Chemical adsorption of salicylate on silver - A systematic approach to the interpretation of surface-enhanced vibrational spectra
Bibliographic record
Abstract
In this work, surface-enhanced vibrational spectroscopy and normal vibrational spectroscopy as well as density functional theory (DFT) computational methods have been employed to investigate the nature of the chemical adsorption and orientation of the surface species generated from salicylic acid at silver surfaces. The structure of salicylic acid and its IR and Raman spectra are determined at the B3LYP/6-311+G(d,p) level of theory. These results are used in the assignment of the vibrational spectra. Surface-enhanced Raman scattering (SERS) spectra obtained from silver island films thinly coated with salicylic acid confirm chemical adsorption on the Ag nanostructures. To probe the nature of this surface complex, the optimized geometries and IR and Raman spectra of two model salicylate-silver complexes (Ag1 and Ag2) were calculated at the B3LYP/Lanl2DZ level of theory. It was found that good agreement exists between experimentally observed SERS spectra and the simulated SERS spectra of a complex with the salicylate monoanion bound to a Ag + ion through its carboxylate group (Ag1). The carboxylate silver salt of salicylic acid (essentially the Ag1 complex) was also prepared, and its IR and Raman spectra were recorded for comparison with the surface-enhanced vibrational spectra. These results, along with the application of surface selection rules, suggest that salicylic acid is deprotonated at silver surfaces, interacting through its carboxylate group alone, and is preferentially in a tilted head-on orientation.Key words: chemisorption, salicylic acid, silver, density functional theory, surface-enhanced Raman scattering, reflection-absorption IR spectroscopy, surface-enhanced IR absorption.
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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".