Advances in fabrication, physio-chemical properties, and sensing applications of non-metal boron nitride and boron carbon nitride-based nanomaterials
Bibliographic record
Abstract
Boron carbon nitride (BCN) and boron nitride (BN), semiconductor nanomaterials with high versatility, have attracted attention because of their mechanical toughness, thermal conductivity, electrical insulation, improved in terms of oxidation resistance, and chemical stability. While BCN is commonly recognized for its ability to adjust the bandgap, the porosity is a captivating yet relatively unexplored characteristic. The presence of pores in BCN offers advantages such as a large surface area and pore size, which can significantly improve efficiency compared to non-porous materials. Moreover, BCN exhibits diverse morphologies, including nanoparticles, nanosheets, nanotubes, nanoplates, and aerogels, each possessing distinctive and remarkable properties when compared to similar structures. These composites can be separated into ceramic and polymer nanocomposites depending on the matrix utilized. This study reviews the definition, properties, various types, and synthesis methods of BN and BCN-based nanocomposite materials. Moreover, their significant use in developing electrochemical and optical sensing and biosensing platforms, gas sensors, pH sensors, and pressure sensors has been reported. These composites have several uses in pollutant degradation, photocatalysts, photovoltaics, and drug delivery. Furthermore, the future of semiconductor/BN and BCN composites and the challenges associated with producing composite materials on a large scale are examined. This review will assist in the production of highly efficient BN and BCN-based materials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".