Transient simulation of the oscillatory injection of water–ethanol mixtures into an inductively-coupled radio frequency thermal plasma
Bibliographic record
Abstract
Abstract This study investigates the effects of oscillatory water–ethanol injection on the temperature, velocity, and species distributions within an inductively-coupled radio frequency (RF) thermal plasma, using a transient 2D-axisymmetric computational fluid dynamics model. The simulation models a TEKNA PL-35 RF plasma torch, operated with an argon–oxygen gas mixture, accounting for various species such as electrons, atoms, molecules, and ions. The water–ethanol solution is injected through a probe into the plasma core with sinusoidal velocity, replicating the effects of a peristaltic pump. Validation of the model is performed through comparison with previously published numerical data, demonstrating good conformity. The study explores the influence of frequency and molar concentration of the injected water–ethanol mixture, and operating pressure on key plasma characteristics, including electron density and temperature distribution. Results indicate that applying an oscillatory velocity pattern for central fluid injection into the plasma reduces the overall plasma temperature, particularly near the probe outlet. Additionally, it leads to a more uniform temperature distribution by decreasing sharp temperature gradients at the torch outlet. Furthermore, higher operating pressures increase electron density, which enhances energy transfer through mechanisms such as electron impact reactions. This improved energy transfer promotes a more uniform temperature distribution, leading to greater plasma uniformity. These results suggest that the oscillatory injection can promote more uniform heating of precursor materials, which is vital for applications such as coating deposition and powder synthesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".