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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).
Jobflow is a free, open-source library for writing and executing workflows. Complex workflows can be defined using simple python functions and executed locally or on arbitrary computing resources using the FireWorks workflow manager.
Some features that distinguish jobflow are dynamic workflows, easy compositing and connecting of workflows, and the ability to store workflow outputs across multiple databases.
Multiscale modeling of linear elastic heterogeneous structures via localized model order reduction
(2024)
In this paper, a methodology for fine scale modeling of large scale linear elastic structures is proposed, which combines the variational multiscale method, domain decomposition and model order reduction. The influence of the fine scale on the coarse scale is modelled by the use of an additive split of the displacement field, addressing applications without a clear scale separation. Local reduced spaces are constructed bysolving an oversampling problem with random boundary conditions. Herein, we inform the boundary conditions by a global reduced problem and compare our approach using physically meaningful correlated samples with existing approaches using uncorrelated samples. The local spaces are designed such that the local contribution of each subdomain can be coupled in a conforming way, which also preserves the sparsity pattern of standard finite element assembly procedures. Several numerical experiments show the accuracy and efficiency of the method, as well as its potential to reduce the size of the local spaces and the number of training samples compared to the uncorrelated sampling
This is a set of use examples for the HDF5Translator framework. This framework lets you translate measurement files into a different (e.g. NeXus-compatible) structure, with some optional checks and conversions on the way. For an in-depth look at what it does, there is a blog post here.
The use examples provided herein are each accompanied by the measurement data necessary to test and replicate the conversion. The README.md files in each example show the steps necessary to do the conversion for each.
We encourage those who have used or adapted one or more of these exampes to create their own conversion, to get in touch with us so we may add your example to the set.
In the field of computational science and engineering, workflows often entail the application of various software, for instance, for simulation or pre- and postprocessing. Typically, these components have to be combined in arbitrarily complex workflows to address a specific research question. In order for peer researchers to understand, reproduce and (re)use the findings of a scientific publication, several challenges have to be addressed. For instance, the employed workflow has to be automated and information on all used software must be available for a reproduction of the results. Moreover, the results must be traceable and the workflow documented and readable to allow for external verification and greater trust. In this paper, existing workflow management systems (WfMSs) are discussed regarding their suitability for describing, reproducing and reusing scientific workflows. To this end, a set of general requirements for WfMSswere deduced from user stories that we deem relevant in the domain of computational science and engineering. On the basis of an exemplary workflow implementation, publicly hosted at GitHub (https:// this http URL), a selection of different WfMSs is compared with respect to these requirements, to support fellow scientists in identifying the WfMSs that best suit their requirements.
KupferDigital mechanical testing datasets: Stress relaxation and low-cycle fatigue (LCF) tests
(2024)
The KupferDigital project deals with the development of a data ecosystem for digital materials research on the basis of ontology-based digital representations of copper and copper alloys. This document provides exemplary mechanical testing datasets for training the developed KupferDigital infrastructures. Different types of cast copper alloys were provided for this research and their mechanical testing (stress relaxation and low-cycle fatigue) was performed in the accredited materials testing laboratory, while the test results were reported according to the DIN/ISO standards and attached with the maximum possible metadata about the sample history, equipment, and calibration. The attached content file consisted of the obtained primary raw testing data as well as the secondary datasets of these tests containing the detailed metadata of mechanical testing methods. Such test data files are processed by the KupferDigital digital tools to be converted to standardized machine-readable data files.
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.
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.
2PP-TestArtifact
(2023)
This repository contains a test artifact (TA), also called test structure, designed for two-photon polymerization (also known as Direct Laser Writing (DLW) or Two/Multi-photon lithography (2PA/MPA)). Test artifacts can be used to compare structures, to check options used by the slicer, check the state of the 2PP machine itself or to get a construction guidelines for a certain combination of power, velocity and settings.
The associated paper can be found here: https://dx.doi.org/10.1088/1361-6501/acc47a
General ideas behind the test artifact:
1. optimized for 2PP-DLW
2. should be fast and easy to analyse with optical microscopy or 3. scanning electron microscopy without tilt.
3. short time to fabricate
4. include a reasonable amount of different features
5. bulk and small structures on the substrate
Particle size determination of a commercially available CeO2 nano powder - SOPs and reference data
(2023)
Compilation of detailed SOPs for characterization of a commercially available CeO2 nano powder including
- suspension preparation (indirect and direct sonication),
- particle size determination (Dynamic Light Scattering DLS and Centrifugal Liquid Sedimentation CLS) with reference data, respectively.
For sample preparation and analysis by Scanning Electron Microscopy (SEM) of this powder see related works (submitted, coming soon).
