We present a novel simulation technique derived from Brownian cluster dynamics used so far to study the isotropic colloidal aggregation. It now implements the classical KernFrenkel potential to describe patchy interactions between particles. This technique gives access to static properties, dynamics and kinetics of the system, even far from the equilibrium. Particle thermal motions are modeled using billions of independent small random translations and rotations, constrained by the excluded volume and the connectivity. This algorithm, applied to a single polymer chain leads to correct static and dynamic properties, in the framework where hydrodynamic interactions are ignored. By varying patch angles, various chain flexibilities can be obtained. We have used this new algorithm to model step-growth polymerization under various solvent qualities. The polymerization reaction is modeled by an irreversible aggregation between patches while an isotropic finite square-well potential is superimposed to mimic the solvent quality. In bad solvent conditions, a competition between a phase separation (due to the isotropic interaction) and polymerization (due to patches) occurs. Surprisingly, an arrested network with a very peculiar structure appears. It is made of strands and nodes. Strands gather few stretched chains that dip into entangled globular nodes. These nodes act as reticulation points between the strands. The system is kinetically driven and we observe a trapped arrested structure. That demonstrates one of the strengths of this new simulation technique. It can give valuable insights about mechanisms that could be involved in the formation of stranded gels.

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Shortly after mixing cement grains with water, a cementitious fluid paste is formed that immediately transforms into a solid form by a phenomenon known as setting. Setting actually corresponds to the percolation of emergent network structures consisting of dissolving cement grains glued together by nanoscale hydration products, mainly calcium-silicate-hydrates. As happens in many percolation phenomena problems, the theoretical identification of the percolation threshold (i.e. the cement setting) is still challenging, since the length scale where percolation becomes apparent (typically the length of the cement grains, microns) is many times larger than the nanoscale hydrates forming the growing spanning network. Up to now, the long-lasting gap of knowledge on the establishment of a seamless handshake between both scales has been an unsurmountable obstacle for the development of a predictive theory of setting. Herein we present a true multi-scale model which concurrently provides information at the scale of cement grains (microns) and at the scale of the nano-hydrates that emerge during cement hydration. A key feature of the model is the recognition of cement setting as an off-lattice bond percolation process between cement grains. Inasmuch as this is so, the macroscopic probability of forming bonds between cement grains can be statistically analysed in smaller local observation windows containing fewer cement grains, where the nucleation and growth of the nano-hydrates can be explicitly described using a kinetic Monte Carlo Nucleation and Growth model. The most striking result of the model is the finding that only a few links (~12%) between cement grains are needed to reach setting. This directly unveils the importance of explicitly including nano-texture on the description of setting and explains why so low amount of nano-hydrates is needed for forming a spanning network. From the simulations, it becomes evident that this low amount is least affected by processing variables like the water-to-cement ratio and the presence of large quantities of nonreactive fillers. These counter-intuitive predictions were verified by ex-professo experiments that we have carried out to check the validity of our model.

The addition of external nanoparticles, mainly nano silica during the hydration of cement is a field of investigation in high performance cements. The added particles act as seeds and initiate early nucleation and subsequent growth of C-S-H gel. Nucleation is triggered very early, before enough clinker grains are dissolved or in other words, before the super saturation condition is attained. Hence, depending on the amount of added seed, the morphology and the mechanical properties of the product differ. Experimental studies in this area are less favored due to economic reasons and simulation studies are rare in the literature. An earlier work by some of the authors introduced a Monte Carlo model which dealt with the kinetics of the hydration process at early ages. The colloidal model incorporated random nucleation in the bulk, followed by an Avramian style layer by layer growth of 5nm sized C-S-H particles. The model was based on a Random Sequential Addition scheme and enabled a satisfactory rationalization of the early growth of C-S-H gel. In the present study, we extend this model for the addition of extra seeds and for different water to cement ratios.

The composition and structure of Calcium-Silicate-Hydrate (C-S-H) phases depends on various reaction parameters leading to its formation. Molecular Dynamic simulation studies probing the formation and structure of C-S-H are generally computationally expensive and can reach only very short time scales. Herein, we propose a coarse graining approach to model the formation of C-S-H, using patchy particles and a modified Patchy Brownian Cluster Dynamics algorithm. The simulations show that patchy particle systems can recover the qualitative kinetic evolution of C-S-H formation, and the obtained final structures were comparable to previously reported molecular dynamics studies and experiments. The model was extended to study the effect of water in the polymerization of tetraethoxysilane oligomers, the principal component of an impregnation treatment for deteriorated concrete surfaces. The intermediate system properties predicted by the simulations, such as viscosity and gel time, and structure were found to be well in accordance with the tailored experiments.