Published March 21, 2025 | Version v1
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Data for "Anisotropic DLVO-like interaction for charge patchiness in colloids and proteins"

  • 1. Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
  • 2. Department of Physics and Astronomy, University of Padova, 35131 Padova, Italy
  • 3. NFN, Sezione di Padova, via Marzolo 8, I-35131 Padova, Italy
  • 4. Department of Theoretical Physics, Joˇzef Stefan Institute, Jamova 39, SI-1000 Ljubljana, Slovenia
  • 5. Institut f¨ur Theoretische Physik, TU Wien, Wiedner Hauptstraße 8-10, A-1040 Wien, Austria
  • 6. CNR-ISC, Uos Sapienza, Piazzale A. Moro 2, 00185 Roma, Italy

Description

The behaviour and stability of soft and biological matter depend significantly on electrostatic interactions, as particles such as proteins and colloids acquire a charge when dispersed in an electrolytic solution. A typical simplification used to understand bulk phenomena involving electrostatic interactions is the isotropy of the charge on the particles. However, whether arising naturally or by synthesis, charge distributions are often inhomogeneous, leading to an intricate particle-particle interaction landscape and complex assembly phenomena. The fundamental complexity of these interactions gives rise to models based on distinct assumptions and varying degrees of simplifications which can blur the line between genuine physical behaviour and artefacts arising from the choice of a particular electrostatic model. Building upon the widely-used linearized Poisson-Boltzmann theory, we propose a theoretical framework that -- by bridging different models -- provides a robust DLVO-like description of electrostatic interactions between inhomogeneously charged particles. By matching solely the single-particle properties of two different mean-field models, we find a quantitative agreement between the pair interaction energies over a wide range of system parameters. Our work identifies a strategy to merge different models of inhomogeneously charged particles and paves the way to a reliable, accurate, and computationally affordable description of their interactions.

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