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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!
The datasets from (Hard Energy) X-ray photoelectron spectroscopy, X-ray diffraction and Scanning Electron Microsopy are related to the publication
G. Chemello, X. Knigge, D. Ciornii, B.P. Reed, A.J. Pollard, C.A. Clifford, T. Howe, N. Vyas, V.-D. Hodoroaba, J. Radnik
"Influence of the morphology on the functionalization of graphene nanoplatelets analyzed by comparative photoelectron spectroscopy with soft and hard X-rays"
Advanced Materials Interfaces (2023), DOI: 10.1002/admi.202300116.
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.
X-Ray computed tomography (XCT) scan of 11 individual metallic powder particles, made of (Mn,Fe)2(P,Si) alloy. The data set consists of 4 single XCT scans which have been stitched together [3] after reconstruction. The powder material is an (Mn,Fe)2(P,Si) alloy with an average density of 6.4 g/cm³. The particle size range is about 100 - 150 µm with equivalent pore diameters up to 75 µm. The powder and the metallic alloy are described in detail in [1, 2].
Wide-range X-ray scattering datasets and analyses for all samples described in the 2020 publication "Gold and silver dichroic nanocomposite in the quest for 3D printing the Lycurgus cup". These datasets are composed by combining multiple small-angle x-ray scattering and wide-angle x-ray scattering curves into a single dataset. They have been analyzed using McSAS to extract polydispersities and volume fractions. They have been collected using the MOUSE project (instrument and methodology).
X-ray scattering datasets for samples described in the 2022 publication "Side chain length dependent dynamics and conductivity in self assembled ion channels". This dataset includes both raw and processed X-ray scattering data for samples ILC8, ILC10, ILC12, ILC14 and ILC16 alongside background measurement files (BKG).
X-ray scattering datasets for samples described in the 2022 publication "Molecular Mobility of Polynorbornenes with Trimethylsiloxysilyl side groups: Influence of the Polymerization Mechanism". This dataset includes both raw and processed X-ray scattering data for samples APTCN and MPTCN, alongside background measurements files (BKG).
X-ray scattering datasets for samples described in the 2020 publication "Molecular Dynamics of Janus Polynorbornenes: Glass Transitions and Nanophase Separation". This dataset includes both raw and processed X-ray scattering data for samples PTCHSiO-Pr, Bu, Hx, Oc and De, alongside background measurements files (BKG). This data was collected using the MOUSE project (instrument and methodology).
This dataset contains the processed and analysed small-angle X-ray scattering data associated with all samples from the publications "Bio-SAXS of Single-Stranded DNA-Binding Proteins: Radiation Protection by the Compatible Solute Ectoine" (https://doi.org/10.1039/D2CP05053F).
Files associated with McSAS3 analyses are included, alongside the relevant SAXS data, with datasets labelled in accordance to the protein (G5P), its concentration (1, 2 or 4 mg/mL), and if Ectoine is present (Ect) or absent (Pure). PEPSIsaxs simulations of the GVP monomer (PDB structure: 1GV5 ) and dimer are also included.
TOPAS-bioSAXS-dosimetry extension for TOPAS-nBio based particle scattering simulations can be obtained from https://github.com/MarcBHahn/TOPAS-bioSAXS-dosimetry which is further described in https://doi.org/10.26272/opus4-55751.
This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant number 442240902 (HA 8528/2-1 and SE 2999/2-1). We acknowledge Diamond Light Source for time on Beamline B21 under Proposal SM29806. This work has been supported by iNEXT-Discovery, grant number 871037, funded by the Horizon 2020 program of the European Commission.
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.
WEBSLAMD
(2022)
The objective of SLAMD is to accelerate materials research in the wet lab through AI. Currently, the focus is on sustainable concrete and binder formulations, but it can be extended to other material classes in the future.
1. Summary
Leverage the Digital Lab and AI optimization to discover exciting new materials Represent resources and processes and their socio-economic impact.
