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

Turnover Number and Catalytic Efficiency01:19

Turnover Number and Catalytic Efficiency

10.0K
The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
Chymotrypsin is a pancreatic enzyme that breaks down proteins during digestion....
10.0K
Coefficient of Correlation01:12

Coefficient of Correlation

6.1K
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...
6.1K
Correlation of Experimental Data01:23

Correlation of Experimental Data

227
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
227
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

5.9K
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. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
5.9K
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

1.6K
In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the...
1.6K
Correlations02:20

Correlations

32.7K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
32.7K

You might also read

Related Articles

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

Sort by
Same author

Combined spectroscopic and computational study for optimising catalyst design in hydrocarbon transformations.

Chemical communications (Cambridge, England)·2022
Same author

Hybrid catalysts based on N-heterocyclic carbene anchored on hierarchical zeolites.

RSC advances·2022
Same author

Rational Design and Application of Covalent Organic Frameworks for Solar Fuel Production.

Molecules (Basel, Switzerland)·2021
Same author

Understanding catalytic CO<sub>2</sub> and CO conversion into methanol using computational fluid dynamics.

Faraday discussions·2021
Same author

Bimetallic PdAu Catalysts within Hierarchically Porous Architectures for Aerobic Oxidation of Benzyl Alcohol.

Nanomaterials (Basel, Switzerland)·2021
Same author

Probing the Design Rationale of a High-Performing Faujasitic Zeotype Engineered to have Hierarchical Porosity and Moderated Acidity.

Angewandte Chemie (International ed. in English)·2020
Same journal

Soft catalytic platforms: hydrogel-catalyst synergies for spatiotemporally controlled therapy.

Chemical communications (Cambridge, England)·2026
Same journal

Cobalt-porphyrin/Bi<sub>19</sub>S<sub>27</sub>Br<sub>3</sub> nanorods enable photocatalytic CO<sub>2</sub> reduction to C<sub>2</sub>H<sub>4</sub>.

Chemical communications (Cambridge, England)·2026
Same journal

Controllable protein assembly: from design strategies to functional applications.

Chemical communications (Cambridge, England)·2026
Same journal

Silicon-supported 2D conductive metal-organic framework nanorod arrays for alkaline water and urea electrooxidation.

Chemical communications (Cambridge, England)·2026
Same journal

Zn<sub>3-<i>x</i></sub>H<sub>2<i>x</i></sub>(OH)<sub>2</sub>(MoO<sub>4</sub>)<sub>2</sub>·H<sub>2</sub>O: a crystallographically accurate Φ<sub><i>y</i></sub>-type structure of "Zn<sub>5</sub>Mo<sub>2</sub>O<sub>11</sub>·5H<sub>2</sub>O".

Chemical communications (Cambridge, England)·2026
Same journal

Frontiers in CO<sub>2</sub> reduction: employing alloy and high-entropy catalysts <i>via</i> photocatalytic and electrocatalytic pathways.

Chemical communications (Cambridge, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 15, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K

Rationalising catalytic performance using a unique correlation matrix.

Maciej G Walerowski1, Stylianos Kyrimis1,2, Victoria A Hewitt1

  • 1School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK. R.Raja@soton.ac.uk.

Chemical Communications (Cambridge, England)
|August 22, 2024
PubMed
Summary
This summary is machine-generated.

Precise control over nanoparticle size during catalyst synthesis was achieved by adjusting solvent properties and drying temperature. A new correlation matrix aids in designing better catalysts by linking synthesis, structure, and performance.

More Related Videos

On the Preparation and Testing of Fuel Cell Catalysts Using the Thin Film Rotating Disk Electrode Method
12:12

On the Preparation and Testing of Fuel Cell Catalysts Using the Thin Film Rotating Disk Electrode Method

Published on: March 16, 2018

21.9K
Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture
09:53

Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture

Published on: May 13, 2018

8.3K

Related Experiment Videos

Last Updated: Jun 15, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.2K
On the Preparation and Testing of Fuel Cell Catalysts Using the Thin Film Rotating Disk Electrode Method
12:12

On the Preparation and Testing of Fuel Cell Catalysts Using the Thin Film Rotating Disk Electrode Method

Published on: March 16, 2018

21.9K
Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture
09:53

Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture

Published on: May 13, 2018

8.3K

Area of Science:

  • Materials Science
  • Chemical Engineering
  • Nanotechnology

Background:

  • Understanding the relationship between catalyst synthesis, structure, and performance is crucial for developing efficient catalytic materials.
  • Precise control over nanoparticle size is a key factor influencing catalyst activity and selectivity.

Purpose of the Study:

  • To investigate the intricate relationships between catalyst synthesis parameters, resulting nanoparticle structure, and overall catalytic performance.
  • To develop a predictive tool for designing improved catalysts based on synthesis and structural characteristics.

Main Methods:

  • Systematic variation of solvent volume, drying temperature, and solvent polarity during nanoparticle synthesis.
  • Characterization of synthesized nanoparticles to determine size and structural properties.
  • Development and application of a multidimensional correlation matrix integrating synthetic, structural, and catalytic data.

Main Results:

  • Achieved precise control over nanoparticle size by manipulating solvent volume, drying temperature, and solvent polarity.
  • Established clear correlations between synthesis conditions, nanoparticle structure (size), and catalyst performance.
  • Demonstrated the utility of the multidimensional correlation matrix in rationalizing catalyst behavior.

Conclusions:

  • Tailoring synthesis conditions offers a viable route for precise nanoparticle size control in catalysts.
  • The developed multidimensional correlation matrix provides a powerful framework for understanding and predicting catalyst performance.
  • This approach can significantly aid in the rational design of next-generation catalysts with enhanced properties.