Physiological and anti-oxidative response of biologically and chemically synthesized iron oxide: Zea mays a case study
Bibliographic record
Abstract
The synthesis methodology, particle size and shape, dose optimization, and toxicity studies of nano-fertilizers are vital prior to their field application. This study investigates the comparative response of chemically synthesized and biologically synthesized iron oxide nanorods (NRs) using moringa olefera along with bulk FeCl 3 on summer maize ( Zea mays ). It is found that FeCl 3 salt and chemically synthesized iron oxides NRs caused growth retardation and impaired plant physiological and anti-oxidative activities at a concentration higher than 25 mg/L due to toxicity by over accumulation. While iron released form biologically synthesized NRs have shown significantly positive results even at 50 mg/L due to their low toxicity, an improved leaf area (13%), number of leaves per plant (26%), total chlorophyll content (80%) and nitrate content (6%) with biologically synthesized NRs are obtained. Moreover, the plant anti-oxidative activity also increased on treatment with biologically synthesized NRs because of their ability to form a complex with metal ions. These findings suggest that biologically synthesized iron oxides NRs are an efficient iron source and can last for a long time. Thus, proving that nanofertilizer are required to have specific surface chemistry to release the nutrient in an appropriate concentration for better plant growth.
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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".