Influence of Impurities from Manufacturing Process on the Toxicity Profile of Boron Nitride Nanotubes
Bibliographic record
Abstract
The toxicity of boron nitride nanotubes (BNNTs) has been the subject of conflicting reports, likely due to differences in the residuals and impurities that can make up to 30-60% of the material produced based on the manufacturing processes and purification employed. Four BNNTs manufactured by induction thermal plasma process with a gradient of BNNT purity levels achieved through sequential gas purification, water and solvent washing, allowed assessing the influence of these residuals/impurities on the toxicity profile of BNNTs. Extensive characterization including infrared and X-ray spectroscopy, thermogravimetric analysis, size, charge, surface area, and density captured the alteration in physicochemical properties as the material went through sequential purification. The material from each step is screened using acellular and in vitro assays for evaluating general toxicity, mechanisms of toxicity, and macrophage function. As the material increased in purity, there are more high-aspect-ratio particulates and a corresponding distinct increase in cytotoxicity, nuclear factor-κB transcription, and inflammasome activation. There is no alteration in macrophage function after BNNT exposure with all purity grades. The cytotoxicity and mechanism of screening clustered with the purity grade of BNNTs, illustrating that greater purity of BNNT corresponds to greater toxicity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".