Related Experiment Video
Updated: May 29, 2026
11:25
Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain
Published on: May 14, 2009
Built-in reduction of statistical fluctuations of partitioning objects
E DelRe1, B Crosignani, P Di Porto
1Department of Electrical and Information Engineering, University of L'Aquila, I-67100 L'Aquila, Italy.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|September 21, 2011
Summary
Thermal equilibrium in microtubule systems is delayed by an exponential increase in time with more particles. This mechanical process may overcome randomness in microscale and nanoscale systems.
Area of Science:
- Physics
- Biophysics
- Nanotechnology
Background:
- Microscale and nanoscale systems are often governed by randomness due to ergodic constraints.
- Understanding the dynamics of particle-filled systems is crucial for manipulating them.
Purpose of the Study:
- To investigate the dynamics of an object partitioning a microtubule filled with particles.
- To determine the time scales for reaching thermal equilibrium in such systems.
- To explore the potential for overcoming inherent randomness in confined systems.
Main Methods:
- Theoretical modeling of particle-object interactions within a microtubule.
- Numerical simulations to analyze system dynamics and equilibrium attainment.
Main Results:
- The time to reach thermal equilibrium increases exponentially with the number of particles.
- A fundamental mechanical process was identified that influences system dynamics.
- The findings suggest a mechanism to bypass ergodic constraints on accessible time scales.
Conclusions:
- The study reveals a significant delay in achieving thermal equilibrium in particle-filled microtubules, dependent on particle count.
- This mechanical process offers a potential pathway to control randomness in microscale and nanoscale systems.
- The research provides insights into the fundamental physics governing confined particle dynamics.
Related Concept Videos
01:14Extraction: Partition and Distribution Coefficients
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
For extracting a solute from an aqueous phase into an organic...
01:23Sampling Plans
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
01:17Probability Histograms
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
01:19Testing a Claim about Standard Deviation
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
01:22Block Diagram Reduction
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
01:12Sampling Distribution
Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...