Valorization of red mud and biomass waste via pre-pyrolysis activation for high-performance magnetic biochar in heavy metal remediation
Bibliographic record
Abstract
Heavy metal contamination of water remains a critical environmental challenge, demanding efficient, low-cost, and sustainable treatment technologies. This study presents an innovative strategy to address both wastewater pollution and industrial waste disposal by converting red mud (RM), a hazardous byproduct of aluminum production, and maple wood (MW) biomass into magnetic biochar (MBC) adsorbents. Unlike traditional post-pyrolysis biochar (BC) activation methods, a novel pre-pyrolysis biomass chemical activation approach was employed using acid (HNO 3 ) and base (KOH) to tailor the surface properties of the biomass-RM mixture prior to co-pyrolysis. The resulting materials, HNO 3 -MBC and KOH-MBC, displayed distinct physicochemical characteristics and adsorption behaviors. Despite having a lower surface area, KOH-MBC exhibited superior removal efficiencies (∼100 %) for Cu 2+ and Pb 2+ due to its abundant oxygen-containing functional groups (–OH, –COOH). HNO 3 -MBC achieved slightly lower removal (∼95 %) but offered higher mesoporosity. Adsorption was governed by chemisorption mechanisms, including electrostatic attraction, ion exchange, complexation, precipitation, and redox reactions, with both materials fitting pseudo-second-order kinetics and Langmuir isotherm models. Economic analysis highlighted the cost advantage of KOH-MBC (CAD 15.47/kg) over HNO 3 -MBC (CAD 41.29/kg), reinforcing its potential for scalable environmental applications. Overall, this work offers a sustainable and cost-effective pathway to transform industrial wastes into high-performance adsorbents for heavy metal remediation in water.
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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) | 0.001 | 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".