Non‐Toxic Dry‐Coated Nanosilver for Plasmonic Biosensors
Bibliographic record
Abstract
Abstract The plasmonic properties of noble metals facilitate their use for in vivo bio‐applications such as targeted drug delivery and cancer cell therapy. Nanosilver is best suited for such applications as it has the lowest plasmonic losses among all such materials in the UV‐visible spectrum. Its toxicity, however, can destroy surrounding healthy tissues and thus, hinders its safe use. Here, that toxicity against a model biological system (Escherichia coli) is “cured” or blocked by coating nanosilver hermetically with a about 2 nm thin SiO2 layer in one‐step by a scalable flame aerosol method followed by swirl injection of a silica precursor vapor (hexamethyldisiloxane) without reducing the plasmonic performance of the enclosed or encapsulated silver nanoparticles (20–40 nm in diameter as determined by X‐ray diffraction and microscopy). This creates the opportunity to safely use powerful nanosilver for intracellular bio‐applications. The label‐free biosensing and surface bio‐functionalization of these ready‐to‐use, non‐toxic (benign) Ag nanoparticles is presented by measuring the adsorption of bovine serum albumin (BSA) in a model sensing experiment. Furthermore, the silica coating around nanosilver prevents its agglomeration or flocculation (as determined by thermal annealing, optical absorption spectroscopy and microscopy) and thus, enhances its biosensitivity, including bioimaging as determined by dark field illumination.
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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 | 0.001 | 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".