The dataset provided in this repository comprises data obtained from a series of full-notch creep tests (FNCT) performed on selected high-density polyethylene (PE-HD) materials (for further details, see section 1 Materials in this document) in accordance with the corresponding standard ISO 16770.
The FNCT is one of the mechanical testing procedures used to characterize polymer materials with respect to their environmental stress cracking (ESC) behavior. It is widely applied for PE-HD materials, that are predominantly used for pipe and container applications. It is based on the determination of the time to failure for a test specimen under constant mechanical load in a well-defined and temperature controlled liquid environment. The test device used here also allows for continuous monitoring of applied force, specimen elongation and temperature.
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.
Here a dataset of XPS, HAXPES and SEM measurements for the physico-chemical characterization of Au nanoparticles is presented. The measurements are part of the H2020 project “NanoSolveIT”.
Here a dataset of XPS, HAXPES and SEM measurements for the physico-chemical characterization of Fe3O4 nanoparticles is presented. The measurements are part of the H2020 project “NanoSolveIT”.
McSAS3
(2023)
McSAS3 is a refactored version of the original McSAS (see DOI 10.1107/S1600576715007347). This software fits scattering patterns to obtain size distributions without assumptions on the size distribution form. The refactored version has some neat features:
- Multiprocessing is included, spread out over as many cores as number of repetitions!
- Full state of the optimization is stored in an organized HDF5 state file.
- Histogramming is separate from optimization and a result can be re-histogrammed as many times as desired.
- SasModels allow a wide range of models to be used
- If SasModels does not work (e.g. because of gcc compiler issues on Windows or Mac), an internal sphere model is supplied
- Simulated data of the scattering of a special shape can also be used as a McSAS fitting model. Your models are infinite!
- 2D fitting also works.
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.
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.
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.
Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. This open-source software package powers the Materials Project.
In this particular contribution, the handling of obital-resolved "ICOHPLIST.lobster" files from Lobster was implemented in the software package (github handle: @JaGeo).
The elastic properties (Young's modulus, shear modulus) of titanium alloy Ti-6Al-4V were investigated between room temperature and 400 °C in an additively manufactured variant (laser-based directed energy deposition with powder as feedstock, DED-LB/M) and from a conventional process route (hot rolled bar). The moduli were determined using the dynamic resonance method. The data set includes information on processing parameters, heat treatments, microstructure, grain size, specimen dimensions and weight, Young’s and shear modulus as well as their measurement uncertainty.
The dataset was generated in an accredited testing lab using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. It consists of two kinds of json files. Smaller lightweight JSONS consists of summarized bonding information for each of the compounds. The files are named as per ID numbers in the materials project database.
Here we provide also the larger computational data json files for 700 compounds. This files consists of all important LOBSTER computation output files data stored as dictionary.
This database consists of bonding data computed using Lobster for 1520 solid-state compounds consisting of insulators and semiconductors. The files are named as per ID numbers in the materials project database.
Here we provide the larger computational data JSON files for the rest of the 820 compounds. This file consists of all important LOBSTER computation output files data stored as a dictionary.
This dataset consists of indentation data measured with a conospherical tip in a Hysitron-Bruker TI980 Nanoindenter on the surface of a <100> Silicon wafer and a polished cross-sectional cut of a Zr65Cu25Al10 bulk metallic glass.
It is associated with the following publication:
Birte Riechers, Catherine Ott, Saurabh Mohan Das, Christian H. Liebscher, Konrad Samwer, Peter M. Derlet and Robert Maass "On the elastic microstructure of bulk metallic glasses" Materials and Design xxx, (2023) 111929. https://doi.org/10.1016/j.matdes.2023.111929
All experimental information can be found in this paper and in the accompanying supplementary information.
This electronic version of the data was published on the "Zenodo Data repository" found at http://zenodo.org/deposit in the community "Bundesanstalt fuer Materialforschung und -pruefung (BAM)".
The authors have copyright to these data. You are welcome to use the data for further analysis, but are requested to cite the original publication whenever use is made of the data in publications, presentations, etc.
Any questions regarding the data can be addressed to birte.riechers@bam.de who would also appreciate a note if you find the data useful.
## Summary:
This notebook and associated datasets (including VASP details) accompany a manuscript available on the ArXiv (https://doi.org/10.48550/arXiv.2303.13435) and hopefully soon in a journal as short communication as well. Most of the details needed to understand this notebook are explained in that paper with the same title as above. For convenience, the abstract is repeated here:
## Paper abstract:
We demonstrate a strategy for simulating wide-range X-ray scattering patterns, which spans the small- and wide scattering angles as well as the scattering angles typically used for Pair Distribution Function (PDF) analysis. Such simulated patterns can be used to test holistic analysis models, and, since the diffraction intensity is presented coupled to the scattering intensity, may offer a novel pathway for determining the degree of crystallinity.
