Valorization of red mud and biomass waste via pre-pyrolysis activation for high-performance magnetic biochar in heavy metal remediation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it