arXiv subjects

Edward Thomas Jr.

Publications and source records attributed to Edward Thomas Jr..

3 recordsLinked to original sources

arXiv2025

Non-perturbative 2D spatial measurements of electric fields within a plasma sheath

We introduce an all-optical quantum-enhanced diagnostic for electric fields in low-temperature plasmas. Trace amounts of rubidium vapor, added to argon plasma, allow us to produce spectrally narrow electric field-sensitive optical resonances via quantum optical effect of Rydberg electromagnetically induced transparency, and to non-invasively measure electric field in plasma with sensitivity exceeding 1 V/cm. By collecting fluorescence from the illuminated region of interest, we reconstruct a 2D spatial profile of the electric field magnitude with $30~\mu$m resolution. As a proof-of-principle demonstration, we measured the changes in electric field within the plasma sheath surrounding a biased Langmuir probe tip. This method holds significant potential for studying sheath structures in low-temperature plasmas.

physics.plasm-ph↗

arXiv2023

Introducing dusty plasma particle growth of nanospherical titanium dioxide

In dusty plasma environments, the spontaneous growth of nanoparticles from reactive gases has been extensively studied for over three decades, primarily focusing on hydrocarbons and silicate particles. Here, we introduce the growth of titanium dioxide, a wide band gap semiconductor, as dusty plasma nanoparticles. The resultant particles exhibited a spherical morphology and reached a maximum homogeneous radius of 230 $\pm$ 17 nm after an elapsed time of 70 seconds. The particle grew linearly and the growth displayed a cyclic behavior; that is, upon reaching their maximum radius, the largest particles fell out of the plasma, and a new growth cycle immediately followed. The particles were collected after being grown for different amounts of time and imaged using scanning electron microscopy. Further characterization was carried out using energy dispersive X-ray spectroscopy, X-ray diffraction and Raman spectroscopy to elucidate the chemical composition and crystalline properties of the maximally sized particles. Initially, the as-grown particles after 70 seconds exhibited an amorphous structure. However, annealing treatments at temperatures of 400 $^\circ$C and 800 $^\circ$C induced crystallization, yielding anatase and rutile phases, respectively. Notably, annealing at 600 $^\circ$C resulted in a mixed phase of anatase and rutile. These findings open new avenues for a rapid and controlled growth technique of titanium dioxide as dusty plasma.

physics.plasm-ph↗

arXiv2019

Microparticle cloud imaging and tracking for data-driven plasma science

Large data sets give rise to the `fourth paradigm' of scientific discovery and technology development, extending other approaches based on human intuition, fundamental laws of physics, statistics and intense computation. Both experimental and simulation data are growing explosively in plasma science and technology, motivating data-driven discoveries and inventions, which are currently in infancy. Here we describe recent progress in microparticle cloud imaging and tracking (mCIT, $μ$CIT) for laboratory plasma experiments. Three types of microparticle clouds are described: from exploding wires, in dusty plasmas and in atmospheric plasmas. The experimental data sets are obtained with one or more imaging cameras at a rate up to 100k frames per second (fps). A physics-constrained motion tracker, a Kohonen neural network (KNN) or self-organizing map (SOM), the feature tracking kit (FTK), and U-Net are described and compared with each other for particle tracking using the datasets. Particle density and signal-to-noise ratio have been identified as two important factors that affect the tracking accuracy. Fast Fourier transform (FFT) is used to reveal how U-Net, a deep convolutional neural network (CNN) developed for non-plasma applications, achieves the improvements for noisy scenes. The fitting parameters for a simple polynomial track model are found to group into clusters that reveal the geometry information about the camera setup. The mCIT or $μ$CIT techniques, when enhanced with data models, can be used to study the microparticle- or Debye-length scale plasma physics. The datasets are also available for ML code development and comparisons of algorithms.

physics.plasm-ph↗