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Acrylic based pressure sensitive adhesives (PSAs) are a widely used class of adhesive in which the suitability for a given application is heavily dependent on a delicate balance of the viscoelastic properties of the material. In this work, we present a strategy for the H-bond directed structural reinforcement of acrylic PSAs with enhanced shear resistance. The system is composed of a water-based acrylic polymer dispersion functionalized with pyrrolidone groups, and tannic acid, a natural occurring polyphenol, dissolved in water. It is demonstrated that during the drying of the dispersions, the H-bonds between the pyrrolidone groups in the polymer particles and the hydroxyl groups of TA in the aqueous phase promote the spontaneous formation of a percolating TA network around the soft acrylic polymer particles. Even when present as a fraction of a percent of the total formulation, the synergistic combination of the H-bond network and the structural reinforcement leads to PSAs with substantially improved shear resistance, whilst maintaining good peel strength. Unlike most approaches to improving shear resistance of acrylic PSAs, the proposed strategy is particularly interesting in that it offers the potential for post-synthesis tuning of the adhesive behavior of the PSA. [Display omitted] • Tannic acid was blended with a pyrrolidone functionalized acrylic latex. • H-bond interactions led to a stiff, percolating TA network in the dried film. • The formation of the TA network led to PSAs with enhanced shear strength. • The microstructure formation did not significantly reduce peel adhesion. • This strategy offers a route for easy post-synthesis tuning of adhesive properties. [ABSTRACT FROM AUTHOR]

The stochastic simulation algorithm (SSA) and the corresponding Monte Carlo (MC) method are among the most common approaches for studying stochastic processes. They relies on knowledge of interevent probability density functions (PDFs) and on information about dependencies between all possible events. Analytical representations of a PDF are difficult to specify in advance, in many real life applications. Knowing the shapes of PDFs, and using experimental data, different optimization schemes can be applied in order to evaluate probability density functions and, therefore, the properties of the studied system. Such methods, however, are computationally demanding, and often not feasible. We show that, in the case where experimentally accessed properties are directly related to the frequencies of events involved, it may be possible to replace the heavy Monte Carlo core of optimization schemes with an analytical solution. Such a replacement not only provides a more accurate estimation of the properties of the process, but also reduces the simulation time by a factor of order of the sample size (at least ≈104). The proposed analytical approach is valid for any choice of PDF. The accuracy, computational efficiency, and advantages of the method over MC procedures are demonstrated in the exactly solvable case and in the evaluation of branching fractions in controlled radical polymerization (CRP) of acrylic monomers. This polymerization can be modeled by a constrained stochastic process. Constrained systems are quite common, and this makes the method useful for various applications. [ABSTRACT FROM AUTHOR]