Adsorption of Cu(II) and Pb(II) in Aqueous Solution by Biochar Composites
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In this study, straw biochar (TB) was prepared by pyrolysis at 500 °C, and biochar composite material (TBS) was prepared by a 1:4 mass ratio with sludge (TS). Scanning electron microscopy and Fourier transform infrared spectroscopy were utilized to characterize the material before and after adsorption. The results demonstrated that TBS possesses significant pore structure characteristics and abundant active functional groups such as hydroxyl, carboxyl, and carbonyl groups, providing a structural basis for its efficient adsorption of heavy metal ions in aqueous solutions. The adsorption performance of the remediation materials for Cu(II) and Pb(II) in aqueous solution was systematically investigated. Experimental data showed that TBS achieved maximum adsorption capacities of 60.86 and 46.98 mg/g for Cu(II) and Pb(II) at equilibrium, respectively, exhibiting superior adsorption efficiency. Through fitting analysis using adsorption kinetic models and isothermal adsorption models, it was found that the pseudo-second-order kinetic model and Freundlich isothermal model could more accurately describe the adsorption process of the two heavy metal ions, indicating that chemical adsorption was the dominant mechanism and characterized by multilayer adsorption. Thermodynamic parameter calculations revealed negative Δ G values and positive Δ H and Δ S values, suggesting that the adsorption process was a spontaneous, entropy-increasing, and endothermic reaction. These research results fully validate the excellent removal capabilities of TBS for Cu(II) and Pb(II). This study has shown that TBS can be considered a promising and cost-effective adsorbent, demonstrating its potential to adsorb heavy metal ions 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".