The ``Ultima Ratio'' strategy is demonstrated on a 64-nm Metal Organic Framework (MOF) particle, calculated from $Q<0.01$\,$\mathrm{nm}^{-1}$ up to $Q\approx150$\,$\mathrm{nm}^{-1}$, with a resolution of 0.16\,\AA. The computations exploit a modified 3D Fast Fourier Transform (3D-FFT), whose modifications enable the transformations of matrices at least up to $8000^3$ voxels in size. Multiple of these modified 3D-FFTs are combined to improve the low-$Q$ behaviour.
The resulting curve is compared to a wide-range scattering pattern measured on a polydisperse MOF powder.
While computationally intensive, the approach is expected to be useful for simulating scattering from a wide range of realistic, complex structures, from (poly-)crystalline particles to hierarchical, multicomponent structures such as viruses and catalysts.
Electric Safety Interlock
(2023)
This interlock is designed to prevent electrical shock from high voltage (>60V) equipment.
While the general safety interlock can be generically applied, this particular example employs an external vacuum-activated switch. It is for safeguarding human operations inside a vacuum sample chamber while the chamber doors are open. The circuit is closed (output is active) when a sufficient level of vacuum is reached, i.e. when all accessible openings are necessarily closed.
The initial application is to interrupt power to a 220V, 250W heating cartridge (itself mounted inside a small sample holder with potentially exposed contacts) when the sample chamber is open.
The external circuit can be modified to use different interlock mechanisms as needed. Note that the external interlock circuit is only a single circuit (with two signal lines) and thus is not protected against external shorts.
To accomodate a range of safety interlocks, the 4-pin M12 connector is wired as follows: Pin 1 (Brown): +24V for power supply, max current 0.6A Pin 2 (White): Safety interlock system signal 1 (0 or 24V) Pin 3 (Blue) : Safety interlock system signal 2 (0 or 24V) Pin 4 (Black): 0V for power supply
The safety is interlocked (output active) when both signal pins are set high (24V), with sufficient current to activate the two relays. Pin 1 and 4 can be used to power safety hardware (such as light curtains or proximity detectors) with 24VDC up to a current of 0.6A. A larger power supply can be installed when higher currents are needed, while staying within the current limits imposed by the wiring cross-section.
Despite the advances in hardware and software techniques, standard numerical methods fail in providing real-time simulations, especially for complex processes such as additive manufacturing applications. A real-time simulation enables process control through the combination of process monitoring and automated feedback, which increases the flexibil- ity and quality of a process. Typically, before producing a whole additive manufacturing structure, a simplified experiment in form of a bead-on- plate experiment is performed to get a first insight into the process and to set parameters suitably. In this work, a reduced order model for the transient thermal problem of the bead-on-plate weld simulation is devel- oped, allowing an efficient model calibration and control of the process. The proposed approach applies the proper generalized decomposition (PGD) method, a popular model order reduction technique, to decrease the computational effort of each model evaluation required multiple times in parameter estimation, control and optimization. The welding torch is modeled by a moving heat source, which leads to difficulties separating space and time, a key ingredient in PGD simulations. A novel approach for separating space and time is applied and extended to 3D problems allowing the derivation of an efficient separated representation of the tem- perature. The results are verified against a standard finite element model showing excellent agreement. The reduced order model is also leveraged in a Bayesian model parameter estimation setup, speeding up calibrations and ultimately leading to an optimized real-time simulation approach for welding experiment using synthetic as well as real measurement data.
Test artifact for fs-LDW
(2023)
FenicsXConcrete
(2023)
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
Mechanical testing ontology
(2023)
The materials mechanical testing ontology (MTO) was developed by collecting the mechanical testing vocabulary from ISO 23718 standard, as well as the standardized testing processes described for various mechanical testing of materials like tensile testing, Brinell hardness test, Vickers hardness test, stress relaxation test, and fatigue testing. Confirming the ISO/IEC 21838-2 standard, MTO utilizes the Basic Formal Ontology (BFO), Common Core Ontology (CCO), Industrial Ontologies Foundry (IOF), Quantities, Units, Dimensions, and data Types ontologies (QUDT), and Material Science and Engineering Ontology (MSEO) as the upper-level ontologies.
