Utilization of a Novel Chitosan/Clay/Biochar Nanobiocomposite for Immobilization of Heavy Metals in Acid Soil Environment
Bibliographic record
Abstract
An organic–inorganic composite of chitosan, nanoclay, and biochar (named as MTCB) was chosen to develop a bionanocomposite to simultaneously immobilize Cu, Pb, and Zn metal ions within the contaminated soil and water environments. The composite material was structurally and chemically characterized with the XRD, TEM, SEM, BET, and FT-IR techniques. XRD and TEM results revealed that a mixed exfoliated/intercalated morphology was formed upon addition of small amounts of nanoclay (5% by weight). Batch adsorption experiments showed that the adsorption capacity of MTCB for Cu 2+ , Pb 2+ , and Zn 2+ were much higher than that of the pristine biochar sample (121.5, 336, and 134.6 mg g −1 for Cu 2+ , Pb 2+ , and Zn 2+ , respectively). The adsorption isotherm for Cu 2+ and Zn 2+ fitted satisfactorily to a Freundlich model while the isotherm of Pb 2+ was best represented by a Temkin model. That the adsorption capacity increased with increasing temperature is indicative of the endothermic nature of the adsorption process. According to the FTIR analysis, the main mechanism involved in immobilization of metals is binding with –NH 2 groups. Results from this study indicated that modification of biochar by chitosan/clay nanocomposite enhances its potential capacity for immobilization of heavy metals, rendering the bionanocomposite into an efficient heavy metal sorbent in mine-impacted acidic waters and soils.
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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 | 0.000 | 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".