Fe<sub>3</sub>O<sub>4</sub> Nanoparticles and Carboxymethyl Cellulose: A Green Option for the Removal of Atmospheric Benzene, Toluene, Ethylbenzene, and <i>o</i>-Xylene (BTEX)
Why this work is in the frame
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Bibliographic record
Abstract
In this work, we investigate the interaction of gaseous benzene, toluene, ethylbenzene, and o -xylene (BTEX) with Fe 3 O 4 nanoparticles and demonstrate the potential application of Fe 3 O 4 nanoparticles as adsorbents for BTEX. On the basis of X-ray diffraction, transmission electron microscopy, gas chromatography–mass spectrometry, and gas chromatography–flame ionization detection results, using toluene as a model compound, we find that adsorption is of a heterogeneous nature. At relatively high concentrations of toluene (300–2790 ppmv), X-ray photoelectron spectroscopy results indicate an increase in the divalent cations relative to the trivalent cations of Fe 3 O 4 nanoparticles, which is possibly triggered by nanoscale effects. Removal efficiency experiments show that Fe 3 O 4 nanoparticles (4 g) reduce 100 ppmv of BETX in air by 83 ± 8%, 95 ± 5%, 97 ± 1%, and 98 ± 2%, respectively. Comparable removal efficiencies were observed for recycled Fe 3 O 4 nanoparticles. Toluene was also removed from a flow by Fe 3 O 4 nanoparticles bound together with carboxymethyl cellulose, without releasing undesired aerosols. Fe 3 O 4 nanoparticles (bare and as a composite) show potential as practical and environmental friendly materials for the remediation of BTEX from air.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it