Preparation of Magnetic Activated Carbon by Activation and Modification of Char Derived from Co-Pyrolysis of Lignite and Biomass and Its Adsorption of Heavy-Metal-Containing Wastewater
Bibliographic record
Abstract
Adsorption with activated carbon (AC) is an important method for the treatment of heavy metal wastewater, but there are still certain challenges in the separation and reuse of activated carbon. The preparation of magnetic activated carbon (MAC) by modifying AC is one of the effective means to realize the separation of AC from solution after the adsorption process. In this work, lignite and poplar leaves were used as raw materials for co-pyrolysis, and the co-pyrolysis char was activated and modified to prepare MAC. The structure and properties were characterized by VSM, N2 adsorption, SEM, XRD, and FT-IR. At the same time, the adsorption performance of MAC on wastewater containing Pb and Cd ions was studied. The results show that the prepared MAC contains Fe3O4, and the saturation magnetization (Ms) of the MAC is 13.83 emu/g; the specific surface area of the MAC is 805.86 m2/g, and the micropore volume is 0.23 cm3/g; the MAC exhibited a good porous structure. When the pH value of the solution was 5, the adsorption time was 120 min, the dosage of MAC was 4 g/L, the initial concentration of Pb ion solution was 50 mg/L, and that of Cd ion solution was 25 mg/L, and the adsorption temperature was 30 °C, the adsorption efficiency of Pb, Cd ions were 84.40 and 78.80%, respectively, and the adsorption capacities were 10.55 and 4.93 mg/g, respectively. The adsorption of Pb and Cd ions by MAC conforms to the Langmuir adsorption model, which is a monolayer adsorption. The adsorption process is mainly chemical adsorption, which can be better described by the pseudo-second-order model. The adsorption thermodynamic analysis showed that the adsorption of Pb and Cd ions by MAC was a spontaneous reaction, and the higher the temperature, the stronger the spontaneity.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".