High sensitivity surface enhanced Raman scattering detection of organic molecules and amino acids
Bibliographic record
Abstract
Surface enhanced Raman scattering is a promising technique for high sensitivity analytical applications. However, there are issues which have to be addressed in order to make SERS a reliable technique such as the optimization of conditions for any given analyte, understanding the kinetic processes of binding of the target molecules to the nanostructures and understanding the evolution and coagulation of the nanostructures, in the case of colloidal solutions. The background electrolyte is a very important factor in SERS experiments. Here we report a detailed study of the influence of the addition of different types of electrolytes on the amplitude and kinetics of the SERS signal in silver colloids. Different amino acids and organic dyes were used as test molecules in the concentration range of 10-8 to 10-4 M. We found that a new proposed electrolyte containing HCO3, CO3, Cl and SO4 ions provides very high enhancement of Raman signal in organic molecules we studied. The advantages of the composite electrolyte are especially noticeable at low concentration of tryptophan where we observed 108 enhancement of Raman signal, approximately 300 times larger than for the case of commonly used electrolyte sodium chloride.
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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 | 0.001 | 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".