Microwave-Assisted Generation of Standard Gas Mixtures
Bibliographic record
Abstract
Microwave heating was employed for preparation of the standard gas of volatile organic compounds (VOCs) and semivolatile organic compounds (semi-VOCs) by using a 1000 W commercial domestic microwave oven and 1 L gas-sampling bulbs. The VOCs investigated were benzene, chloroform, 1,3-dichlorobenzene, tetrachloroethylene, toluene, and 1,1,2-trichloroethane, and the semi-VOCs used were the polychlorinated biphenyls (PCBs) PCB 1016 and PCB 1248. Since these weakly or nonpolar molecules are very poor absorbers of microwave energy, an appropriate amount of water was introduced to accept microwave radiation and act as the thermal source to accelerate their evaporation. The glass bulb may also contribute thermal energy to the VOCs/semi-VOCs by accepting microwave energy to a small degree. For 0.5 microL of liquid VOCs on 10 mg of glass wool, it was shown that 15 microL of H2O and 60 s of microwave heating yielded a very efficient evaporation [97.2-106.4%, compared with a classic method (Muller, L; Gorecki, T.; Pawliszyn, J. Fresenius' J. Anal. Chem. 1999, 364, 610-616)]. For 1 microL of PCB solution (1000 microg/mL in hexane), 15 microL of H2O and 90 s of microwave heating also provided a complete evaporation. The addition of water was particularly significant for microwave-assisted evaporation of PCBs because semi-VOCs are much more difficult to evaporate than VOCs. This developed microwave technique proved to be quite simple, powerful, rapid, accurate, and safe for the preparation of VOC/semi-VOC standard gas. Solid- phase microextraction combined with gas chromatography was used for the gas analysis.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".