Advancing phosphorus foliar fertilization with nano-hydroxyapatite: mechanisms and agricultural relevance
Bibliographic record
Abstract
Abstract Phosphorus (P) use in agriculture remains inefficient and poses long-term environmental concerns. We investigate the uptake, redistribution, and efficacy of foliar-applied nano-hydroxyapatite (nHAp) in P-deficient barley (Hordeum vulgare), aiming to clarify its mode of action and evaluate its potential as a practical alternative to conventional phosphate salts. Using a simple, low-cost wet synthesis, we produced chemically-labelled nHAp with uniform elongated morphology (median length and width of 33.6 and 5.3 nm, respectively), excellent colloidal stability, pH-dependent solubility, and full redispersibility, suitable for highly concentrated foliar formulations. Infiltration experiments reveal that nHAp dissolves gradually in situ, with peak phosphate release between 1-3 days post-treatment, enabling local and systemic P recovery in deficient plants without inducing leaf scorching, unlike conventional P fertilizers. Bioimaging reveals that nHAp, applied as droplets to the leaf surface, penetrates via stomata and diffuses through the apoplast, where it dissolves and releases P without entering mesophyll cells. Uptake efficiency was strongly influenced by formulation surface tension and air humidity, and varied markedly between crop species. When comparing barley with potato, it was shown that potato exhibited high uptake at single applications, whereas barley required repeated treatments due to lower uptake efficiency. These findings position nHAp as a promising nanobiotechnological approach for sustainable and slow-release foliar P fertilization.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".