Calculate complex compositions and enrich them with detailed material knowledge. Integrate laboratory data and apply it to novel formulations. Tailor materials to the purpose to achieve the best solution.
Workflow
Digital Lab
Specify resources: From base materials to manufacturing processes – "Base" enables a detailed and consistent description of existing resources
Combine resources: The combination of base materials and processes offers an almost infinite optimization potential. "Blend" makes it easier to design complex configurations.
Digital Formulations: With "Formulations" you can effortlessly convert your resources into the entire spectrum of possible concrete formulations. This automatically generates a detailed set of data for AI optimization.
AI-Optimization
Materials Discovery: Integrate data from the "Digital Lab" or upload your own material data. Enrich the data with lab results and adopt the knowledge to new recipes via artificial intelligence. Leverage socio-economic metrics to identify recipes tailored to your requirements.
Trinamic TMCL IOC is a Python package designed for controlling stepper motors connected to a Trinamic board using the TMCL language (all boards supported by PyTrinamic should now work, has been tested on the TMCM 6110 and the TMCM 6214). Since it is implementing the TMCL protocol, it should be easy to adapt to other Trinamic motor controller boards. This package assumes the motor controller is connected over a machine network via a network-to-serial converter, but the underlying PyTrinamic package allows for other connections too.
This allows the control of attached motors via the EPICS Channel-Access virtual communications bus. If EPICS is not desired, plain Pythonic control via motion_control should also be possible. An example for this will be provided in the example.ipynb Jupyter notebook.
This package leverages Caproto for EPICS IOCs and a modified PyTrinamic library for the motor board control, and interfaces between the two via an internal set of dataclasses. Configuration for the motors and boards are loaded from YAML files (see tests/testdata/example_config.yaml).
The modifications to PyTrinamic involved extending their library with a socket interface. This was a minor modification that should eventually find its way into the official package (a pull request has been submitted).
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.
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 "
Simulates X-ray and Neutron scattering patterns from arbitrary shapes defined by STL files.
Features:
- Uses multithreading to compute a number of independent solutions, then uses the variance of the results to estimate an uncertainty on the output.
- Can be launched from the command line using an excel sheet to define settings, or from a jupyter notebook.
- Outputs scattering patterns in absolute units if the contrast is set.
- A Gaussian size distribution is available, where the relative scaling of objects for each repetion can be varied. Recommended to be used with limited width (max. 10%) to avoid artefacts.
- Writes results with settings to an archival HDF5 file.
Application examples:
This software has been used in several studies to date. For example, it has been used here to simulate a model scattering pattern for a cuboid shape, which was then fed forward into the McSAS3 analysis program for analyzing scattering patterns of polydisperse cuboids. A second use is here, where it was used for the modeling of flattened helices. In this paper, scattering pattern features could be matched with particular morphological changes in the structure. Lastly, this paper has an example where it was used to validate the analytical analysis model, and explore the realistic limits of application of the analytical model.
Test artifact for fs-LDW
(2023)
Tensile Test Ontology (TTO)
(2023)
This is the stable version 2.0.1 of the PMD ontology module of the tensile test (Tensile Test Ontology - TTO) as developed on the basis of the 2019 standard ISO 6892-1: Metallic materials - Tensile Testing - Part 1: Method of test at room temperature.
The TTO was developed in the frame of the PMD project. The TTO provides conceptualizations valid for the description of tensile test and corresponding data in accordance with the respective standard. By using TTO for storing tensile test data, all data will be well structured and based on a common vocabulary agreed on by an expert group (generation of FAIR data) which will lead to enhanced data interoperability. This comprises several data categories such as primary data, secondary data and metadata. Data will be human and machine readable. The usage of TTO facilitates data retrieval and downstream usage. Due to a close connection to the mid-level PMD core ontology (PMDco), the interoperability of tensile test data is enhanced and data querying in combination with other aspects and data within the broad field of material science and engineering (MSE) is facilitated.
