Boosting the Transparency of Thin Layers by Coatings of Opposing Susceptibility: How Metals Help See Through Dielectrics
Bibliographic record
Abstract
We propose a hypothesis that a very thin layer can be made more transparent by adding a thin coating with susceptibility of opposing sign. Two experimental tests backed by a theoretical model support this hypothesis. First, we show that the visible and near-infrared transmission through a semi-transparent silver film can be enhanced by up to ~70% and spectrally tailored depending on the type and thickness of the dielectric coating. Material types explored as dielectric coating layers include conventional metal oxides (titanium dioxide) and lesser-explored elemental semiconductors (undoped silicon, p-type silicon, and germanium). Second, and more surprisingly, we show that coating a 50-nm-thick silicon nitride membrane with a 10-nm-thick silver layer can modestly enhance the transmission by up to 6 ± 1% in the blue part of the spectrum. Transmission enhancements are observed for three silver-coated membranes in different configurations. Thinner silver coatings are theoretically capable of enhancement factors greater than 10%, but implementation is restricted by challenges in making smooth and continuous silver films below 10 nm in thickness. This study is important because it is the first demonstration of reciprocity with respect to the transmission enhancements achieved by combining thin metallic and dielectric layers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".