Dynamic Terahertz Plasmonics Enabled by Phase‐Change Materials
Bibliographic record
Abstract
Abstract Phase‐change phenomena have been an attractive research theme for decades due to the dynamic transition of material properties providing extraordinary capabilities for versatile optical device applications. Even at the terahertz (THz) frequency regime, phase‐change materials (PCMs) promote the development of dynamic devices, especially when combined with a plasmonic approach delivering strong field enhancement and localization. According to the design of plasmonic metamaterials or hybrid composites, PCMs can actively modulate the electromagnetic properties of THz waves through thermal, electrical, and optical means. In turn, THz waves can affect the PCM properties in the nonlinear regime due to the intense field strength enhancement by plasmonic structures. Here, a few types of PCMs demonstrating promising potential in THz plasmonic applications are introduced. Starting from the best‐known transition metal oxide, vanadium dioxide (VO2), which possesses an insulator‐to‐metal phase transition near room temperature, superconductors, chalcogenides, ferroelectrics, liquid crystals, and liquid metals are covered along with their phase‐change properties and the control mechanisms infused with THz plasmonic applications. The corresponding recent progress presenting how PCMs combined with plasmonic structures can demonstrate effective THz modulation is reviewed. This general overview may provide a better understanding of dynamic THz plasmonics and new ideas for future THz technology.
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 | 0.000 | 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".