Toxicity of Nanoparticles Used in Minimum Quantity Lubrication (MQL) Machining: A Sustainability Analysis
Bibliographic record
Abstract
Abstract Minimum quantity lubrication (MQL) with nanocomposite particles is among the new areas of study and has proven to provide very good cooling and lubrication in the machining of difficult to cut materials, such as titanium, Inconel and ADI. It is therefore imperative to understand their effects on the environment in the early stages of investigation, prior to their wide scale usage in industry. This study focuses on the different nanocomposite particles used in previous research, which is available in the literature, and evaluates their sustainability characteristics by investigating the toxicity of these nanocomposite particles on humans. The cooling capabilities of each of the nanoparticles considered is first established from the existing literature and summarized. Human cell viability measured from in vitro toxicity studies of nanoparticles is used as a variable to easily capture the toxicity of nanoparticles. Six different human cell lines were chosen to represent the effects of possible exposure through inhalation [human lung epithelial cells (A549), and bronchial epithelial cells (NL-20)], ingestion (AGS, and HepG2) and dermal contact (THP-1, and human peripheral blood cells). A comparison table was developed (Table 2.0), which provides easy interpretation of the toxicity levels of the five nanoparticles that were considered using all three human cell lines. The drawback of this comparison is the lack of sufficient data to assign conclusive toxicity levels to the nanoparticles. The toxicity studies of nanoparticles on humans is still in its infancy and contradictory results exist for some of the nanoparticles. This is the first attempt to combine the results of the experimental investigations of nano-MQL cooling and the toxicity studies of nanoparticles, allowing researchers to make informed decisions in the selection of the most sustainable nanoparticles in the nano-MQL machining process.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".