Magnetic adsorbents from co‐pyrolysis of non‐woody biomass and red mud for water decontamination
Bibliographic record
Abstract
Abstract Red mud (RM) and non‐woody biomass are both underutilized resources for renewable composite materials, which could be used in environmental decontamination processes. This study aims to investigate the efficacy of co‐pyrolyzing non‐woody biomass with RM to produce a magnetic biochar composite. When pyrolyzed, RM is reduced to magnetic iron while the non‐woody biochar is responsible for the adsorption of organic compounds. Ibuprofen, acetaminophen, methyl orange, and methylene blue were used as test compounds to investigate the overall adsorptive capacity of the composite and to determine the possible adsorption mechanisms of biochar produced from RM pyrolyzed with switch grass, phragmites, rice husk, and miscanthus. The composite produced from a 1 to 1 mixture of RM and miscanthus showed the highest adsorption capacity with 13.8 and 8.34 mg/g of ibuprofen and acetaminophen adsorbed, respectively, which is attributed to its greater ‐interactions as a result of lower surface oxygen sites. Different ratios of RM to biomass were also tested for the production of the miscanthus composite, where it was found that the 1:2 ratio showed the best overall adsorption with 25.9 mg/g removal of acetaminophen, surpassing the miscanthus biochar's at 17.9 mg/g.
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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 | 0.001 | 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) | 0.001 | 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".