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Record W4280498785 · doi:10.1186/s40580-022-00312-y

Spontaneous formation of boron nitride nanotube fibers by boron impurity reduction in laser ablation of ammonia borane

2022· article· en· W4280498785 on OpenAlexaff
Dong-Su Bae, Chunghun Kim, Hunsu Lee, Omar Khater, Keun Su Kim, Homin Shin, Kun‐Hong Lee, Myung Jong Kim

Bibliographic record

VenueNano Convergence · 2022
Typearticle
Languageen
FieldMaterials Science
TopicBoron and Carbon Nanomaterials Research
Canadian institutionsNational Research Council CanadaMcGill University
FundersDefense Acquisition Program AdministrationMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaKorea Institute of Science and TechnologyMinistry of Trade, Industry and EnergyNational Research FoundationKorea Research Institute for Defense Technology Planning and Advancement
KeywordsAmmonia boraneBoronBoron nitrideMaterials scienceLaser ablationBoraneImpurityReduction (mathematics)NanotubeAblationNanotechnologyLaserCarbon nanotubeChemistryComposite materialOpticsInternal medicineOrganic chemistryMedicine

Abstract

fetched live from OpenAlex

Highly crystalline and few-walled boron nitride nanotubes (BNNTs) had been synthesized by laser ablation using only ammonia borane as a precursor. As a molecular precursor, ammonia borane supplied both B and N atoms with a ratio of 1:1, and BNNTs were formed via the homogeneous nucleation of BN radicals, not the growth from boron nano-droplets, which is a generally accepted growth mechanism of the laser-grown BNNTs. Owing to the absence of amorphous boron impurities, the van der Waals interaction among BNNTs became effective and thus a BNNT fibers was formed spontaneously during the BNNT synthesis. The BNNT growth and the subsequent fiber formation are found to occur only at high pressures of a surrounding gas. The mechanism behind the critical role of pressure was elucidated from the perspective of reaction kinetics and thermal fluid behaviors. A polarized Raman study confirmed that the BNNT fiber formed exhibits a good alignment of BNNTs, which implies great potential for continuous production of high-quality BNNT fibers for various applications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2022
Admission routes1
Has abstractyes

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