The Effect of Silicon–Melatonin Nanoparticles on Improving Germination Parameters and Reducing Salinity Toxicity by Maintaining Ion Homeostasis in Cyamopsis tetragonoloba L. Seedlings
Bibliographic record
Abstract
The salinity of water and soil is a constraint that has an extreme effect on germination and the establishment of crops. Therefore, it is pivotal to boost crop salt tolerance in global semi-arid regions. By mixing Si in an ME medium, a new complex of nanoparticles (Si-CTS-HPC-ME NPs) was synthesized, and we investigated the role of Si-CTS-HPC-ME NPs on Cyamopsis tetragonoloba germination and tolerance against salinity stress. Thus, this study examined the influence of Si-CTS-HPC-ME NPs at different concentrations (N1: 0, N2: 40 and N3: 80 mg L−1) on some germination and seedling growth parameters and the ion homeostasis of Cyamopsis tetragonoloba L. (cluster bean) seedlings under three salinity levels (S1: 0, S2: 6 and S3: 12 dS m−1). With increasing salinity, the energy of germination (GE), index of germination (GI), index of vitality (VI), seedling vigor index (SVI), fresh weight (SFW) and dry (SDW) weight of seedlings, plumule length (PL), and radicle length (RL) parameters gradually decreased, while the mean germination time (MGT) and coefficient of velocity of germination (CVG) increased in salt-stressed cluster bean seedlings in comparison to the control. However, the usage of Si-CTS-HPC-ME NPs was effective in enhancing cluster bean tolerance to salinity by enhancing total phenols and flavonoids and improving K+, Si, and Ca2+ uptake, thus reducing lipid peroxidation, decreasing sodium ion uptake and potassium leakage, and promoting germination parameters compared with non-NP-treated seedlings. Meanwhile, 40 mg L−1 Si-CTS-HPC-ME NPs exhibited an effective response in saline conditions compared with the other NP treatments. Consequently, the application of Si-CTS-HPC-ME NPs in salt-stressed cluster bean seedlings can serve as an effective technique to enhance salinity tolerance in saline conditions under arid and semi-arid climatic conditions.
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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".