Low-Temperature Triazine Functionalization of Boron Nitride Nanotubes for Applications from Drug Delivery to Advanced Composites
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Boron nitride nanotubes (BNNTs) are emerging nanomaterials with impressive mechanical properties and unique multifunctional properties including high thermal stability, electrical insulation, optical transparency, neutron absorption capability, and piezoelectricity. To harness the true potential of BNNTs for applications from drug delivery to advanced coatings and composites, functionalization is necessary to enhance their solubility and processability. Current functionalization approaches require aggressive reaction conditions such as long reaction times, high temperatures, high pressures, aggressive reagents, and an inert atmosphere. Herein, we present the development of a low temperature covalent functionalization method for enhanced solution processing of BNNTs utilizing a nitrene based (2 + 1) cycloaddition reaction that results in enhanced BNNT processability and solubility in N -methyl-2-pyrrolidone and N, N -dimethylformamide. Confirmation and degree of covalent functionalization are established using infrared (IR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and thermal gravimetric analysis (TGA). Poly(3-hexylthiophene) (P3HT) is used to assess the quality of the BNNTs postfunctionalization to again confirm covalent functionalization. Finally, secondary functionalization of the functionalized BNNTs with propargyl chloride is achieved to demonstrate that this functionalization not only enhances the processability of the BNNTs but also results in a BNNT surface platform in which a plethora of molecules and nanomaterials can be grafted based on the application case. This nondestructive method preserves the extraordinary properties of BNNTs while opening their widespread use in many new and exciting opportunities in materials development.
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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".