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Record W4385525313 · doi:10.1109/ectc51909.2023.00387

A Comparison of Wearable Metasurfaces

2023· article· en· W4385525313 on OpenAlexaff
Adria L. Kajenski, Shahriar Khushrushahi, Guinevere Strack, Alkim Akyurtlu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsInkwellMaterials scienceThread (computing)CoatingSubstrate (aquarium)OptoelectronicsComposite materialComputer science

Abstract

fetched live from OpenAlex

A metasurface was printed on two industrial fabrics using two commercially available inks, CM127-48 (Creative Materials), and PE876 (Dupont), via direct-write printing. The fabrics both had a polyurethane (PU) coating on the reverse side for mechanical reinforcement and for preventing ink from bleeding through to the other side. The woven nylon fabrics were distinguished by their thread mass (70D and 200 D). The metasurface was first designed for band stop at 6 GHz and characterized by radio frequency (RF) measurements and sheet resistance, Rs. All combinations of ink and substrate performed similarly and were consistent with simulations. By assessing initial performance and longevity, the best combination of materials was determined to be CM127-48 on EX. These materials were then used to fabricate a double-sided design, with a grid printed on the reverse side of the fabric, leading to a dual-band metasurface with rejection at 4.8 GHz and band pass at 2.6 GHz. This work presents, for the first time, an additive approach to fabricating novel fabric based dual-band filters using direct-write printing and addresses the challenges in controlling alignment when printing on both sides of a fabric substrate and printing on surfaces with different coatings.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.320
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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