Effective medium theories in surface enhanced infrared spectroscopy: The pentacene example
Bibliographic record
Abstract
Effective medium theory (EMT) is a semiempirical approach developed to predict the response properties of composites. In particular, EMT has been applied to the study of rough metal surfaces that can enhance the absorption of electromagnetic radiation in the infrared [surface-enhanced infrared absorption (SEIRA) or in the visible (surface-enhanced visible]. The application of EMT provides a formalism to simulate the effective dielectric function of the inhomogeneous medium, for instance, of metal-organic thin films. The EMT is widely applied when the dimensions of the inhomogeneties (granular components in the mixed film) are smaller than the wavelength of the incident radiation. The computational approach to SEIRA using EMT, and the experimental SEIRA results for pentacene are presented here. First, enhancement factors are calculated using EMT method for pentacene on Ag, Cu, and Sn. Vibrational intensities for each symmetry species are obtained using DFT B3LYP at the 6-31G(d) level of theory. Second, from the set of experimental data provided by reflection- absorption infrared (RAIRS) and transmission FT-IR spectra of a 15 nm pentacene film evaporated onto reflecting and IR transparent support substrates, respectively, the molecular orientation and the interpretation of the observed spectra in conformity with the surface selection rules was extracted. The interpretation of the pentacene SEIRA spectra of a 15 nm pentacene film on silver and tin islands is presented and its compliance with surface selection rules is discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".