Single- and double-walled boron nitride nanotubes: Controlled synthesis and application for water purification
Bibliographic record
Abstract
Abstract Research interest in boron nitride nanotubes (BNNTs) has increased after the recent success of large-scale BNNT syntheses using high-temperature-pressure laser ablation or high-temperature plasma methods. Nonetheless, there are limits to the application and commercialization of these materials because of the difficulties associated with their fine structural control. Herein, the growth kinetics of BNNTs were systemically studied for this purpose. The growth pressure of the nitrogen feed gas was varied while the growth temperature remained constant, which was confirmed by black body radiation measurements and calculations based on a heat loss model. Changing from the diffusion-limited regime to the supply-limited regime of growth kinetics based on the optimized BNNT synthesis condition afforded the control of the number of BNNT walls. The total amount of BNNTs possessing single and double walls was over 70%, and the BNNT surface area increased to 278.2 m2/g corresponding to small wall numbers and diameters. Taking advantage of the large surface area and high-temperature durability of the material, BNNTs utilized as a recyclable adsorbent for water purification. The efficiency of the BNNTs for capturing methylene blue particles in water was approximately 94%, even after three repetition cycles, showing the potential of the material for application in the filter industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".