Broadband Measurement of Dielectric Properties of 3D-Printed Plastics
Bibliographic record
Abstract
Obtaining broadband measurements of dielectric properties is a challenge, particularly at high frequencies. An established approach is to use an open coaxial probe resting on a sample of the material under test (MUT) that has a known thickness. We describe the application of this technique using Speag’s DAK-TL2 system to characterize several 3D-printed plastics. 3D-printed materials have very little published data about their electrical properties, and plastics are very convenient for making microwave components from coil formers to antennas. Low-loss materials are of particular interest, exacerbating the measurement challenge. Ideally, the MUT samples are homogenous, have uniform thickness, and the surfaces are very smooth [1]. None of these properties can be attained with most plastics, particularly with 3D-printed plastics, with the most challenging property being surface smoothness. For low-loss materials, the loss tangents are close to or less than the measurement uncertainty of the system. Much of this uncertainty is due to the losses in the probe and the detail of the interface between the probe and the MUT. The procedure, measurements, and repeatability behaviour of the complex dielectric constants of a variety of 3D-printed plastics are presented for frequencies up to 20 GHz.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".