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Record W2897788662 · doi:10.2351/1.5063135

Laser post processing of deposited graphene patterns

2014· article· en· W2897788662 on OpenAlexaff
Elahe Jabari, Ehsan Toyserkani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGrapheneMaterials scienceFabricationInkwellPrinted electronicsNanotechnologyScanning electron microscopeConductive inkCarbon nanotubeElectrical conductorInterconnectionOptoelectronicsLaserComposite materialSheet resistanceOpticsComputer scienceLayer (electronics)

Abstract

fetched live from OpenAlex

A wide potential application range of graphene from super capacitors and transparent conductors to antennas, has made it a prominent competitor over traditional metallic elements and carbon nanotubes (CNTs). Ink-based printing processes are of the most favorable fabrication processes for printed electronics. Ink-based printed graphene interconnects usually contain several extra polymer-based components of the ink including solvents and surfactants. Therefore, a post heating process is usually needed to not only eliminate all unwanted components in printed patterns, but also improve the printed graphene performance as an interconnect. In this study, a 1550 nm CW fiber laser irradiation is used to heat treat graphene-based printed patterns fabricated by an aerosol-based micro-scale additive manufacturing technique. The manuscript will address the preliminary optimization of the laser power and speed, in order to degrade undesired elements and obtain pure graphene patterns after post processing. Optical microscopy and scanning electron microscopy (SEM) are employed to investigate the topography and microstructure of graphene patterns.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score0.263

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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