Magnetic skyrmions are stable spin textures with quasi-particle behavior and attract significant interest in fundamental and applied physics. The metastability of magnetic skyrmions at zero magnetic ...field is particularly important to enable, for instance, a skyrmion racetrack memory. Here, the results of the nucleation of stable skyrmions and formation of ordered skyrmion lattices by magnetic force microscopy in (Pt/CoFeSiB/W) n multilayers, exploiting the additive effect of the interfacial Dzyaloshinskii–Moriya interaction, are presented. The appropriate conditions under which skyrmion lattices are confined with a dense two-dimensional liquid phase are identified. A crucial parameter to control the skyrmion lattice characteristics and the number of scans resulting in the complete formation of a skyrmion lattice is the distance between two adjacent scanning lines of a magnetic force microscopy probe. The creation of skyrmion patterns with complex geometry is demonstrated, and the physical mechanism of direct magnetic writing of skyrmions is comprehended by micromagnetic simulations. This study shows a potential of a direct-write (maskless) skyrmion (topological) nanolithography with sub-100 nm resolution, where each skyrmion acts as a pixel in the final topological image.
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Surface plasmon resonance is sensitive to the size and shape of gold nanoparticles. The quantitative analysis of the ultraviolet–visible spectra provides information about the structural parameters ...of the nanoparticles. This task is related to the inverse design problem where machine learning (ML) algorithms show superior performance over classical approaches for problems with many degrees of freedom. If a ML algorithm is used as a black box, it often fails when target experimental data have systematic differences with the theoretical training data set. Our work aims to assess the uncertainties in the structural analysis of gold nanoparticles performed using optical spectroscopy. Therefore, ML is trained over a theoretical data set and then used as a tool to predict the spectrum for any combination of structural parameters. The region of a feasible solution is analyzed via L2 norm contour plots, and the method is extended to multicomponent mixtures where Gaussian distribution mimics the particle size distribution. We also demonstrate that the ML algorithm is able to select only informative features of the spectrum (descriptors) and establish an analytical relation between descriptors of spectra and structural parameters. This work extends the capabilities of optical spectroscopy as an analytical tool for noble metal nanoparticles.
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Valence tautomer transition occurs mainly in 3d metalorganic complexes with redox-active ligands and makes them potential candidates for single-molecular switches. The transition occurs under ...temperature, pressure, or light-induced stimuli and is strongly affected by the intermolecular interactions. However single-crystal x-ray diffraction is not always applicable to such systems when crystal structure is destroyed upon transition or system is studied in the solution. Such an example is bis(o-semiquinonato) cobalt complex with TEMPO-functionalized iminopyridine ancillary ligand. In this work we apply two complementary techniques-ligand-sensitive Fourier transform infrared spectroscopy (FTIR) and metal sensitive Co K-edge x-ray absorption spectroscopy (XAS). In a solid state, a temperature hysteresis of magnetization larger than 40 K was observed upon cyclic cooling-heating. So, the temperature of phase transition upon cooling is about 40 K lower than that upon heating. In solution, the x-ray absorption spectra for high-temperature and low-temperature states were similar to that in the solid form, but the hysteresis was absent. Two methods are can probe valence tautomer transition, but XAS has an advantage for the liquid phase analysis and FTIR has larger sensitivity to the ligand related interactions in solid.
Cu-SSZ-13 is a highly active NH
-SCR catalyst for the abatement of harmful nitrogen oxides (NO
,
= 1, 2) from the exhausts of lean-burn engines. The study of Cu-speciation occurring upon thermal ...dehydration is a key step for the understanding of the enhanced catalytic properties of this material and for identifying the SCR active sites and their redox capability. Herein, we combined FTIR, X-ray absorption (XAS) and emission (XES) spectroscopies with DFT computational analysis to elucidate the nature and location of the most abundant Cu sites in the activated catalyst. Different Cu species have been found to be dominant as a function of the dehydration temperature and conditions. Data analysis revealed that the dehydration process of Cu cations is essentially completed at 250 °C, with the formation of dehydrated CuOH
species hosted in close proximity to 1-Al sites in both d6r and 8r units of the SSZ-13 matrix. These species persist at higher temperatures only if a certain amount of O
is present in the gas feed, while under inert conditions they undergo virtually total "self-reduction" as a consequence of an OH extra-ligand loss, resulting in bi-coordinated bare Cu
cations. Synchrotron characterization supported by computational analysis allowed an unprecedented quantitative refinement of the local environment and structural parameters of these Cu(ii) and Cu(i) species.
