Synthesis of boron nitride nanosheets powders using a plasma based bottom-up approach
Bibliographic record
Abstract
Abstract Boron nitride nanosheets (BNNS) are graphene-like materials with large bandgap and excellent thermal/chemical stability. Current BNNS synthesis methods show low yield and/or purity preventing effective implementation in real-life applications. This work reports two catalyst-free bottom-up approaches for BNNS synthesis using induction thermal plasma. High enthalpy and cooling rates of this plasma allow BNNS to form homogeneously when using solid ammonia borane (AB) as a precursor. In this case, clusters of B x N y H z nucleate to form particles of critical sizes on which BNNS propagate while releasing H 2 . Using boron powders instead of AB produces BNNS through a heterogeneous route. In this case, boron undergoes spheroidization while active nitrogen species diffuse on the liquid surface to form boron nitride nanowalls which propagate into BNNS. The operating pressure and nitrogen loading are shown to control BNNS nucleation and growth and minimize by-products. An optimum operating pressure of 62 kPa provides axial velocities necessary for decomposing AB and melting boron. The synthesis process is well controlled yielding homogeneously grown BNNS of 30 × 30 nm sheet lateral sizes and ∼8 atomic layers, while the heterogeneously grown BNNS are of 100 × 100 nm and ∼20 layers. Both homogeneous and heterogeneous routes lead to BNNS structures showing exceptional crystallinity with defect-free stacking of the BN planes.
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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) | 0.001 | 0.001 |
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.
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".