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Record W4304480085 · doi:10.1039/d2nr04205c

Improvement of field emission performances by DMSO and PEDOT:PSS treated freestanding CNT clusters

2022· article· en· W4304480085 on OpenAlexafffund
Jiayu Alexander Liu, Jiaqi Wang, Elahe Cheraghi, Siyuan Chen, Yonghai Sun, John T. W. Yeow

Bibliographic record

VenueNanoscale · 2022
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of Waterloo
FundersLloyd's RegisterCMC Microsystems
KeywordsMaterials sciencePEDOT:PSSField electron emissionNanotechnologyField (mathematics)Carbon nanotubeComposite materialOptoelectronicsPolymer

Abstract

fetched live from OpenAlex

In this paper, we present two methods to improve the field emission (FE) performance of vertically aligned carbon nanotube emitters that are treated with dimethyl sulfoxide (DMSO) solution and poly(3,4-ethylene dioxythiophene):polystyrene sulfonate (PEDOT:PSS) solution, respectively, and compared their performances. Both treatments force CNTs to be bundled into clusters, but the formed structures are very different. After treatment, both methods reduced the threshold electric field due to the reduction in screening effects, whereas the PEDOT:PSS treatment significantly reduced the threshold field to far lower than that of DMSO treatment. In addition, the FE efficiency and lifetime of treated CNT emitters are significantly improved. For both treated emitters, there is only slight degradation of the emission current after 80 hours of continuous FE at around 50 μA. In addition, the uniformity of both treated CNT emitters is improved which enables more CNTs to contribute to the overall current emission. This, in turn, lowers the current emitted by individual CNTs, and thereby increases the lifetime of the emitters. Therefore, this study demonstrates that these simple treatment methods of bundling CNTs into unique cluster-structures significantly improve the lifetime of FE and make them excellent candidates for large currents and long-term FE.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.611

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 head0.007
GPT teacher head0.229
Teacher spread0.223 · 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

Citations9
Published2022
Admission routes2
Has abstractyes

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