Feasibility Study on Reducing Lead and Cadmium Absorption by Alfalfa (Medicago scutellata L.) in a Contaminated Soil Using Nano-Activated Carbon and Natural Based Nano-Zeolite
Bibliographic record
Abstract
The first risk posed by heavy metal pollution in an ecosystem is metal accumulation in the biomass of growing plants, which has harmful effects on human health. Natural-based nanoparticles are efficient in remediating environmental pollutants because they have a high surface/volume ratio, high chemical activity and produce no harmful side-products. The present study investigates the capacity of natural-based nano-porous adsorbents for reducing the availability of heavy metals to annual alfalfa (Medicago scutellata L.) roots and keeps them in soil. In a factorial experiment based on a randomized design (with four replications), three nano-adsorbents (nano-activated carbon, natural nano-zeolite and modified nano-zeolite) and two heavy metals (lead and cadmium) have been tested. The results demonstrated that applying the highest rate of activated carbon and modified nano-zeolite reduced shoot Pb content by 34% and 33.2%, and shoot Cd content by 35.5% and 46.7%, respectively, compared with the adsorbent-free control. ********* In press - Online First. Article has been peer reviewed, accepted for publication and published online without pagination. It will receive pagination when the issue will be ready for publishing as a complete number (Volume 47, Issue 4, 2019). The article is searchable and citable by Digital Object Identifier (DOI). DOI link will become active after the article will be included in the complete issue. *********
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 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 | 0.001 |
| 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".