Evaluation of Gas‐Phase Filter Performance for a Gas Mixture
Bibliographic record
Abstract
Adsorption‐based granular activated carbon (GAC) filter is one of the common techniques for removing gas phase contaminants. However, a large group of gas phase contaminants is present in indoor environment and limited studies have been carried out to investigate the impact of contaminants type, their mixture and indoor air humidity on the GAC performance. This paper reports the outcomes of a series of experimental work which was carried out on a full‐scale system to study the impact of gas phase contaminant type (toluene, n ‐hexane, and methyl ethyl ketone (MEK)), their mixture, and indoor air relative humidity (RH) level on the performance of GAC filter. It was observed that the GAC filter performed well in removing toluene in both scenarios (single and mixture) due to its high molecular weight, boiling point, and polarizability to ion formation with the GAC, followed by n ‐hexane and MEK. It was also noted that the GAC filter did not perform well in removing MEK due to its weak attractive forces with GAC as compare to n ‐hexane and toluene. Among the different physical properties of indoor contaminant, removal performance and service life of the tested GAC filter were positively correlated to the contaminant molecular weight. In addition, the lower dipole moment and interaction energy made the adverse effects of indoor air RH on n ‐hexane more visible than toluene and MEK. Finally, a significant difference was observed between quantification indexes of the filter in removing indoor air contaminants as a single gas and as a mixture gas. The removal efficiency of GAC filter for the mixture was significantly reduced compared to those for the single gases due to the presence of other compounds in gas mixture competing for the same free space on carbon media.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".