Surface charge patchiness of different charge types can influence the solution behaviours of colloidal particles and globular proteins. Herein, coiled-coil ‘bundlemer’ nanoparticles that display only a single type of surface charge (SC) are computationally designed to compare their solution behaviours to mixed charge-type (MC) counterparts with both positively and negatively charged side chains. Nematic and columnar liquid crystal phases are discovered in low concentrations of SC particles, indicative of particle end-to-end stacking into columns combined with lateral electrostatic repulsion between columns, while MC particles with the same net charge and particle shape produced only amorphous, soluble aggregates. Similarly, porous lattices are formed in mixtures of SC/MC particles of opposite charges while MC/MC mixtures of opposite charges produce only amorphous aggregates. The lattice structure is inferred with a machine learning optimization approach. The differences between SC and MC particle behaviours directly show the importance of surface electrostatic patchiness.

不同电荷类型的表面电荷斑块会影响胶体颗粒和球状蛋白质的溶解行为。在本文中，仅显示单一类型表面电荷 （SC） 的卷曲线圈“束聚体”纳米颗粒被计算设计成将其溶液行为与具有带正电荷和带负电荷侧链的混合电荷型 （MC） 对应物进行比较。在低浓度的 SC 颗粒中发现了向列和柱状液晶相，这表明颗粒端到端堆积成柱，并结合了柱之间的横向静电排斥，而具有相同净电荷和颗粒形状的 MC 颗粒仅产生无定形的可溶性聚集体。同样，多孔晶格是在相反电荷的 SC/MC 颗粒混合物中形成的，而相反电荷的 MC/MC 混合物仅产生无定形聚集体。晶格结构是使用机器学习优化方法推断的。SC 和 MC 颗粒行为之间的差异直接表明了表面静电斑块的重要性。