Abiotic Stress-Triggered Nanocarriers for Seed Nanopriming and Early-Stage Plant Development
Bibliographic record
Abstract
Climate change and increasing soil salinity threaten global food security, particularly in arid and semiarid regions where crop productivity is already compromised. Addressing these challenges requires sustainable agricultural innovations that enhance the plant resilience to abiotic stress. Kinetin (Kn), a phytohormone that regulates plant growth and development, shows strong potential to enhance stress tolerance but suffers from poor solubility and instability under environmental conditions, limiting its agricultural use. To overcome these limitations, we developed zinc-caffeic acid-based metal-phenolic nanocarriers (CAFZin) to encapsulate kinetin. The resulting system, CAFZin-K, was synthesized through coordination-driven self-assembly and thoroughly characterized for its morphology, loading efficiency, and release behavior. CAFZin-K enabled sustained kinetin release and improved its stability, achieving a 3.7-fold increase in uptake compared to that of free Kn. When used for seed nanopriming, CAFZin-K significantly enhanced germination rate, shoot and root growth, and overall biomass compared to nonencapsulated Kn and untreated controls under both normal and saline conditions. These findings demonstrate the potential of metal-phenolic nanocarriers as cost-effective, green, and eco-designed systems for the protection and controlled release of bioactive molecules, supporting crop performance under challenging environmental 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) | 0.001 | 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".