Statistical Correlation Between SERS Intensity and Nanoparticle Cluster Size
Bibliographic record
Abstract
Surface enhanced Raman scattering (SERS) mapping of biomarkers has shown great promise in determining the distribution of proteins of interest in cells and tissues. Metallic nanoparticle (NP) probes are generally used in such mapping. Since SERS intensities from NPs are dependent on size, shape, and interparticle distance/distribution, it is unclear if this method can provide reliable biomarker quantification. To address this problem, we investigated a statistical approach to the quantification of SERS from SERS probe clusters. The investigation began by considering multiple biotinylated surfaces that had been exposed to pegylated NPs (designed for biological SERS mapping) functionalized with streptavidin (defined as SERS probes). The surfaces were imaged with a scanning electron microscope and SERS-mapped with a Raman microscope. Statistical distributions of the SERS probe clusters and mapped SERS intensities on the surfaces were developed. It was found that there was a smooth polynomial relationship between SERS intensity and probe cluster size. Our result is in contrast to the sharp, highly variable intensity increases observed in studies of unmodified NPs. Based on the polynomial relationship found, it is clear that pegylated NP SERS probes might be useful for quantification in the SERS mapping of biological material, as the SERS intensity can be potentially related back to the number of probes at the acquisition point.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".