MétaCan
Menu
Back to cohort
Record W4293565822 · doi:10.1016/j.rineng.2022.100533

Studying and evaluation physical characteristic of composite substrate chip and, its application

2022· article· en· W4293565822 on OpenAlexafffund
Ameen Abdelrahman, Fouad Erchiqui, Mourad Nedil

Bibliographic record

VenueResults in Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
FundersUniversité du Québec à MontréalUniversité du Québec en Abitibi-Témiscamingue
KeywordsMaterials scienceDielectricGrapheneComposite numberOptoelectronicsFluidicsSubstrate (aquarium)ChipComposite materialElectrical impedanceElectronic engineeringNanotechnologyComputer scienceElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

We aim to demonstrate the creation of a fabricated fluidic antenna based on dielectric PDMS substrate, its unique properties of conductivity, flexibility, robustness, and use as an antenna. We evaluated an appropriate fluidic solution comprised of a polyethyleneimine (PEI) matrix assembled from nanoparticle Ttinum oxides and graphene. Various investigations of its mechanical flexibility (stress), thermal properties (DSC), and IR have been carried out on fabricated PDMS substrates, and a dielectric was recorded at 2.67. In addition, (TEM), IR, UV, and Electrochemical Impedance (EIS) tests have been performed to evaluate the PEI matrix-like surface morphology. The measurement and simulation outcomes show that the fabricated antenna operates at 1.8–2.6 GHz, which covers the WLAN band area.

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 score0.005

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.244
Teacher spread0.226 · 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

Citations8
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueResults in EngineeringSame topicAdvanced Sensor and Energy Harvesting MaterialsFrench-language works237,207