In situ self-assembly of gold nanoparticles on hydrophilic and hydrophobic substrates for influenza virus-sensing platform
Bibliographic record
Abstract
Abstract Nanomaterials without chemical linkers or physical interactions that reside on a two-dimensional surface are attractive because of their electronic, optical and catalytic properties. An in situ method has been developed to fabricate gold nanoparticle (Au NP) films on different substrates, regardless of whether they are hydrophilic or hydrophobic surfaces, including glass, 96-well polystyrene plates, and polydimethylsiloxane (PDMS). A mixture of sodium formate (HCOONa) and chloroauric acid (HAuCl 4 ) solution was used to prepare Au NP films at room temperature. An experimental study of the mechanism revealed that film formation is dependent on surface wettability and inter particle attraction. The as-fabricated Au NP films were further applied to the colorimetric detection of influenza virus. The response to the commercial target, New Caledonia/H1N1/1999 influenza virus, was linear in the range from 10 pg/ml to 10 μg/ml and limit of detection was 50.5 pg/ml. In the presence of clinically isolated influenza A virus (H3N2), the optical density of developed color was dependent on the virus concentration (10–50,000 PFU/ml). The limit of detection of this study was 24.3 PFU/ml, a limit 116 times lower than that of conventional ELISA (2824.3 PFU/ml). The sensitivity was also 500 times greater than that of commercial immunochromatography kits.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".