Surface Enhanced Infrared Studies of 4-Methoxypyridine Adsorption on Gold Film Electrodes
Bibliographic record
Abstract
This work uses electrochemical surface sensitive vibrational spectroscopy to characterize the adsorption of a known metal nanoparticle stabilizer and growth director, 4-methoxypyridine (MOP). Surface enhanced infrared absorption spectroscopy (SEIRAS) is employed to study the adsorption of 4-methoxypyridine on gold films. Experiments are performed under electrochemical control and in different electrolyte acidities to identify both the extent of protonation of the adsorbed species as well as its orientation with respect to the electrode surface. No evidence of adsorbed conjugated acid is found even when the electrolyte pH is considerably lower than the pKa. Through an analysis of the transition dipole moments, determined from DFT calculations, the SEIRA spectra support an adsorption configuration through the ring nitrogen which is particularly dominant in neutral pH conditions. Adsorption is dependent on both the electrical state of the Au film electrode as well as the presence of ions in the electrolyte that compete for adsorption sites at positive potentials. Combined differential capacitance measurements and spectroscopic data demonstrate that both a horizontal adsorption geometry and a vertical adsorption phase can be induced, with the former being found on negatively charged surfaces in acidic media and the latter over a wide range of polarizations in neutral solutions.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".