Sustainable synthesis of multiple-metal-doped Fe2O3 nanoparticles with enhanced photocatalytic performance from Fe-bearing dust
Bibliographic record
Abstract
An Fe-abundant solid waste, fabric filter dust (FFD) was used as the starting material for the preparation of metal-doped Fe2O3 (M−Fe2O3) nanoparticles for the photocatalytic degradation of MO aqueous solution. Valuable elements including Fe, Ti, Al, Na, Si, Ca, and Mg were extracted from the dust by hydrochloric acid leaching, transformed into sediments by increasing the pH of the lixivium orderly, and converted into M−Fe2O3 nanoparticles by the sol–gel technology. The effects of pH for the precipitation process and firing temperature for the sol–gel method were investigated systematically. The M−Fe2O3 samples were characterised using X-ray diffraction, field emission scanning electron microscopy, Brunauer–Emmett–Teller analysis, UV–vis spectra, and energy dispersive spectroscopy. The performances of the dust-derived nanoparticles during photocatalytic process were appraised by visible light photodegradation of methyl orange (MO) aqueous solution, indicating that the M−Fe2O3 (M = Ti and Al) sample prepared with a precipitation pH of 4 and a firing temperature of 500 °C exhibits the most impressive photocatalytic behaviour. The product produces a degradation rate of 82.99% for 100 mL of MO solution (10 mg/L) after visible-light degradation for 180 min, which is increased from 38.94% achieved by an undoped Fe2O3 sample prepared under the same conditions.
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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".