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1. Minimum reaction network necessary to describe Ar/CF4 plasma etch

   https://doi.org/10.1117/12.2297502  

   Chopra, Meghali J.; Bonnecaze, Roger T.; Helpert, Sofia (March 2018, Proc. SPIE 10589, Advanced Etch Technology for Nanopatterning VII)

   Predicting the etch and deposition profiles created using plasma processes is challenging due to the complexity of plasma discharges and plasma-surface interactions. Volume-averaged global models allow for efficient prediction of important processing parameters and provide a means to quickly determine the effect of a variety of process inputs on the plasma discharge. However, global models are limited based on simplifying assumptions to describe the chemical reaction network. Here a database of 128 reactions is compiled and their corresponding rate constants collected from 24 sources for an Ar/CF4 plasma using the platform RODEo (Recipe Optimization for Deposition and Etching). Six different reaction sets were tested which employed anywhere from 12 to all 128 reactions to evaluate the impact of the reaction database on particle species densities and electron temperature. Because many the reactions used in our database had conflicting rate constants as reported in literature, we also present a method to deal with those uncertainties when constructing the model which includes weighting each reaction rate and filtering outliers. By analyzing the link between a reaction’s rate constant and its impact on the predicted plasma densities and electron temperatures, we determine the conditions at which a reaction is deemed necessary to the plasma model. The results of this study provide a foundation for determining which minimal set of reactions must be included in the reaction set of the plasma model. 

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2. Alfvén eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks

   https://doi.org/10.1088/1741-4326/ac3be7  

   Jalalvand, Azarakhsh; Kaptanoglu, Alan A.; Garcia, Alvin V.; Nelson, Andrew O.; Abbate, Joseph; Austin, Max E.; Verdoolaege, Geert; Brunton, Steven L.; Heidbrink, William W.; Kolemen, Egemen (December 2021, Nuclear Fusion)

   Abstract Modern tokamaks have achieved significant fusion production, but further progress towards steady-state operation has been stymied by a host of kinetic and MHD instabilities. Control and identification of these instabilities is often complicated, warranting the application of data-driven methods to complement and improve physical understanding. In particular, Alfvén eigenmodes are a class of ubiquitous mixed kinetic and MHD instabilities that are important to identify and control because they can lead to loss of confinement and potential damage to the walls of a plasma device. In the present work, we use reservoir computing networks to classify Alfvén eigenmodes in a large labeled database of DIII-D discharges, covering a broad range of operational parameter space. Despite the large parameter space, we show excellent classification and prediction performance, with an average hit rate of 91% and false alarm ratio of 7%, indicating promise for future implementation with additional diagnostic data and consolidation into a real-time control strategy. 

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3. Plasma Chemistry and Applications of Atmospheric Pressure Transient Electrical Discharges

   Dhali, Shirshak K (July 2025, Springer Series on Atomic, Optical, and Plasma Physics; Springer: Berlin/Heidelberg, Germany.)

   Guharay, S; Wada, M (Ed.)

   At or near atmospheric pressure, overvolted gas breakdown results in a streamer formation. In many applications of non-thermal plasma where efficient excited species generation is critical, the streamers are quenched to prevent it from reaching the arc phase. This can be achieved by repetitive nano second pulsing or dielectric barrier discharges were the dielectric charging quenches the arc formation. In such discharges, the plasma characteristics such as electron and ion densities and the production of excited species is determined by the streamer properties. Over the past five decades, a vast amount of experimental and computational work has been accumulated to establish a well-accepted theory of streamer formation and propagation. In this article we discuss the fluid models for streamers and quantify some macroscopic properties which can inform specific applications. We discuss in detail the fluid equations needed to model streamers and several schemes of parametrization of the transport and electron collisional processes. From an application point of view, the steamer simulations are used to quantify the excited species production by electron impact. This information is used to predict the specific outcomes via the plasma chemical conversion pathways. We present results of streamer discharges for three applications which are of technological importance to illustrate this approach: Plasma-assisted combustion, remediation of toxic gases, and plasma medicine. For plasma-assisted combustion the results of hydrogen ignition are discussed since non-hydrocarbon-based fuels such as hydrogen and ammonia are potential fuel candidates to reduce greenhouse gases. For the remediation of toxic gases, we discuss the removal of SOX/NOx from flue gas. Plasma medicine is a relatively new field and repetitive nano-second pulsed discharges in a helium gas carrier shows promise as a reactive plasma source for treating biological material. We discuss the helium metastable production in a streamer discharge since this species leads to the production of OH radicals which plays an important role. 

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4. Self-pulsing of dielectric barrier discharges at low driving frequencies

   https://doi.org/10.1063/5.0198876  

   Thagunna, Shanti_K; Kolobov, Vladimir_I; Zank, Gary_P (May 2024, Physics of Plasmas)

   This paper investigates the self-pulsing of dielectric barrier discharges (DBDs) at low driving frequencies. In particular, (a) the dependence of current on the product pd of gas pressure p and the gas gap length d, (b) the effects of lossy dielectrics (in resistive discharges) and large dielectric permittivity (in ferroelectrics) on current dynamics, (c) the transition from Townsend to a dynamic capacitively coupled plasma (CCP) discharge with changing pd values, and (d) the transition from Townsend to a high-frequency CCP regime with increasing the driving frequency. A one-dimensional fluid model of argon plasma is coupled to an equivalent RC circuit for lossy dielectrics. Our results show multiple current pulses per AC period in Townsend and CCP discharge modes which are explained by uncoupled electron–ion transport in the absence of quasineutrality and surface charge deposition at dielectric interfaces. The number of current pulses decreases with an increasing applied frequency when the Townsend discharge transforms into the CCP discharge. The resistive barrier discharge with lossy dielectrics exhibits Townsend and glow modes for the same pd value (7.6 Torr cm) for higher and lower resistances, respectively. Finally, we show that ferroelectric materials can amplify discharge current in DBDs. Similarities between current pulsing in DBD, Trichel pulses in corona discharges, and subnormal oscillations in DC discharges are discussed. 

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5. Predicting Human Intent to Interact with a Public Robot: The People Approaching Robots Database (PAR-D)

   https://doi.org/10.1145/3678957.3685706  

   Thompson, Sydney; Lew, Alexander; Li, Yifan; Stanish, Elizabeth; Huang, Alex; Phanse, Rohan; Vázquez, Marynel (November 2024, ACM)

   This work studies the problem of predicting human intent to interact with a robot in a public environment. To facilitate research in this problem domain, we first contribute the People Approaching Robots Database (PAR-D), a new collection of datasets for intent prediction in Human-Robot Interaction. The database includes a subset of the ATC Approach Trajectory dataset [28] with augmented ground truth labels. It also includes two new datasets collected with a robot photographer on two locations of a university campus. Then, we contribute a novel human-annotated baseline for predicting intent. Our results suggest that the robot’s environment and the amount of time that a person is visible impacts human performance in this prediction task. We also provide computational baselines for intent prediction in PAR-D by comparing the performance of several machine learning models, including ones that directly model pedestrian interaction intent and others that predict motion trajectories as an intermediary step. From these models, we find that trajectory prediction seems useful for inferring intent to interact with a robot in a public environment. 

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