Role of Nanoparticle Surface Charge in Surface-Enhanced Raman Scattering
Bibliographic record
Abstract
In this work, the role of nanoparticle surface charge in surface-enhanced Raman scattering (SERS) is examined for the common case of measurements made in colloidal solutions of Ag and Au. Average SERS intensities obtained for several analytes (salicylic acid, pyridine, and 2-naphthalenethiol) on Ag and Au colloids are correlated with the pH and zeta potential (zeta) values of the nanoparticle solutions from which they were recorded. The consequence of the electrostatic interaction between the analyte and the metallic nanoparticle is stressed. The zeta potentials of three commonly used colloidal solutions are reported as a function of pH, and a discussion is given on how these influence SERS intensity. Also examined is the importance of nanoparticle aggregation (and colloidal solution collapse) in determining SERS intensities, and how this varies with the pH of the solution. The results show that SERS enhancement is highest at zeta potential values where the colloidal nanoparticle solutions are most stable and where the electrostatic repulsion between the particles and the analyte molecules is minimized. These results suggest some important criteria for consideration in all SERS measurements and also provide important insights into the problem of predicting SERS activities for different molecular systems.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".