Dates (Phoenix Dactylifera L.) extracts derived nanoparticles and its application
Bibliographic record
Abstract
Plant-mediated green synthesis of metallic nanoparticles (NPs) has become the most deserving alternative to chemical synthesis as this process is economical and energy-efficient, and environmentally benign. For the last twenty to thirty years, different plant sources are being utilized for the fabrication of green NPs, and few of them have used the extract of Phoenix Dactylifera L. as reducing, capping, or stabilizing agents. This review provides a detailed outline of the extraction method from various parts of dates and their synthesis with different metal salts using these extracts. The applied techniques of characterization and application of these nanoparticles have also been thoroughly discussed. The phytochemicals present in the extract were responsible for reducing the metals. Except for a few, all the investigations reported the spherical NPs but have variations in their size. These NPs have high prospects in applications such as antimicrobial, anticancer, antioxidant, and catalytic activities. This work may lead the path for additional advancement in this field, and researchers may take up the future work for the large scale production of NPs and their application using date extracts.
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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".