Preparation and characterization of manganese‐containing biochars and their lead ion adsorption mechanism
Bibliographic record
Abstract
Abstract Heavy metal pollution causes severely adverse impacts on human and ecosystem health. Adsorption is one of the most effective technologies used in the removal of heavy metal ions from contaminated water. However, its widespread applications are limited, owing to its high cost and low adsorbent selectivity. In this study, modified manganese‐containing biochars were synthesized to improve their Pb 2+ adsorption ability. Fraxinus mandshurica sawdust samples were impregnated with pure water as well as KMnO 4 and MnSO 4 aqueous solutions (FM‐BC, FM‐M7BC, and FM‐M2BC, respectively) for 24 hours at 25°C and pyrolyzed at 400°C. The resulting biochars were characterized using x‐ray diffraction (XRD), scanning electron microscopy (SEM), energy‐dispersive x‐ray analyses (EDX), Fourier‐transform infrared spectroscopy (FTIR), N 2 adsorption‐desorption isotherms, and Boehm titration. Batch adsorption experiments revealed the kinetics and magnitude of Pb 2+ adsorption onto the biochars. The Pb 2+ adsorption capacity of FM‐M7BC (156.25 mg · g −1 ) was significantly higher than those of FM‐M2BC (25.35 mg · g −1 ) and FM‐BC (11.99 mg · g −1 ), and based on FTIR and Boehm titration results before and after Pb 2+ adsorption, an adsorption mechanism was proposed. FM‐M7BC showed the highest adsorption enhancement owing to the strong affinity of MnCO 3 particles and O‐containing groups for Pb 2+ . Additionally, the influence of the different oxidation states of manganese (Mn(VII), and Mn(II)) in the manganese components on the Pb 2+ adsorption performance of the biochars was extremely strong. FM‐M7BC was an effective adsorbent for removing Pb 2+ .
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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".