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

Diffusion01:12

Diffusion

217.3K
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...
217.3K
Diffusion01:21

Diffusion

6.3K
Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
6.3K
Coefficient of Correlation01:12

Coefficient of Correlation

8.5K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
8.5K
Confidence Coefficient01:24

Confidence Coefficient

10.5K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
10.5K
Coefficient of Variation01:10

Coefficient of Variation

8.3K
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...
8.3K
Factors Affecting Activity Coefficient01:17

Factors Affecting Activity Coefficient

1.5K
The extended Debye-Hückel equation indicates that the activity coefficient of an ion in an aqueous solution at 25°C depends on three partially interdependent properties: the ionic strength of the solution, the charge of the ion, and the ion size. 
The activity coefficient value for an ion is close to one when the solution has almost zero ionic strength, i.e., when the solution shows close to ideal behavior. As the ionic strength of the solution increases from 0 to 0.1 mol/L, a...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Information Entropy Metrics to Address the Complexity of Cooperative Gating of Ion Channels.

Entropy (Basel, Switzerland)·2026
Same author

Flux Enhancement in Hybrid Pervaporation Membranes Filled with Mixed Magnetic Chromites ZnCr<sub>2</sub>Se<sub>4</sub>, CdCr<sub>2</sub>Se<sub>4</sub> and CuCr<sub>2</sub>Se<sub>4</sub>.

Molecules (Basel, Switzerland)·2025
Same author

Applying Entropic Measures, Spectral Analysis, and EMD to Quantify Ion Channel Recordings: New Insights into Quercetin and Calcium Activation of BK Channels.

Entropy (Basel, Switzerland)·2025
Same author

Innovative Multilayer Biodegradable Films of Chitosan and PCL Fibers for Food Packaging.

Foods (Basel, Switzerland)·2025
Same author

Improving Antimicrobial Properties of Biopolymer-Based Films in Food Packaging: Key Factors and Their Impact.

International journal of molecular sciences·2024
Same author

Synthesis of Bis(isodecyl Terephthalate) from Waste Poly(ethylene Terephthalate) Catalyzed by Lewis Acid Catalysts.

International journal of molecular sciences·2024

Related Experiment Video

Updated: Jan 26, 2026

Measuring Diffusion Coefficients via Two-photon Fluorescence Recovery After Photobleaching
07:00

Measuring Diffusion Coefficients via Two-photon Fluorescence Recovery After Photobleaching

Published on: February 26, 2010

11.7K

A Simple Methodology to Estimate the Diffusion Coefficient in Pervaporation-Based Purification Experiments.

Gabriela Dudek1, Przemysław Borys2

  • 1Department of Physical Chemistry and Technology of Polymers, Faculty of Chemistry, Silesian University of Technology, Strzody 9, 44-100 Gliwice, Poland. gmdudek@polsl.pl.

Polymers
|April 10, 2019
PubMed
Summary
This summary is machine-generated.

This study presents a new method to estimate diffusion coefficients in hydrophilic membranes for pervaporation. The technique effectively determines the diffusion coefficient from permeation data, crucial for purification processes.

Keywords:
diffusion coefficientethanol/water separationpervaporation

More Related Videos

Experimental Methodology for Estimation of Local Heat Fluxes and Burning Rates in Steady Laminar Boundary Layer Diffusion Flames
10:29

Experimental Methodology for Estimation of Local Heat Fluxes and Burning Rates in Steady Laminar Boundary Layer Diffusion Flames

Published on: June 1, 2016

12.4K
Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy
11:43

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy

Published on: May 10, 2014

11.2K

Related Experiment Videos

Last Updated: Jan 26, 2026

Measuring Diffusion Coefficients via Two-photon Fluorescence Recovery After Photobleaching
07:00

Measuring Diffusion Coefficients via Two-photon Fluorescence Recovery After Photobleaching

Published on: February 26, 2010

11.7K
Experimental Methodology for Estimation of Local Heat Fluxes and Burning Rates in Steady Laminar Boundary Layer Diffusion Flames
10:29

Experimental Methodology for Estimation of Local Heat Fluxes and Burning Rates in Steady Laminar Boundary Layer Diffusion Flames

Published on: June 1, 2016

12.4K
Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy
11:43

Easy Measurement of Diffusion Coefficients of EGFP-tagged Plasma Membrane Proteins Using k-Space Image Correlation Spectroscopy

Published on: May 10, 2014

11.2K

Area of Science:

  • Chemical Engineering
  • Membrane Science
  • Separation Processes

Background:

  • Pervaporation is a key technique for purifying liquid mixtures.
  • Accurate estimation of diffusion coefficients is vital for optimizing pervaporation processes.
  • Existing models may not fully account for membrane pre-conditioning and system geometry.

Purpose of the Study:

  • To develop a procedure for estimating diffusion coefficients in hydrophilic membranes using solution-diffusion models.
  • To incorporate membrane pre-filling and tubing length effects into the model.
  • To validate the procedure using experimental data for water-ethanol separation.

Main Methods:

  • A series solution of the general permeation problem was employed.
  • The model accounts for membrane states (water-filled or feed-filled) before measurement.
  • The influence of tubing length on permeation measurements was analyzed.

Main Results:

  • The diffusion coefficient can be effectively estimated from the time course of transported mass.
  • Analysis of well-defined time lags in the permeation curve provides accurate diffusion coefficient values.
  • The procedure was successfully illustrated using water-ethanol separation data with chitosan membranes.

Conclusions:

  • The proposed procedure offers an effective method for diffusion coefficient estimation in pervaporation.
  • The model's consideration of pre-filling and tubing length enhances accuracy.
  • This work contributes to the optimization of hydrophilic membrane-based purification systems.