Surface Wave Enhanced Sensing in the Terahertz Spectral Range: Modalities, Materials, and Perspectives
Bibliographic record
Abstract
The terahertz spectral range (frequencies of 0.1-10 THz) has recently emerged as the next frontier in non-destructive imaging and sensing. Here, we review amplitude-based and phase-based sensing modalities in the context of the surface wave enhanced sensing in the terahertz frequency band. A variety of surface waves are considered including surface plasmon polaritons on metals, semiconductors, and zero gap materials, surface phonon polaritons on polaritonic materials, Zenneck waves on high-k dielectrics, as well as spoof surface plasmons and spoof Zenneck waves on structured interfaces. Special attention is paid to the trade-off between surface wave localization and sensor sensitivity. Furthermore, a detailed theoretical analysis of the surface wave optical properties as well as the sensitivity of sensors based on such waves is supplemented with many examples related to naturally occurring and artificial materials. We believe our review can be of interest to scientists pursuing research in novel high-performance sensor designs operating at frequencies beyond the visible/IR band.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".