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Self-assembling fractal particle networks

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Self-assembling fractal particle networks

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Self-assembling fractal particle networks

Joseph Jun and Alfred Hübler

Center for Complex Systems Research

University of Illinois at Urbana-Champaign

Research supported in part by the National Science Foundation

(PHY-01-40179 and DMS-03725939 ITR)

random initial distribution

compact initial distribution

- Experiment: Agglomeration of conducting particles in an electric field
- 1) We focus on the dynamics of the system
- 2) We explore the topology of the networks using graph theory.
- 3) We explore a variety of initial conditions.
- Results:
- three growth stages: strand formation, boundary connection, and geometric expansion.
- networks are open loop
- statistically robust features: number of termini, number of branch points, resistance, initial condition matters somewhat
- 4) Minimum spanning tree growth model predicts emerging pattern

source electrode

Basic experiment consists of two electrodes, a source electrode and a boundary electrode connected to opposite terminals of a power supply.

battery

boundary electrode

source electrode

Basic experiment consists of two electrodes, a source electrode and a boundary electrode connected to opposite terminals of a power supply.

The boundary electrode lines a dish made of a dielectric material such as glass or acrylic.

The dish contains particles and a dielectric medium (oil)

battery

particle

boundary electrode

oil

20 kV

battery maintains a voltage difference of 20 kV between boundary and source electrodes

source electrode sprays charge over oil surface

20 kV

source electrode sprays charge over oil surface

20 kV

air gap between source electrode and oil surface approx. 5 cm

source electrode sprays charge over oil surface

20 kV

air gap between source electrode and oil surface approx. 5 cm

boundary electrode has a diameter of 12 cm

needle electrode sprays charge over oil surface

20 kV

air gap between needle electrode and oil surface approx. 5 cm

boundary electrode has a diameter of 12 cm

oil height is approximately 3 mm, enough to cover the particles

castor oil is used: high viscosity, low ohmic heating, biodegradable

needle electrode sprays charge over oil surface

20 kV

air gap between needle electrode and oil surface approx. 5 cm

ring electrode forms boundary of dish

has a radius of 12 cm

oil height is approximately 3 mm, enough to cover the particles

castor oil is used: high viscosity, low ohmic heating, biodegradable

particles are non-magnetic stainless steel, diameter D=1.6 mm

particles sit on the bottom of the dish

The growth of the network proceeds in three stages:I) strand formation

II) boundary connection

III) geometric expansion

{

12 cm

stage I:

strand formation

t=0s

10s

5m 13s

14m 7s

{

12 cm

stage I:

strand formation

t=0s

10s

5m 13s

14m 7s

14m 14s

stage II:

boundary connection

{

12 cm

stage I:

strand formation

t=0s

10s

5m 13s

14m 7s

{

14m 14s

14m 41s

15m 28s

stage II:

boundary connection

stage III: geometric expansion

{

12 cm

stage I:

strand formation

t=0s

10s

5m 13s

14m 7s

{

14m 14s

14m 41s

15m 28s

77m 27s

stage II:

boundary connection

stage III: geometric expansion

stationary state

The motion of the lead particles of the six largest strands from a single experiment.

The motion of the lead particles of the six largest strands from a single experiment.

Distance of lead particle of a strand correlates well with number of particles in strand.

N=591

N=784

N=1044

Comparing for different numbers of particles, N. The growth of the strands still tend to correlate for higher N.

Phenomenology: stage II (boundary connection)

Stage II begins when the “winning” strand connects to the boundary. It is brief in duration, and is best characterized by the particles binding to the boundary.

Phenomenology: stage III (geometric expansion)

After all the particles bind together, they will now be like charged and spread apart. This expansion into the available space is the main characteristic of stage III.

Termini = particles touching only one other particle

Branching points = particles touching three or more other particles

Trunks = particles touching only two other particles

Particles become one of the above three types in stage II and III. This occurs over a relatively short period of time.

Graph theory measures for trees

We allow the physical locations of the particles to define the adjacency.

The particles’ positions are digitized.

Each particle is considered a node.

When the distance between two particles is shorter than a cutoff length, they are considered adjacent; we put a link between them.

c=5

c=3

red circles indicate cutoff length

yellow lines indicate distance between centers of particles

Adjacency (number of neighbors)

We can define the average adjacency mathematically as:

ci is the adjacency of particle i

Θ is the Heaviside step function

N is the total number of particles

ri & rj are the positions of particles i & j respectively

rcut is the cutoff length

Ideally, rcut = D, where D is the diameter of a particle. But because of the noise in digitizing the position of the particles, we use a slightly larger value, usually 1.16 ≤ rcut/D ≤ 1.28.

