Simultaneous removal of ammonium, phosphate, and phenol via self-assembled biochar composites CBCZrOFe <sub>3</sub> O <sub>4</sub> and its utilization as soil acidity amelioration
Bibliographic record
Abstract
High concentrations of ammonium, phosphate, and phenol are recognized as water pollutants that contribute to the degradation of soil acidity. In contrast, small quantities of these nutrients are essential for soil nutrient cycling and plant growth. Here, we reported composite materials comprising biochar, chitosan, ZrO, and Fe3O4, which were employed to mitigate ammonium, phosphate, and phenol contamination in water and to lessen soil acidity. Batch adsorption experiments were conducted to assess the efficacy of the adsorbents. Initially, comparative studies on the simultaneous removal of NH4, PO4, and phenol using CB (biochar), CBC (biochar + chitosan), CBCZrO (biochar + chitosan + ZrO), and CBCZrOFe3O4 (biochar + chitosan + ZrO + Fe3O4) were conducted. The results discovered that CBCZrOFe3O4 exhibited the highest removal percentage among the adsorbents (P < 0.05). Adsorption data for CBCZrOFe3O4 were well fitted to the second-order kinetic and Freundlich isotherm models, with maximum adsorption capacities of 112.65 mg/g for NH4, 94.68 mg/g for PO4 and 112.63 mg/g for phenol. Subsequently, the effect of CBCZrOFe3O4-loaded NH4, PO4, and phenol (CBCZrOFe3O4-APP) on soil acidity was studied over a 60-day incubation period. The findings showed no significant changes (P < 0.05) in soil exchangeable acidity, H+, Mg, K, and Na. However, there was a substantial increase in the soil pH, EC, available P, CEC, N-NH4, and N-NO3. A significant reduction was also observed in the available soil exchangeable Al and Fe (P < 0.05). This technique demonstrated multi-functionality in remediating water pollutants and enhancing soil acidity.
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 | 0.001 |
| 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".