An Effective Medium Theory Description of Surface-Enhanced Infrared Absorption from Metal Island Layers Grown on Conductive Metal Oxide Films
Bibliographic record
Abstract
The influence of a thin film of a supporting layer of conductive metal oxide (CMO) on the surface-enhanced infrared spectra generated from metal island layers is studied using an effective medium (EM) approximation and compared to experimental results. Gold island films electrodeposited on indium tin oxide (ITO) coated on an internal reflection element (IRE) give rise to asymmetric (bimodal or derivative-looking) line shapes and have strong surface-enhanced infrared absorption spectroscopy (SEIRAS) intensities for adsorbed cyanate using both p- (transverse magnetic) and s- (transverse electric) plane-polarized light. The dependence of the SEIRAS intensity on the angle of incidence is very different compared to metal films directly deposited on the surface of the IRE, as larger magnitude SEIRAS intensities are observed at angles close to the critical angle. The role of additional enhancement effects from possible plasmonic phenomena arising in the ITO layer is shown not to contribute to the ATR-SEIRAS mechanism. The observed spectra are modeled using an EM treatment of the gold island film, and a good qualitative and semiquantitative agreement is found between the calculated and experimental results. Using the Fresnel equations and EM-determined optical constants, the reflectivity of the interface is shown to be highly dependent on the volume fraction of gold in the enhancing layer, and asymmetric and even inverted bands arise near the metal percolation threshold. Electric field analysis shows that the low free charge carrier density (relative to metals) of ITO gives rise to field distributions similar to those of an absorbing dielectric layer on the surface of an IRE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".