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Data Publication

Data from manuscript: Experimental and simulation data of cluster size distribution and average cluster size

Shelke, Yogesh | Marín-Aguilar, Susana | Camerin, Fabrizio | Dijkstra, Marjolein | Kraft, Daniela J.

4TU.ResearchData

(2022)

Descriptions

Datasets belonging to research on the self-assembly of colloidal molecules prepared from positively charged colloidal hematite cubes and negatively charged colloidal spheres. The data sets contain experimental and simulated data on the cluster size distribution and average cluster size of colloidal molecules. Experimental cluster size was determined by counting at least 100 clusters from microscopy images after self-assembly driven by electrostatic interactions. Simulation cluster size was determined by counting 1000 clusters from Monte Carlo simulations.

Keywords


Originally assigned keywords
Condensed Matter Physics
FOS: Physical sciences
Colloidal Clusters
Colloidal Molecules Cluster Size Distribution
Average Cluster Size

MSL enriched keywords
minerals
oxide mineral
hematite


Source publisher

4TU.ResearchData


DOI

10.4121/19773601.v1


Creators

Shelke, Yogesh

https://orcid.org/:

0000-0003-4094-8499

Marín-Aguilar, Susana

Camerin, Fabrizio

Dijkstra, Marjolein

https://orcid.org/:

0000-0002-9166-6478

Kraft, Daniela J.


Contributors

Leiden Instituut Onderzoek Natuurkunde LION-Biological & Soft Matter, Leiden University,

Other

Soft Condensed Matter, Debye Institute For Nanomaterials Science, Department Of Physics, Utrecht University,

Other


Citation

Shelke, Y., Marín-Aguilar, S., Camerin, F., Dijkstra, M., & Kraft, D. J. (2022). Data from manuscript: Experimental and simulation data of cluster size distribution and average cluster size (Version 1) [Data set]. 4TU.ResearchData. https://doi.org/10.4121/19773601.V1


Dates

Issued:

2022-05-31


Funding References

Funder name: unknown

Award title: European Research Council (ERC Starting Grant number 758383, RECONFMAT)

Funder name: unknown

Award title: European Research Council (ERC464 Advanced Grant number ERC-2019-ADV-H2020 884902, SoftML)


Rights

Creative Commons Zero v1.0 Universal


Datacite version

1