Differentiation of Nanoparticles Isolated from Distinct Plant Species Naturally Growing in a Heavy Metal Polluted Site
Bibliographic record
Abstract
Leaves harvested from the plants of two different species (Dittrichia viscosa and Cichorium intybus) grown in their autogenous environment near a steel manufacturing company were characterized for naturally accumulated nanoparticles. These plant species are known to accumulate heavy metals. It was, however, unknown if these species would also accumulate these heavy metals in the form of nanoparticles. The isolated solid fractions were analyzed using dynamic light scattering, X-ray fluorescence, and transmission electron microscopy. These analyses revealed the presence of nanoparticles within the plants. The composition of nanoparticles found in each plant species is distinct: (i) for Dittrichia viscosa, the nanoparticle composition matched the heavy metal pollution anticipated from the surrounding industries; (ii) for Cichorium intybus, the nanoparticle composition was similar to the most abundant elements in the soil. The different behavior is a reflection of the phytoaccumulator characteristics of both species. This study provides the first evidence of sequestration of heavy metals in the form of nanoparticles by plants grown autogenously in polluted areas and will have implications in waste management of phytoremediation systems and in understanding the heavy metal life-cycle in the environment.
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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".