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Record W4311887598 · doi:10.1557/s43578-022-00841-6

Recent advances and perspective on boron nitride nanotubes: From synthesis to applications

2022· article· en· W4311887598 on OpenAlexafffund
Michael B. Jakubinek, Keun Su Kim, Myung Jong Kim, Angel A. Martı́, Matteo Pasquali

Bibliographic record

VenueJournal of materials research/Pratt's guide to venture capital sources · 2022
Typearticle
Languageen
FieldMaterials Science
TopicBoron and Carbon Nanomaterials Research
Canadian institutionsUniversity of TorontoUniversity of OttawaUniversity of New BrunswickNational Research Council Canada
FundersDivision of ChemistryNational Research FoundationNational Research Council CanadaDefense Acquisition Program AdministrationMinistry of Science and ICT, South KoreaRice UniversityNational Research Foundation of KoreaAir Force Office of Scientific ResearchMinistry of Trade, Industry and EnergyGachon UniversityKorea Institute of Science and TechnologyWelch FoundationNational Science Foundation
KeywordsBoron nitrideNanotechnologyMaterials scienceCarbon nanotubeNanomaterialsTransparency (behavior)Computer science

Abstract

fetched live from OpenAlex

Abstract Boron nitride nanotubes (BNNTs) are emerging nanomaterials with analogous structures and similarly impressive mechanical properties to carbon nanotubes (CNTs), but unique chemistry and complimentary multifunctional properties, including higher thermal stability, electrical insulation, optical transparency, neutron absorption capability, and piezoelectricity. Over the past decade, advances in synthesis have made BNNTs more broadly accessible to the nanomaterials and other research communities, removing a major barrier to their utilization and research. Therefore, the field is poised to grow rapidly and see the emergence of BNNT applications ranging from electronics to aerospace materials. A key challenge, that is being gradually overcome, is the development of manufacturing processes to make “neat” BNNT materials. This overview highlights the history and current status of the field, providing both an introduction to this Focus Issue—BNNTs: Synthesis to Applications—as well as a perspective on advances, challenges, and opportunities for this emerging material. Graphical abstract

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.363
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.022
GPT teacher head0.342
Teacher spread0.321 · 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

Citations43
Published2022
Admission routes2
Has abstractyes

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