Colloids follow the laws of statistical mechanics

Colloids do follow the laws of statistical mechanics, though their behavior can be more complex than simpler systems due to their unique nature. Colloids are mixtures where fine particles are dispersed within a continuous medium, and they are larger than typical molecules but small enough to exhibit Brownian motion. The interaction between these particles and the solvent allows the principles of statistical mechanics to be applied,

with some considerations:

🐳Ref.1

sankey-beta
Ref.,colloids follow the laws of statistical mechanics,2
Ref.,light scattering of colloids,2
Ref.,particle-resolved studies (PRS),5
Ref.,the birth of a crystal nucleus,1
Ref.,dynamical heterogeneity,2
Ref.,active colloidal systems,7
Ref.,imaging of (quasi) 2D colloidal model systems ,1
Ref.,tracking colloidal particles in 3D ,1
Ref.,smaller particles or suspensions at high volume fraction,2

Light scattering of colloids

Particle-resolved studies (PRS)

The birth of a crystal nucleus

Dynamical heterogeneity

Active colloidal systems

Imaging (quasi) 2D colloidal model systems

Tracking colloidal particles in 3D

Smaller particles or suspensions at high volume fraction

</aside>

🐳Ref.2

sankey-beta
Ref.,using smaller colloids,1
Ref.,the glass transition,3
Ref.,colloid tracking,1
Ref.,a boxcar filter followed by grayscale dilation,1
Ref.,novel particle tracking method,2
Ref.,Real-space structure of colloidal hard-sphere glasses,1
Ref.,identify contacts between colloids through ultra-high precision coordinate location,2
Ref.,STimulated Emisison via Depletion,2

Using smaller colloids

The glass transition

Colloid tracking

A boxcar filter followed by grayscale dilation

Novel particle tracking method

Real-space structure of colloidal hard-sphere glasses

Identify contacts between colloids through ultra-high precision coordinate location

STimulated Emisison via Depletion

</aside>

🐳Ref.3

sankey-beta
Ref.,Tracking colloids is often specimen dependant,2
Ref.,obtaining particle coordinates from microscopy images through machine learning,1
Ref.,remote sensing imagery,1
Ref.,molecular imaging,1
Ref.,Transformers for image recognition,1
Ref.,Bounding box detection and instance segmentation,2
Ref.,3d instance segmentation of confocal image,1
Ref.,Unlisted,1
Ref.,dense pose-prediction,1

Tracking colloids is often specimen dependant

Obtaining particle coordinates from microscopy images through machine learning

Remote sensing imagery

Molecular imaging

Transformers for image recognition

Bounding box detection and instance segmentation

3d instance segmentation of confocal image

Dense pose-prediction

</aside>

🐳Ref.4

sankey-beta
Ref.,tracking of submicron-scale particles in 2d and 3d,1
Ref.,Unlisted,7
Ref.,3D U-net,1
Ref.,residual encoder,1
Ref.,Attention U-net,1
Ref.,a least squares Gaussian approximation method,1
Ref.,Perlin noise,1
Ref.,contrast to noise ratio (CNR) measurement,1

Tracking of submicron-scale particles in 2d and 3d

3D U-net

Residual encoder

Attention U-net

Least squares Gaussian approximation method

Perlin noise

Contrast to noise ratio (CNR) measurement

</aside>

🐳Ref.5

sankey-beta
Ref.,heuristics approach,3
Ref.,Unlisted,1
Ref.,ground truth deficient problem,2
Ref.,Radial Distribution Function (RDF),1
Ref.,Topological Cluster Classifcation (TCC) ,2
Ref.,the fluorescent dye at the centre of the particle,2
Ref.,Resnet18 layer encoder,1
Ref.,SWISH function and instance normalisation,2
Ref.,Integrating the information across frames,1
Ref.,clustering approaches,2
Ref.,End-to-End Instance Segmentation,1

Heuristics approach

Ground truth deficient problem

Radial Distribution Function (RDF)

Topological Cluster Classifcation (TCC)

The fluorescent dye at the centre of the particle

Resnet18 layer encoder

SWISH function and instance normalisation

Integrating information across multiple frames

Semantically labeling input

End-to-End Instance Segmentation

</aside>

📃Ref.

R. Evans, D. Frenkel, and M. Dijkstra M., From simple liquids to colloids and soft matter, Physics Today 72, 38 (2019).

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