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xraylib 3.1.0
(2014)
Quantitative estimate of elemental composition by spectroscopic and imaging techniques using X-ray fluorescence requires the availability of accurate data of X-ray interaction with matter. Although a wide number of computer codes and data sets are reported in literature, none of them is presented in the form of freely available library functions which can be easily included in software applications for X-ray fluorescence. This work presents a compilation of data sets from different published works and an xraylib interface in the form of callable functions. Although the target applications are on X-ray fluorescence, cross sections of interactions like photoionization, coherent scattering and Compton scattering, as well as form factors and anomalous scattering functions, are also available.
xraylib provides access to some of the most respected databases of physical data in the field of x-rays. The core of xraylib is a library, written in ANSI C, containing over 40 functions to be used to retrieve data from these databases. This C library can be directly linked with any program written in C, C++ or Objective-C. Furthermore, the xraylib package contains bindings to several popular programming languages: Fortran 2003, Perl, Python, Java, IDL, Lua, Ruby, PHP and .NET, as well as a command-line utility which can be used as a pocket-calculator. Although not officially supported, xraylib has been reported to be useable from within Matlab and LabView.
The source code is known to compile and run on the following platforms: Linux, Mac OS X, Solaris, FreeBSD and Windows.
Development occurs on Github: http://github.com/tschoonj/xraylib
Downloads are hosted by the X-ray Micro-spectroscopy and Imaging research group of Ghent University: http://lvserver.ugent.be/xraylib
Version 3.1.0 release notes:
- Database of commonly used radionuclides for X-ray sources added (new API: GetRadioNuclideDataByName, GetRadioNuclideDataByIndex, GetRadioNuclideDataList and FreeRadioNuclideData)
- numpy Python bindings added, generated with Cython. Performance basically the same as the core C library. (suggested by Matt Newville)
- docstring support added to Python bindings (suggested by Matt Newville)
- Windows SDKs now have support for Python 3.4.
- Windows 64-bit SDK now comes with IDL bindings
- Confirmed support for LabView (thanks to Dariush Hampai!)
- Universal Intel 32/64 bit Framework built for Mac OS X
- Perl support for Debian/Ubuntu
- Several bugfixes: thanks to those that reported them!
XMI-MSIM 5.0
(2014)
XMI-MSIM is an open source tool designed for predicting the spectral response of energy-dispersive X-ray fluorescence spectrometers using Monte-Carlo simulations. It comes with a fully functional graphical user interface in order to make it as user friendly as possible. Considerable effort has been taken to ensure easy installation on all major platforms.
Development of this package was part of my PhD thesis. The algorithms were inspired by the work of my promotor Prof. Laszlo Vincze of Ghent University. Links to his and my own publications can be found in our manual.
A manuscript has been published in Spectrochimica Acta Part B that covers the algorithms that power XMI-MSIM. Please include a reference to this publication in your own work if you decide to use XMI-MSIM for academic purposes.
A second manuscript was published that covers our XMI-MSIM based quantification plug-in for PyMca. Soon information on using this plug-in will be added to the manual.
XMI-MSIM is released under the terms of the GPLv3.
Development occurs at Github: http://github.com/tschoonj/xmimsim
Downloads are hosted by the X-ray Micro-spectroscopy and Imaging research group of Ghent University: http://lvserver.ugent.be/xmi-msim
Version 5.0 release notes:
Changes:
1. Custom detector response function: build a own plug-in containing your own detector response function and load it at run-time to override the builtin routines. Instructions can be found in the manual.
2. Escape peak improvements: new algorithm is used to calculate the escape peak ratios based on a combined brute-force and variance-reduction approach. Ensures high accuracy even at high incoming photon energies and thin detector crystals. Downside: it's slower…
3. Removed maximum convolution energy option. Was a bit confusing anyway.
4. Number of channels: moved from simulation controls into input-file
5. Radionuclide support added: Now you can select one or more commonly used radionuclide sources from the X-ray sources widget.
6. Advanced Compton scattering simulation: a new alternative implementation of the Compton scattering has been implemented based on the work of Fernandez and Scot (http://dx.doi.org/10.1016/j.nimb.2007.04.203), which takes into account unpopulated atomic orbitals. Provides an improved simulation of the Compton profile, as well as fluorescence contributions due to Compton effect (extremely low!), but slows the code down considerably. Advanced users only. Default: OFF
7. Plot spectra before convolution in results
8. Windows: new Inno Setup installers. Contains the headers and import libraries
9. Windows: compilers changed to GCC 4.8.1 (TDM-GCC)
10. Windows: rand_s used to generate seeds on 64-bit version (requires Vista or later)
11. Windows: new gtk runtime for the 64-bit version (see also https://github.com/tschoonj/GTK-for-Windows-Runtime-Environment-Installer)
12. Mac OS X: compilers changed to clang 5.1 (Xcode) and gfortran 4.9.1 (MacPorts)
13. Original input-files from our 2012 publication (http://dx.doi.org/10.1016/j.sab.2012.03.011) added to examples
14. Updater performs checksum verification after download
15. X-ray sources last used values stored in preferences.ini
16. xmimsimdata.h5 modified: even bigger now...
