Fabrication and characterization of a composite TiO<sub>2</sub>-polypropylene high-refractive-index solid immersion lens for super-resolution THz imaging
Bibliographic record
Abstract
Terahertz (THz) near-field imaging is attracting a lot of attention for its potential applications in medical diagnosis and material characterization. However, the spatial resolution of the recorded THz image was mainly limited by the diffraction limit of the commonly used lens- and mirror-based THz optical systems. Alternatively, a solid immersion lens (SIL) can be a promising approach for achieving super-resolution imaging as it reduces the spot size of the focused THz beam by a factor of 1/ n , where n is the refractive index (RI) of the lens material. In this work, we present the design and fabrication of hemispherical THz SIL using powder mixes of titanium dioxide (TiO 2 ) and polypropylene (PP) whose RIs are ≈10 and ≈1.51, respectively, at 1.0 THz. In particular, we present two different lens fabrication strategies that are simple and cost-effective solutions. The first strategy uses pressing the TiO 2 powder with a PP powder at the Vicat temperature of PP while controlling the concentration of TiO 2 and the resultant lens porosity. The second design consists in pressing the TiO 2 powder in a hollow hemisphere that is 3D printed using PP. The fabricated lenses are then characterized physically and optically, and their RIs are compared to the theoretical estimates using the Bruggeman model of the effective media. From the experimental measurements of the proposed SIL, a resolution limit as low as 0.2 λ was achieved at 0.09 THz ( λ ≈ 3.3 mm), which is comparable to the best resolutions reported in the literature.
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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 | 0.001 | 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".