Co‐Application of Wood Biochar and Nano‐Titanium Dioxide to Immobilize Vanadium in Alkaline Soils
Bibliographic record
Abstract
ABSTRACT Titanium dioxide (TiO2) and biochar have been used as amendments to adsorb vanadium (V) in aqueous solutions; however, their simultaneous application in the remediation of V‐contaminated neutral‐alkaline soils is rare. TiO2 nanoparticles, biochar, and a blend of biochar+TiO2 were investigated as amendments for an alkaline V‐contaminated soil. Treatments involved mixing (weight basis) 1% TiO2 (T1), 5% biochar (T2), 1% TiO2 + 5% biochar (T3) with soil, and an unamended soil (T4). An adsorption edge study was performed from pH 4 to 10 at the V concentration of 200 mg L−1. A standard vanadium (V) solution was prepared using NaVO3. An incubation study was conducted over 3 months with V‐contaminated soil at a rate of 200 mg kg−1, at 80% field capacity. At the end of the incubation period, the treated soils were subjected to V fractionation. X‐ray diffraction (XRD) patterns and FTIR spectra of TiO2 nanoparticles, biochar, uncontaminated soil, and four treated soils were obtained. The adsorption edge of V was below pH 4.6, suggesting reduced retention of V in alkaline soils. In the combined treatment, the adsorption edge was elevated by one unit compared to the control. V adsorption increased in TiO2, biochar, and combined TiO2+biochar treatments at 7%, 4%, and 20%, respectively, compared with the unamended soil (control) at a pH of ~7.6. Functional groups revealed the possibility of inner‐sphere and outer‐sphere adsorption mechanisms between vanadate and the mix amendment of TiO2+biochar. The labile V fraction decreased, and the nonlabile V fraction increased, significantly in the TiO2+biochar amended soil compared to the unamended soil. Applying a blend of biochar+TiO2 reduced the mobility of V in a neutral‐alkaline soil, thereby preventing it from contaminating the nearby soil and water bodies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".