Monte Carlo Aggregation Code (MCAC) Part 1: Fundamentals

Design, characterization and applications of nanocolloidal hydrogels

Nanocolloidal gels (NCGs) are an emerging class of soft matter, in which nanoparticles act as building blocks of the colloidal network. Chemical or physical crosslinking enables NCG synthesis and assembly from a broad range of nanoparticles, polymers, and low-molecular weight molecules. The synergistic properties of NCGs are governed by nanoparticle composition, dimensions and shape, the mechanism of nanoparticle bonding, and the NCG architecture, as well as the nature of molecular crosslinkers. Nanocolloidal gels find applications in soft robotics, bioengineering, optically active coatings and sensors, optoelectronic devices, and absorbents. This review summarizes currently scattered aspects of NCG formation, properties, characterization, and applications. We describe the diversity of NCG building blocks, discuss the mechanisms of NCG formation, review characterization techniques, outline NCG fabrication and processing methods, and highlight most common NCG applications. The review is concluded with the discussion of perspectives in the design and development of NCGs.

A Fast and Improved Tunable Aggregation Model for Stochastic Simulation of Spray Fluidized Bed Agglomeration

Agglomeration in spray fluidized bed (SFB) is a particle growth process that improves powder properties in the chemical, pharmaceutical, and food industries. In order to analyze the underlying mechanisms behind the generation of SFB agglomerates, modeling of the growth process is essential. Morphology plays an imperative role in understanding product behavior. In the present work, the sequential tunable algorithm developed in previous studies to generate monodisperse SFB agglomerates is improved and extended to polydisperse primary particles. The improved algorithm can completely retain the given input fractal properties (fractal dimension and prefactor) for polydisperse agglomerates (with normally distributed radii of primary particles having a standard deviation of 10% from the mean value). Other morphological properties strongly agreed with the experimental SFB agglomerates. Furthermore, this tunable aggregation model is integrated into the Monte Carlo (MC) simulation. The kinetics of the overall agglomeration at various operating conditions, like binder concentration and inlet fluidized gas temperature, are investigated. The present model accurately predicts the morphological descriptors of SFB agglomerates and the overall kinetics under various operating parameters.

Monte Carlo Aggregation Code (MCAC) Part 2: Application to soot agglomeration, highlighting the importance of primary particles

During the agglomeration of nanoparticles and in particular, soot, a change in both the flow regime (from free molecular to near continuum) as well as the change of agglomeration regime (from ballistic to diffusive) is expected. However, these effects are rarely taken into account in numerical simulations of particle agglomeration and yet, they are suspected to have an important impact on the agglomeration kinetics, particle morphologies, and size distributions. This work intends to study these properties by using the Monte Carlo Aggregation Code (MCAC) presented in the preceding work (part 1), focusing on the physical impacts of varying the particle volume fraction and monomers size and polydispersity. The results show an important sensitivity of the kinetics of agglomeration, coagulation homogeneity, and agglomerate morphology to the size of monomers. First, for smaller monomer diameters, the agglomeration kinetic is enhanced and agglomerates are characterized by larger fractal dimensions. Second, for large monomer diameters, fractal dimensions down to 1.67 can be found being smaller than the classical 1.78 for Diffusion Limited Cluster Agglomeration (DLCA) mechanism. One important conclusion is that variation in time of both regimes has to be considered for a more accurate simulation of the agglomerate size distribution and morphology.