Safer Formulation Concept for Flame-Generated Engineered Nanomaterials
Bibliographic record
Abstract
The likely success or failure of the nanotechnology industry depends on the environmental health and safety of engineered nanomaterials (ENMs). While efforts toward engineering safer ENMs are sparse, such efforts are considered crucial to the sustainability of the nanotech industry. A promising approach in this regard is to coat potentially toxic nanomaterials with a biologically inert layer of amorphous SiO 2 . Core–shell particles exhibit the surface properties of their amorphous SiO 2 shell while maintaining specific functional properties of their core material. A major challenge in the development of functional core–shell particles is the design of scalable high-yield processes that can meet large-scale industrial demand. Here, we present a safer formulation concept for flame-generated ENMs based on a one-step, in flight SiO 2 encapsulation process, which was recently introduced by the authors as a means for a scalable manufacturing of SiO 2 -coated ENMs. First, the versatility of the SiO 2 -coating process is demonstrated by applying it to four ENMs (CeO 2, ZnO, Fe 2 O 3, Ag) marked by their prevalence in consumer products as well as their range in toxicity. The ENM-dependent coating fundamentals are assessed, and process parameters are optimized for each ENM investigated. The effects of the SiO 2 -coating on core material structure, composition, and morphology, as well as the coating efficiency on each nanostructured material, are evaluated using state-of-the-art analytical methods (XRD, N 2 adsorption, TEM, XPS, isopropanol chemisorption). Finally, the biological interactions of SiO 2 -coated vs uncoated ENMs are evaluated using cellular bioassays, providing valuable evidence for reduced toxicity for the SiO 2 -coated ENMs. Results indicate that the proposed “safer by design” concept bears great promise for scaled-up application in industry in order to reduce the toxicological profile of ENMs for certain applications.
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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 | 0.000 | 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 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".