MétaCan
Menu
Back to cohort
Record W2790489520 · doi:10.3390/nano8020123

Printed Graphene Derivative Circuits as Passive Electrical Filters

2018· article· en· W2790489520 on OpenAlexafffund
Dogan Sinar, George K. Knopf

Bibliographic record

VenueNanomaterials · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGrapheneMaterials scienceElectronic circuitDerivative (finance)Electronic engineeringOptoelectronicsNanotechnologyElectrical engineeringEngineeringBusiness

Abstract

fetched live from OpenAlex

The objective of this study is to inkjet print resistor-capacitor (RC) low pass electrical filters, using a novel water-based cellulose graphene ink, and compare the voltage-frequency and transient behavior to equivalent circuits constructed from discrete passive components. The synthesized non-toxic graphene-carboxymethyl cellulose (G-CMC) ink is deposited on mechanically flexible polyimide substrates using a customized printer that dispenses functionalized aqueous solutions. The design of the printed first-order and second-order low-pass RC filters incorporate resistive traces and interdigitated capacitors. Low pass filter characteristics, such as time constant, cut-off frequency and roll-off rate, are determined for comparative analysis. Experiments demonstrate that for low frequency applications (<100 kHz) the printed graphene derivative circuits performed as well as the circuits constructed from discrete resistors and capacitors for both low pass filter and RC integrator applications. The impact of mechanical stress due to bending on the electrical performance of the flexible printed circuits is also investigated.

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 categoriesMeta-epidemiology (narrow)
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 score0.010
Threshold uncertainty score1.000

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)0.0010.000

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 head0.236
Teacher spread0.224 · 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.

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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueNanomaterialsSame topicAdvanced Sensor and Energy Harvesting MaterialsFrench-language works237,207