Towards Integration of Two-Dimensional Hexagonal Boron Nitride (2D h-BN) in Energy Conversion and Storage Devices
Bibliographic record
Abstract
The prominence of two-dimensional hexagonal boron nitride (2D h-BN) nanomaterials in the energy industry has recently grown rapidly due to their broad applications in newly developed energy systems. This was necessitated as a response to the demand for mechanically and chemically stable platforms with superior thermal conductivity for incorporation in next-generation energy devices. Conventionally, the electrical insulation and surface inertness of 2D h-BN limited their large integration in the energy industry. However, progress on surface modification, doping, tailoring the edge chemistry, and hybridization with other nanomaterials paved the way to go beyond those conventional characteristics. The current application range, from various energy conversion methods (e.g., thermoelectrics) to energy storage (e.g., batteries), demonstrates the versatility of 2D h-BN nanomaterials for the future energy industry. In this review, the most recent research breakthroughs on 2D h-BN nanomaterials used in energy-based applications are discussed, and future opportunities and challenges are assessed.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".