Application of ZnO Nanoparticles Encapsulated in Mesoporous Silica on the Abaxial Side of a <i>Solanum lycopersicum</i> Leaf Enhances Zn Uptake and Translocation via the Phloem
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Foliar application of nutrient nanoparticles (NPs) is a promising strategy for improving fertilization efficiency in agriculture. Phloem translocation of NPs from leaves is required for efficient fertilization but is currently considered to be feasible only for NPs smaller than a cell wall pore size exclusion limit of <20 nm. Using mass spectrometry imaging, we provide here the first direct evidence for phloem localization and translocation of a larger (∼70 nm) fertilizer NP comprised of ZnO encapsulated in mesoporous SiO 2 (ZnO@MSN) following foliar deposition. The Si content in the phloem tissue of the petiole connected to the dosed leaf was ∼10 times higher than in the xylem tissue, and ∼100 times higher than the phloem tissue of an untreated tomato plant petiole. Direct evidence of NPs in individual phloem cells has only previously been shown for smaller NPs introduced invasively in the plant. Furthermore, we show that uptake and translocation of the NPs can be enhanced by their application on the abaxial (lower) side of the leaf. Applying ZnO@MSN to the abaxial side of a single leaf resulted in a 56% higher uptake of Zn as well as higher translocation to the younger (upper) leaves and to the roots, than dosing the adaxial (top) side of a leaf. The higher abaxial uptake of NPs is in alignment with the higher stomatal density and lower density of mesophyll tissues on that side and has not been demonstrated before.
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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".