Reconfigurable Terahertz Moiré Frequency Selective Surface Based on Additive Manufacturing Technology
Bibliographic record
Abstract
We designed and fabricated a terahertz (THz) frequency selective surface (FSS) based on two distinct additive manufacturing technologies, namely, printable electronics (PE) and three-dimensional (3D) printing. Silver nanoparticle ink was printed on a polyethylene terephthalate (PET) substrate utilizing a large-scale roll-to-roll industrial PE technique with a flexographic printed unit, while the 3D-printed THz FSS was fabricated based on a powder bed fusion-selective laser melting system. The filtering characteristics of both types of FSS were verified through calculation, simulation, and experiments. Furthermore, the rotational tuning approach was applied to two identical FSS to form reconfigurable FSS which could be defined as Moiré FSS. Based on the numerical results obtained, our proposed technique which used a PE-based Moiré FSS achieves a 58% modulation depth at 0.25 THz, while experimental verification found a modulation depth of 41% at 0.22 THz, confirming that its adoption is simple and cost-effective. To the best of our knowledge, this is the first demonstration of a Moiré reconfigurable printed FSS operating in the THz region.
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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) | 0.001 | 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".