Inverse patchy particles are promising colloids to develop new architectures in ceramic materials based on their self-assembly. Nonetheless, a good understanding of their aggregation is required. Several previous studies have shown that the behavior of ceramic colloids can be well described by the DLVO interaction potential. In the present paper, we develop new coarse-grained Brownian dynamics simulations, where particles are represented by an assembly of beads interacting via DLVO interactions, whose parameters can be directly linked to experimental characterization. First, the validity of the simulations is proved by studying the heteroaggregation of homogeneously charged particles. Then, simulations are applied to one-patch inverse patchy particles to study the effect of the patch size. They show that the smaller the patch, the more elongated the aggregates. Simulations are also performed to understand the role of the Debye screening length in the particular case of large patches and they show that aggregation leads always to compact aggregates.

Download full-text PDF

Source
http://dx.doi.org/10.1039/c9cp04247dDOI Listing

Publication Analysis

Top Keywords

inverse patchy
12
patchy particles
12
brownian dynamics
8
dynamics simulations
8
one-patch inverse
8
simulations
5
particles
5
simulations one-patch
4
particles inverse
4
particles promising
4

Similar Publications

One of the frontiers of nanotechnology is advancing beyond the periodic self-assembly of materials. Icosahedral quasicrystals, aperiodic in all directions, represent one of the most challenging targets that has yet to be experimentally realized at the colloidal scale. Previous attempts have required meticulous human-designed building blocks and often resulted in interactions beyond the current experimental capabilities.

View Article and Find Full Text PDF

Protein engineering enables the creation of tailor-made proteins for a variety of applications. ImmTACs stand out as promising therapeutics for cancer and other treatments while also presenting unique challenges for stability, formulation, and delivery. We have shown that ImmTACs behave as Janus particles in solution, leading to self-association at low concentrations, even when the average protein-protein interactions suggest that the molecule should be stable.

View Article and Find Full Text PDF
Article Synopsis
  • Alopecia areata is a condition that causes hair loss and can be treated with a medicine called DPCP.
  • Researchers studied 97 kids with this condition to see how well DPCP worked over time.
  • After a year, about 9% of the kids had their hair fully regrow, and those who started with less severe symptoms did better with the treatment.
View Article and Find Full Text PDF

Inverse design of a pyrochlore lattice of DNA origami through model-driven experiments.

Science

May 2024

School of Molecular Sciences and Center for Molecular Design and Biomimetics, The Biodesign Institute, Arizona State University, 1001 South McAllister Avenue, Tempe, AZ 85281, USA.

Sophisticated statistical mechanics approaches and human intuition have demonstrated the possibility of self-assembling complex lattices or finite-size constructs. However, attempts so far have mostly only been successful in silico and often fail in experiment because of unpredicted traps associated with kinetic slowing down (gelation, glass transition) and competing ordered structures. Theoretical predictions also face the difficulty of encoding the desired interparticle interaction potential with the experimentally available nano- and micrometer-sized particles.

View Article and Find Full Text PDF

Background: Alopecia areata (AA) is an autoimmune condition characterized by sudden and unpredictable hair loss, with a lifetime incidence of 2%. AA can be divided into three categories: patchy alopecia, alopecia totalis, and alopecia universalis. It can affect a person's psychological health and overall quality of life.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!