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

Inductively Coupled Plasma–Mass Spectrometry (ICP–MS): Overview01:19

Inductively Coupled Plasma–Mass Spectrometry (ICP–MS): Overview

1.1K
In inductively coupled plasma–mass spectrometry (ICP–MS), an inductively coupled plasma (ICP) torch is used as an atomizer and ionizer. Solid samples are dissolved and volatilized before being introduced into the high-temperature argon plasma, while solution samples are nebulized and passed through the high-temperature argon plasma. Plasma dissociates the analytes and ionizes their component atoms to form a mixture of positive ions and molecular species. The positive ions are then...
1.1K
Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences01:20

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences

712
Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and...
712
Fluid Mosaic Model01:19

Fluid Mosaic Model

14.2K
Scientists identified the plasma membrane in the 1890s and its principal chemical components (lipids and proteins) by 1915. The model for plasma membrane structure, proposed in 1935 by Hugh Davson and James Danielli, was the first model to be widely accepted in the scientific community. The model was based on the plasma membrane's "railroad track" appearance in early electron micrographs. Davson and Danielli theorized that the plasma membrane's structure resembled a sandwich...
14.2K
Interfacial Electrochemical Methods: Overview01:06

Interfacial Electrochemical Methods: Overview

526
Interfacial electrochemical methods focus on the phenomena occurring at the boundary between an electrode and a solution, as opposed to bulk methods that concentrate on the solution's overall properties. These interfacial methods are classified as either static or dynamic based on the presence of a nonzero current in the electrochemical cell and the consistency of analyte concentrations. Static methods, such as potentiometry, measure the cell's potential without any significant current...
526
Inductively Coupled Plasma Atomic Emission Spectroscopy: Principle01:19

Inductively Coupled Plasma Atomic Emission Spectroscopy: Principle

1.1K
Inductively coupled plasma (ICP) is the most widely used plasma source in atomic emission spectroscopy (AES), also known as Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). The ICP source, or torch, consists of three concentric quartz tubes with argon gas flowing through them. A spark from a Tesla coil initiates the ionization of argon, generating a high-temperature plasma.
The ions and electrons produced interact with the fluctuating magnetic field created by a water-cooled...
1.1K
Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

369
Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy  (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used....
369

You might also read

Related Articles

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

Sort by
Same author

Revealing liquid-gas transitions with finite-size scaling in experimental and simulation systems confined by an external field.

Soft matter·2026
Same author

Vibrational Modes and Particle Rearrangements in Sheared Quasi-Two-Dimensional Complex Plasmas.

Physical review letters·2025
Same author

Wave dispersion in a three-dimensional complex plasma solid under microgravity conditions.

Physical review. E·2025
Same author

Observation of the hexatic phase in a two-dimensional complex plasma using machine learning.

Soft matter·2024
Same author

Dissipative solitary waves in a two-dimensional complex plasma: Amorphous versus crystalline.

Physical review. E·2023
Same author

Reflection and transmission of an incident solitary wave at an interface of a binary complex plasma in a microgravity condition.

Physical review. E·2021
Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026
Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026
Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026
Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026
Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026
Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026
See all related articles

Related Experiment Video

Updated: Oct 22, 2025

Total Internal Reflection Absorption Spectroscopy TIRAS for the Detection of Solvated Electrons at a Plasma-liquid Interface
08:50

Total Internal Reflection Absorption Spectroscopy TIRAS for the Detection of Solvated Electrons at a Plasma-liquid Interface

Published on: January 24, 2018

14.0K

Identification of the Interface in a Binary Complex Plasma Using Machine Learning.

He Huang1, Mierk Schwabe2, Cheng-Ran Du1,3

  • 1College of Science, Donghua University, Shanghai 201620, China.

Journal of Imaging
|August 30, 2021
PubMed
Summary
This summary is machine-generated.

Researchers used a support vector machine (SVM) to identify interfaces in binary complex plasmas. This machine learning approach effectively distinguished between particle sizes and plasma regions.

Keywords:
complex plasmamachine learning

More Related Videos

Building Langmuir Probes and Emissive Probes for Plasma Potential Measurements in Low Pressure, Low Temperature Plasmas
08:10

Building Langmuir Probes and Emissive Probes for Plasma Potential Measurements in Low Pressure, Low Temperature Plasmas

Published on: May 25, 2021

4.7K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.7K

Related Experiment Videos

Last Updated: Oct 22, 2025

Total Internal Reflection Absorption Spectroscopy TIRAS for the Detection of Solvated Electrons at a Plasma-liquid Interface
08:50

Total Internal Reflection Absorption Spectroscopy TIRAS for the Detection of Solvated Electrons at a Plasma-liquid Interface

Published on: January 24, 2018

14.0K
Building Langmuir Probes and Emissive Probes for Plasma Potential Measurements in Low Pressure, Low Temperature Plasmas
08:10

Building Langmuir Probes and Emissive Probes for Plasma Potential Measurements in Low Pressure, Low Temperature Plasmas

Published on: May 25, 2021

4.7K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

12.7K

Area of Science:

  • Plasma physics
  • Complex systems
  • Materials science

Background:

  • Binary complex plasmas contain two dust particle types in ionized gas.
  • Phase separation occurs due to spinodal decomposition and force imbalance, creating an interface.
  • This interface can be dynamic, influenced by external or internal factors.

Purpose of the Study:

  • To develop a method for locating the interface in binary complex plasmas.
  • To apply machine learning for analyzing plasma behavior.
  • To distinguish between different regions within the plasma.

Main Methods:

  • Utilized a support vector machine (SVM) classification method.
  • Employed image brightness as the primary feature for classification.
  • Used scaled mean and variance as input features for the SVM.

Main Results:

  • Successfully distinguished three distinct areas: small particles, big particles, and dust-free plasma.
  • Accurately identified the interface separating small and big particles.
  • Demonstrated the effectiveness of SVM in analyzing complex plasma structures.

Conclusions:

  • Support vector machine (SVM) is a viable tool for interface detection in binary complex plasmas.
  • Image-based feature analysis (mean, variance) enables precise classification of plasma regions.
  • This method aids in understanding the dynamics and structure of complex dusty plasmas.