Transmissive invisibility cloak using phase gradient metasurfaces
Bibliographic record
Abstract
Abstract Different from the reflective approach of the carpet cloak designed to conceal surface irregularities on highly reflective surfaces, the transmission invisibility cloak, often capable of achieving perfect invisibility, operates within a transmission geometry. In this configuration, the cloaking mechanism ensures that an object neither reflects nor refracts incoming waves in free space, presenting opportunities for more versatile applications, though with the requirement for intricate designs. This paper introduces a novel methodology for designing a transmissive invisibility cloak, employing a simplified combination structure of two phase-gradient metasurfaces based on the generalized Snell’s law. Initially, we designed a highly transparent metasurface for the millimeter-wave band to yield diverse phase gradients. We confirmed the effectiveness of this metasurface through the observation of abnormal refraction. Then, through a deliberate arrangement of these phase gradients, we construct a transmissive invisibility cloak that guides electromagnetic waves around the cloaked region. Simulations and experimental measurements conducted under plane-wave conditions demonstrate the cloak’s effectiveness and practical applicability. The ensuing comparative investigations of results among free space, uncloaked objects, and cloaked objects validate the expected cloaking effect, offering valuable insights into the design and functionality of transmissive invisibility cloaks.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".