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Record W4403032197 · doi:10.1021/acsaelm.4c01143

Two-Part Surfactant-Assisted Exfoliation of Hexagonal Boron Nitride Nanosheets to Obtain Highly Stable Two-Dimensional Nanomaterial Dispersions

2024· article· en· W4403032197 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Applied Electronic Materials · 2024
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsHexagonal boron nitrideExfoliation jointNanomaterialsBoron nitrideMaterials sciencePulmonary surfactantHexagonal crystal systemNanotechnologyChemical engineeringGrapheneChemistryCrystallography

Abstract

fetched live from OpenAlex

Printable dielectric materials that exhibit high dielectric constants and low losses at high frequencies are needed for additive manufacturing of electronic devices. One promising nanomaterial for use in such systems is hexagonal boron nitride (hBN). This 2D nanomaterial is insulating due to its wide band gap and has a dielectric constant ranging from 2 to 4, making it an ideal candidate for applications including gate dielectrics, capacitors, and passivation layers in 2D nanoelectronics. However, stabilizing the dispersion of hBN nanosheets for printing applications while minimizing the reliance on toxic solvents and excessive surfactants remains a challenge. Many of the prevailing exfoliation techniques are time-consuming and resource-intensive. This work explores a two-part, surfactant-assisted mechanical exfoliation method to obtain stable hBN nanosheet dispersions from bulk hBN in a relatively short period, using ball milling followed by probe sonication. Exfoliation of hBN nanosheets assisted by various concentrations (from 0 to 1 wt %) of Triton X −100 was explored. The yield of each mixture was quantified by thermogravimetric analysis (TGA), and a maximum yield of 18.4% was achieved using 1 wt % surfactant. Colloidal stability was examined by using UV–vis spectroscopy, and solutions were found to remain stable for up to 30 days. The quality and size of the nanosheets were assessed using X-ray diffraction, scanning electron microscopy, and atomic force microscopy. The dielectric properties of the obtained nanosheets were measured using a vector network analyzer at microwave frequencies, and the real permittivity of the nanosheets ranged from 2.1 to 3.7 with varying concentrations of surfactant used in the synthesis. Furthermore, the nanosheets were found to be insulating and to have low dielectric loss tangents ranging from 0.012 to 0.014. The two-part, surfactant-assisted mechanical exfoliation technique requires much lower processing time than sonication alone and results in highly stable dispersions. The resulting hBN nanosheets exhibited tunable real permittivity and low dielectric loss, positioning these materials as promising options for dielectric ink formulations.

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.

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 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 score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.273
Teacher spread0.261 · 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