Toxicity Analysis of Nano-Minimum Quantity Lubrication Machining—A Review
Bibliographic record
Abstract
The lubrication properties of nanoparticles are of great interest to the manufacturing industry and led to the development of the nano-minimum quantity lubrication (NMQL) cooling strategy. To evaluate the sustainability characteristics of nano-minimum quantity lubrication, apart from analyzing the benefits of increasing machining efficiency, it is also essential to evaluate the potential detrimental effects of nanoparticles on human health and the environment. Existing literature provides substantial data on the benefits of nano-minimum quantity lubrication machining. However, the current literature does not provide researchers in the machining sector a comprehensive analysis of the toxicity of the nanoparticles used in nano-minimum quantity lubrication. This study aims to provide a comprehensive review that addresses the toxicity levels of the most frequently used nanoparticles in NMQL machining. To understand the impacts of nanoparticles on the human body and the environment, in vitro studies that evaluate the nanoparticles’ toxicity on human cells and in vitro/in vivo studies on other living organisms are considered. The results from toxicity studies on each of the chosen nanoparticles are summarized and presented in chronological order. The reviewed studies indicate transition metal dichalcogenides (MoS2 and WS2) exhibit very low toxicity when compared to other nanoparticles. The toxicity of hBN and AL2O3 nanoparticles varies depending on their lengths and crystalline structures, respectively. In conclusion, a chart that maps the toxicity levels of nanoparticles on seven different human cell lines (human lung epithelial cells (A549), human bronchial epithelial cells (Nl-20), AGS human gastric cells, human epidermal cells (HEK), human liver-derived cells (HepG2), human endothelial cells and human peripheral cells), representing exposures by inhalation, ingestion and dermal contact, was developed for easy and quick insights. This is the first attempt in open literature to combine the results of the experimental investigations of nano-minimum quantity lubrication cooling and the toxicity studies of nanoparticles, allowing researchers to make informed decisions in the selection of the most sustainable nanoparticles in the nano-minimum quantity lubrication 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".