Proline doped ZnO nanocomposite alleviates NaCl induced adverse effects on morpho-biochemical response in Coriandrum sativum
Bibliographic record
Abstract
Salinity stress is a major abiotic factor causing destructive impact on plant growth. Therefore, an approach is needed to alleviate the negative effect of salinity stress. Considering the distinct and advantageous effects of proline and zinc oxide nanoparticles (ZnO NPs) on plant growth, ZnO NPs were functionalized by proline (ZnOP NPs) under the concept of nanofertilizer and slow release of molecules. An in vitro study was performed to investigate the effect of nanocomposite on Coriandrum sativum (coriander) exposed to 50 mM NaCl stress. Scanning electron microscopy (SEM) and X-ray diffraction (XRD) revealed 14.73 nm and 20.59 nm size of ZnO NPs and ZnOP NPs, respectively with spherical to hexagonal structures. Vegetative parameters of plants like height and biomass were improved by the application of NPs. ZnOP NPs at 100 mg/L depicted 200% increase in shoot length while 157% increase in root length when applied with NaCl stress. Fresh weight of shoots and roots increased upto 387 mg and 127 mg, respectively at 100 mg/L ZnOP NPs. Antioxidant and phytochemical activities that were increased due to salinity stress, decreased by ZnOP NPs. Radical scavenging activity reduced upto 55%, antioxidant potential upto 35%, and reducing power by 20% in shoots of plants as compared to NaCl stressed plants. A similar trend was also observed in roots. Likewise, phenolic content decreased by 30% in shoots and 43% in roots at 100 mg/L of ZnOP NPs and same for flavonoid contents. Application of ZnOP NPs to salt stressed plants also reduced the concentrations of superoxide dismutase (SOD) and peroxide dismutase (POD). In conclusion, proline functionalized ZnO NPs were proved effective against salinity stress. This nanofertilizer can be promoted as a potential candidate for combating the negative effects of saline condition.
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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".