Nano-oxides immobilize cadmium, lead, and zinc in mine spoils and contaminated soils facilitating plant growth
Bibliographic record
Abstract
Nanoparticles with high reactivity can be applied as amendments to remediate soil metal contaminations by immobilizing toxic elements. Nano-oxides of Fe have been studied but Al and Ti nano-oxides have not been tested for their remediation capacity of toxic metals. The potential of synthesized iron (Fe-O), aluminum (Al-O), and titanium (Ti-O) nano-oxides for stabilizing Cd, Pb, and Zn in mine spoil (Chat) and contaminated soil was compared using adsorption studies and a greenhouse experiment. Chat and soil were amended with nano-oxides at two rates (25 and 50 g·kg−1) and a pot experiment was conducted with sorghum (Sorghum bicolor L. Moench). Leachates were collected twice per week from plant emergence to harvest at maturity and metals were compared against an unamended control. Chat was contaminated with Cd, Pb, and Zn at 84, 1583, and 6154 mg·kg−1, and soil at 15, 1260, and 3082 mg·kg−1, respectively. Adsorption conformed to the Langmuir linear isotherm and adsorption maxima of metals were in the order of Al-O > Ti-O ≥ Fe-O. Nano-oxides reduced Cd concentration by 28% (Fe-O) to 87% (Ti-O) and Zn concentration by 14% (Fe-O) to 85% (Al-O) in plant tissues compared with unamended Chat. Nano-oxides significantly reduced Cd, Pb, and Zn in leachates and available Cd and Zn in Chat/soil relative to the respective unamended controls. Nano-oxides can be used to remediate heavy metal contaminated Chat and soil and facilitate plant growth under proper nutrient supplements. Nano-oxides of Al-O and Ti-O remediated metals more effectively than Fe-O.
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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".