Enabling materials for subwavelength-size low-loss surface modes in the Terahertz spectral range
Bibliographic record
Abstract
Terahertz spectral range (frequencies of 0.1-1 THz) has recently emerged as the next frontier for non-destructive imaging, industrial sensing and ultra-fast wireless communications. Here, we review several classes of materials such as simple metals, semiconductors, high-k dielectrics, polar materials, zero gap materials, as well as structured materials that can support strongly localized electromagnetic modes at material interfaces in the Terahertz spectral range. We present the basic theory of surface waves, detail the requirement of strong modal confinement and low loss for the surface waves propagating at material interfaces and discuss challenges for excitation of such modes at Terahertz frequencies. A large number of examples related to naturally occurring and artificial materials is then presented. A variety of practical applications is envisioned for surface waves at Terahertz frequencies including non-destructive super-resolution imaging and quality control, high sensitivity sensors capable of operation with small volumes of analytes that are opaque in the visible and near-infrared, as well as design of compact optical circuit for the upcoming ultra-high bitrate THz communication devices.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".