Determination, through titration with NO, of the concentration of oxygen atoms in the flowing afterglow of Ar-O<sub>2</sub>and N<sub>2</sub>-O<sub>2</sub>plasmas used for sterilization purposes
Bibliographic record
Abstract
Les méthodes existantes de titrage de N et O d'une post-décharge au moyen de l'intensité d'émission de la molécule NO excitée ne permettant pas d'aller au-delà de x = 5% dans un mélange x O 2 -(100%- x )N 2 , nous présentons une démarche valable pour x ⩽20%. Cette technique est fondée sur la mesure de l'intensité d'émission de NO 2 (A), en fonction du débit de NO introduit, en relation avec une dérivation analytique des équations des concentrations [N] et [O]. La concentration d'oxygène atomique obtenue par cette méthode est validée de façon indépendante à partir de la mesure du rapport des intensités d'émission de NO(B) et de N 2 (B, 11) (celle-ci détectable pour x ⩽8%). Enfin, la méthode proposée est mise en oeuvre pour apprécier l'influence de la valeur de la concentration d'oxygène atomique sur le temps de stérilisation dans une post-décharge en flux à partir d'un plasma de N 2 -O 2 . \engabstract Existing titration methods of N and O in an afterglow based on the emission intensity of the excited NO molecule cannot be used at x values exceeding 5% in the x O 2 -(100%- x )N 2 mixture. Our technique extends the x range to 20%. It utilizes the emission intensity measurement of NO 2 (A), as a function of the introduced NO flow, in relation with analytically derived equations for the O and N concentrations. The atomic oxygen concentration obtained in this way is validated independently through measurements of the emission intensity ratio of NO(B) and N 2 (B, 11) (detectable for x ⩽8%). Finally, the proposed method is used to assess the influence of the oxygen atom concentration on the sterilization time in the flowing afterglow of an N 2 -O 2 plasma.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".