Sapphire Photonic Crystal Waveguides for Terahertz Sensing in Aggressive Environments
Bibliographic record
Abstract
Abstract Terahertz (THz) frequency range opens significant opportunities in various fundamental and applied fields including condensed matter physics and chemistry, biology and medicine, public security and nondestructive testing. Despite significant advances in THz instrumentation, the problem of THz sensing in harsh environments, particularly at high temperatures and pressures, remains acute due to the lack of THz materials and optical components capable for operation under the extreme conditions. To address this problem, the THz hollow‐core photonic crystal sapphire waveguides that are fabricated using shaped crystal growth technique are developed. Numerical analysis and experimental study show that the proposed waveguides operate in a few‐mode regime and allow for the broadband transmission of THz pulses with small dispersions and low propagation losses. Thanks to the unique physical properties of sapphire, the proposed waveguides are capable of operating in a variety of aggressive environments. As an example, the developed waveguides are used to conduct the intra‐waveguide interferometric sensing of phase transitions in sodium nitrite films at high temperatures. It is believed that the proposed sapphire‐based material's platform has strong potential for developing THz guided optics for applications in intra‐waveguide spectroscopy, interferometry, and remote sensing in aggressive environments.
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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) | 0.000 | 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".