Stability of Vanadium in Alkaline Soils Amended With Biochar and Metal Oxide Nanoparticles Under Simulated Seasonal Temperature Fluctuations
Bibliographic record
Abstract
ABSTRACT Vanadium (V) is a re‐emerging environmental contaminant due to its high mobility and potential toxicity in soil and water systems. This study examined the stability of immobilized V in soils amended with biochar (BC, 5% w/w), and biochar combined with iron (BCFe), aluminum (BCAl), or titanium (BCTi) oxide nanoparticles (1% w/w), under simulated seasonal temperature transitions. Soil was incubated for 5 months, beginning with 1 month at 4°C followed by 4 months at 22°C, simulating spring‐to‐summer seasonal transitions. Pore water was periodically sampled and analyzed for V concentrations. At the end of incubation, soils were assessed using sequential extraction, synthetic precipitation leaching procedure (SPLP), scanning electron microscopy equipped with energy‐dispersive X‐ray spectroscopy (SEM‐EDS), and Fourier transform infrared (FTIR) spectroscopy. Pore water V concentrations declined over time, with significantly lower levels during the cold phase. The BCTi treatment achieved the greatest V reduction (43%), followed by BCAl (38%), in pore water. Sequential extraction indicated that BCTi had the lowest exchangeable and highest residual V fractions. SPLP testing showed the lowest leachability of V from BCTi (3.2 mg L −1 ), followed by BCAl (3.7 mg L −1 ) amended soils, while the unamended control showed the highest (4.9 mg L −1 ). SEM‐EDS confirmed co‐location of Ti and V on soil particle surfaces. FTIR analysis revealed the presence of functional groups and nanoparticle integration into the soil matrix. These findings demonstrate that BCTi and BCAl amendments enhanced the V immobilization and the stability of V‐amendment complexes in alkaline soils under fluctuating temperatures.
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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) | 0.000 | 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".