The TTO class structure forms a comprehensible and semantic layer for unified storage of data generated in a tensile test including the possibility to record data from analysis, re-evaluation and re-use. Furthermore, extensive metadata allows to assess data quality and reproduce experiments. Following the open world assumption, object properties are deliberately low restrictive and sparse.
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.
OpenSCAD, STL and technical drawings for the solid sample rack designed primarily for use with The MOUSE instruments.
This solid sample rack can be used in conjunction with:
- Laser-cut sample holder (10.5281/zenodo.7499437)
- Modular sample holder (10.5281/zenodo.7499416)
- Capillary flow-cell for liquid samples (10.5281/zenodo.7499421)
PDF file for the laser-cut sample holder designed primarily for use with The MOUSE instruments.
This sample holder can be used in conjunction with:
- Solid sample rack/plate (10.5281/zenodo.7499424)
- Modular sample holder (10.5281/zenodo.7499416)
- Capillary flow-cell for liquid samples (10.5281/zenodo.7499421)
OpenSCAD, STL and technical drawings for the capillary flow-through cell designed primarily for use with The MOUSE instruments.
This flow-through cell can be used in conjunction with:
- Modular sample holder (10.5281/zenodo.7499416)
- Solid sample rack/plate (10.5281/zenodo.7499424)
- Laser-cut sample holder (10.5281/zenodo.7499437)
In recent years, we have come to appreciate the astounding intricacy of the formation process of minerals from ions in aqueous solutions. In this context, a number of studies have revealed that nucleation in the calcium sulfate system is non-classical, involving the aggregation and reorganization of nanosized prenucleation particles. In a recent work we have shown that this particle-mediated nucleation pathway is actually imprinted in the resultant single micron-sized CaSO4 crystals. This property of CaSO4 minerals provides us with an unique opportunity to search for evidence of non-classical nucleation pathways in geological environments. In particular, we focused on the quintessential single crystals of anhydrite extracted from the Naica mine in Mexico. We elucidated the growth history from this mineral sample by mapping growth defects at different length scales. Based on these data we argue that the nano-scale misalignment of the structural sub-units observed in the initial calcium sulfate crystal seed propagate through different length-scales both in morphological, as well as strictly crystallographic aspects, eventually causing the formation of large mesostructured single crystals of anhydrite. Hence, the nanoparticle mediated nucleation mechanism introduces a 'seed of imperfection', which leads to a macroscopic single crystal, in which its fragments do not fit together at different length-scales in a self-similar manner. Consequently, anisotropic voids of various sizes are formed with very well-defined walls/edges. But, at the same time the material retains its essential single crystal nature. These findings shed new light on the longstanding concept of crystal structure.
Small-angle Scattering Data Analysis Round Robin: anonymized results, figures and Jupyter notebook
(2023)
The intent of this round robin was to find out how comparable results from different researchers are, who analyse exactly the same processed, corrected dataset.
This zip file contains the anonymized results and the jupyter notebook used to do the data processing, analysis and visualisation. Additionally, TEM images of the samples are included
These are four datasets that were made available to the participants of the Small-angle Scattering data analysis round robin. The intent was to find out how comparable results from different researchers are, who analyse exactly the same processed, corrected dataset.
In this repository, there are:
1) a PDF document with more details for the study,
2) the datasets for people to try and fit
3) an Excel spreadsheet to document the results.
Datasets 1 and 2 were modified from: Deumer, Jerome, & Gollwitzer, Christian. (2022). npSize_SAXS_data_PTB (Version 5) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5886834
Datasets 3 and 4 were collected in-house on the MOUSE instrument.
