Two-Part Surfactant-Assisted Exfoliation of Hexagonal Boron Nitride Nanosheets to Obtain Highly Stable Two-Dimensional Nanomaterial Dispersions
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".