Bugfixes:
1. Windows: support for usernames with unicode characters. Fixed using customized builds of HDF5. Thanks to Takashi Omori of Techno-X for the report!
2. Spectrum import from file fixes. Was never properly tested apparently
Note:
For those that compiled XMI-MSIM from source: you will need to regenerate the xmimsimdata.h5 file with xmimsim-db. Old versions of this file will not work with XMI-MSIM 5.0.
Gas chromatography using atmospheric pressure chemical ionization coupled to mass spectrometry (GC/APCI-MS) is an emerging metabolomics platform, providing much-enhanced capabilities for structural mass spectrometry as compared to traditional electron ionization (EI)-based techniques. To exploit the potential of GC/APCI-MS for more comprehensive metabolite annotation, a major bottleneck in metabolomics, we here present the novel R-based tool InterpretMSSpectrum assisting in the common task of annotating and evaluating in-source mass spectra as obtained from typical full-scan experiments. After passing a list of mass-intensity pairs, InterpretMSSpectrum locates the molecular ion (M0), fragment, and adduct peaks, calculates their most likely sum formula combination, and graphically summarizes results as an annotated mass spectrum. Using (modifiable) filter rules for the commonly used methoximated-trimethylsilylated (MeOx-TMS) derivatives, covering elemental composition, typical substructures, neutral losses, and adducts, InterpretMSSpectrum significantly reduces the number of sum formula candidates, minimizing manual effort for postprocessing candidate lists. We demonstrate the utility of InterpretMSSpectrum for 86 in-source spectra of derivatized standard compounds, in which rank-1 sum formula assignments were achieved in 84% of the cases, compared to only 63% when using mass and isotope information on the M0 alone. We further use, for the first time, automated annotation to evaluate the purity of pseudospectra generated by different metabolomics preprocessing tools, showing that automated annotation can serve as an integrative quality measure for peak picking/deconvolution methods. As an R package, InterpretMSSpectrum integrates flexibly into existing metabolomics pipelines and is freely available from CRAN (https://cran.r-project.org/).
Raw data from metabolomics experiments are initially subjected to peak identification and signal deconvolution to generate raw data matrices m × n, where m are samples and n are metabolites. We describe here simple statistical procedures on such multivariate data matrices, all provided as functions in the programming environment R, useful to normalize data, detect biomarkers, and perform sample classification.
In case study one of the CONSENS project, two aromatic substances were coupled by a lithiation reaction, which is a prominent example in pharmaceutical industry. The two aromatic reactants (Aniline and o-FNB) were mixed with Lithium-base (LiHMDS) in a continuous modular plant to produce the desired product (Li-NDPA) and a salt (LiF). The salt precipitates which leads to the formation of particles. The feed streams were subject to variation to drive the plant to its optimum.
The uploaded data comprises the results from four days during continuous plant operation time. Each day is denoted from day 1-4 and represents the dates 2017-09-26, 2017-09-28, 2017-10-10, 2017-10-17.
In the following the contents of the files are explained.
This file contains all the data used for the figures shown in the Dissertation of Ruben Gerrits with the title "An experimental study of fungal olivine weathering".
In this study, the weathering-affecting, rock-inhabiting fungus, Knufia petricola A95 and the Fe-bearing olivine (Fe0.2Mg1.8SiO4) were selected to investigate fungi-induced effects on mineral dissolution. The availability of a melanin-deficient mutant (ΔKppks) of K. petricola A95, that produced more extracellular polymeric substances (EPS) than the wild type (WT), enabled comparative studies of the role of melanin and EPS in weathering processes.
AI-reflectivity is a code based on artificial neural networks trained with simulated reflectivity data that quickly predicts film parameters from experimental X-ray reflectivity curves. This project has a common root with (ML-reflectivity)[https://github.com/schreiber-lab/ML-reflectivity] and evolved in parallel. Both are linked to the following publication:
Fast Fitting of Reflectivity Data of Growing Thin Films Using Neural Networks A. Greco, V. Starostin, C. Karapanagiotis, A. Hinderhofer, A. Gerlach, L. Pithan, S. Liehr, F. Schreiber, S. Kowarik (2019). J. Appl. Cryst.
For an online live demonstration using a pre-trained network have a look at github.
Metabolomics, the analysis of potentially all small molecules within a biological system, has become a valuable tool for biomarker identification and the elucidation of biological processes. While metabolites are often present in complex mixtures at extremely different concentrations, the dynamic range of available analytical methods to capture this variance is generally limited. Here, we show that gas chromatography coupled to atmospheric pressure chemical ionization mass spectrometry (GC-APCI-MS), a state of the art analytical technology applied in metabolomics analyses, shows an average linear range (LR) of 2.39 orders of magnitude for a set of 62 metabolites from a representative compound mixture. We further developed a computational tool to extend this dynamic range on average by more than 1 order of magnitude, demonstrated with a dilution series of the compound mixture, using robust and automatic reconstruction of intensity values exceeding the detection limit. The tool is freely available as an R package (CorrectOverloadedPeaks) from CRAN (https://cran.r-project.org/) and can be incorporated in a metabolomics data processing pipeline facilitating large screening assays.