Also ideally, 0 ≤ ci ≤ 6; we impose this by hand in the algorithm.

Adjacency algorithm

photos from experiment

Digitize the positions of each particle from the photos.

Adjacency algorithm

photos from experiment

digitization of positions

Digitize the positions of each particle from the photos.

Run the adjacency algorithm on the list of particle positions.

Adjacency algorithm

photos from experiment

output from algorithm*

Digitize the positions of each particle from the photos.

Run the adjacency algorithm on the list of particle positions.

The algorithm picks up how particles are connected. It identifies holes and grain boundaries.

*Graphs from algorithm were visualized using the Combinatorica package in Mathematica.

rcut = 1.25•D

Visualizing the stages with the adjacency

By looking at <c> as a function of time from the digitization of the photos, we can see this measure naturally segregates the stages.

The average adjacency versus time.

Visualizing the stages with the adjacency

By looking at <c> as a function of time from the digitization of the photos, we can see this measure naturally segregates the stages.

The top dashed lines is an estimate of <c> at t=0 s, given by (circle):

The bottom dotted line is the value of <c> in the steady-state (single strand):

The average adjacency converges rapidly.

Visualizing the stages with the adjacency

By looking at <c> as a function of time from the digitization of the photos, we can see this measure naturally segregates the stages.

The top dashed lines is an estimate of <c> at t=0 s, given by (circle):

The bottom dotted line is the value of <c> in the steady-state (single strand):

The inset shows the same plot for several values of the cutoff length.

The average adjacency converges rapidly.

Visualizing the stages with the adjacency

A look at the differences in stages between different particle numbers.

The average adjacency converges rapidly for all cases.

We conclude that the topology of the network establishes in a relatively short amount of time following stage II.

Graphs show how the number of termini, T, and branching points, B, scale with the total number of particles in the tree.

b3

b4

b6

b5

Subspecies b5 and b6 have never been observed in the experiment.

Percentage of branching points that connect to four other particles as a function of particle number.

Insets on the left show two particles artificially placed into a loop separate from one another.

The graph on the right shows the separation between the two particles as a function of time.

N = 784

T = 166

B = 161

N = 794

T = 170

B = 162

N = 791

T = 170

B = 164

N = 792

T = 159

B = 153

The mass dimension, dm, is defined by Σρ(r) = N ~ rdm

N = 784

T = 166

B = 161

N = 794

T = 170

B = 162

N = 791

T = 170

B = 164

N = 792

T = 159

B = 153

dm ~ 1.74─1.83

dm ~ 1.76─1.82

dm ~ 1.75─1.91

dm ~ 1.79─1.90

The mass dimension, dm, is defined by Σρ(r) = N ~ rdm

Particles arrange themselves similarly in different experiments.

The radial distribution of particles for different times in the experiment. The system entered stage II after t=847s. The fractal dimension decreases from Dm=2 to Dm=1.8.

The radial distribution of termini for similar number of particles and different number of particles.

N = 752

T = 149

B = 146

N = 785

T = 200

B = 187

N = 720

T = 122

B = 106

N = 752

T = 131

B = 85

Initial conditions are a strong constraint on the final form of tree(s).

?

Will this initial configuration produce a spiral?

No, system is unstable to ramified structures.

Eliminating stage I by artificially placing a connecting strand to the boundary; we call these “cheat” experiments.

Eliminating stage I by artificially placing a connecting strand to the boundary; we call these “cheat” experiments.

In this case, there are no losing strands that become long termini at the perimeter.

Consequently, there are more termini and branching points for the cheat cases.

Initial conditions directly preceding stage II are important to determining the relative number of topological species.

We estimate the resistance,

as

K = height of oil conductivity of oil

I0= total current

Growth of trees occurs in three stages.Average adjacency captures the three stages.Topology of network forms relatively quickly.Particles become one of three species.The relative abundance of each species is statistically reproducible.Initial conditions are a strong constraint to formation of networks.

How does the state of the system directly preceding stage II affect the topology of the trees?Can we predict the final tree at this stage?

Since topology of the networks is established relatively quickly, particles connect to one another before they have moved far.

Thus, we attempt to model the connections formed by the system using only the local information for each particle—it’s neighborhood.

Since topology of the networks is established relatively quickly, particles connect to one another before they have moved far.

Thus, we attempt to model the connections formed by the system using only the local information for each particle—it’s neighborhood.

We use data from the experiments: a snapshot of the particles directly preceding stage II.

Since topology of the networks is established relatively quickly, particles connect to one another before they have moved far.

Thus, we attempt to model the connections formed by the system using only the local information for each particle—it’s neighborhood.

