Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

21.2K
Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
21.2K
Scanning Electron Microscopy01:07

Scanning Electron Microscopy

5.6K
A scanning electron microscope (SEM) is used to study the surface features of a sample by using an electron beam that scans the sample surface in a two-dimensional manner. Typically, areas between ~1 centimeter to 5 micrometers in width can be imaged. SEM can be used to image bacteria, viruses, tissues as well as larger samples like insects. Conventional SEM gives a magnification ranging from 20X to 30,000X and spatial resolution of 50 to 100 nanometers.
Fundamental Principles
Accelerated...
5.6K
Diffusion01:12

Diffusion

221.8K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
221.8K
Approximate Integration01:24

Approximate Integration

58
In many practical and theoretical contexts, the exact value of a definite integral may be inaccessible. This limitation typically arises when the antiderivative of a function is either unknown or cannot be expressed in a closed mathematical form. Alternatively, it can occur when a function is defined not by a formula but by a finite set of empirical data points, such as those collected during experiments. In these cases, approximate integration techniques provide a valuable solution.One of the...
58
Linearization and Approximation01:26

Linearization and Approximation

73
Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
73
Coefficient of Variation01:10

Coefficient of Variation

8.7K
The coefficient of variation measures the dispersion of the data points or distribution around the mean. Using the coefficient of variation, we can compare two data series with drastically different means or different units of measurement. The coefficient of variation for a sample and a population is expressed as a percentage of the ratio of standard deviation to the mean.
The coefficient of variation is a practical statistical tool in finance. It allows investors to assess the volatility or...
8.7K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cross-sectional structure evolution of phase-separated spin-coated ethylcellulose/hydroxypropylcellulose films during solvent quenching.

RSC advances·2022
Same author

Visualisation of individual dopants in a conjugated polymer: sub-nanometre 3D spatial distribution and correlation with electrical properties.

Nanoscale·2022
Same author

Convolutional neural networks for segmentation of FIB-SEM nanotomography data from porous polymer films for controlled drug release.

Journal of microscopy·2021
Same author

Structure evolution during phase separation in spin-coated ethylcellulose/hydroxypropylcellulose films.

Soft matter·2021
Same author

Three-dimensional reconstruction of porous polymer films from FIB-SEM nanotomography data using random forests.

Journal of microscopy·2021
Same author

DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks.

Journal of microscopy·2020

Related Experiment Video

Updated: Feb 11, 2026

Near Simultaneous Laser Scanning Confocal and Atomic Force Microscopy Conpokal on Live Cells
09:20

Near Simultaneous Laser Scanning Confocal and Atomic Force Microscopy Conpokal on Live Cells

Published on: August 11, 2020

7.3K

Massively parallel approximate Bayesian computation for estimating nanoparticle diffusion coefficients, sizes and

M Röding1, M Billeter2

  • 1RISE Research Institutes of Sweden, Bioscience and Materials, Göteborg, Sweden.

Journal of Microscopy
|April 21, 2018
PubMed
Summary
This summary is machine-generated.

We developed a parallel computing method to analyze nanoparticle behavior in liquids. This approach accurately estimates diffusion, size, and concentration from microscopy data, with freely available software for CPU and GPU.

Keywords:
ConcentrationConfocal laser scanning microscopyDiffusion coefficientNanoparticlesParticle tracking

More Related Videos

Video-rate Scanning Confocal Microscopy and Microendoscopy
14:10

Video-rate Scanning Confocal Microscopy and Microendoscopy

Published on: October 20, 2011

28.6K
Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
12:15

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Published on: April 9, 2019

9.2K

Related Experiment Videos

Last Updated: Feb 11, 2026

Near Simultaneous Laser Scanning Confocal and Atomic Force Microscopy Conpokal on Live Cells
09:20

Near Simultaneous Laser Scanning Confocal and Atomic Force Microscopy Conpokal on Live Cells

Published on: August 11, 2020

7.3K
Video-rate Scanning Confocal Microscopy and Microendoscopy
14:10

Video-rate Scanning Confocal Microscopy and Microendoscopy

Published on: October 20, 2011

28.6K
Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
12:15

Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy

Published on: April 9, 2019

9.2K

Area of Science:

  • Physical Chemistry
  • Materials Science
  • Nanotechnology

Background:

  • Accurate characterization of nanoparticle dynamics is crucial for understanding their behavior in liquid suspensions.
  • Traditional methods for estimating diffusion coefficients, sizes, and concentrations can be computationally intensive and limited in scope.
  • Confocal laser scanning microscopy combined with particle tracking offers a powerful approach for observing nanoparticle motion.

Purpose of the Study:

  • To develop and present a novel, massively parallel Population Monte Carlo Approximate Bayesian Computation (PMC-ABC) method for nanoparticle characterization.
  • To estimate key nanoparticle properties including diffusion coefficients, sizes, and concentrations in liquid suspension.
  • To provide accessible analysis software for both central processing unit (CPU) and graphics processing unit (GPU) users.

Main Methods:

  • Implementation of a massively parallel PMC-ABC algorithm.
  • Utilizing confocal laser scanning microscopy and particle tracking data.
  • Modeling the joint probability distribution of diffusion coefficients and residence time within a detection region.

Main Results:

  • Successful estimation of diffusion coefficients, sizes, and concentrations for nanoparticles.
  • Application of the method to characterize both monodisperse and bidisperse samples of fluorescent polystyrene beads.
  • Demonstration of the method's effectiveness using simulated and experimental data.

Conclusions:

  • The developed PMC-ABC method provides an efficient and accurate approach for nanoparticle characterization.
  • Freely available CPU and GPU software enhances accessibility for researchers in nanoparticle science.
  • The method is robust and applicable to complex nanoparticle systems.