Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

Predicting Products: Substitution vs. Elimination

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:

You might also read

Related Articles

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

Sort by

Same author

Overcoming the Speed Limit of Four-Way DNA Branch Migration with Bulges in Toeholds.

Nano letters·2025

Same author

Association of deep learning-derived histologic features of placental chorionic villi with maternal and infant characteristics in the New Hampshire birth cohort study.

Placenta·2025

Same author

Designing enhanced mixing in stagnant microfluidic environments: an artificial cilia approach.

Lab on a chip·2025

Same author

Association of Deep Learning-Derived Histologic Features of Placental Chorionic Villi with Maternal and Infant Characteristics in the New Hampshire Birth Cohort Study.

medRxiv : the preprint server for health sciences·2025

Same author

Kinetic Proofreading Can Enhance Specificity in a Nonenzymatic DNA Strand Displacement Network.

Journal of the American Chemical Society·2024

Same author

Programmable metachronal motion of closely packed magnetic artificial cilia.

Lab on a chip·2024

Search research articles

Related Experiment Video

Updated: Feb 10, 2026

Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture

Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture

Published on: May 13, 2018

The challenge of catalyst prediction.

Rutger A van Santen¹, Aditya Sengar, Erik Steur

¹Institute for Complex Molecular Systems, Eindhoven University of Technology, 5612AZ Eindhoven, The Netherlands. r.a.v.santen@tue.nl.

Faraday Discussions

|May 26, 2018

Summary

Computational catalysis shows promise but faces challenges in predicting catalyst performance. New models address catalyst initiation and deactivation, improving predictions for reactions like alkylation and Fischer-Tropsch synthesis.

More Related Videos

HKUST-1 as a Heterogeneous Catalyst for the Synthesis of Vanillin

HKUST-1 as a Heterogeneous Catalyst for the Synthesis of Vanillin

Published on: July 23, 2016

Imine Metathesis by Silica-Supported Catalysts Using the Methodology of Surface Organometallic Chemistry

Imine Metathesis by Silica-Supported Catalysts Using the Methodology of Surface Organometallic Chemistry

Published on: October 18, 2019

Related Experiment Videos

Last Updated: Feb 10, 2026

Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture

Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture

Published on: May 13, 2018

HKUST-1 as a Heterogeneous Catalyst for the Synthesis of Vanillin

HKUST-1 as a Heterogeneous Catalyst for the Synthesis of Vanillin

Published on: July 23, 2016

Imine Metathesis by Silica-Supported Catalysts Using the Methodology of Surface Organometallic Chemistry

Imine Metathesis by Silica-Supported Catalysts Using the Methodology of Surface Organometallic Chemistry

Published on: October 18, 2019

Area of Science:

Catalysis
Computational Chemistry
Materials Science

Background:

Theoretical catalysis struggles to fully predict catalyst performance.
Transient initiation and deactivation processes are critical but often omitted in modeling.
Accurate catalyst modeling is essential for optimizing chemical processes.

Purpose of the Study:

To highlight new insights and successful applications of computational catalysis.
To address the challenge of incorporating transient initiation and deactivation into catalyst modeling.
To demonstrate improved predictive capabilities through case studies.

Main Methods:

Development of deactivation models for specific catalytic reactions.
Analysis of catalyst structural reorganization induced by reaction conditions.
Application of computational modeling to solid acid and transition metal catalysis.

Main Results:

A deactivation model was developed for the alkylation of isobutane and alkene.
Structural reorganization effects were discussed for transition metal catalysts in the Fischer-Tropsch reaction.
The study illustrates the importance of including dynamic processes in catalysis modeling.

Conclusions:

Computational catalysis can be enhanced by including transient initiation and deactivation.
Accurate modeling of dynamic processes improves the predictability of catalyst performance.
This work provides a foundation for more reliable theoretical predictions in catalysis.