Evaluation of MOF-based adsorbents for sampling and measurements of semi-volatile organic compounds (SVOCS) from contaminated air
Bibliographic record
Abstract
,Understanding the challenges of effectively capturing and analyzing semi-volatile organic compounds (SVOCs) from contaminated air is crucial for environmental monitoring. In this study, selected Zr-containing metal-organic frameworks (MOFs), UiO-67 and UiO-67-Ni, have been investigated for their potential on adsorption of a group of nonpolar SVOCs so called as poly-aromatic hydrocarbons (PAHs). Acenaphthene (Acp) was used as a model SVOC, and adsorption tests were investigated using the static headspace sampling technique, and desorption technique using methanol under ultrasonic conditions. Parameters including desorption time and adsorbent dosage have been investigated as independent variables for extraction performance, where the General Full Factorial design method has been applied for an experimental design. Comparing two different MOFs with same crystal structure and different pore size has demonstrated the exact effect of pore accessibility and interaction of host-guest molecules in adsorption of SVOC. The results showed superior sampling performance for UiO-67-Ni, with an adsorption capacity of 0.226 µg mg-1, representing a 47.8% improvement compared to UiO-67. The gas removal efficiency reached 17.45% for UiO-67-Ni, compared to 11.81% for UiO-67. A collective interpretation of the data from characterization techniques demonstrated that the presence of Ni in the framework substantially increased pore size and specific surface area, which positively impacted the adsorption performance of the MOF. The pronounced pore diameter difference between UiO-67 and UiO-67-Ni (22.4 Å to 25.3 Å), and the total surface area of 1450.5 m² g⁻¹ in the Ni-containing MOF, are the driving forces behind higher adsorption performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".