Influence of magnetic field strength on nanoparticle growth in a capacitively-coupled radio-frequency Ar/C<sub>2</sub>H<sub>2</sub> discharge
Bibliographic record
Abstract
The growth of nanoparticles in a magnetised chemically active discharge (Ar/C 2 H 2 ) is investigated. The influence of the strength of the magnetic field on dust particle growth dynamics is explored. The structure of the grown nanoparticles is studied ex situ . It is revealed that the strength of the magnetic field (up to 2.5 T) has a major impact on discharge parameters (such as the self-bias of the powered electrode) as well as on the growth and morphology of the nanoparticles. At high magnetic field, the dust cloud is confined in the sheath above the grounded electrode while without magnetic field the dust cloud occupies most of the interelectrode space. Moreover, at high magnetic field, large porous spherical agglomerates were grown. The modification of the self-bias is explained by the influence of the magnetic field on the diffusion of charged species resulting in a confinement of the plasma under the powered electrode. Complementary particle-in-cell simulations confirm that the electric field and plasma distributions are strongly affected by the magnetic field explaining the experimentally observed dust cloud localisation. The large porous spherical agglomerates are most probably due to an enhanced agglomeration caused by the modified confinement owing to the magnetic field.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".