Pavol Juhas

Revealing the mechanisms behind SnO2 nanoparticle formation and growth during hydrothermal synthesis: an in situ total scattering study.

The formation and growth mechanisms in the hydrothermal synthesis of SnO(2) nanoparticles from aqueous solutions of SnCl(4)·5H(2)O have been elucidated by means of in situ X-ray total scattering (PDF) measurements. The analysis of the data reveals that when the tin(IV) chloride precursor is dissolved, chloride ions and water coordinate octahedrally to tin(IV), forming aquachlorotin(IV) complexes of the form [SnCl(x)(H(2)O)(6-x)]((4-x)+) as well as hexaaquatin(IV) complexes [Sn(H(2)O)(6-y)(OH)(y)]((4-y)+). Upon heating, ellipsoidal SnO(2) nanoparticles are formed uniquely from hexaaquatin(IV). The nanoparticle size and morphology (aspect ratio) are dependent on both the reaction temperature and the precursor concentration, and particles as small as ~2 nm can be synthesized. Analysis of the growth curves shows that Ostwald ripening only takes place above 200 °C, and in general the growth is limited by diffusion of precursor species to the growing particle. The c-parameter in the tetragonal lattice is observed to expand up to 0.5% for particle sizes down to 2-3 nm as compared to the bulk value. SnO(2) nanoparticles below 3-4 nm do not form in the bulk rutile structure, but as an orthorhombic structural modification, which previously has only been reported at pressures above 5 GPa. Thus, adjustment of the synthesis temperature and precursor concentration not only allows control over nanoparticle size and morphology but also the structure.

Atomic Structures and Gram Scale Synthesis of Three Tetrahedral Quantum Dots

Luminescent semiconducting quantum dots (QDs) are central to emerging technologies that range from tissue imaging to solid-state lighting. However, existing samples are heterogeneous, which has prevented atomic-resolution determination of their structures and obscured the relationship between their atomic and electronic structures. Here we report the synthesis, isolation, and structural characterization of three cadmium selenide QDs with uniform compositions (Cd35Se20(X)30(L)30, Cd56Se35(X)42(L)42, Cd84Se56(X)56(L)56; X = O2CPh, L = H2N-C4H9). Their UV-absorption spectra show a lowest energy electronic transition that decreases in energy (3.54 eV, 3.26 eV, 3.04 eV) and sharpens as the size of the QD increases (fwhm = 207 meV, 145 meV, 115 meV). The photoluminescence spectra of all three QDs are broad with large Stokes shifts characteristic of trap-luminescence. Using a combination of single-crystal X-ray diffraction and atomic pair distribution function analysis, we determine the structures of their inorganic cores, revealing a series of pyramidal nanostuctures with cadmium terminated {111} facets. Theoretical and experimental studies on these materials will open the door to a deeper fundamental understanding of structure-property relationships in quantum-confined semiconductors.

Complex modeling: a strategy and software program for combining multiple information sources to solve ill posed structure and nanostructure inverse problems.

A strategy is described for regularizing ill posed structure and nanostructure scattering inverse problems (i.e. structure solution) from complex material structures. This paper describes both the philosophy and strategy of the approach, and a software implementation, DiffPy Complex Modeling Infrastructure (DiffPy-CMI).

Polymorphism in magic-sized Au144(SR)60 clusters

Ultra-small, magic-sized metal nanoclusters represent an important new class of materials with properties between molecules and particles. However, their small size challenges the conventional methods for structure characterization. Here we present the structure of ultra-stable Au144(SR)60 magic-sized nanoclusters obtained from atomic pair distribution function analysis of X-ray powder diffraction data. The study reveals structural polymorphism in these archetypal nanoclusters. In addition to confirming the theoretically predicted icosahedral-cored cluster, we also find samples with a truncated decahedral core structure, with some samples exhibiting a coexistence of both cluster structures. Although the clusters are monodisperse in size, structural diversity is apparent. The discovery of polymorphism may open up a new dimension in nanoscale engineering.

Quantitative nanostructure characterization using atomic pair distribution functions obtained from laboratory electron microscopes

Abstract Quantitatively reliable atomic pair distribution functions (PDFs) have been obtained from nanomaterials in a straightforward way from a standard laboratory transmission electron microscope (TEM). The approach looks very promising for making electron derived PDFs (ePDFs) a routine step in the characterization of nanomaterials because of the ubiquity of such TEMs in chemistry and materials laboratories. No special attachments such as energy filters were required on the microscope. The methodology for obtaining the ePDFs is described as well as some opportunities and limitations of the method.

