Effect of Mono-Vacant Defects on the Adsorption Properties of Deep Eutectic Solvents onto Hexagonal Boron-Nitride Nanoflakes
Bibliographic record
Abstract
Hexagonal boron nitride is a promising material for a variety of electronic, optical, and material science applications. Both the synthesis of the material through exfoliation, and its various applications almost inevitably require its solvation. Deep eutectic solvents (DES) are extremely useful solvents for these types of applications due to their non-volatility, inflammability, biocompatibility, and reasonable cost. There are many different deep eutectic solvents available, and their suitability for any given application is particularly dependent on the specific of their structure. DES have been examined computationally for use with boron nitride, but these calculations use idealized, perfect boron nitride sheets instead of the more realistic, defect-containing systems. In this report, we investigated four DESs with two experimentally observed defective boron nitride, one with a single boron vacancy, the other with a single nitrogen vacancy. All DESs bound with higher affinity to the defective boron nitride than to the pristine surface. Charge transfer was minimal in all cases although the surfaces tended to donate electron density to the solvents. The interactions between the solvents and the surfaces are primarily non-covalent although in several cases natural bond order analysis indicates a partial covalent interaction that helps explain the higher-than-expected affinity for particular DES. The DESs have little effect on the predicted optical behaviour of the pristine boron nitride but do significantly change the adsorption spectrum of the defective boron nitride nanoflakes; the effect on bulk material might be limited. Together these results suggest that the choice of DES can either be made to limit any effect on the properties of the material (urea-choline chloride) or to affect the optical and electronic nature of the material (benzoic acid-choline chloride).
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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".