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Related Concept Videos

Atomic Emission Spectroscopy: Lab01:29

Atomic Emission Spectroscopy: Lab

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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
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Inductively Coupled Plasma Atomic Emission Spectroscopy: Principle01:19

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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...
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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...
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Atomic Emission Spectroscopy: Interference01:30

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In atomic emission spectroscopy (AES), high-temperature atomizers excite a broad range of elements and molecules that generate complex emissions from sources such as oxides, hydroxides, and flame combustion products in the flame or plasma. Several strategies can be employed to minimize spectral interferences caused by overlapping emission lines or bands. These include increasing instrument resolution, choosing alternative emission lines, optimally placing the detector in low-background regions,...
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Non-conservative Forces01:17

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Non-conservative forces are dissipative forces such as friction or air resistance. These forces take energy away from a system as it progresses. Unlike conservative forces, non-conservative forces do not have potential energy associated with them. This is because the energy is lost to the system and cannot be turned into useful work later.
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An object absorbing an electromagnetic wave would experience a force in the direction of propagation of the wave. This force occurs because electromagnetic waves contain and transport momentum. The force accounts for the wave's radiation pressure exerted on the object. Maxwell's prediction was confirmed in 1903 by Nichols and Hull by precisely measuring radiation pressures with a torsion balance. The measuring instrument had mirrors suspended from a fiber kept inside a glass container.
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Related Experiment Video

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Experimental Methods of Dust Charging and Mobilization on Surfaces with Exposure to Ultraviolet Radiation or Plasmas
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Extracting forces from noisy dynamics in dusty plasmas.

Wentao Yu1, Jonathan Cho1, Justin C Burton1

  • 1Department of Physics, Emory University, Atlanta, Georgia 30322, USA.

Physical Review. E
|October 21, 2022
PubMed
Summary

This study uses machine learning (ML) to accurately extract forces from noisy dusty plasma data. The ML model achieved 50% better accuracy than traditional methods, enabling noncontact measurements.

Area of Science:

  • Physics
  • Plasma Physics
  • Materials Science

Background:

  • Extracting environmental forces from noisy data is challenging in complex physical systems.
  • Machine learning (ML) shows promise but is often limited to simulated data.
  • Dusty plasmas present unique challenges due to non-Gaussian noise.

Purpose of the Study:

  • To apply supervised ML for extracting electrostatic, dissipative, and stochastic forces on charged particles in dusty plasmas.
  • To develop a robust ML model capable of handling non-Gaussian noise artifacts.
  • To enable noncontact measurements of particle properties in plasma environments.

Main Methods:

  • Tracking subpixel motion of micron-sized charged particles in argon plasma using image analysis.
  • Training a supervised ML model on simulated particle trajectories with artifacts like drift and pixel locking.

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  • Utilizing over 100 dynamical and statistical features for force estimation.

    • Main Results:

    • Successfully estimated electrostatic, dissipative, and stochastic forces from particle motion.
    • Achieved 50% greater prediction accuracy compared to conventional methods.
    • Demonstrated noncontact measurement of particle charge and Debye length in interacting particle systems.

    Conclusions:

    • Supervised ML is effective for force extraction in noisy dusty plasma environments.
    • The developed model overcomes limitations of traditional methods by handling non-Gaussian noise.
    • This approach facilitates advanced diagnostics and understanding of complex plasma systems.