Boron Nitride Nanotubes for Optical Fiber Chemical Sensing Applications
Bibliographic record
Abstract
Boron nitride nanotubes (BNNTs) are 1-D hollow fibrous nanomaterials. They are thermally stable up to 800 °C in open air and up to 1000 °C in a pure chlorine atmosphere, are electrically insulating, and possess superlative mechanical properties. Since the BNNT assembly is highly porous and easily penetrated by liquids and gases, BNNT thin film coated on optical fiber can be used as a novel sensing medium with enhanced sensitivity and selectivity. In this letter, uniform BNNT films have been successfully coated on optical fibers and tapered optical fibers (TOFs). A BNNT-coated TOF sensor has been developed for various liquids and gases sensing applications. We demonstrated experimentally that the BNNT-coated TOF can be used as a level sensor for liquids, even for those with refractive indices smaller than that of silica such as the organic solvents like acetone, hexane, tetrahydrofuran, ethyl ether, and dimethylformamide. As to gas sensing, HCl was selectively detected with enhanced sensitivity due to its high polarity and good affinity to the OH/NH2 functionalized BNNTs. The BNNT-coated optical fiber sensors can be potentially used at high temperatures and in some harsh environments.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".