Evaluation of nano-confined catalytic oxidation air purification technology on eliminating marijuana chemicals and odour
Bibliographic record
Abstract
The novel air purification technology, Nano-Confined Catalytic Oxidation (NCCO), has been proven to be effective at eliminating air pollutants. With the increasing legalization and decriminalization of medicinal and recreational cannabis and related products, respectively, in many countries and jurisdictions around the world, concerns have been raised about indoor air quality from smoking cannabis products, such as marijuana, which produce gaseous pollutants and intense odour. In this study, NCCO technology has been evaluated for its effectiveness in reducing key marijuana concentrations in polluted indoor air by direct measurements and odour intensity assessments by human volunteers. For the odour intensity measurements, 20 non-cannabis adults participated in the odour assessment. The results are remarkable and statistically significant. The reduction in Dronabinol, a pharmaceutical form of Tetrahydrocannabinol (THC), Cannabidiol (CBD) and Cannabinol, averages 93.4%, whereas that in airborne marijuana compounds with no air purification only averages 6.2%. The technology also demonstrates statistically significant reductions in PM2.5, PM10 and total volatile organic compounds generated from marijuana smoke. The technology was able to restore high levels of harmful particulate matter to normal baseline levels. Furthermore, the odour assessment conducted by a group of 20 volunteers also confirmed statistically significant reductions in marijuana odour by 55.6% after 50 min of air purification.
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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) | 0.000 | 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".