A Novel Insight on the Geometry of Plasma Channels of Nanosecond Micron-Size Discharges on the Surface of Living Tissues of Plants

Kozhayeva, Julia P.; Lyubimtseva, V. A.; Zuimatch, Elena A.; Дубинов, А. Е.

doi:10.1002/ppap.201400132

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…According to the report of this experimental study, the visible image of the glow discharge showed a striation pattern [ 7 ], which is an evidence of importance in electric potential profiles to form a plasma path in the corresponding experimental apparatus. The authors also performed an experiment of surface microdischarges [ 10 ], and this maze-solving method was applied to biomaterial processing [ 11 ], which is a technical application of this plasma maze-solving method.…”

Section: Introductionmentioning

confidence: 99%

Maze-solving in a plasma system based on functional analogies to reinforcement-learning model

Sakai,

Karasaki,

Ito

et al. 2024

PLoS ONE

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Maze-solving is a classical mathematical task, and is recently analogously achieved using various eccentric media and devices, such as living tissues, chemotaxis, and memristors. Plasma generated in a labyrinth of narrow channels can also play a role as a route finder to the exit. In this study, we experimentally observe the function of maze-route findings in a plasma system based on a mixed discharge scheme of direct-current (DC) volume mode and alternative-current (AC) surface dielectric-barrier discharge, and computationally generalize this function in a reinforcement-learning model. In our plasma system, we install two electrodes at the entry and the exit in a square lattice configuration of narrow channels whose cross section is 1×1 mm2 with the total length around ten centimeters. Visible emissions in low-pressure Ar gas are observed after plasma ignition, and the plasma starting from a given entry location reaches the exit as the discharge voltage increases, whose route converging level is quantified by Shannon entropy. A similar short-path route is reproduced in a reinforcement-learning model in which electric potentials through the discharge voltage is replaced by rewards with positive and negative sign or polarity. The model is not rigorous numerical representation of plasma simulation, but it shares common points with the experiments along with a rough sketch of underlying processes (charges in experiments and rewards in modelling). This finding indicates that a plasma-channel network works in an analog computing function similar to a reinforcement-learning algorithm slightly modified in this study.

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Section: Introductionmentioning

confidence: 99%

Maze-solving in a plasma system based on functional analogies to reinforcement-learning model

Sakai,

Karasaki,

Ito

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Simple discharge cells similar to the cells described in refs − are prepared for the experiment. It is made in the following way: Standard microscope slides with dimensions of 1″ × 3″ and a thickness of 1 mm are taken.…”

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confidence: 99%