-SC0007991 . The authors acknowledge the use of shared facilities provided through the Materials Research Science and Engineering Center (MRSEC) program of the National Science Foundation under Award Number DMR-1420072. VG acknowledges support from The Margaret Strauss Kramer Fellowship. Publisher Copyright: © 2022 The Authors

PY - 2022/9

Y1 - 2022/9

N2 - The assemblies of colloidal particles and their characterization are becoming more complex as new particle shapes, materials, and assembly methods are being introduced. Consequentially, there is a need for tools that can analyze videos and extract quantitative data from qualitative media to elucidate the dynamic processes of assembly formations. This contribution presents an algorithm that analyzes videos of the 2D assembly process of ellipsoidal particles. Particles are both classified using abstracted interparticle properties and tracked over time. Our algorithm is customized to quantiatively analyze the dynamic assembly of the ellipsoidal colloidal particles into chains, which break and reform over time. By analyzing the yielded data from our algorithm, we better elucidate the dynamic nature of particle chain formation. We foresee our algorithm being capable of analyzing videos in real time to study the dynamic assembly behavior of diverse matter such as biological samples and nanoparticles of various shapes.

AB - The assemblies of colloidal particles and their characterization are becoming more complex as new particle shapes, materials, and assembly methods are being introduced. Consequentially, there is a need for tools that can analyze videos and extract quantitative data from qualitative media to elucidate the dynamic processes of assembly formations. This contribution presents an algorithm that analyzes videos of the 2D assembly process of ellipsoidal particles. Particles are both classified using abstracted interparticle properties and tracked over time. Our algorithm is customized to quantiatively analyze the dynamic assembly of the ellipsoidal colloidal particles into chains, which break and reform over time. By analyzing the yielded data from our algorithm, we better elucidate the dynamic nature of particle chain formation. We foresee our algorithm being capable of analyzing videos in real time to study the dynamic assembly behavior of diverse matter such as biological samples and nanoparticles of various shapes.

KW - Colloidal chains

KW - Dynamic assembly

KW - Image processing

KW - Particle classification

KW - Particle detection

KW - Particle tracking

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Tools for the quantification of the dynamic assembly of colloidal chains of ellipsoidal particles

Abstract

Keywords

ASJC Scopus subject areas

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