Silica polymerization has been extensively used to synthesize various fascinating materials for industrial and technological applications. The polymerization protocol is modified by altering several parameters (such as the concentration of the precursor, temperature, pH) heuristically to obtain the desired end product. To properly understand the effect of such parameters, knowledge of molecular events occurring during the process of polymerization is essential. In this work, we developed algorithms to capture molecular events such as translation, rotation, and reactions using the reaction ensemble Monte Carlo (REMC) technique. Our algorithms simulate molecular events in accordance with physical time by correctly scaling the movements of a cluster with the monomer, thereby capturing the kinetics of the process. We studied the polymerization of the four coordinated silica (f) precursor using our algorithm and observed excellent agreement between simulation results and experimental data. The algorithm was also used to study the polymerization of the three coordinated silica (f) precursor and it was found that our simulations capture experimental kinetics well, thereby confirming that the developed algorithms are robust. We studied the effect of the functionality of the precursor on polymerization kinetics and the resulting structure by simulating silica systems having a mixture of two, three and four functional (f, f, and f) silica precursors. We observed that network formation and cluster size decrease with the increase in the concentration of the f precursor. The radius of gyration (R) of the system initially increases due to network formation and decreases later due to the collapse of a large cluster. The R is directly correlated with the total number of primitive rings present in the system. The molecular level understanding obtained will be useful in the design of tailored silica nanoparticles.

The porous morphology drives the application of nano-and mesoporous materials in a variety of areas. However, limited information is available on the porosity development during their synthesis at various times due to challenges in experimental and simulation techniques. In this work, we have probed the porosity development in silica particles using Monte Carlo simulation techniques. We have developed an algorithm to measure the porosity of small, irregular shaped, finite-size particles formed during the polymerization process. We observed that smaller and denser clusters are formed initially, which later aggregate to form porous clusters in the range of 500−1000 h for the system having a concentration of silica precursor = 104 mg/cm 3 in a reactive solvent. In the case of nonreactive solvents, the porosity development is significantly different and occurs from initial stages of polymerization and lasts until the aggregation stage, in the range of 2−2000 h for similar concentrations. This significant change is due to faster kinetics of polymerization. Further polymerization leads to the formation of denser clusters due to aging in both cases. The mechanism of porosity formation in reactive solvent systems is due to random aggregation of denser clusters, whereas in a nonreactive solvent, it occurs by merging of smaller porous clusters. We also prepared a phase diagram of porosity evolution at various concentrations and observed that it has a tremendous effect on porosity development. We find three concentration ranges where silica cluster transforms from small denser to large denser particles in multiple stages. For a nonreactive solvent, the porosity evolution phase diagram shifts and stretches toward lower concentration ranges. We believe that this detailed understanding of porosity development will be useful to control the porous morphology of nano-and mesoporous materials during their synthesis.

Ring structures are ubiquitous in porous materials and play a crucial role in the functioning of these materials. Understanding rings formation and breaking mechanism are essential for designing and controlling...

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