Synthesis of MgO-coated corncob biochar and its application in lead stabilization in a soil washing residue
Bibliographic record
Abstract
In this study, a magnesium oxide (MgO) coated corncob biochar (MCB) was synthesized by pyrolyzing MgCl2 pretreated corncob, for a better performance in lead immobilization in a contaminated soil compared with corncob biochar (CB). The properties and microstructures of CB and MCB were investigated. It was observed that MgO particles ranging from 1 to 2 μm were well coated on MCB, and the MgO content in MCB was calculated at 29.90% in w/w. The surface area of the biochar was significantly enhanced from 0.07 to 26.56 m2/g after the MgO coating. The MgO coating also significantly facilitated the lead removal percentage from 23% to 74% in aqueous solution by biochar. CB failed to immobilize lead in a soil washing residue and could not reduce its environmental risks in a laboratory incubation study. In contrast, MCB was applied to the soil and resulted in a significant reduction in TCLP leached lead from 10.63 to 5.24 mg/L (reduced by 50.71%). The comparison between MCB and other amendments suggests that the biochar component of MCB adsorbed lead onto its surface through cation-π interaction and increased surface adsorption due to higher surface area, and then the MgO coated on MCB's surface further enhanced the adsorption through precipitation. The synergistic roles of biochar-mineral composites make them a promising candidate for soil remediation.
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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".