A Direct Immunoassay Based on Surface-Enhanced Spectroscopy Using AuNP/PS-b-P2VP Nanocomposites
Bibliographic record
Abstract
A biosensor was developed for directly detecting human immunoglobulin G (IgG) and adenosine triphosphate (ATP) based on stable and reproducible gold nanoparticles/polystyrene-b-poly(2-vinylpyridine) (AuNP/PS-b-P2VP) nanocomposites. The substrates were functionalized with carboxylic acid groups for the covalent binding of anti-IgG and anti-ATP and the detection of IgG and ATP (1 to 150 μg/mL). SEM images of the nanocomposite show 17 ± 2 nm AuNP clusters adsorbed over a continuous porous PS-b-P2VP thin film. UV-VIS and SERS were used to characterize each step of the substrate functionalization and the specific interaction between anti-IgG and the targeted IgG analyte. The UV-VIS results show a redshift of the LSPR band as the AuNP surface was functionalized and SERS measurements showed consistent changes in the spectral features. Principal component analysis (PCA) was used to discriminate between samples before and after the affinity tests. Moreover, the designed biosensor proved to be sensitive to different concentrations of IgG with a limit-of-detection (LOD) down to 1 μg/mL. Moreover, the selectivity to IgG was confirmed using standard solutions of IgM as a control. Finally, ATP direct immunoassay (LOD = 1 μg/mL) has demonstrated that this nanocomposite platform can be used to detect different types of biomolecules after proper functionalization.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".