Supporting Information
Supporting Information File 1: The sheet details information on the metal’s oxides with zeta potential and cell damage endpoint along with the external set used in the present work. Supporting Information File 2: PLS graphs for both cell damage and zeta potential data.
| Supporting Information File 1: Additional experimental data. | ||
| Format: XLSX | Size: 62.0 KB | Download |
| Supporting Information File 2: Additional figures. | ||
| Format: PDF | Size: 556.7 KB | Download |
Cite the Following Article
Exploring the relationships between physiochemical properties of nanoparticles and cell damage to combat cancer growth using simple periodic table-based descriptors
Joyita Roy and Kunal Roy
Beilstein J. Nanotechnol. 2024, 15, 297–309.
https://doi.org/10.3762/bjnano.15.27
How to Cite
Roy, J.; Roy, K. Beilstein J. Nanotechnol. 2024, 15, 297–309. doi:10.3762/bjnano.15.27
Download Citation
Citation data can be downloaded as file using the "Download" button or used for copy/paste from the text window
below.
Citation data in RIS format can be imported by all major citation management software, including EndNote,
ProCite, RefWorks, and Zotero.
Presentation Graphic
| Picture with graphical abstract, title and authors for social media postings and presentations. | ||
| Format: PNG | Size: 12.9 MB | Download |
Citations to This Article
Up to 20 of the most recent references are displayed here.
Scholarly Works
- Fioressi, S. E.; Bacelo, D. E.; Duchowicz, P. R. Quantitative Structure–Property Relationships (QSPR) for Materials Science. Challenges and Advances in Computational Chemistry and Physics; Springer Nature Switzerland, 2025; pp 61–79. doi:10.1007/978-3-031-78736-2_4
- Roy, J.; Roy, K. Insights into nanoparticle toxicity against aquatic organisms using multivariate regression, read-across, and ML algorithms: Predictive models for Daphnia magna and Danio rerio. Aquatic toxicology (Amsterdam, Netherlands) 2024, 276, 107114. doi:10.1016/j.aquatox.2024.107114
- Kar, S.; Yang, S. Introducing third-generation periodic table descriptors for nano-qRASTR modeling of zebrafish toxicity of metal oxide nanoparticles. Beilstein journal of nanotechnology 2024, 15, 1142–1152. doi:10.3762/bjnano.15.93
- Yang, H.; Niu, S.; Guo, M.; Xue, Y. Molecular mechanisms of silver nanoparticle-induced neurotoxic injury and new perspectives for its neurotoxicity studies: A critical review. Environmental pollution (Barking, Essex : 1987) 2024, 362, 124934. doi:10.1016/j.envpol.2024.124934
- Uzokboev, S.; Akhmadbekov, K.; Nuritdinova, R.; Tawfik, S. M.; Lee, Y.-I. Unveiling the potential of alginate-based nanomaterials in sensing technology and smart delivery applications. Beilstein journal of nanotechnology 2024, 15, 1077–1104. doi:10.3762/bjnano.15.88
- Meng, F.; Xu, W.; Qian, Y.; Sun, F.; Sun, B.; Yang, Z. Artificial intelligence (AI)-enabled thermochemical risk modeling via self-attentive deep neural networks for predicting the SADT of organic peroxides. Journal of Loss Prevention in the Process Industries 99, 105827. doi:10.1016/j.jlp.2025.105827