Development of a Recyclable Remediation System for Gaseous BTEX: Combination of Iron Oxides Nanoparticles Adsorbents and Electrochemistry
Bibliographic record
Abstract
We designed a two-step green technique to remove and recycle selected gaseous air pollutants. The first step includes the assessment of adsorption efficiencies of BTEX (benzene, toluene, ethylbenzene, and xylenes) on magnetite, hematite, and their composite surfaces. Improvement of the synthesis method led to BTEX adsorption (>85%; 200 ppmv) on 1.0 g of nanoparticles within a time scale of minutes. The second element included the design of an electrochemical reactor for the regeneration of used nanoparticles. NaOH showed superior performance as an electrolyte in comparison to NaCl and Na 2 CO 3 . The stripping efficiency for cathodic regeneration was higher than the anodic one. Under optimized conditions, the stripping efficiency was up to 85%. Iron oxides nanoparticles were regenerated (∼90%). Using high-resolution transmission electron microscopy, X-ray diffraction, NanoScan, and Brunauer–Emmett–Teller, selected physical and chemical properties of nanosurfaces were analyzed, revealing that the physical properties of nanoparticles remained unchanged during the regeneration process.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 teacher head, 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".