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Record W2022899820 · doi:10.1143/jjap.46.l1096

Improvement of Carbon Nanotubes using Cryogenic Treatment

2007· article· en· W2022899820 on OpenAlexaff
Dae-Weon Kim, Eui-Yun Jang, Seung Min Lee, Wal-Jun Kim, Jong‐Hoon Lee, Jacob I. Kleiman

Bibliographic record

VenueJapanese Journal of Applied Physics · 2007
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsIntegrity Testing Laboratory (Canada)University of Toronto
Fundersnot available
KeywordsKaptonPolyimideLiquid nitrogenMaterials scienceCarbon nanotubeAnnealing (glass)Cryogenic treatmentCryogenic temperatureComposite materialNitrogenCarbon fibersStictionLayer (electronics)Analytical Chemistry (journal)NanotechnologyComposite numberChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Functional improvement of single-walled carbon nanotubes coated on polyimide Kapton HN was studied by means of cyclic cryogenic treatment. Immersing in liquid nitrogen at 77 K joining in heat annealing at 500 K induced the increase of D–G ratio and electrical conductivity in conjecture to the partial remedial junction with the coexisted stiction on surface. The coated thickness was deliberately able to adjust associated with cycling number of cryogenic treatment through the sequential detachment from the outmost layer owing to the axial shrunken effect by new atomic cross links at defects derived from the close bonding distance.

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 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 score0.005
Threshold uncertainty score0.589

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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
Published2007
Admission routes1
Has abstractyes

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