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Record W2908207323 · doi:10.1109/ifetc.2018.8583855

Thermal Transfer Printing with Donor Ribbon for Flexible Hybrid RFID Antenna Fabrication

2018· article· en· W2908207323 on OpenAlexaff
Philippe Descent, Ricardo Izquierdo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsFabricationRibbonElectronicsFlexible electronicsPrinted circuit boardMaterials scienceTransfer printingAntenna (radio)Electronic circuitTextileComputer scienceElectrical engineeringNanotechnologyEngineering

Abstract

fetched live from OpenAlex

Printed electronics is more and more used for the fabrication of devices in multiple fields like wearable, disposable and health domains. Benefits of using these technics are mainly guided by the reduction of manufacturing cost, the possibility to print on flexible substrates and to deposit a large variety of materials. Here, we present the adaptation of the printing technic of thermal transfer using a donor ribbon for the deposition of metal layers. Flexible hybrid printed antennas were made by using this technic. Their characterizations and S-parameter tests are shown. These flexible antennas can be easily integrated to more complex circuits which may include sensors and other components and lead to hybrid portable devices able to monitor human condition, such on a smart bandage or on a on-skin glucometer.

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)0.000
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.210
Teacher spread0.197 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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