Zero-Valent Iron Nanoparticles Remediate Nickel-Contaminated Aqueous Solutions and Biosolids-Amended Agricultural Soil
Bibliographic record
Abstract
Nickel (Ni+2) accumulation in wastewater treatment sludge poses a potential environmental risk with biosolids-land application. An incubation experiment was conducted to evaluate the effect of nanoparticles of zero-valent iron (nZVI) on Ni+2 sorption in biosolids-treated agricultural soils. Two application rates of biosolids (0, 5%, w/w) and four treatment levels (0, 1, 5, and 10 g/kg) of nZVI were examined, either separately or interactively. The results of this study showed significant differences in Ni+2 sorption capacity between different nZVI treatments. The initial Ni+2 concentration in biosolids-amended soil significantly affected Ni sorption in the soil treated with nZVI. The “H-shape” of sorption isotherm in nZVI-treated soil reflects strong interaction between the Ni concentration and the nZVI treatment, while the C-shape of sorption isotherm in biosolids-amended soil without the nZVI treatment indicates intermediate affinity for Ni+2 sorption. Nickel retention in soil was increased with the increase of nZVI levels. The removal efficiency of Ni+2 by nZVI from solution was increased with the increase of pH from 5 to 11 and reached a maximum of 99.56% at pH 11 and nZVI treatment of 10 g/kg. The Ni+2 desorption rate decreased from 92 to 7, 4, and 1% with increasing nZVI treatment levels from 0 to 1, 5, and 10 g/kg, respectively, with a soil Ni+2 concentration of 50 mg/L. The maximum adsorption capacity (qmax) of 10 g/kg nZVI-treated soil was 333.3 mg/g, which was much higher than those from the other treatments of 0 (5 mg/g), 1 (25 mg/g), and 5 g/kg (125 mg/g). The underlying mechanism for Ni+2 immobilization using nZVI in an aquatic environment is controlled by a sorption process, reduction of metal ion to zero-valent metal, as well as (co)precipitation. Moreover, increasing the nZVI treatment level in biosolids-amended soil significantly decreased bioavailable Ni+2 concentrations in the soil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".