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

Electron Paramagnetic Resonance (EPR) Spectroscopy: Organic Radicals01:17

Electron Paramagnetic Resonance (EPR) Spectroscopy: Organic Radicals

3.4K
Ideally, an unpaired electron shows a single peak in the EPR spectrum due to the transition between the two spin energy states. However, coupling interactions can occur between the spins of the unpaired electron and any neighboring spin-active nuclei. This hyperfine coupling results in hyperfine splitting, where the EPR signal is split into multiplets. The signals split into 2nI + 1 peaks, where n is the number of equivalent nuclei and I is the nuclear spin. These splitting patterns provide...
3.4K
Dimensional Analysis03:40

Dimensional Analysis

62.2K
Dimensional analysis, also known as the factor label method, is a versatile approach for mathematical operations. The main principle behind this approach is: the units of quantities must be subjected to the same mathematical operations as their associated numbers. This method can be applied to computations ranging from simple unit conversions to more complex and multi-step calculations involving several different quantities and their units.
Conversion Factors and Dimensional Analysis
The unit...
62.2K
Dimensional Analysis01:27

Dimensional Analysis

660
Dimensional analysis is a valuable technique in fluid mechanics for simplifying complex problems by reducing them into dimensionless groups. These groups capture the essential relationships between the variables involved, allowing researchers and engineers to analyze fluid flow without dealing with each variable individually. This approach reduces the number of independent variables, allowing for easier analysis and better understanding of physical phenomena.
In fluid mechanics, dimensional...
660
Dimensional Analysis01:23

Dimensional Analysis

2.1K
Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
Dimensional analysis allows us to analyze and compare physical quantities on a...
2.1K
Dimensional Analysis02:19

Dimensional Analysis

24.0K
The concept of dimension is important because every mathematical equation linking physical quantities must be dimensionally consistent, implying that mathematical equations must meet the following two rules. The first rule is that, in an equation, the expressions on each side of the equal sign must have the same dimensions. This is fairly intuitive since we can only add or subtract quantities of the same type (dimension). The second rule states that, in an equation, the arguments of any of the...
24.0K
Emission Spectra02:39

Emission Spectra

76.2K
When solids, liquids, or condensed gases are heated sufficiently, they radiate some of the excess energy as light. Photons produced in this manner have a range of energies, and thereby produce a continuous spectrum in which an unbroken series of wavelengths is present.
76.2K

You might also read

Related Articles

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

Sort by
Same author

High-resolution structure of monomorphic Aβ<sub>1-40</sub> fibrils.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Through-space donor-acceptor homoconjugation strategies for emissive radical species.

Chemical science·2026
Same author

Aducanumab binding to Aβ<sub>1-42</sub> fibrils alters dynamics of the N-terminal tail while preserving the fibril core.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Atomic Structure of GNNQQNY Nanocrystals: A Validated Approach for Polymorphic Amyloids.

The journal of physical chemistry letters·2025
Same author

Aducanumab Binding to Aβ<sub>1-42</sub> Fibrils Alters Dynamics of the N-Terminal Tail While Preserving the Fibril Core.

bioRxiv : the preprint server for biology·2025
Same author

Diboron-Incorporated Indenofluorene: Isolation of Crystalline Neutral and Reduced States of 6,12-Diboraindeno[1,2-<i>b</i>]fluorene.

Journal of the American Chemical Society·2025

Related Experiment Video

Updated: Jan 28, 2026

Rapid Scan Electron Paramagnetic Resonance Opens New Avenues for Imaging Physiologically Important Parameters In Vivo
08:01

Rapid Scan Electron Paramagnetic Resonance Opens New Avenues for Imaging Physiologically Important Parameters In Vivo

Published on: September 26, 2016

9.8K

Convolutional Neural Network Analysis of Two-Dimensional Hyperfine Sublevel Correlation Electron Paramagnetic

Alexander T Taguchi1, Ethan D Evans1, Sergei A Dikanov2

  • 1Department of Chemistry , Massachusetts Institute of Technology , 77 Massachusetts Avenue , Cambridge , Massachusetts 02139 , United States.

The Journal of Physical Chemistry Letters
|February 22, 2019
PubMed
Summary

A novel machine learning method analyzes complex electron paramagnetic resonance (EPR) spectra without experimental training. This approach accurately predicts magnetic coupling parameters from simulations alone, advancing spectroscopic analysis.

More Related Videos

Tumor Hypoxia Assessment: In Vivo 3D Oxygen Imaging Through Electron Paramagnetic Resonance
07:07

Tumor Hypoxia Assessment: In Vivo 3D Oxygen Imaging Through Electron Paramagnetic Resonance

Published on: February 14, 2025

3.8K
Use of Electron Paramagnetic Resonance in Biological Samples at Ambient Temperature and 77 K
06:45

Use of Electron Paramagnetic Resonance in Biological Samples at Ambient Temperature and 77 K

Published on: January 11, 2019

9.8K

Related Experiment Videos

Last Updated: Jan 28, 2026

Rapid Scan Electron Paramagnetic Resonance Opens New Avenues for Imaging Physiologically Important Parameters In Vivo
08:01

Rapid Scan Electron Paramagnetic Resonance Opens New Avenues for Imaging Physiologically Important Parameters In Vivo

Published on: September 26, 2016

9.8K
Tumor Hypoxia Assessment: In Vivo 3D Oxygen Imaging Through Electron Paramagnetic Resonance
07:07

Tumor Hypoxia Assessment: In Vivo 3D Oxygen Imaging Through Electron Paramagnetic Resonance

Published on: February 14, 2025

3.8K
Use of Electron Paramagnetic Resonance in Biological Samples at Ambient Temperature and 77 K
06:45

Use of Electron Paramagnetic Resonance in Biological Samples at Ambient Temperature and 77 K

Published on: January 11, 2019

9.8K

Area of Science:

  • Spectroscopy
  • Computational Chemistry
  • Machine Learning

Background:

  • Electron Paramagnetic Resonance (EPR) spectroscopy, particularly Hyperfine Sublevel Correlation (HYSCORE) techniques, provides detailed molecular information.
  • Analyzing complex 2D HYSCORE EPR spectra often requires expert knowledge and can be challenging due to data limitations.

Purpose of the Study:

  • To develop a machine learning (ML) algorithm capable of interpreting complex 2D HYSCORE EPR spectra.
  • To enable accurate prediction of magnetic coupling parameters and their distributions using only simulated data.

Main Methods:

  • A computer vision-based machine learning algorithm was developed.
  • The algorithm learns spin physics exclusively from simulated HYSCORE EPR spectra, eliminating the need for experimental training data.
  • The neural network was applied to analyze 14N HYSCORE spectra.

Main Results:

  • The ML approach successfully predicted hyperfine (a, T) and 14N quadrupole (K, η) coupling constants.
  • Predicted constants showed minimal deviation from manual analyses (average deviations: 0.11 MHz, 0.09 MHz, 0.19 MHz, and 0.09, respectively).
  • The method effectively utilizes the full information content of 2D spectra.

Conclusions:

  • The developed ML algorithm provides expert-level analysis of complex 2D HYSCORE EPR spectra.
  • This simulation-trained approach is robust and applicable even with limited experimental data.
  • The method offers a powerful new tool for extracting previously inaccessible magnetic coupling information.