Non‐Toxic Dry‐Coated Nanosilver for Plasmonic Biosensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 SiO 2 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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it