Fabrication and characterization of a composite TiO<sub>2</sub>-polypropylene high-refractive-index solid immersion lens for super-resolution THz imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it