Non-thermal plasma technology for air pollution control and bacterial deactivation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The exploration of innovative technologies for effective pollution control is crucial for both environmental and human health. Non-thermal plasma has emerged as a promising solution due to its dynamic nature and versatile applications. This work investigates the role of non-thermal plasma in air pollution control, covering the decomposition of various volatile organic compounds, including toluene, formaldehyde, ethanol, hydrogen sulfide, and sulfur dioxide, as well as the deactivation of E. coli. The findings revealed that toluene, formaldehyde, and ethanol reach more than 90% decomposition, while hydrogen sulfide undergoes a complete conversion. Meanwhile, the sulfur dioxide removal efficiency stands at 27%. Additionally, E. coli deactivation in fixed feeding mode demonstrates robust bactericidal capabilities within 30 min, while continuous feeding for 4 h achieves 100% bacterial inactivation. These quantitative outcomes provide insights for optimizing non-thermal plasma systems in pollution control, environmental remediation, and sterilization processes.
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.
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 it