Transient heating and evaporation of metallic particles under plasma conditions
Bibliographic record
Abstract
Abstract The synthesis of metallic nanopowders through the vaporization and condensation of metallic precursors under plasma condition is increasingly accepted for a wide range of applications in the electronic and electrical industry. The present study aims at the computer simulation of the basic processes involved. Specific attention is given to the particle evaporation rate and the associated energy balance. Computation carried out for the vaporization of 50 µ m iron and copper particles in an atmospheric pressure argon plasma at 9000 K provide means of quantifying the different energy requirements of the process including; • energy needed for the initial heating of the particle to its vaporization temperature, • energy required for particle vaporization, • energy lost by surface radiation from the particle during the evaporation process, and • energy radiated from the plasma/metal vapor cloud. The results show that energy lost by volume radiation for the plasma/metal vapor cloud is by far the most important energy requirement of the process that deserves special attention in terms of reactor engineering design in order to reduce its value and consequently increase process productivity. As expected, the results are to a large extent material dependent, varying widely with the radiative properties of the metal involved.
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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 | 0.000 |
| 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".