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Record W4410220094 · doi:10.1021/acsnano.4c16853

Activated Diffusion of 1D J-Aggregates in Boron Nitride Nanotubes by Curvature Patterning

2025· article· en· W4410220094 on OpenAlexafffund
Jean-Baptiste Marceau, Juliette Le Balle, Duc-Minh Ta, Alberto Aguilar, Annick Loiseau, Richard Martel, Pierre Bon, Raphaël Voituriez, Gaëlle Recher, Étienne Gaufrès

Bibliographic record

VenueACS Nano · 2025
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversité de Montréal
FundersCentre National de la Recherche ScientifiqueNatural Sciences and Engineering Research Council of CanadaH2020 European Research CouncilCanada Research ChairsAgence Nationale de la RechercheCanada Foundation for Innovation
KeywordsBoron nitrideMaterials scienceDiffusionNanotechnologyCurvatureNitrideBoronChemical engineeringChemistryLayer (electronics)GeometryOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

The directed assembly of molecules into micrometer-scale patterns and advanced materials holds broad relevance across fields such as life sciences, photovoltaics, and quantum photonics. However, these processes are often challenged by competing forces such as Brownian motion, capillary interactions, drift, and nonspecific adsorption. Here, we demonstrate a reactivated and guided diffusion mechanism of luminescent dye aggregates after encapsulation within boron nitride nanotubes (BNNTs). Correlative analyses between BNNT curvature and molecular positioning along the nanotube axis reveal efficient long-range migration of dye molecules from curved to straight sections of the BNNT. This curvature-activated diffusion leads to the formation of J-aggregate clusters, arranged in periodic patterns with precise spacings and defined lengths. A phenomenological model of curvature-guided molecular motility is developed to describe 1D diffusion within BNNTs, accurately predicting the size and spacing of J-aggregates as a function of the nanotube length. Finally, this mechanism is exploited using different substrates, such as exfoliated MoS 2 topological steps or micropatterned gratings on the photonic device, to induce local bending of the BNNTs and deterministically control molecular cluster formation.

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)0.000
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 score0.004
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.264
Teacher spread0.257 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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