Sample self‐absorption in surface‐enhanced <scp>Raman</scp> spectroscopy (SERS): influence of the resonance, dilution and depth of the measurements
Bibliographic record
Abstract
In this paper, we demonstrate that sample self‐absorption can strongly attenuate the surface‐enhanced Raman spectroscopy (SERS) signal when resonant conditions are used. Below a monolayer coverage of the SERS substrate (silver nanoparticles), the signal is weakly attenuated, and a linear relationship to the concentration is maintained, as suggested by the fact that the scattered signal is proportional to the concentration. For concentrations of analytes superior to a monolayer coverage, the intensity reaches a maximal plateau value, and the increasing amount of molecules in solution acts as filters. As a result, it strongly attenuates the intensity of the SERS scattered main peaks and of the incident excitation light. These molecules contribute to the absorption but not to the Raman scattering. At this point, the attenuation of the light follows an exponential relationship to the concentration, and a decrease in intensity is observed for very concentrated solutions. A practical example is given with the analysis of an orange dye, having a strong absorption in the visible (~400–500 nm). We show that the effect of self‐absorption is even more pronounced when depth‐related measurements are made. We also demonstrate that exciting the sample off‐resonance eliminates the self‐absorption and that a linear relationship of the Raman intensities to the concentration is respected. We finally compare the surface‐enhanced with normal Raman measures. Copyright © 2017 John Wiley & Sons, Ltd.
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 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 | 0.001 | 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) | 0.000 | 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".