Advanced Nanomaterials for Biological Applications
Bibliographic record
Abstract
Nanomaterials have been widely studied for many years and they have also generated an intense scientific interest due to a wide variety of potential applications in biomedical, optical, and electronic fields.Nanomaterials have drawn attention based on the few properties they exhibit like their surface to mass ratio and the reactivity of their surface.Also, the control of composition, size, shape, and morphology of nanomaterials is an essential cornerstone for the development and application of nanomaterials and nanoscale devices.The selection of material depends on factors such as (i) required size of nanoparticles, (ii) aqueous solubility and stability, (iii) surface characteristics as charge and permeability, or (iv) degree of biodegradability, biocompatibility, and toxicity.This special issue holds 3 reviews and 10 original research articles.Various nanomaterials including gold nanoparticles, TiO 2 , ZnO, magnetic nanomaterials, graphene, heparinbased nanoparticles, and polymer nanocomposites were used to show their potential applications in multimodal imaging contrast agents, cancer detection, drug delivery, cytotoxicity and genotoxicity, biosensing, antibiofilm, protein binding, and tumor destruction via heating (hyperthermia).J. Morán-Martínez and coworkers reported the utilization of coating with TiO 2 nanoparticles for the improvement of the characteristics of NiTi archwires and discussed their effect on histopathological, cytotoxic, and genotoxic properties.They have used male rats in four groups with different treatments, and the amount of TiO 2 nanoparticles was changed gradually.Their results showed that cell viability in lymphocytes treated with TiO 2 NPs did not cause genotoxicity.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".