Shape-controlled synthesis of zinc nanostructures mediating macromolecules for biomedical applications
Bibliographic record
Abstract
Zinc nanostructures (ZnONSs) have attracted much attention due to their morphological, physicochemical, and electrical properties, which were entailed for various biomedical applications such as cancer and diabetes treatment, anti-inflammatory activity, drug delivery. ZnONS play an important role in inducing cellular apoptosis, triggering excess reactive oxygen species (ROS) production, and releasing zinc ions due to their inherent nature and specific shape. Therefore, several new synthetic organometallic method has been developed to prepare ZnO crystalline nanostructures with controlled size and shape. Zinc oxide nanostructures' crystal size and shape can be controlled by simply changing the physical synthesis condition such as microwave irradiation time, reaction temperature, and TEA concentration at reflux. Physicochemical properties which are determined by the shape and size of ZnO nanostructures, directly affect their biological applications. These nanostructures can decompose the cell membrane and accumulate in the cytoplasm, which leads to apoptosis or cell death. In this study, we reviewed the various synthesis methods which affect the nano shapes of zinc particles, and physicochemical properties of zinc nanostructures that determined the shape of zinc nanomaterials. Also, we mentioned some macromolecules that controlled their physicochemical properties in a green and biological approaches. In addition, we present the recent progress of ZnONSs in the biomedical fields, which will help centralize biomedical fields and assist their future research development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".