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Record W2799844810 · doi:10.1049/joe.2018.0070

SE adjustment of planar mesh screen by fine‐tuning metal thickness

2018· article· en· W2799844810 on OpenAlexaff
Yonathan Corredores, Xavier Castel, Philippe Besnier, Cyril Dupeyrat, Patrice Foutrel

Bibliographic record

VenueThe Journal of Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsSafran Electronics (Canada)
FundersEuropean Regional Development FundRégion BretagneMinistère de l'Education Nationale, de l'Enseignement Superieur et de la RechercheEuropean Commission
KeywordsMaterials sciencePlanarTransparency (behavior)MicrowaveElectromagnetic shieldingMetalMicroelectronicsOptoelectronicsSubstrate (aquarium)Electrical impedanceOpticsComposite materialComputer scienceElectrical engineeringTelecommunicationsMetallurgyEngineering

Abstract

fetched live from OpenAlex

The study presents the thickness effect of mesh metal films printed onto glass substrate on the shielding effectiveness (SE) of such transparent screens. Currently, standards in microelectronic technology use a metal film thickness of between ∼100 nm and few micrometres, depending on the available deposition and implementation techniques. This study demonstrates that the thickness of mesh metal films is a key parameter which needs to be adjusted precisely. A theoretical model based on SE, complex impedance and optical transparency has been developed for this purpose. At microwaves, a relevant selection of the metal thickness values from 0.1 to 2 µm changes the SE at low frequency (from 36 to 40 dB at 2 GHz, respectively) while maintaining it constantly at a higher frequency (close to 20 dB at 18 GHz). The optical transparency of such mesh screens is kept constant over the entire visible light spectrum (78%). Experimental data are in complete agreement with the theoretical values.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.013
GPT teacher head0.212
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2018
Admission routes1
Has abstractyes

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