Open Access
Open access
volume 12 pages 167120-167152

A comparative review: research in safety and sustainability of carbon nanomaterials without and with machine learning assistance

Liqing Wang 1
Hongyan Wang 2
Mingyu Bai 3
Mei Bai 3
Yin Wu 2
Tongshu Guo 2, 4
Ting Guo 2
Dirui Cai 5
D. Cai 5
Peiyan Sun 2, 4
Na Xiao 6
Ansheng Li 1
Wuyi Ming 2, 4
Publication typeJournal Article
Publication date2024-11-08
scimago Q1
wos Q2
SJR0.849
CiteScore9.0
Impact factor3.6
ISSN21693536
Abstract
In recent years, the rapid development of nanomaterials and nanoproducts has led to their widespread application in energy, aerospace, agriculture, industry, and biomedicine. However, carbon nanomaterials (CNMs) have been shown to possess toxic properties that negatively impact both the environment and human health. Then, toxicological risk assessment of CNMs are necessary to identify their potential adverse effects. This review examines the safety and sustainability of fullerenes, graphene, and carbon nanotubes in critical areas. First, the toxicity of CNMs in medicine, environmental engineering, energy, food, cosmetic and agriculture sectors using traditional detection methods is summarized. Then, the application of machine learning techniques for assessing the toxicity of these types of CNMs is reviewed. Finally, a comparative analysis of traditional and machine learning methods for detecting carbon nanomaterial toxicity is presented, highlighting key issues that need to be addressed in future research.
Found 
Found 

Top-30

Journals

1
Carbon Letters
1 publication, 25%
RSC Advances
1 publication, 25%
Artificial Intelligence in Data and Big Data Processing
1 publication, 25%
Microchimica Acta
1 publication, 25%
1

Publishers

1
2
3
Springer Nature
3 publications, 75%
Royal Society of Chemistry (RSC)
1 publication, 25%
1
2
3
  • We do not take into account publications without a DOI.
  • Statistics recalculated weekly.

Are you a researcher?

Create a profile to get free access to personal recommendations for colleagues and new articles.
Metrics
4
Share
Cite this
GOST |
Cite this
GOST Copy
Wang L. et al. A comparative review: research in safety and sustainability of carbon nanomaterials without and with machine learning assistance // IEEE Access. 2024. Vol. 12. pp. 167120-167152.
GOST all authors (up to 50) Copy
Wang L., Wang H., Bai M., Bai M., Wu Y., Tongshu Guo, Guo T., Cai D., Cai D., Sun P., Xiao N., Li A., Ming W. A comparative review: research in safety and sustainability of carbon nanomaterials without and with machine learning assistance // IEEE Access. 2024. Vol. 12. pp. 167120-167152.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1109/access.2024.3494549
UR - https://ieeexplore.ieee.org/document/10747328/
TI - A comparative review: research in safety and sustainability of carbon nanomaterials without and with machine learning assistance
T2 - IEEE Access
AU - Wang, Liqing
AU - Wang, Hongyan
AU - Bai, Mingyu
AU - Bai, Mei
AU - Wu, Yin
AU - Tongshu Guo
AU - Guo, Ting
AU - Cai, Dirui
AU - Cai, D.
AU - Sun, Peiyan
AU - Xiao, Na
AU - Li, Ansheng
AU - Ming, Wuyi
PY - 2024
DA - 2024/11/08
PB - Institute of Electrical and Electronics Engineers (IEEE)
SP - 167120-167152
VL - 12
SN - 2169-3536
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2024_Wang,
author = {Liqing Wang and Hongyan Wang and Mingyu Bai and Mei Bai and Yin Wu and Tongshu Guo and Ting Guo and Dirui Cai and D. Cai and Peiyan Sun and Na Xiao and Ansheng Li and Wuyi Ming},
title = {A comparative review: research in safety and sustainability of carbon nanomaterials without and with machine learning assistance},
journal = {IEEE Access},
year = {2024},
volume = {12},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
month = {nov},
url = {https://ieeexplore.ieee.org/document/10747328/},
pages = {167120--167152},
doi = {10.1109/access.2024.3494549}
}
Profiles