A Comparative Review: Biological Safety and Sustainability of Metal Nanomaterials Without and with Machine Learning Assistance
In recent years, metal nanomaterials and nanoproducts have been developed intensively, and they are now widely applied across various sectors, including energy, aerospace, agriculture, industry, and biomedicine. However, nanomaterials have been identified as potentially toxic, with the toxicity of m...
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| Language: | English |
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2072-666X/16/1/15 |
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| author | Na Xiao Yonghui Li Peiyan Sun Peihua Zhu Hongyan Wang Yin Wu Mingyu Bai Ansheng Li Wuyi Ming |
| author_facet | Na Xiao Yonghui Li Peiyan Sun Peihua Zhu Hongyan Wang Yin Wu Mingyu Bai Ansheng Li Wuyi Ming |
| author_sort | Na Xiao |
| collection | DOAJ |
| description | In recent years, metal nanomaterials and nanoproducts have been developed intensively, and they are now widely applied across various sectors, including energy, aerospace, agriculture, industry, and biomedicine. However, nanomaterials have been identified as potentially toxic, with the toxicity of metal nanoparticles posing significant risks to both human health and the environment. Therefore, the toxicological risk assessment of metal nanomaterials is essential to identify and mitigate potential adverse effects. This review provides a comprehensive analysis of the safety and sustainability of metallic nanoparticles (such as Au NPs, Ag NPs, etc.) in key domains such as medicine, energy, and environmental protection. Using a dual-perspective analysis approach, it highlights the unique advantages of machine learning in data processing, predictive modeling, and optimization. At the same time, it underscores the importance of traditional methods, particularly their ability to offer greater interpretability and more intuitive results in specific contexts. Finally, a comparative analysis of traditional methods and machine learning techniques for detecting the toxicity of metal nanomaterials is presented, emphasizing the key challenges that need to be addressed in future research. |
| format | Article |
| id | doaj-art-051304255e1746f5a7f487a1721f74c8 |
| institution | Kabale University |
| issn | 2072-666X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj-art-051304255e1746f5a7f487a1721f74c82025-01-24T13:41:50ZengMDPI AGMicromachines2072-666X2024-12-011611510.3390/mi16010015A Comparative Review: Biological Safety and Sustainability of Metal Nanomaterials Without and with Machine Learning AssistanceNa Xiao0Yonghui Li1Peiyan Sun2Peihua Zhu3Hongyan Wang4Yin Wu5Mingyu Bai6Ansheng Li7Wuyi Ming8Department of Engineering, Huanghe University of Science and Technology, Zhengzhou 450008, ChinaHenan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaHenan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaHenan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaHenan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaHenan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaGuangdong HUST Industrial Technology Research Institute, Huazhong University of Science and Technology, Dongguan 523808, ChinaHenan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaHenan Key Lab of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaIn recent years, metal nanomaterials and nanoproducts have been developed intensively, and they are now widely applied across various sectors, including energy, aerospace, agriculture, industry, and biomedicine. However, nanomaterials have been identified as potentially toxic, with the toxicity of metal nanoparticles posing significant risks to both human health and the environment. Therefore, the toxicological risk assessment of metal nanomaterials is essential to identify and mitigate potential adverse effects. This review provides a comprehensive analysis of the safety and sustainability of metallic nanoparticles (such as Au NPs, Ag NPs, etc.) in key domains such as medicine, energy, and environmental protection. Using a dual-perspective analysis approach, it highlights the unique advantages of machine learning in data processing, predictive modeling, and optimization. At the same time, it underscores the importance of traditional methods, particularly their ability to offer greater interpretability and more intuitive results in specific contexts. Finally, a comparative analysis of traditional methods and machine learning techniques for detecting the toxicity of metal nanomaterials is presented, emphasizing the key challenges that need to be addressed in future research.https://www.mdpi.com/2072-666X/16/1/15metal nanomaterialsnanotoxicologysafetysustainabilityenergyenvironment |
| spellingShingle | Na Xiao Yonghui Li Peiyan Sun Peihua Zhu Hongyan Wang Yin Wu Mingyu Bai Ansheng Li Wuyi Ming A Comparative Review: Biological Safety and Sustainability of Metal Nanomaterials Without and with Machine Learning Assistance Micromachines metal nanomaterials nanotoxicology safety sustainability energy environment |
| title | A Comparative Review: Biological Safety and Sustainability of Metal Nanomaterials Without and with Machine Learning Assistance |
| title_full | A Comparative Review: Biological Safety and Sustainability of Metal Nanomaterials Without and with Machine Learning Assistance |
| title_fullStr | A Comparative Review: Biological Safety and Sustainability of Metal Nanomaterials Without and with Machine Learning Assistance |
| title_full_unstemmed | A Comparative Review: Biological Safety and Sustainability of Metal Nanomaterials Without and with Machine Learning Assistance |
| title_short | A Comparative Review: Biological Safety and Sustainability of Metal Nanomaterials Without and with Machine Learning Assistance |
| title_sort | comparative review biological safety and sustainability of metal nanomaterials without and with machine learning assistance |
| topic | metal nanomaterials nanotoxicology safety sustainability energy environment |
| url | https://www.mdpi.com/2072-666X/16/1/15 |
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