Synthesis of nanoparticles, their biocompatibility, and toxicity behavior for biomedical applications
Bibliographic record
Abstract
Nanomaterials research has in part been focused on their use in biomedical applications for more than several decades. However, in recent years this field has been developing to a much more advanced stage by carefully controlling the size, shape, and surface-modification of nanoparticles. This review provides an overview of two classes of nanoparticles, namely iron oxide and NaLnF4, and synthesis methods, characterization techniques, study of biocompatibility, toxicity behavior, and applications of iron oxide nanoparticles and NaLnF4 nanoparticles as contrast agents in magnetic resonance imaging. Their optical properties will only briefly be mentioned. Iron oxide nanoparticles show a saturation of magnetization at low field, therefore, the focus will be MLnF4 (Ln = Dy3+, Ho3+, and Gd3+) paramagnetic nanoparticles as alternative contrast agents which can sustain their magnetization at high field. The reason is that more potent contrast agents are needed at magnetic fields higher than 7 T, where most animal MRI is being done these days. Furthermore we observe that the extent of cytotoxicity is not fully understood at present, in part because it is dependent on the size, capping materials, dose of nanoparticles, and surface chemistry, and thus needs optimization of the multidimensional phenomenon. Therefore, it needs further careful investigation before being used in clinical applications.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".