1,721,009 research outputs found
Chemical selectivity in structure determination by the time dependent analysis of in situ XRPD data: a clear view of Xe thermal behavior inside a MFI zeolite
No full textQuantifying API polymorphs in formulations using X-ray powder diffraction and multivariate standard addition method combined with net analyte signal analysis
Get PDFThe direct quantification of Active Pharmaceutical Ingredients in solid formulations is a challenging open issue. A consolidated analytical technique based on X-ray Powder Diffraction is available, being the definitive test for the identification of polymorphs and crystal phases. However, its application for quantitative analysis is hindered by matrix effects: refinement methods (e.g. Rietveld method) require a complete knowledge of samples’ composition, while univariate calibration methods require the matrix effect to be studied and severely suffer from the co-presence of crystalline and amorphous phases in the sample. Multivariate analysis is the only way to bypass problems affecting refinements procedures and univariate calibration. In particular, the multivariate standard addition method (SAM) is promising; however, it is straightforward only when the analytical blank (matrix devoid of analyte) is available: in that case SAM is applied by simply extrapolating the SAM model to the matrix experimental signal. In this work, the quantitative analysis of polymorphic forms of Active Pharmaceutical Ingredients based on X-ray Powder Diffraction is performed for the first time by a method based on multivariate standard addition method combined with net analyte signal procedure; it allows for reliable quantification of polymorphs of active principles in solid formulations, which are rapidly analyzed without any sample pre-treatment. Two test cases are presented: quantification of two polymorphs of piracetam in binary mixtures (forms II and III), and quantification of paracetamol (form I) in Tachifludec®
Digital Fingerprinting of Coffee Blending by Sensitive Crystallization
Get PDFThe authenticity and quality of productions is an area of priority interest that involves safety of consumers and potential economic damages deriving from frauds on origin, adulteration and labeling of products. Several investigation techniques are currently used to characterize food matrices from physical-chemical-biological point of view using different methods in order to limit possible adulterations. In this work, we have developed an experimental and computational framework to improve the potentialities of sensitive crystallization: an experimental technique known since 1936, but never used for quantitative assessment of food quality. As a test case, it has been applied to investigate the geographical traceability and quality of coffee samples. An extensive statistical analysis associated with a careful choice of advanced image descriptors allows gathering quantitative information about the samples, which can constitute a digital fingerprint of their composition. With this new tool we are able to distinguish with blind tests high-quality coffee brands from low-quality mixtures, different coffee species, green from toasted condition of beans and, to a lesser extent, the macro-geographical provenience. A powder X-ray diffraction analysis reinforces the results obtained by sensitive crystallization for the case where crystalline domains are present in the coffee sample.</jats:p
Multivariate Analysis Applications in X-ray Diffraction
Get PDF: Multivariate analysis (MA) is becoming a fundamental tool for processing in an efficient
way the large amount of data collected in X-ray diffraction experiments. Multi-wedge data
collections can increase the data quality in case of tiny protein crystals; in situ or operando setups
allow investigating changes on powder samples occurring during repeated fast measurements;
pump and probe experiments at X-ray free-electron laser (XFEL) sources supply structural
characterization of fast photo-excitation processes. In all these cases, MA can facilitate the extraction
of relevant information hidden in data, disclosing the possibility of automatic data processing even
in absence of a priori structural knowledge. MA methods recently used in the field of X-ray
diffraction are here reviewed and described, giving hints about theoretical background and possible
applications. The use of MA in the framework of the modulated enhanced diffraction technique is
described in detail
Multivariate versus traditional quantitative phase analysis of X-ray powder diffraction and fluorescence data of mixtures showing preferred orientation and microabsorption
Get PDFIn materials and earth science, but also in chemistry, pharmaceutics and engineering, the quantification of elements and crystal phases in solid samples is often essential for a full characterization of materials. The most frequently used techniques for this purpose are X-ray fluorescence (XRF) for elemental analysis and X-ray powder diffraction (XRPD) for phase analysis. In both methods, relations between signal and quantity do exist but they are expressed in terms of complex equations including many parameters related to both sample and instruments, and the dependence on the active element or phase amounts to be determined is convoluted among those parameters. Often real-life samples hold relations not suitable for a direct quantification and, therefore, estimations based only on the values of the relative intensities are affected by large errors. Preferred