These files contain cell models for TOPAS/Geant4 and the inclusion of nano particles in particle scattering simulations. A simple spherical cell with nanoparticles can be generated in a fast manner. The user has the option to include the following organelles: nucleus, mitochondria, cell membrane. Additionally nanoparticles can be included in the cytosol and at the surface of the nucleus and/or the mitochondria.
The C++ classes in this repository extend the functionality of the TOPAS (http://www.topasmc.org/) Monte-Carlo program, which is itself a wrapper of the Geant4 MCS Toolkit (http://geant4.org). The sourcecode together with examples and scorers are provided.
"If you use this extension please cite the following literature:
Hahn, M.B., Zutta Villate, J.M. "Combined cell and nanoparticle models for TOPAS to study radiation dose enhancement in cell organelles." Sci Rep 11, 6721 (2021).
https://doi.org/10.1038/s41598-021-85964-2 "
To simulate the movement of the macroscopic magnetic moment in ferromagnetic systems under the influence of elevated temperatures, the stochastic version of the Landau-Lifshitz (LL) or the Landau-Lifshitz-Gilbert equation with a spin density of one per unit cell has to be used.
To apply the stochastic LL to micromagnetic simulations, where the spin density per unit cell is generally higher, a conversion has to be performed. OOMMF sample files MIF) are provided which can be used to determine the Curie temperature for the classical bulk magnets, iron, nickel and cobalt.
This dataset is a complete set of raw, processed and analyzed data, complete with Jupiter notebooks, associated with the manuscript mentioned in the title.
In the manuscript, we provide a "systems architecture"-like overview and detailed discussions of the methodological and instrumental components that, together, comprise the "MOUSE" project (Methodology Optimization for Ultrafine Structure Exploration). Through this project, we aim to provide a comprehensive methodology for obtaining the highest quality X-ray scattering information (at small and wide angles) from measurements on materials science samples.
Dataset and Jupyter worksheet interpreting the (results from) small- and wide-angle scattering data from a series of boehmite/epoxy nanocomposites. Accompanies the publication "Competition of nanoparticle-induced mobilization and immobilization effects on segmental dynamics of an epoxy-based nanocomposite", by Paulina Szymoniak, Brian R. Pauw, Xintong Qu, and Andreas Schönhals.
Datasets are in three-column ascii (processed and azimuthally averaged data) from a Xenocs NanoInXider SW instrument. Monte-Carlo analyses were performed using McSAS 1.3.1, other analyses are in the Python 3.7 worksheet. Graphics and result tables are output by the worksheet.
This is the repository of all experimental raw data used in the Scientific Reports publication "Specific adsorption sites and conditions derived by thermal decomposition of activated carbons and adsorbed carbamazepine" by Daniel Dittmann, Paul Eisentraut, Caroline Goedecke, Yosri Wiesner, Martin Jekel, Aki Sebastian Ruhl, and Ulrike Braun.
It includes
- overview_measurements.xlsx and overview_measurements.ods containing a list of all TGA experiments (TGA, TGA-FTIR, TED-GC-MS, and ramp-kinetics)
- TED-GC-MS.zip containing gas chromatography-mass spectrometry experimtent files for the Chemstation and OpenChrom
- TGA.zip containing thermogravimetric analyses raw data on evolved gas analyses experiments (TGA-FTIR and TED-GC-MS)
- TGA_kinetics.zip containing thermogravimetric analyses raw data on decomposition kinetic experiments (ramp-kinetics)
- TGA-FTIR.zip containing Fourier-transform infrared spectroscopy series files for OMNIC
- XRF.zip containing x-ray flourescence data on elemental composition
Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans.
Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (i) Training data based on combinations of measured pure component spectra and (ii) Training data based on a spectral model.
Synthetic low-field NMR spectra
First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum.
Xi (“pure component spectra dataset”)
Xii (“spectral model dataset”)
Experimental low-field NMR spectra from MNDPA-Synthesis
This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included.
Scientific welding data covers a wide range of physical domains and timescales and are measured using various different sensors. Complex and highly specialized experimental setups at different welding institutes complicate the exchange of welding research data further. The WelDX research project aims to foster the exchange of scientific data inside the welding community by developing and establishing a new open source file format suitable for the documentation of experimental welding data and upholding associated quality standards. In addition to fostering scientific collaboration inside the national and international welding community an associated advisory committee will be established to oversee the future development of the file format. The proposed file format will be developed with regard to current needs of the community regarding interoperability, data quality and performance and will be published under an appropriate open source license. By using the file format objectivity, comparability and reproducibility across different experimental setups can be improved.