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

Magnetic Vector Potential01:15

Magnetic Vector Potential

730
In electrostatics, the electric field can be written as the negative gradient of the potential. In magnetostatics, the zero divergence of the magnetic field ensures that the magnetic field can be expressed as the curl of a vector potential. This potential is known as the magnetic vector potential.
Consider an ideal solenoid with n turns per unit length and radius R. If I is the current through the solenoid, the magnetic field inside the solenoid is expressed as the product of vacuum...
730
Magnetostatic Boundary Conditions01:28

Magnetostatic Boundary Conditions

1.0K
An electric field suffers a discontinuity at a surface charge. Similarly, a magnetic field is discontinuous at a surface current. The perpendicular component of a magnetic field is continuous across the interface of two magnetic mediums. In contrast, its parallel component, perpendicular to the current, is discontinuous by the amount equal to the product of the vacuum permeability and the surface current. Like the scalar potential in electrostatics, the vector potential is also continuous...
1.0K

You might also read

Related Articles

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

Sort by
Same author

Levodopa Sensing with a Nanosensor Array via a Low-Cost Near Infrared Readout.

Analytical chemistry·2025
Same author

Microcontroller-Optimized Measurement Electronics for Coherent Control Applications of NV Centers.

Sensors (Basel, Switzerland)·2024
Same author

Excited-State Lifetime of NV Centers for All-Optical Magnetic Field Sensing.

Sensors (Basel, Switzerland)·2024
Same author

Compact and Fully Integrated LED Quantum Sensor Based on NV Centers in Diamond.

Sensors (Basel, Switzerland)·2024
Same author

Correction to "Equilibrium Contact Angle and Adsorption Layer Properties with Surfactants".

Langmuir : the ACS journal of surfaces and colloids·2019
Same author

Equilibrium Contact Angle and Adsorption Layer Properties with Surfactants.

Langmuir : the ACS journal of surfaces and colloids·2018
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Aug 10, 2025

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains
07:42

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

Published on: July 20, 2022

2.8K

Edge-Machine-Learning-Assisted Robust Magnetometer Based on Randomly Oriented NV-Ensembles in Diamond.

Jonas Homrighausen1, Ludwig Horsthemke2, Jens Pogorzelski2

  • 1Department of Engineering Physics, Münster University of Applied Sciences, Stegerwaldstraße 39, 48565 Steinfurt, Germany.

Sensors (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a low-cost quantum magnetometer using nitrogen vacancy centers and edge machine learning for precise magnetic field sensing. The system utilizes an ESP32 microcontroller for real-time data analysis and magnetic field deduction.

Keywords:
NV center in diamondedge machine learningmagnetometrymicrocontrollerneural networksoptically detected magnetic resonancequantum sensing

More Related Videos

High-Speed Magnetic Tweezers for Nanomechanical Measurements on Force-Sensitive Elements
08:50

High-Speed Magnetic Tweezers for Nanomechanical Measurements on Force-Sensitive Elements

Published on: May 12, 2023

2.2K
Magnetic Tweezers for the Measurement of Twist and Torque
11:41

Magnetic Tweezers for the Measurement of Twist and Torque

Published on: May 19, 2014

23.4K

Related Experiment Videos

Last Updated: Aug 10, 2025

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains
07:42

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

Published on: July 20, 2022

2.8K
High-Speed Magnetic Tweezers for Nanomechanical Measurements on Force-Sensitive Elements
08:50

High-Speed Magnetic Tweezers for Nanomechanical Measurements on Force-Sensitive Elements

Published on: May 12, 2023

2.2K
Magnetic Tweezers for the Measurement of Twist and Torque
11:41

Magnetic Tweezers for the Measurement of Twist and Torque

Published on: May 19, 2014

23.4K

Area of Science:

  • Quantum sensing
  • Materials science
  • Machine learning

Background:

  • Optically detected magnetic resonance (ODMR) of nitrogen vacancy (NV) centers in diamonds offers high precision for magnetic field sensing.
  • Existing quantum magnetometers can be complex and costly, limiting widespread adoption.

Purpose of the Study:

  • To develop a low-cost, stand-alone quantum magnetometer using NV centers and edge machine learning.
  • To demonstrate the feasibility of using an embedded microcontroller for real-time magnetic field measurement.

Main Methods:

  • A continuous-wave ODMR setup was used to acquire data for training an artificial neural network.
  • An ESP32 microcontroller was employed for controlling data acquisition and performing network inference (edge machine learning).
  • The trained neural network was deployed on the ESP32 to deduce magnetic field strength from ODMR spectra.

Main Results:

  • The developed sensor setup successfully measured magnetic fields with high precision.
  • The edge machine learning approach enabled real-time magnetic field deduction on a low-power embedded device.
  • Proof-of-concept demonstrated the system's capability across a wide measuring range.

Conclusions:

  • The proposed low-cost quantum magnetometer with edge machine learning is a viable and accessible solution for precise magnetic field sensing.
  • This technology has the potential to enable robust and widespread sensor applications.
  • Further development could lead to portable and cost-effective magnetic field measurement tools.