István Lagzi

How and Why Nanoparticle’s Curvature Regulates the Apparent pKa of the Coating Ligands

Dissociation of ionizable ligands immobilized on nanopaticles (NPs) depends on and can be regulated by the curvature of these particles as well as the size and the concentration of counterions. The apparent acid dissociation constant (pK(a)) of the NP-immobilized ligands lies between that of free ligands and ligands self-assembled on a flat surface. This phenomenon is explicitly rationalized by a theoretical model that accounts fully for the molecular details (size, shape, conformation, and charge distribution) of both the NPs and the counterions.

Maze solving by chemotactic droplets

Droplets emitting surface-active chemicals exhibit chemotaxis toward low-pH regions. Such droplets are self-propelled and navigate through a complex maze to seek a source of acid placed at one of the mazes exits. In doing so, the droplets find the shortest path through the maze. Chemotaxis and maze solving are due to an interplay between acid/base chemistry and surface tension effects.

Nanoparticle oscillations and fronts

Chemical oscillations can be coupled to the dynamic self-assembly of nanoparticles. Periodic pH changes translate into protonation and deprotonation of the ligands that stabilize the nanoparticles, thus altering repulsive and attractive interparticle forces. In a continuous stirred-tank reactor, rhythmic aggregation and dispersion is observed; in spatially distributed media, propagation of particle aggregation fronts is seen.

Bridging Interactions and Selective Nanoparticle Aggregation Mediated by Monovalent Cations

Selective aggregation and precipitation of like-charged nanoparticles (NPs) covered with carboxylate ligands can be induced by different monovalent cations. The ordering of critical concentrations required for NP precipitation is Cs(+) ≫ K(+) > Li(+) > Na(+) > Rb(+) and does not correlate with the size of hydrated cations M(+), nor can it be predicted by the Hofmeister series. On the other hand, different anions have no effect on the precipitation trends. These observations are rationalized by a theoretical model combining the elements of the DLVO theory with molecular-level calculations. The key component of the model is the cation-specific binding of various metal cations to the carboxylate ligands.

Charged nanoparticles as supramolecular surfactants for controlling the growth and stability of microcrystals

Microcrystals of desired sizes are important in a range of processes and materials, including controlled drug release, production of pharmaceutics and food, bio- and photocatalysis, thin-film solar cells and antibacterial fabrics. The growth of microcrystals can be controlled by a variety of agents, such as multivalent ions, charged small molecules, mixed cationic-anionic surfactants, polyelectrolytes and other polymers, micropatterned self-assembled monolayers, proteins and also biological organisms during biomineralization. However, the chief limitation of current approaches is that the growth-modifying agents are typically specific to the crystalizing material. Here, we show that oppositely charged nanoparticles can function as universal surfactants that control the growth and stability of microcrystals of monovalent or multivalent inorganic salts, and of charged organic molecules. We also show that the solubility of the microcrystals can be further tuned by varying the thickness of the nanoparticle surfactant layers and by reinforcing these layers with dithiol crosslinks.

Air pollution modelling using a Graphics Processing Unit with CUDA

The Graphics Processing Unit (GPU) is a powerful tool for parallel computing. In the past years the performance and capabilities of GPUs have increased, and the Compute Unified Device Architecture (CUDA) – a parallel computing architecture – has been developed by NVIDIA to utilize this performance in general purpose computations. Here we show for the first time a possible application of GPU for environmental studies serving as a basement for decision making strategies. A stochastic Lagrangian particle model has been developed on CUDA to estimate the transport and the transformation of the radionuclides from a single point source during an accidental release. Our results show that parallel implementation achieves typical acceleration values in the order of 80–120 times compared to CPU using a single-threaded implementation on a 2.33 GHz desktop computer. Only very small differences have been found between the results obtained from GPU and CPU simulations, which are comparable with the effect of stochastic transport phenomena in atmosphere. The relatively high speedup with no additional costs to maintain this parallel architecture could result in a wide usage of GPU for diversified environmental applications in the near future.

Liesegang rings engineered from charged nanoparticles

Functionalized nanoparticles (NPs) serve as building blocks of self-organizing chemical patterns comprising periodic zones of nanoparticle precipitation. In contrast to ions, which underlie most pattern-forming chemical systems and whose properties cannot be readily modified, NPs allow for flexible adjustment of particle charges and/or material properties. In particular, changes in the particle charges control the precipitation behavior and ultimately the morphologies of the emerging patterns. The phenomenon of NP-based periodic precipitation is explained by reaction-diffusion modeling and can be used for the fractionation of NPs of different sizes.

Controlling and engineering precipitation patterns.

Controlling and engineering chemical structures are the most important scientific challenges in material science. Precipitation patterns from ions or nanoparticles are promising candidates for designing bulk structure for catalysis, energy production, storage, and electronics. There are only a few procedures and techniques to control precipitation (Liesegang) patterns in gel media (e.g., using an electric field, varying the initial concentration of the electrolytes). However, those methods provide just a limited degree of freedom. Here, we provide a robust and transparent way to control and engineer Liesegang patterns by varying gel concentration and inducing impurity by addition of gelatin to agarose gel. Using this experimental method, different precipitation structures can be obtained with different width and spatial distribution of the formed bands. A new variant of a sol-coagulation model was developed to describe and understand the effect of the gel concentration and impurities on Liesegang pattern formation.

Dispersion modeling of air pollutants in the atmosphere: a review

Modeling of dispersion of air pollutants in the atmosphere is one of the most important and challenging scientific problems. There are several natural and anthropogenic events where passive or chemically active compounds are emitted into the atmosphere. The effect of these chemical species can have serious impacts on our environment and human health. Modeling the dispersion of air pollutants can predict this effect. Therefore, development of various model strategies is a key element for the governmental and scientific communities. We provide here a brief review on the mathematical modeling of the dispersion of air pollutants in the atmosphere. We discuss the advantages and drawbacks of several model tools and strategies, namely Gaussian, Lagrangian, Eulerian and CFD models. We especially focus on several recent advances in this multidisciplinary research field, like parallel computing using graphical processing units, or adaptive mesh refinement.

Formation of Liesegang patterns in an electric field

Evolution of Liesegang patterns in an electric field was studied experimentally in the AgNO3/K2Cr2O7/gelatine system. The distance of the last (nth) band as a function of their appearance time can be described by the equation Xn = c1t1/2 + c2t + c3. A numerical model, based on Ostwalds supersaturation theory, predicted the same functional law. Experiments showed that the ratio of the distances of two consecutive rings, the spacing coefficient, decreases with increasing electric field strength and this behaviour was also reproduced by the numerical model.