Iron Oxide Nanoparticles as a Potential Iron Fertilizer for Peanut (Arachis hypogaea)
Bibliographic record
Abstract
Nanomaterials are used in practically every aspect of modern life, including agriculture. The aim of this study was to evaluate the effectiveness of iron oxide nanoparticles (Fe2O3 NPs) as a fertilizer to replace traditional Fe fertilizers, which have various shortcomings. The effects of the Fe2O3 NPs and a chelated-Fe fertilizer (ethylenediaminetetraacetic acid-Fe; EDTA-Fe) fertilizer on the growth and development of peanut (Arachis hypogaea), a crop that is very sensitive to Fe deficiency, were studied in a pot experiment. The results showed that Fe2O3 NPs increased root length, plant height, biomass, and SPAD values of peanut plants. The Fe2O3 NPs promoted the growth of peanut by regulating phytohormone contents and antioxidant enzyme activity. The Fe contents in peanut plants with Fe2O3 NPs and EDTA-Fe treatments were higher than the control group. We used energy dispersive X-ray spectroscopy (EDS) to quantitatively analyze Fe in the soil. Peanut is usually cultivated in sandy soil, which is readily leached of fertilizers. However, the Fe2O3 NPs adsorbed onto sandy soil and improved the availability of Fe to the plants. Together, these results show that Fe2O3 NPs can replace traditional Fe fertilizers in the cultivation of peanut plants. To the best of our knowledge, this is the first research on the Fe2O3 NPs as the iron fertilizer.
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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".