Covalent Functionalization of Boron Nitride Nanotubes <i>via</i> Reduction Chemistry
Bibliographic record
Abstract
Boron nitride nanotubes (BNNTs) exhibit a range of properties that hold great potential for many fields of science and technology; however, they have inherently low chemical reactivity, making functionalization for specific applications difficult. Here we propose that covalent functionalization of BNNTs via reduction chemistry could be a highly promising and viable strategy. Through density functional theory calculations of the electron affinity of BNNTs and their binding energies with various radicals, we reveal that their chemical reactivity can be significantly enhanced via reducing the nanotubes (i.e., negatively charging). For example, a 5.5-fold enhancement in reactivity of reduced BNNTs toward NH2 radicals was predicted relative to their neutral counterparts. The localization characteristics of the BNNT π electron system lead the excess electrons to fill the empty p orbitals of boron sites, which promote covalent bond formation with an unpaired electron from a radical molecule. In support of our theoretical findings, we also experimentally investigated the covalent alkylation of BNNTs via reduction chemistry using 1-bromohexane. The thermogravimetric measurements showed a considerable weight loss (12-14%) only for samples alkylated using reduced BNNTs, suggesting their significantly improved reactivity over neutral BNNTs. This finding will provide an insight in developing an effective route to chemical functionalization of BNNTs.
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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".