Towards the extraction of the crystal cell parameters from pair distribution function profiles.

The approach based on atomic pair distribution function (PDF) has revolutionized structural investigations by X-ray/electron diffraction of nano or quasi-amorphous materials, opening up the possibility of exploring short-range order. However, the ab initio crystal structural solution by the PDF is far from being achieved due to the difficulty in determining the crystallographic properties of the unit cell. A method for estimating the crystal cell parameters directly from a PDF profile is presented, which is composed of two steps: first, the type of crystal cell is inferred using machine-learning approaches applied to the PDF profile; second, the crystal cell parameters are extracted by means of multivariate analysis combined with vector superposition techniques. The procedure has been validated on a large number of PDF profiles calculated from known crystal structures and on a small number of measured PDF profiles. The lattice determination step has been benchmarked by a comprehensive exploration of different classifiers and different input data. The highest performance is obtained using the k-nearest neighbours classifier applied to whole PDF profiles. Descriptors calculated from the PDF profiles by recurrence quantitative analysis produce results that can be interpreted in terms of PDF properties, and the significance of each descriptor in determining the prediction is evaluated. The cell parameter extraction step depends on the cell metric rather than its type. Monometric, dimetric and trimetric cells have top-1 estimates that are correct 40, 20 and 5% of the time, respectively. Promising results were obtained when analysing real nanocrystals, where unit cells close to the true ones are found within the top-1 ranked solution in the case of monometric cells and within the top-6 ranked solutions in the case of dimetric cells, even in the presence of a crystalline impurity with a weight fraction up to 40%.

Principal component analysis for automatic extraction of solid-state kinetics from combined in situ experiments.

Solid-state reactivity is often studied by in situ experiments with a multi-technique approach, where complementarity of different probes is exploited. In situ data are usually analysed using a complex protocol: first the reaction model most suited to describe the specific solid-state reaction is chosen, second the reaction coordinate is obtained from the data, the order of reaction is then calculated by applying a specific kinetic equation, and finally kinetic parameters are obtained with an Arrhenius plot. The approach is both time consuming and subject to errors due to the arbitrariness of extraction of the reaction coordinate, typically from individual peak intensity variations during the reaction. In addition, application of the different kinetic equations to obtain the best fitting one is tedious and no general method to select the best model with an unbiased approach is available. Here we propose a new procedure based on principal component analysis to get kinetic information from in situ data, which simplifies and speeds up the process of kinetic parameter calculation from a three- to a two- or even a one-step form, reaching a high degree of automation and the ability to manage the huge amount of data produced by in situ multi-technique experiments. The new approach treats data as a whole, without biases introduced by manual methods of obtaining the reaction coordinate by peak intensity evaluation from individual patterns typical of the traditional approach. The procedure is described in its theoretical framework and applied to the formation of a molecular complex, monitored by in situ X-ray powder diffraction and Raman measurements.

Improved multivariate analysis for fast and selective monitoring of structural dynamics by in situ X-ray powder diffraction.

The development of two solid-state reactions, Xe absorption into MFI and molecular complex formation, where samples are affected by changes of crystal lattice due to temperature or pressure variation was structurally monitored through in situ or in operando X-ray powder diffraction experiments. Consequent variations of the peak positions prevent collective analysis of measured patterns, aiming at investigating structural changes occurring within the crystal cell. Moreover, an intrinsic and variable error in peak position is unavoidable when using the Bragg-Brentano geometry and, in some cases (sticky, bulky, aggregate samples) the sample mounting can increase the error within a dataset. Here we present a general multivariate analysis method to process in a fast and automatic way in situ XRPD data collected on charge transfer complexes and porous materials, with the capacity of disentangling peak shifts from intensity and shape variations in diffraction signals, thus allowing an efficient separation of the contribution of crystal lattice changes from structural changes. The peak shift correction allowed an improved PCA analysis that turned out to be more sensible than the traditional single pattern Rietveld analysis. The developed algorithms allowed, with respect to the traditional approach, the location of two new Xe positions into MFI with a better interpretation of the experimental data, while a much faster and more efficient recovery of the reaction coordinate was achieved in the molecular complex formation reaction.

ROI-Based On-Board Compression for Hyperspectral Remote Sensing Images on GPU.

In recent years, hyperspectral sensors for Earth remote sensing have become very popular. Such systems are able to provide the user with images having both spectral and spatial information. The current hyperspectral spaceborne sensors are able to capture large areas with increased spatial and spectral resolution. For this reason, the volume of acquired data needs to be reduced on board in order to avoid a low orbital duty cycle due to limited storage space. Recently, literature has focused the attention on efficient ways for on-board data compression. This topic is a challenging task due to the difficult environment (outer space) and due to the limited time, power and computing resources. Often, the hardware properties of Graphic Processing Units (GPU) have been adopted to reduce the processing time using parallel computing. The current work proposes a framework for on-board operation on a GPU, using NVIDIA's CUDA (Compute Unified Device Architecture) architecture. The algorithm aims at performing on-board compression using the target's related strategy. In detail, the main operations are: the automatic recognition of land cover types or detection of events in near real time in regions of interest (this is a user related choice) with an unsupervised classifier; the compression of specific regions with space-variant different bit rates including Principal Component Analysis (PCA), wavelet and arithmetic coding; and data volume management to the Ground Station. Experiments are provided using a real dataset taken from an AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) airborne sensor in a harbor area.

Bioinspired Synthesis of CaCO3 Superstructures through a Novel Hydrogel Composite Membranes Mineralization Platform: A Comprehensive View.

Hydrogel composite membranes (HCMs) are used as novel mineralization platforms for the bioinspired synthesis of CaCO3 superstructures. A comprehensive statistical analysis of the experimental results reveals quantitative relationships between crystallization conditions and crystal texture and a strong selectivity toward complex morphologies when monomers bearing carboxyl and hydroxyl groups are used together in the hydrogel layer synthesis in HCMs.