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In this work we have applied machine learning methods (Extra Trees, Ridge Regression and Neural Networks) to predict structural parameters of the system based on its XANES spectrum. We used two ML ...approaches: direct one, i.e. when ML model is trained to predict the structural parameters directly from the XANES spectrum and inverse one when ML model is used to approximate spectrum as a function of structural parameters. We show the applicability of several ML approaches to predict the geometry of CO2 molecule adsorbed on Ni2+ surface sites hosted in the channels of CPO-27-Ni metal-organic framework. Quantitative fitting is based on difference XANES spectra and we discuss advantages and disadvantages of the two ML approaches and critically examine the overfitting phenomenon, caused by systematic differences of experimental data and learning dataset.
•Machine learning methods were applied for quantitative analysis of XANES spectra.•Distance and bond angle in molecular adsoprtion were predicted.•Direct and inverse mashine learning methods have been proposed.•Extra Trees, Neural Network and Ridge Regression methods were compared.•Extra Trees method showed best agreement with experimental data.
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A set of Sm-doped iron oxide magnetic nanoparticles (Fe x O y NPs) of different sizes as an example of ferromagnetic NPs at room temperature was synthesized by microware-assisted methods. Powder ...X-ray diffraction and transmission electron microscopy showed Fe x O y NPs with an inverse spinel structure. Mössbauer and X-ray absorption spectroscopy (XAS) were used to study the local atomic and electronic structure of iron in the NPs. Linear combination of XAS spectra of reference iron oxides failed in reproducing the spectra of Fe x O y NPs Piquer, C. ; J. Phys. Chem. C 2014, 118, 1332−1346 . We attribute this fact to the nonstoichiometric distribution of tetrahedral, Td 3+, and octahedral, Oh 3+ and Oh 2+, sites in NPs compared to bulk references. This distribution was successfully reproduced by linear combination of theoretical XAS spectra obtained for clusters where iron was in Td 3+, Oh 3+, and Oh 2+ defined oxidation and symmetry states. This approach allowed us to obtain the quantitative speciation of the fraction of Td 3+, Oh 3+, and Oh 2+ sites as a function of the different size of the Sm-doped Fe x O y NPs based on the XAS data. In contrast to the standard XAS reference compounds analysis where experimental spectra fits are limited to constant Td 3+/Oh 3+ and Td 3+/Oh 3+/Oh 2+ ratios and to Mössbauer spectroscopy that requires liquid helium temperatures, the proposed method opens a new possibility to quantitatively estimate the amount of each independent inequivalent iron site in terms of symmetry and oxidation state at room temperature.
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7.
Study of Nanomaterials for X-Ray Photodynamic Therapy Soldatov, M. A.; Medvedev, P. V.; Butova, V. V. ...
Surface investigation, x-ray, synchrotron and neutron techniques,
2021/1, Volume:
15, Issue:
1
Journal Article
Peer reviewed
—The prototype of an apparatus for studying the X-ray fluorescence properties of nanomaterials for X-ray photodynamic therapy is developed. The X-ray fluorescence characteristics of nanomaterials ...based on gadolinium fluorides are studied. For a series of samples, X-ray fluorescence spectra are obtained in the region of 600–700 nm, which allows the conclusion that such materials are promising for use as components of radiosensitizers for X-ray photodynamic therapy. The data obtained are important for optimizing the characteristics of nanomaterials and for further modification of the parameters of methods for synthesizing new materials for X-ray photodynamic therapy.
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A novel approach for the analysis of extended X-ray absorption fine structure (EXAFS) spectra is developed exploiting an inverse machine learning-based algorithm. Through this approach, it is ...possible to explore and account for, in a precise way, the nonlinear geometry dependence of the photoelectron backscattering phases and amplitudes of single and multiple scattering paths. In addition, the determined parameters are directly related to the 3D atomic structure, without the need to use complex parametrization as in the classical fitting approach. The applicability of the approach, its potential and the advantages over the classical fit were demonstrated by fitting the EXAFS data of two molecular systems, namely, the KAu (CN)2 and the RuCl2(CO)32 complexes.
