Insight mechanism of magnetic activated catalyst derived from recycled steel residue for black liquor degradation
Bibliographic record
Abstract
The present work deals with developing a method for revalorizing steel residues to create sunlight-active photocatalysts based on iron oxides. Commercial-grade steel leftovers are oxidized under different combinations of pH and temperature (50–90 °C and 3 ≥ pH ≤ 5) in a low energy-intensive setup. The material with the highest production efficiency (yield > 12%) and magnetic susceptibility (χm = 387 × 10−6 m3/kg) was further explored and modified by diffusion of M2+ (Zn and Co) ions within the structure of the oxide using a hydrothermal method to create ZnFe2O4, CoFe2O4 and combined Co–Zn ferrite. (Co–Zn)Fe2O4 displayed a bandgap of 2.02 eV and can be activated under sunlight irradiation. Electron microscopy studies show that (Co–Zn)Fe2O4 consists of particles with diameters between 400 and 700 nm, homogeneous size, even distribution, and good dispersibility. Application of the developed materials in the sunlight catalysis of black liquors from cellulose extraction resulted in a reduction of the Chemical Oxygen Demand (− 15% on average) and an enhancement in biodegradability (> 0.57 BOD/COD) after 180 min of reaction. Since the presented process employs direct solar light, it opens the possibility to large-scale water treatment and chemical upgrading applications.
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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) | 0.002 | 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".