Tunable Infrared Plasmon Response of Lithographic Sn‐doped Indium Oxide Nanostructures
Bibliographic record
Abstract
Abstract Transparent conductive oxides (TCOs) have strong potential for plasmonic applications. Given their easily tunable properties and low energy response, significant challenges in the controlled fabrication and precise characterization of TCOs must be better understood before this potential can be realized. Here, the mid‐ to near‐infrared plasmonic response of Sn‐doped In 2 O 3 (ITO) nanostructures is presented, fabricated top‐down using electron beam lithography and radio‐frequency sputtering. These equilateral ITO triangles of different side lengths are imaged at high spatial and energy resolution with monochromated electron energy loss spectroscopy (EELS) in a scanning transmission electron microscope. Applying the Richardson–Lucy (RL) deconvolution algorithm to experimental EELS spectra reveals localized surface plasmon (LSP) excitations between 150 and 550 meV and a 730 meV bulk plasmon. This very‐low‐energy response to an electron beam is compared with boundary element method simulations of nanostructures. These simulations use the dielectric functions of continuous thin films of the same materials, characterized by ellipsometry, 4‐point probe, and Hall effect tests. Additionally, upon rapid thermal annealing of ITO, blue‐shifts in LSP energy, and longer LSP lifetimes are examined as a consequence of an amorphous‐to‐polycrystalline transformation and an increase in the free carrier density.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".