Electronic and structural properties of Möbius boron-nitride and carbon nanobelts
Bibliographic record
Abstract
For the development of nanofilters and nanosensors, we wish to know the impact of size on their geometric, electronic, and thermal stabilities. Using the semiempirical tight binding method as implemented in the xTB program, we characterized Möbius boron-nitride and carbon-based nanobelts with different sizes and compared them to each other and to normal nanobelts. The calculated properties include the infrared spectra, the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), the energy gap, the chemical potential, and the molecular hardness. The agreement between the peak positions from theoretical infrared spectra compared with experimental ones for all systems validates the methodology that we used. Our findings show that for the boron-nitride-based nanobelts, the calculated properties have an opposite monotonic relationship with the size of the systems, whereas for the carbon-based nanobelts, the properties show the same monotonic relationship for both types of nanobelts. Also, the torsion presented on the Möbius nanobelts, in the case of boron-nitride, induced an inhomogeneous surface distribution for the HOMO orbitals. High-temperature molecular dynamics also allowed us to contrast carbon-based systems with boron-nitride systems at various temperatures. In all cases, the properties vary with the increase in size of the nanobelts, indicating that it is possible to choose the desired values by changing the size and type of the systems. This work has many implications for future studies, for example our results show that carbon-based nanobelts did not break as we increased the temperature, whereas boron-nitride nanobelts had a rupture temperature that varied with their size; this is a meaningful result that can be tested when the use of more accurate simulation methods become practical for such systems in the future.
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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".