Synergistic relationship of endophyte-nanomaterials to alleviate abiotic stress in plants
Bibliographic record
Abstract
Plant responses to abiotic stresses through diverse mechanisms and strategic measures in utilizing nanomaterials have positively impacted crop productivity. Stress can cause membrane depletion, reactive oxygen species formation, cell toxicity and death, and reduction in plant growth. However, nanomaterials can mitigate some of the negative impacts of abiotic stresses and enhance crop yield. Some endophytic microbes can synthesize nanomaterials, which can maintain and enhance plant health and growth via nitrogen fixation, siderophore production, phytohormones synthesis, and enzyme production without any pathological effects. Nanoparticle-synthesizing endophytes also help boost plant biochemical and physiological functions by ameliorating the impact of abiotic stresses. The increase in the use and implementation of nano-growth enhancers from beneficial microbes, such as nano-biofertilizers, nano-pesticides, nano-herbicides, and nano-fungicides are considered safe and eco-friendly in ensuring sustainable agriculture and reduction of agrochemical usage. Promisingly, nanotechnology concepts in agriculture aim to sustain plant health and protect plants from oxidative stresses through the activation of anti-oxidative enzymes. The mechanisms and the use of nanomaterials to relieve abiotic plant stress still require further discussion in the literature. Therefore, this review is focused on endophytic microbes, the induction of abiotic stress tolerance in plants, and the use of nanomaterials to relieve abiotic plant stresses.
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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) | 0.001 | 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".