orientation (PO) and microabsorption (MA) in XRPD cannot usually be avoided, and traditional corrections in Rietveld refinement, such as the Brindley MA correction, are not able, in general, to restore the correct phase quantification. In this work, a multivariate approach, where principal component analysis is exploited alone or combined with regression methods, is used on XRPD profiles collected on ad hoc designed mixtures to face and overcome the typical problems of traditional approaches. Moreover, the partial or no known crystal structure (PONKCS) method was tested on XRPD data, as an example of a hybrid approach between Rietveld and multivariate approaches, to correct for the MA effect. Particular attention is given to the comparison and selection of both method and pre-process, the two key steps for good performance when applying multivariate methods to obtain reliable quantitative estimations from XRPD data, especially when MA and PO are present. A similar approach was tested on XRF data to deal with matrix effects and compared with the more classical fundamental-parameter approach. Finally, useful indications to overcome the difficulties of the general user in managing the parameters for a successful application of multivariate approaches for XRPD and XRF data analysis are given
The Crystal Structure of N-[(2E)-3-(4-Chlorophenyl)prop-2-en-1-yl]-4-methoxy-N-methylbenzenesulfonamide
No full textThe title compound C17H18ClNO3S crystallizes in the monoclinic P21/c space group with unit cell parameters a = 15.040 (3) Å, b = 9.151 (6) Å, c = 13.868 (7) Å, β = 116.38 (5)°. Chlorophenyl and methoxyphenyl ring planes are approximately perpendicular and the two moieties form an angle of 82.2°. The crystal packing is stabilized by π–π and Cl–π interactions, which occur between parallel methoxyphenyl and chlorophenyl moieties. Dipolar intermolecular contacts, mainly involving the oxygen atoms and C–H, also contribute to the crystal network. Graphical Abstract: The title compound is formed in the synthetic route for the production of potent inhibitors of calmodulin. Its crystal structure shows the chlorophenyl and methoxyphenyl moieties forming an angle of 82.2°. The crystal packing is stabilized by π–π, Cl–π and dipolar intermolecular contacts. [Figure not available: see fulltext.]
Unveiling the Influence of Hydrated Deep Eutectic Solvents on the Dynamics of Water–Soluble Proteins
No full textDeep eutectic solvents (DESs) are mixtures of two ormore purecompounds (e.g., Lewis or Bronsted acids and bases, anionic and/orcationic species) in a well-defined stoichiometric proportion, witha melting point lower to that of an ideal liquid mixture. These neotericsolvents are highly tunable through varying the structure or relativeratio of parent components and have been evaluated as solvents ableto improve biomolecules' performance, specifically their stabilityand biocatalytic properties. Inspired by a recent crystallographicstudy, we have explored through molecular dynamics (MD) simulationsthe dynamic properties of two different proteins (hen egg-white lysozymeand the human VH antibody fragment HEL4) in a (20% w/w) hydrated solutionof choline chloride-glycerol (1:2). We have developed properforce fields to account for DES, protein, and DES-protein interactions,which have been calibrated using pair distribution function measurementsof pure DES solutions. MD results show that the presence of DES quenchesthe protein motion, increasing the rigidity of the overall proteinstructure. Specific interactions among DES components and proteinresidues, such as those between choline ions and two Tryptophan residuesof lysozyme, may amplify the protein-DES interactions and leadto protein crystallization in the presence of hydrated DES. Thesefindings open new horizons to improve or achieve control on proteinproperties by a proper choice of hydrated DESs used as solvents
A combined approach for characterisation of fresh and brined vine leaves by X-ray powder diffraction, NMR spectroscopy and direct infusion high resolution mass spectrometry
No full textX-ray powder diffraction was combined, for the first time, with Nuclear Magnetic Resonance spectroscopy
and direct infusion mass spectrometry to characterise fresh and brined grape leaves. Covariance
analysis of data generated by the three techniques was performed with the aim to correlate information
deriving from the solid part with those obtained for soluble metabolites. The results obtained indicate
that crystalline components can be correlated to the metabolites contained in the grape leaves, paving
the way to the use of X-ray diffraction analysis for food fingerprinting purposes. Moreover it was ascertained
that, differently from most of the metabolites present in the fresh vine leaves, linolenic acid (an
omega-3-fatty acid) and quercetin-3-O-glucuronide (a polyphenol metabolite) do not undergo sensible
degradation during the brining process, which is used as preservative method for the grape leaves
Tailored Multivariate Analysis for Modulated Enhanced Diffraction
Get PDFModulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited for in situ and operando structural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scores and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. The multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). When applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. To develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis
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