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Record W4294132520 · doi:10.1021/acsanm.2c03100

Reduced Graphene Oxide-Based Dielectric Nanocomposites with Small Dielectric Relaxation Times for Emerging Dielectric Electronics with High-Frequency Performance Demands

2022· article· en· W4294132520 on OpenAlexaff
Danian Liu, Jingwen Wang, Wenhai Peng, Xinzhu Wang, Hua Ren, Donald W. Kirk

Bibliographic record

VenueACS Applied Nano Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsUniversity of Toronto
FundersAeronautical Science Foundation of ChinaNanjing University of Aeronautics and AstronauticsNational Natural Science Foundation of China
KeywordsDielectricGrapheneMaterials scienceNanocompositeElectronicsOxideRelaxation (psychology)OptoelectronicsNanotechnologyElectrical engineeringEngineeringPsychology

Abstract

fetched live from OpenAlex

To meet the demand of emerging devices for high-frequency dielectric performances, we proposed semiconductor polyaniline in situ modified rGO nanosheets to keep the dielectric constant stable at high frequencies. The π–π interface interaction between rGO and semiconductor polyaniline was found to block the migration of electrons, redistributing the delocalized electrons of rGO and leading to a reduction in relaxation time and a delay in the dielectric relaxation at higher frequencies. This mechanism gave the dielectric materials a stable dielectric constant at high frequencies. The dielectric relaxation time was reduced from 2.75 × 10–8 to 5.68 × 10–10 at a filler load of 10 wt %. The dielectric constant only decreased by 19% from 100 to 1 × 108 Hz. For the same range, unmodified rGO showed an 85% decrease. This mechanism could also further increase the dielectric constant and decrease the dielectric loss. This has a great potential for emerging high-frequency devices (DC/DC converter, sensors, energy harvesters, etc.).

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)0.001
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.172
Teacher spread0.167 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

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