Understanding Particle Diffusion: Analyzing Scatter Plots in MATLAB

In summary, the speaker is asking for help in visualizing the spread of different types of particles in an arena over time. They have collected endpoints but not individual trajectories. They are wondering if there are any tutorials or methods to better understand the level of scattering and are considering the concept of a "correlation time". They also mention the possibility of clumping or natural attractors in the insects' behavior and ask if the diffusion coefficient could be affected by insect concentration. They provide a link to an article about locusts swarming due to a critical density.
  • #1
splinewave
4
0
hi all:
i have some insects/particles that i placed in the center of an arena, and tracked their position after a set period of time. what i can get in MATLAB is a 2d scatter plot (several hundred x,y points). i have several different types of particles that show a larger or smaller degree of spread from the center, and have collected their endpoints (i don't have individual trajectories).

can i do something more interesting to communicate just 'how scattered' these particles are after time t? any good tutorials on this given my data (time 1, distance 0; time 2, distance X)? i think i might not be looking for a "diffusion coefficient", since I'm looking at single particles and not concentrations.

i think i might be looking to understand something called a "correlation time".

Thanks for any ideas
 
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  • #2
When you say 2d scatter plot, do you mean t, r, θ or equivalent (t, x, y)? If you are tracking insects, do you expect any "clumping" meaning natural attractors (social behavior)? Do you have several different simultaneous insect types? Like predator-prey? Measurement of many individual insects is sufficient to get a diffusion coefficient. Sometimes the diffusion coefficient depends on insect concentration (density), like locusts. Locusts begin swarming when a critical density is reached. See

http://www.k8science.org/news/news.cfm?art=2564

Bob S
 

Related to Understanding Particle Diffusion: Analyzing Scatter Plots in MATLAB

1. What is particle diffusion and why is it important?

Particle diffusion is the process by which particles move from an area of higher concentration to an area of lower concentration. This movement is driven by random molecular motion and is important in many fields such as chemistry, biology, and physics. Understanding particle diffusion allows us to better understand and predict the behavior of particles in different environments.

2. How do I analyze scatter plots in MATLAB to understand particle diffusion?

To analyze scatter plots in MATLAB for particle diffusion, you can use the built-in function "scatter" to create a scatter plot of your data. You can then use the "fit" function to fit a regression line to your data and calculate the diffusion coefficient. Additionally, you can use the "histogram" function to visualize the distribution of particles over time.

3. What is the difference between normal and anomalous particle diffusion?

Normal particle diffusion is when particles move in a random, Brownian motion. Anomalous particle diffusion, on the other hand, is when particles exhibit non-random movement due to interactions with other particles or obstacles. Anomalous diffusion can be characterized by a non-linear relationship between the mean squared displacement and time, whereas normal diffusion follows a linear relationship.

4. How can I use MATLAB to simulate particle diffusion?

To simulate particle diffusion in MATLAB, you can use the "random" function to generate random positions for the particles at each time step. You can then use the "scatter" function to plot the positions of the particles and analyze the resulting scatter plot as described above. Additionally, you can use the "ode45" function to solve the diffusion equation and simulate the movement of particles over time.

5. What are some real-world applications of understanding particle diffusion?

Understanding particle diffusion has many real-world applications, such as predicting the spread of pollutants in the environment, modeling the movement of molecules in biological systems, and designing drug delivery systems. It is also important in fields such as materials science, where the diffusion of particles can affect the properties of materials. Overall, understanding particle diffusion allows us to better understand and manipulate the behavior of particles in various environments.

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