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Iron oxide nanoparticles have numerous and versatile biological properties, ranging from direct and immediate biochemical effects to prolonged influences on tissues. Most applications have strict ...requirements with respect to the chemical and physical properties of such agents. Therefore, developing rational design methods of synthesis of iron oxide nanoparticles remains of vital importance in nanobiomedicine.
Low toxic superparamagnetic iron oxide nanoparticles (SPIONs) for theranostic applications in oncology having spherical shape and maghemite structure were produced using the fast microwave synthesis technique and were fully characterized by several complementary methods (transmission electron microscopy TEM, X-ray diffraction XRD, dynamic light scattering DLS, X-ray photoelectron spectroscopy XPS, X-ray absorption near edge structure XANES, Mossbauer spectroscopy, and HeLa cells toxicity testing).
TEM showed that the majority of the obtained nanoparticles were almost spherical and did not exceed 20 nm in diameter. The averaged DLS hydrodynamic size was found to be ~33 nm, while that of nanocrystallites estimated by XRD waŝ16 nm. Both XRD and XPS studies evidenced the maghemite (γ-Fe
O
) atomic and electronic structure of the synthesized nanoparticles. The XANES data analysis demonstrated the structure of the nanoparticles being similar to that of macroscopic maghemite. The Mossbauer spectroscopy revealed the γ-Fe
O
phase of the nanoparticles and vibration magnetometry study showed that reactive oxygen species in HeLa cells are generated both in the cytoplasm and the nucleus.
Quasispherical Fe
SPIONs having the maghemite structure with the average size of 16 nm obtained by using the fast microwave synthesis technique are expected to be of great value for theranostic applications in oncology and multimodal anticancer therapy.
X-ray absorption near-edge spectroscopy (XANES) is becoming an extremely popular tool for material science thanks to the development of new synchrotron radiation light sources. It provides ...information about charge state and local geometry around atoms of interest in operando and extreme conditions. However, in contrast to X-ray diffraction, a quantitative analysis of XANES spectra is rarely performed in the research papers. The reason must be found in the larger amount of time required for the calculation of a single spectrum compared to a diffractogram. For such time-consuming calculations, in the space of several structural parameters, we developed an interpolation approach proposed originally by Smolentsev and Soldatov (2007). The current version of this software, named PyFitIt, is a major upgrade version of FitIt and it is based on machine learning algorithms. We have chosen Jupyter Notebook framework to be friendly for users and at the same time being available for remastering. The analytical work is divided into two steps. First, the series of experimental spectra are analyzed statistically and decomposed into principal components. Second, pure spectral profiles, recovered by principal components, are fitted by theoretical interpolated spectra. We implemented different schemes of choice of nodes for approximation and learning algorithms including Gradient Boosting of Random Trees, Radial Basis Functions and Neural Networks. The fitting procedure can be performed both for a XANES spectrum or for a difference spectrum, thus minimizing the systematic errors of theoretical simulations. The problem of several local minima is addressed in the framework of direct and indirect approaches.
Program title: PyFitIt.
Program Files doi:http://dx.doi.org/10.17632/ydkgfdc38t.1
Licensing provisions: GNU General Public License 3.
Programming language: Python, Jupyter Notebook framework.
Nature of problem: Quantitative structural refinements of the X-ray absorption near-edge structure spectra (XANES). Identification of the pure spectral and concentration profiles associated with an experimental XANES dataset.
Solution method: The fitting procedure of the experimental XANES spectra or of their differences is realized by means of the inverse and direct approaches based on the training set and approximation machine learning algorithms. The spectral resolution method is based on the PCA technique involving the usage of a target transformation matrix.
Additional comments including restrictions and unusual features: The current version is compatible with the free FDMNES program package for XANES simulations. However, users can prepare their own matrices of spectra calculated by an arbitrary software and the corresponding structural parameters to perform the fitting procedure in PyFitIt. The complete set of examples is distributed along with the program.
References: PyFitIt web page: http://hpc.nano.sfedu.ru/pyfitit/
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