Characterization of the Conversion of Benzene to Nitrosobenzene in a Helium Low-Temperature Plasma
Bibliographic record
Abstract
The ionization of benzene (C6H6) through helium low-temperature plasma (He-LTP) offers a promising solution to the pervasive issue of its pollution in our daily lives. This study aimed to optimize the conditions for generating specific compounds, namely M+ and [M + 30]+, during the ionization of benzene in low-temperature plasma (LTP) with determination by mass spectrometry (MS). When the discharge voltage is 2.5 kV, a sample gas to discharge gas flow rate ratio of 2:3 is the best condition for generating M+ ions. When the discharge voltage is 3.5 kV, the gas flow rate ratio of 1:3 is the most suitable for generating [M + 30]+ ions. The LTP exhibits optimal ionization efficiency at a total gas flow rate of 40 mL/min. Additionally, we conducted a comparative analysis on the optical emission of benzene in the LTP which revealed the generation of free radicals associated with N, H, and O. The produced nitroso radical (ON•) combines with ionized benzene to yield the [M + 30]+ ion, identified to be nitrosobenzene. Notably, 95% conversion was achieved in the transformation from benzene to nitrosobenzene.
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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) | 0.001 | 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".