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)
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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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) | 0.000 | 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".