Mechanism of Hierarchical Porosity Development in Hexagonal Boron Nitride Nanocrystalline Microstructures for Biomedical and Industrial Applications
Bibliographic record
Abstract
A well-known ceramic material, hexagonal boron nitride (h-BN), has a number of unique properties, including structural and porosity features, that make it suitable for a wide range of industrial applications. Hierarchical porosity and high specific surface area are desirable properties for adsorption processes such as water and air cleaning, hydrogen storage, and drug delivery. These characteristics could be controlled and optimized by synthesis procedures; however, this process requires an understanding of the factors and mechanisms of nanocrystalline h-BN porosity development and textural properties. In this study, we demonstrate that hierarchical porosity displays evidence of the consecutive h-BN synthesis steps and thermal decomposition of intermediants. In addition, evidence shows that h-BN nanosheets can be folded as a result of van der Waals force interactions at elevated temperatures, which is corroborated by a computational modeling. The biocompatibility of the prepared h-BN was also evaluated to confirm the nontoxicity of the material. The results of this research could aid in the optimization and scaling up of an environmentally friendly h-BN synthesis process and assist in the development of new methods for the production of h-BN at a commercial level.
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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 | 0.000 |
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".