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Record W4411028089 · doi:10.3390/nano15110861

Characterization of the Second Harmonic Generation of Boron Nitride Nanotube Macroscopic Assemblies

2025· article· en· W4411028089 on OpenAlexaff
Ping Lü, Jingwen Guan, Cyril Hnatovsky, Huimin Ding, Manny De Silva, Liliana Gaburici, Christopher T. Kingston, Stephen J. Mihailov

Bibliographic record

VenueNanomaterials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicBoron and Carbon Nanomaterials Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceBoron nitrideFemtosecondSecond-harmonic generationWavelengthCoatingOptoelectronicsBoronInfraredCharacterization (materials science)NanotechnologyLaserPolarization (electrochemistry)OpticsChemistry

Abstract

fetched live from OpenAlex

Boron nitride nanotubes (BNNTs) are predicted to be promising one-dimensional nonlinear optical materials, but to date, only one experimental observation has been made using individual nanotubes. In this work, second harmonic generation (SHG) was achieved from free-standing bulk BNNT sheets and BNNT coatings on silica substrates. Focusing femtosecond infrared (fs-IR) laser pulses with a wavelength of 800 nm onto the BNNT assemblies resulted in strong SHG at a wavelength of 400 nm. It was observed that due to the thickness variation of the BNNT assemblies and orientational alignment of BNNTs in the assemblies, the intensity of the second-harmonic (SH) radiation changed dramatically when different locations on the samples were investigated. Among all the BNNT assemblies tested, the localized SH response and its dependence on the polarization of the excitation fs-IR pulses were the strongest in BNNT coatings produced by a dip-coating process. By measuring the SH response, the uniformity, reproducibility, and efficiency of BNNT deposition processes could be assessed. For applications requiring a high SH response from BNNT assemblies, the process of dip coating is preferred.

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 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 score0.010
Threshold uncertainty score0.998

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.284
Teacher spread0.260 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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