Related Experiment Video

Updated: Jan 26, 2026

Colorimetric Detection of Bacteria Using Litmus Test
10:05

Colorimetric Detection of Bacteria Using Litmus Test

Published on: September 17, 2016

9.8K

A dual-mode highly selective and sensitive Schiff base chemosensor for fluorescent colorimetric detection of Ni2+ and

Amit Kumar Manna1, Kalyani Rout, Shubhamoy Chowdhury

  • 1Department of Chemistry, Guru Ghasidas Vishwavidyalaya, Bilaspur, C.G, India. patra29in@yahoo.co.in.

Photochemical & Photobiological Sciences : Official Journal of the European Photochemistry Association and the European Society for Photobiology
|April 13, 2019
PubMed

More Related Videos

ampliPHOX Colorimetric Detection on a DNA Microarray for Influenza09:32

ampliPHOX Colorimetric Detection on a DNA Microarray for Influenza

Published on: June 9, 2011

10.4K
Design and Development of Aptamer–Gold Nanoparticle Based Colorimetric Assays for In-the-field Applications08:23

Design and Development of Aptamer–Gold Nanoparticle Based Colorimetric Assays for In-the-field Applications

Published on: June 23, 2016

12.7K

Related Concept Videos

What is a Mode?01:07

What is a Mode?

The mode is one of the commonly used measures of a central tendency. It is defined as the most frequent value in a data set.
There can be more than one mode in a data set if multiple values have the same highest frequency. For instance, suppose that the Statistics exam scores of 20 students are: 50; 53; 59; 59; 63; 63; 72; 72; 72; 72; 72; 76; 78; 81; 83; 84; 84; 84; 90; 93. Here, the mode is 72, as it occurs most frequently, five times.
A data set with two modes is called bimodal. For example,...
25.2K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.9K
Detection of Black Holes01:10

Detection of Black Holes

Although black holes were theoretically postulated in the 1920s, they remained outside the domain of observational astronomy until the 1970s.
Their closest cousins are neutron stars, which are composed almost entirely of neutrons packed against each other, making them extremely dense. A neutron star has the same mass as the Sun but its diameter is only a few kilometers. Therefore, the escape velocity from their surface is close to the speed of light.
Not until the 1960s, when the first neutron...
2.5K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
8.2K
Effects of EDTA on End-Point Detection Methods01:18

Effects of EDTA on End-Point Detection Methods

Different methods, such as visual observance of metal-ion indicators, spectroscopic techniques, and potentiometric methods, can determine the endpoint of an EDTA titration.
In the visual method, metal-ion indicators (metallochromic dyes), which have distinct colors in their free and complex forms, are added to the mixture to signal the titration's end point. They form stable complexes with metal ions, but these complexes are weaker than the corresponding metal–EDTA complexes. As a...
634
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
10.9K