Surface enhanced Raman scattering of amino acids and peptides
Bibliographic record
Abstract
Abstract Surface Enhanced Raman Scattering (SERS) has been used to observe spectra of several amino acids and peptides. SERS signals from amino acids (tryptophan, phenylalanine and glycine) and peptides (Trp‐Trp and Gly‐Gly‐Gly) in silver colloids have been studied in three experimental configurations 1) free volume 2) liquid core waveguide (LCW) and 3) microfluidic channel. Even though all amino acids contain carboxyl and amine functional groups, the conditions for observing the SERS spectra are different for different amino acids. The impact of the type of electrolyte used for colloid aggregation and the experimental procedures for mixing of the components on the amplitude and the stability of the SERS signal have been studied. The optimum conditions for observing SERS signals for the above mentioned amino acids and peptides have been found. For Trp amino acid a SERS enhancement of Raman signal up to 107 was observed. Additional enhancement of 10 to 50 times can be obtained with the help of a liquid core waveguide technique. The results obtained will be useful in the development and optimization of microfluidic chip devices utilizing SERS. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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".