Bibliographic record
Abstract
In complex plasmas, dust particles are charged through their interactions with the electrons and ions of the surrounding plasma. In low-temperature laboratory plasmas, dust particles most commonly acquire a negative charge. In particular, in a laboratory glow-discharge plasma, the typical charge for a micrometer-size grain generally attains a few thousands of electronic charges. Under stable discharge conditions, this large negative charge is relatively well-characterized. However, for unsteady discharge conditions, the charge can differ and even fluctuate. In particular, when the power source of the discharge is turned off, the charged species of the plasma diffuse away and recombine into neutral species: this is a temporal afterglow. When dust particles are present inside a temporal plasma afterglow, the diffusion of charged species and the plasma decay dynamics are affected. Moreover, the dust particle charges also evolve during the afterglow period. In the late afterglow, dust particles are known to keep residual charges. The value of these residual charges strongly depends on the ambipolar-to-free diffusion transition. In addition, the presence of a constant electric field, causing ions to drift through the neutral gas, has a strong influence on the final dust particle residual charges, eventually leading to large positive residual charges. In this review article, the dynamics of temporal complex plasma afterglow are discussed. Experimental and theoretical results are presented. The basics of temporal afterglow modeling are also given.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".