A comparative study of fibre Bragg grating for spatially and temporally resolved gas temperature measurements in cold atmospheric pressure plasma jets
Bibliographic record
Abstract
Abstract Cold atmospheric pressure plasma jets (CAP-Jet) are successfully used in medical therapy for healing of chronic wounds and are widely researched in inactivation of pathogens and in assisting in cancer therapy. A crucial parameter for these plasma applications is that CAP-Jets operate at temperatures that are tolerable for biological tissues. While tools characterizing the plasma’s gas temperature are well developed, there are only a few methods that work with an agreeable limit of uncertainty, complexity and limited perturbation properties to accurately determine that the studied plasma jet operates at tissue tolerable temperatures at all times. In the current work, time resolved measurements of the gas temperature in the effluent of a CAP-Jet are performed using the innovative technique of a fibre Bragg grating (FBG), in which the temperature dynamics is measured by a shift of the FBGs resonant wavelength through its thermo-optic coefficient. Comparing with other temporal and spatial diagnostic tools such as thermocouple measurement, Schlieren imaging, and optical emission spectroscopy, we demonstrate reliable calorimetric measurements at different plasma duty cycles. The plasma source maintains tissue tolerable temperatures inside the plasma active zone with values below 35 °C at 1 cm distance from the jet nozzle. The calorimetric measurements have revealed that the heat power dissipation in comparison to electric energy of our plasma source is at least 50%.
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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 | 0.001 | 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) | 0.000 | 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 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".