Nanoparticle Properties
Bibliographic record
Abstract
As a constantly expanding class of materials with several applications, NPs have drawn enormous interest. They are being used at an accelerated rate in diverse fields like cosmetics, food industry, and electronics. In the sphere of modern medicine too, NPs have become an inevitable paradigm. Due to the extremely high ratio of atoms on their surface to those inside the particle, NPs have what are known as quantum characteristics. As properties like shape, size, and morphology of NPs diverge from those of bulk materials, their catalytic characteristics improve and so is their applicability. Cellular interactions, behavior of NPs, and their effects are influenced by various properties of NPs, size, shape, and charge being the most prominent. However, while opening the new horizons, unique properties of NPs can also account for toxicity. Thus, it is crucial to understand properties of NPs. The chapter focuses on important properties of NPs. Cellular interactions and toxicity of NPs have been discussed in brief. In addition, the characterization techniques for determining surface properties have been tabulated.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.026 |
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".