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Record W2982463629 · doi:10.1038/s41598-019-52247-w

Dual growth mode of boron nitride nanotubes in high temperature pressure laser ablation

2019· article· en· W2982463629 on OpenAlexaff
Jun Hee Kim, Hyunjin Cho, Thang Pham, Jae Hun Hwang, Seokhoon Ahn, Se Gyu Jang, Hunsu Lee, Cheol Park, Cheol Sang Kim, Myung Jong Kim

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldMaterials Science
TopicBoron and Carbon Nanomaterials Research
Canadian institutionsNational Research Council Canada
FundersKorea Institute of Science and TechnologyMinistry of Trade, Industry and Energy
KeywordsBoron nitrideNucleationMaterials scienceLaser ablationBoronGrowth rateChemical physicsChemical engineeringNanotechnologyTransmission electron microscopyLaserChemistryOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The morphological analysis of the end of boron nitride nanotubes (BNNTs) using high-resolution transmission electron microscopy (HR-TEM) can provide valuable insight into the growth mechanism in high temperature pressure (HTP) laser ablation where the best quality of BNNT materials can be obtained so far. Two growth modes of BNNT coexisting during the synthesis process have been proposed based on HR-TEM observation and length analysis. One is the root growth mode, in which boron nitride (BN) species formed via the surface interaction between surrounding N 2 molecules and boron nanodroplets incorporate into the tubular structure. Another mode called open-end growth mode means the prolongation of tube growth from the exposed BN edge surrounding the surface of boron nanodroplets which is constructed by the heterogeneous nucleation of absorbed BN radicals from the gas plume. The statistical data, the proportions of end structures and the length of BNNTs, could be fitted to two growth modes, and the open-end growth mode is found to be especially effective in producing longer nanotubes with a higher growth rate. The scientific understanding of the growth mechanism is believed to provide the control for optimized production of BNNTs.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.243
Teacher spread0.236 · 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

Citations36
Published2019
Admission routes1
Has abstractyes

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