Modelling pair distribution functions (PDFs) of organic compounds: describing both intra- and intermolecular correlation functions in calculated PDFs

The methods currently used to calculate atomic pair distribution functions (PDFs) from organic structural models do not distinguish between the intramolecular and intermolecular distances. Owing to the stiff bonding between atoms within a molecule, the PDF peaks arising from intramolecular atom–atom distances are much sharper than those of the intermolecular atom–atom distances. This work introduces a simple approach to calculate PDFs of molecular systems without building a supercell model by using two different isotropic displacement parameters to describe atomic motion: one parameter is used for the intramolecular, the other one for intermolecular atom–atom distances. Naphthalene, quinacridone and paracetamol were used as examples. Calculations were done with the DiffPy-CMI complex modelling infrastructure. The new modelling approach produced remarkably better fits to the experimental PDFs, confirming the higher accuracy of this method for organic materials.

On the estimation of statistical uncertainties on powder diffraction and small-angle scattering data from two-dimensional X-ray detectors

Optimal methods are explored for obtaining one-dimensional powder pattern intensities from two-dimensional planar detectors with good estimates of their standard deviations. Methods are described to estimate uncertainties when the same image is measured in multiple frames as well as from a single frame. The importance of considering the correlation of diffraction points during the integration and the resampling process of data analysis is shown. It is found that correlations between adjacent pixels in the image can lead to seriously overestimated uncertainties if such correlations are neglected in the integration process. Off-diagonal entries in the variance–covariance (VC) matrix are problematic as virtually all data processing and modeling programs cannot handle the full VC matrix. It is shown that the off-diagonal terms come mainly from the pixel-splitting algorithm used as the default integration algorithm in many popular two-dimensional integration programs, as well as from rebinning and resampling steps later in the processing. When the full VC matrix can be propagated during the data reduction, it is possible to get accurate refined parameters and their uncertainties at the cost of increasing computational complexity. However, as this is not normally possible, the best approximate methods for data processing in order to estimate uncertainties on refined parameters with the greatest accuracy from just the diagonal variance terms in the VC matrix is explored.

Robust structure and morphology parameters for CdS nanoparticles by combining small-angle X-ray scattering and atomic pair distribution function data in a complex modeling framework

In this work, the concept of complex modeling (CM) is tested by carrying out a co-refinement of the atomic pair distribution function and small-angle X-ray scattering data from CdS nanoparticles. It is shown that, compared with either single technique alone, the CM approach yields a more accurate and robust structural insight into the atomic structure and morphology of nanoparticles. This work opens the door for the application of CM to a wider class of nanomaterials and for the incorporation of additional experimental and theoretical techniques into these studies.

The unassigned distance geometry problem

Studies of distance geometry problems (DGP) have focused on cases where the vertices at the ends of all or most of the given distances are known or assigned, which we call assigned distance geometry problems (aDGPs). In this contribution we consider the unassigned distance geometry problem (uDGP) where the vertices associated with a given distance are unknown, so the graph structure has to be discovered. uDGPs arises when attempting to find the atomic structure of molecules and nanoparticles using X-ray or neutron diffraction data from non-crystalline materials. Rigidity theory provides a useful foundation for both aDGPs and uDGPs, though it is restricted to generic realizations of graphs, and key results are summarized. Conditions for unique realization are discussed for aDGP and uDGP cases, build-up algorithms for both cases are described and experimental results for uDGP are presented.

SrRietveld: a program for automating Rietveld refinements for high-throughput powder diffraction studies

SrRietveld is a highly automated software toolkit for Rietveld refinement. Compared to traditional refinement programs, it is more efficient to use and easier to learn. It is designed for modern high-throughput diffractometers and is capable of processing large numbers of data sets with minimal effort. The software currently uses conventional Rietveld refinement engines, automating GSAS and FullProf refinements. However, as well as automating and extending many tasks associated with these programs, it is designed in a flexible and extensible way so that in the future these engines can be replaced with new refinement engines as they become available. SrRietveld is an open-source software package developed in Python.