Nanoparticle-based surface enhanced Raman spectroscopic imaging of biological arrays
Bibliographic record
Abstract
Surfaces serve as the communication link between the adsorbate and the substrate. Hence, a thorough understanding of the surface chemistries directly interfacing with biological molecules and other adsorbates would provide insight into the fabrication approach as well as the adsorption characteristics of biomolecules adsorbed on the surface. This paper presents a surface-enhanced Raman spectroscopy (SERS) method for high-sensitivity detection and reading of protein microarrays based on gold nanoparticle labels. The reagent employed was 30 nm gold nanoparticles modified with a bifunctional Raman reporter molecule, 5,5'-dithiobis(succinimidyl-2-nitrobenzoate) (DSNB), to integrate anti-bovine IgG for an antigen response in the immunoassay and generate an intense SERS signal. The signal from the DSNB reporter molecule, particularly the strong symmetric nitro stretch was used for the detection of antigen-antibody interactions. Issues related to the sensitivity and selectivity of the assay were also addressed. This work provides useful insights into SERS-based immunoassays and serves as the basis for an eventful adventure into interfacial biomolecular interactions.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".