We take data from the experiments: a snapshot of the particles directly preceding stage II.

Digitize the positions.

Run the adjacency algorithm to obtain a base neighborhood.

cutoff length = 3 particle diameter

loner

From the base neighborhood, we apply algorithms to generate trees.

In other words, particles can only connect to particles that neighbor it. All the links shown on the left are potential connections for the final tree.

Algorithms run until all available particles connect into a tree.

Some particles will not connect to any others (loners). They commonly appear in experiments.

loner

loner

From the base neighborhood, we apply algorithms to generate trees.

In other words, particles can only connect to particles that neighbor it. All the links shown on the left are potential connections for the final tree.

Algorithms run until all available particles connect into a tree.

Some particles will not connect to any others (loners). They commonly appear in experiments.

loner

We chose three algorithms to implement:1) random (RAN)

2) minimum spanning tree (MST)

3) propagating front model (PFM)

The random algorithm randomly selects a link from the neighborhood graph and determines whether to connect the two particles based on whether the link maintains or violates a tree structure.

In practice, we do this by tracking a “tree label” for each particle.

If two particles in a potential connection have the same label, the connection would produce a cycle, and consequently it is rejected.

particle 1

particle 2

summary of RAN connection rule

movie of random algorithm

Typical connection structure from RAN algorithm.

Distribution of termini produced from 105 permutations run on a single experiment.

Number of termini produced for all experiments, plotted as a function of N.

Uses the identical acceptance/rejection criterion as RAN.

The difference between the two is in how the potential connections are chosen.

MST picks shortest links first (particles that are closest to one another).

Since there are degeneracies in links, we run the algorithm through 105permutations of degenerate ordering.

graph (non-tree)

tree (non-minimal)

tree (minimal)

movie of minimum spanning tree algorithm

Typical connection structure from MST algorithm.

Distribution of termini produced from 105 permutations run on a single experiment.

Number of termini produced for all experiments, plotted as a function of N.

Since only one strand reaches the boundary, the connections should propagate from a particular direction.

To capture this, we propose a model where particles link in order by their geographic location.

Particles can connect only when they are adjacent to a particle that already belongs to the boundary.

Since only one strand reaches the boundary, the connections should propagate from a particular direction.

To capture this, we propose a model where particles link in order by their geographic location.

Particles can connect only when they are adjacent to a particle that already belongs to the boundary.

grey thatched particles are already in the network, connections are shown in black lines

white particles are available to connect

dotted particles are not allowed to connect because they are not yet adjacent to a particle in the network

Since only one strand reaches the boundary, the connections should propagate from a particular direction.

To capture this, we propose a model where particles link in order by their geographic location.

Particles can connect only when they are adjacent to a particle that already belongs to the boundary.

the grey filled particle was randomly chosen

it must now randomly select one of its neighbors that are already in the network

To capture this, we propose a model where particles link in order by their geographic location.

the chosen particle joined the network

any particles adjacent to it are now added to the list of particles that may connect

To capture this, we propose a model where particles link in order by their geographic location.

the process repeats until all particles join the boundary.

movie of propagating front model

Typical connection structure from PFM algorithm.

Distribution of termini produced from 105 permutations run on a single experiment.

Number of termini produced for all experiments, plotted as a function of N.

The number of termini and branching points for all three models and the natural experiments.

MST produces the closest match with experiments.

cheat initial condition (without stage I) and natural initial condition

natural

cheat

<T>-<B>=1+b4+2b5+3b6

<T>-<B> is independent of b3 subspecies

Thus, PFM and RAN are not only generating more branching points, they are generating higher order branching points.

We applied three algorithms to produce trees using local connection rules.We found that the algorithm which uses the interparticle spacing but neglects the direction of connection produces the best match to the experiments.

Hebbian Learning in a three-electrode system

M. Sperl, A Chang, N. Weber, A. Hubler, Hebbian Learning in the Agglomeration of Conducting Particles, Phys.Rev.E. 59, 3165 (1999)

random initial distribution

compact initial distribution

- Experiment:J. Jun, A. Hubler, PNAS 102, 536 (2005)
- Three growth stages: strand formation, boundary connection, and geometric expansion;
- Networks are open loop;
- Statistically robust features: number of termini, number of branch points, resistance, initial condition matters somewhat;
- 4) Minimum spanning tree growth model predicts emerging pattern.
- 5) To do: derive result from first principles,random initial condition, predict other observables, control network growth
- Applications: Hardware implementation of neural nets, nano neural nets with SC particles - M. Sperl, A Chang, N. Weber, A. Hubler, Hebbian Learning in the Agglomeration of Conducting Particles, Phys.Rev.E. 59, 3165 (1999)