Functionalization of a nanostructured hydroxyapatite with Cu(II) compounds as a pesticide: <i>in situ</i> transmission electron microscopy and environmental scanning electron microscopy observations of treated <scp><i>Vitis vinifera</i></scp> L. leaves
Bibliographic record
Abstract
BACKGROUND: The present study evaluated a biocompatible material for plant protection with the aim of reducing the amount of active substance applied. We used a synthetic hydroxyapatite (HA) that has been studied extensively as a consequence of its bioactivity and biocompatibility. An aggregation between HA nanoparticles and four Cu(II) compounds applied to Vitis vinifera L. leaves as a pesticide was studied. Formulations were characterized by X-ray diffraction (XRD), dynamic light scattering (DLS) and electron microscopy and applied in planta to verify particle aggregation and efficiency in controlling the pathogen Plasmopara viticola. RESULTS: The XRD patterns showed different crystalline phases dependig on the Cu(II) compound formulated with HA particles, DLS showed that nanostructured particles are stable as aggregates out of the nanometer range and, in all formulations, transmission electron microscopy (TEM) and environmental scanning electron microscopy (ESEM) microscopy showed large aggregates which were partially nanostructured and were recognized as stable in their micrometric dimensions. Such particles did not show phytotoxic effects after their application in planta. CONCLUSION: A formulation based on HA and a soluble Cu(II) compound showed promising results in the control of the fungal pathogen, confirming the potential role of HA as an innovative delivery system of Cu(II) ions. The present work indicates the possibility of improving the biological activity of a bioactive substance by modifying its structure through an achievable formulation with a biocompatible material. © 2018 Society of Chemical Industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".