Adsorption and Desorption of Mixtures of Organic Vapors on Beaded Activated Carbon
Bibliographic record
Abstract
In this study, adsorption and desorption of mixtures of organic compounds commonly emitted from automotive painting operations were experimentally studied. A mixture of two alkanes and a mixture of eight organic compounds were adsorbed onto beaded activated carbon (BAC) and then thermally desorbed under nitrogen. Following both adsorption and regeneration, samples of the BAC were chemically extracted. Gas chromatography-mass spectrometry (GC-MS) was used to quantify the compounds in the adsorption and desorption gas streams and in the BAC extracts. In general, for both adsorbate mixtures, competitive adsorption resulted in displacing low boiling point compounds by high boiling point compounds during adsorption. In addition to boiling point, adsorbate structure and functionality affected adsorption dynamics. High boiling point compounds such as n-decane and 2,2-dimethylpropylbenzene were not completely desorbed after three hours regeneration at 288 °C indicating that these two compounds contributed to heel accumulation on the BAC. Additional compounds not present in the mixtures were detected in the extract of regenerated BAC possibly due to decomposition or other reactions during regeneration. Closure analysis based on breakthrough curves, solvent extraction of BAC and mass balance on the reactor provided consistent results of the amount of adsorbates on the BAC after adsorption and/or regeneration.
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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".