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

Introduction to Normal Distributions01:29

Introduction to Normal Distributions

90
Standardized test scores often follow a symmetric distribution that can be modeled with the normal distribution, a fundamental concept in statistics. This distribution is particularly useful for interpreting test performance fairly across populations, as it provides a mathematical framework for understanding variability and central tendency in large datasets.From Histogram to Frequency DistributionRaw test data are often displayed using histograms, where the height of each bar represents the...
90
Central Limit Theorem01:14

Central Limit Theorem

20.4K
The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
20.4K
Contaminants and Errors01:16

Contaminants and Errors

405
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
405
Random Error01:04

Random Error

9.9K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
9.9K

You might also read

Related Articles

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

Sort by
Same author

Toward an understanding of the fluorescence intensity changes observed on fluorescein 5'-Isothiocyanate-Na(+),K (+)-ATPase.

Journal of fluorescence·2013
Same author

Influence of lipid composition and membrane curvature on fluorescence and solvent relaxation kinetics in unilamellar vesicles.

Journal of fluorescence·2013
Same author

Inhomogeneous high harmonic generation in krypton clusters.

Physical review letters·2013
Same author

High-harmonic transient grating spectroscopy of NO2 electronic relaxation.

The Journal of chemical physics·2012
Same author

Conical intersection dynamics in NO2 probed by homodyne high-harmonic spectroscopy.

Science (New York, N.Y.)·2011
Same author

Efficient generation of near infra-red single photons from the zero-phonon line of a single molecule.

Optics express·2010
Same journal

Enhanced-Sampling Simulations Reveal Distinct Intermediates in SARS-CoV-2 FSE Pseudoknot Interconversion.

Biophysical journal·2026
Same journal

Structure-based simulations of the full Flock House virus capsid reveal pathways and energetics of an infection-critical peptide externalization event.

Biophysical journal·2026
Same journal

Quantifying the Peripheral Surface Information Entropy from Conformational Ensembles of Globular Protein-Peptide Complexes.

Biophysical journal·2026
Same journal

Anisotropic unbinding and location-dependent hovering of a kinesin motor head over microtubule.

Biophysical journal·2026
Same journal

Kinesin-5/Cut7 C-terminal tail phosphorylation influence on motor regulation through multi-scale molecular modeling.

Biophysical journal·2026
Same journal

Dynamic conformations of fluorophores on self-labeling protein tags.

Biophysical journal·2026
See all related articles

Related Experiment Video

Updated: Feb 20, 2026

Dynamic Light Scattering Analysis for the Determination of the Particle Size of Iron-Carbohydrate Complexes
04:40

Dynamic Light Scattering Analysis for the Determination of the Particle Size of Iron-Carbohydrate Complexes

Published on: July 7, 2023

3.3K

Effects of normalization errors on size distributions obtained from dynamic light scattering data.

H Ruf1

  • 1Max-Planck-Institut für Biophysik, Frankfurt, Federal Republic of Germany.

Biophysical Journal
|July 1, 1989
PubMed
Summary
This summary is machine-generated.

Normalization errors in photon correlation spectroscopy significantly skew size distribution results. A new method identifies and corrects these baseline errors for more accurate nanoparticle sizing.

More Related Videos

Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy
09:16

Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy

Published on: January 9, 2017

14.9K
Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels
11:34

Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels

Published on: September 8, 2016

10.8K

Related Experiment Videos

Last Updated: Feb 20, 2026

Dynamic Light Scattering Analysis for the Determination of the Particle Size of Iron-Carbohydrate Complexes
04:40

Dynamic Light Scattering Analysis for the Determination of the Particle Size of Iron-Carbohydrate Complexes

Published on: July 7, 2023

3.3K
Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy
09:16

Measurement of Particle Size Distribution in Turbid Solutions by Dynamic Light Scattering Microscopy

Published on: January 9, 2017

14.9K
Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels
11:34

Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels

Published on: September 8, 2016

10.8K

Area of Science:

  • Physical Chemistry
  • Materials Science
  • Nanotechnology

Background:

  • Photon correlation spectroscopy (PCS) is a key technique for determining particle size distributions.
  • Data normalization is a critical preprocessing step in PCS analysis.
  • Potential errors in normalization can impact the accuracy of derived size distributions.

Purpose of the Study:

  • To investigate the influence of normalization errors on size distributions derived from PCS.
  • To demonstrate the impact of systematic baseline errors on autocorrelation function analysis.
  • To develop a novel method for quantifying and correcting baseline errors in PCS data.

Main Methods:

  • Computer-generated autocorrelation functions simulating light scattering from monomodal Schulz distributions.
  • Analysis of simulated data with varying degrees of systematic normalization errors.
  • Application of a modified CONTIN algorithm for size distribution analysis and error determination.

Main Results:

  • Even minor systematic baseline errors during normalization lead to significant inaccuracies in estimated particle size distributions.
  • Normalization errors in the first-order autocorrelation function introduce error components with positive exponents of delay time.
  • A new method effectively determines the relative baseline error by inverting the PCS data.

Conclusions:

  • Accurate normalization is crucial for reliable size distribution analysis using photon correlation spectroscopy.
  • The identified characteristics of normalization errors allow for their quantitative assessment.
  • The developed method, integrated with the CONTIN algorithm, provides a robust approach to correct for baseline errors in PCS.