SLAMD-FIB-Case-Study
(2022)
With 8% of man-made CO2 emissions, cement production is an important driver of the climate crisis. By using alkali-activated binders part of the energy-intensive clinker production process can be dispensed with. However, because numerous chemicals are involved in the manufacturing process here, the complexity of the materials increases by orders of magnitude. Finding a properly balanced cement formulation is like looking for a needle in a haystack. We have shown for the first time that artificial intelligence (AI)-based optimization of cement formulations can significantly accelerate research. The „Sequential Learning App for Materials Discovery“ (SLAMD) aims to accelerate practice transfer. With SLAMD, materials scientists have low-threshold access to AI through interactive and intuitive user interfaces. The value added by AI can be determined directly. For example, the CO2 emissions saved per ton of cement can be determined for each development cycle: the more efficient the AI optimization, the greater the savings. Our material database already includes more than 120,000 data points of alternative cements and is constantly being expanded with new parameters. We are currently driving the enrichment of the data with a life cycle analysis of the building materials. Based on a case study we show how intuitive access to AI can drive the adoption of techniques that make a real contribution to the development of resource-efficient and sustainable building materials of the future and make it easy to identify when classical experiments are more efficient.
SI Files for "Towards automation of the polyol process for the synthesis of silver nanoparticles"
(2022)
The graphml file: reaction_graph_AgNP.graphml is included. It contains topological information (Fig. 1 in the main text) about the reaction setup and metadata with reaction condtions. It used by the Python API used to control the Chemputer.
SAXS reports. The complete report sheets generated by McSAS are included. They contain extended information characterising the size distributions and the fitting parameters.
NP3_I: saxs_report_NP3_I.pdf
NP3_II: saxs_report_NP3_II.pdf
NP3_III: saxs_report_NP3_III.pdf
NP3_IV: saxs_report_NP3_IV.pdf
NP5_I: saxs_report_NP5_I.pdf
NP5_II: saxs_report_NP5_II.pdf
NP5_III: saxs_report_NP5_III.pdf
This is a set of drawings accompanying the submitted paper entitled "Extending Synchrotron SAXS instrument ranges through addition of a portable, inexpensive USAXS module with vertical rotation axes". The parts described herein will combine with commercial off-the-shelf components to build a high precision pair of rotation stages for accurate measurement of scattering angles with a sub-microradian precision.
SASfit 0.94.12
(2023)
Small-angle scattering is an increasingly common method for characterizing particle ensembles in a wide variety of sample types and for diverse areas of application. SASfit has been one of the most comprehensive and flexible curve-fitting programs for decades, with many specialized tools for various fields.
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 dataset is a complete set of raw, processed and analyzed data, associated with the manuscript mentioned in the title.
All associated metadata and processing history has been added. Particle size distribution analyses using McSAS are included as well.
The samples consisted of a 4.2 mass% dispersion of yttria-stabilized zirconia nanoparticles in a cross-linked matrix. The measurements show a good dispersion with minimal agglomeration. The wide-angle region shows diffraction information consistent with zirconia.
PMD Core Ontology (PMDco)
(2023)
The PMD Core Ontology (PMDco) is a comprehensive framework for representing knowledge that encompasses fundamental concepts from the domains of materials science and engineering (MSE). The PMDco has been designed as a mid-level ontology to establish a connection between specific MSE application ontologies and the domain neutral concepts found in established top-level ontologies. The primary goal of the PMDco is to promote interoperability between diverse domains. PMDco's class structure is both understandable and extensible, making it an efficient tool for organizing MSE knowledge. It serves as a semantic intermediate layer that unifies MSE knowledge representations, enabling data and metadata to be systematically integrated on key terms within the MSE domain. With PMDco, it is possible to seamlessly trace data generation. The design of PMDco is based on the W3C Provenance Ontology (PROV-O), which provides a standard framework for capturing the generation, derivation, and attribution of resources. By building on this foundation, PMDco facilitates the integration of data from various sources and the creation of complex workflows. In summary, PMDco is a valuable tool for researchers and practitioners in the MSE domains. It provides a common language for representing and sharing knowledge, allowing for efficient collaboration and promoting interoperability between diverse domains. Its design allows for the systematic integration of data and metadata, enabling seamless traceability of data generation. Overall, PMDco is a crucial step towards a unified and comprehensive understanding of the MSE domain. PMDco at GitHub: https://github.com/materialdigital/core-ontology