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Cited by 290

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Designing Nanoparticle Translocation through Membranes by Computer Simulations

Ding, Hong-ming, Tian, Wen-de, Ma, Yu-qiang·2012·ACS Nano

Abstract

Nanoparticle penetration into cells is an important process in drug/gene delivery. Here, we successfully design one type of novel nanoparticles with ligands decorating its surface by dynamic bonds and find that the nanoparticle can spontaneously penetrate through membranes by using dissipative particle dynamics simulations. Moreover, the physical parameters of both ligands (for example, ligand type and density) and nanoparticles (such as size and shape) have significant effects on penetration efficiency and translocation time. Especially for nanoparticles with anisotropic shapes or asymmetric surface decoration, the penetration efficiency may reach about 80%. We also provide insights into the interaction between nanoparticles and asymmetric membranes and find that the membrane asymmetry can even increase the penetration efficiency to above 90%. The present study suggests a potential way to translocate novel nanoparticles through membranes, which may provide new ideas for future experimental nanoparticle design and drug delivery.

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Metadata

Authors
Ding, H.-M., Tian, W.-D., & Ma, Y.-Q.
Venue
ACS Nano
Published
2012-01-10
Volume
6
Issue
2
Pages
1230-1238
ISSN
  • 1936-0851 (ISSN-L)
  • 1936-086X
OpenAlex
W2317597468
Cited by
290
References
3
FWCI
11.90
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Designing Nanoparticle Translocation through Membranes by Computer Simulations. https://app.cochat.ai/papers/10.1021/nn2038862
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Ding, H.-M., Tian, W.-D., & Ma, Y.-Q. (2012). Designing Nanoparticle Translocation through Membranes by Computer Simulations. ACS Nano. https://doi.org/10.1021/nn2038862

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.