This contribution contains the raw data used to compare experimental results with thermodynamic calculations using the CALPHAD method, which is related to the research article “The AlMo0.5NbTa0.5TiZr refractory high entropy superalloy: experimental findings and comparison with calculations using the CALPHAD method” [1] , and therefore this article can be used as a basis for interpreting the data contained therein. The AlMo0.5NbTa0.5TiZr refractory superalloy was characterized in the cast and annealed condition (1400 °C for 24 h) in order to measure grain size and to identify and measure the size and area fraction of the phases present. The raw data of this article include X-ray diffraction (XRD) measurements, microstructural characterization by scanning and transmission electron microscopy (SEM and TEM), and elemental analysis by energy dispersive X-ray spectroscopy (EDX). XRD includes the determination of phases and the lattice parameters (A2, B2, and hexagonal structure). Microstructural analysis by scanning and transmission electron microscopy includes (1) identification of composition, size, and volume fraction of the present phases and (2) determination of grain size. Based on these experimental data, it is possible to identify similarities and discrepancies with the data calculated using the CALPHAD method for the alloy under study in Ref. [1] , which provides the basis for better and more efficient development of reliable databases.
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.
The KupferDigital project aims to develop digital methods, tools, and data space infrastructures for digitalizing the entire life cycle of copper materials. The mechanical testing process is one of the main chains of such life cycles which generates lots of important testing data about the mechanical properties of the materials and their related materials and testing metadata. To train the digitalization of the mechanical testing process, different kinds of copper alloys were provided for this project, and their mechanical properties were measured by typical methods like Brinell and Vickers hardness and tensile testing. The primary raw testing data as well as the secondary datasets of these tests are provided. The detailed materials specifications, the utilized mechanical testing methods, and provided datasets are described in the content file. The test data files of heterogeneous structures are processed by the KupferDigital digital tools to be converted to standardized machine-readable data files.
The elastic properties (Young's modulus, shear modulus) of Ni-based alloy Inconel IN718 were investigated between room temperature and 800 °C in an additively manufactured variant (laser powder bed fusion, PBF‑LB/M) and from a conventional process route (hot rolled bar). The moduli were determined using the dynamic resonance method. The data set includes information on processing parameters, heat treatments, grain size, specimen dimensions and weight, Young’s and shear modulus as well as their measurement uncertainty.
The dataset was generated in an accredited testing lab using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.
The elastic properties (Young's modulus, shear modulus) of austenitic stainless steel AISI 316L were investigated between room temperature and 900 °C in an additively manufactured variant (laser powder bed fusion, PBF‑LB/M) and from a conventional process route (hot rolled sheet). The moduli were determined using the dynamic resonance method. The data set includes information on processing parameters, heat treatments, grain size, specimen dimensions and weight, Young’s and shear modulus as well as their measurement uncertainty.
The dataset was generated in an accredited testing lab using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.
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
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).
The dataset contains experimental mechanical data from complex service-like creep-fatigue experiments performed isothermally at 620 °C and a low strain amplitude of 0.2 % on tempered martensite-ferritic grade P92 steel. The data sets in text file format provide cyclic deformation (min. and max. stresses) and the total (hysteresis) data of all recorded fatigue cycles for three different creep-fatigue experiments: 1) a standard relaxation fatigue (RF) test with symmetrical dwell times of three minutes introduced at minimum and maximum strain, 2) a fully strain-controlled service-like relaxation (SLR) test combining these three-minute peak strain dwells with a 30-minute dwell in between at zero strain, and 3) a partly stress-controlled service-like creep (SLC) test combining the three-minute peak strain dwells with 30-minute dwells at constant stress.
Further information on data and data acquisition, analysis, and experimental details are given in “Experimental Data from Service-Like Creep-Fatigue Experiments on Grade P92 Steel”, submitted to Data in Brief. Additional analyses of these datasets, as well as experimental findings and discussions are presented in “Creep-Fatigue of P92 in Service-Like Tests with Combined Stress- and Strain-Controlled Dwell Times”, submitted to International Journal of Fatigue.
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.
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.
The dataset provided in this repository comprises data obtained from a series of characterization tests performed to a sheet of typical S355 (material number: 1.0577) structural steel (designation of steel according to DIN EN 10025-2:2019). The tests include methods for the determination of mechanical properties such as, e.g., tensile test, Charpy test and sonic resonance test. This dataset is intended to be extended by the inclusion of data obtained from further test methods. Therefore, the entire dataset (concept DOI) comprises several parts (versions), each of which is addressed by a unique version DOI.
The data were generated in the frame of the digitization project Innovationplatform MaterialDigital (PMD) which, amongst other activities, aims to store data in a semantically and machine understandable way. Therefore, data structuring and data formats are focused in addition to aspects in the field of material science and engineering (MSE). Hence, this data is supposed to provide reference data as basis for experimental data inclusion, conversion and structuring (data management and processing) that leads to semantical expressivity as well as for MSE experts being generally interested in the material properties and knowledge.
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.
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.
This dataset accompanies the following publication, first published in Scientific Reports (www.nature.com/articles/s41598-021-84287-6):
B. Ruehle, J. Krumrey, V.-D. Hodoroaba, Scientific Reports, Workflow towards Automated Segmentation of Agglomerated, Non-Spherical Particles from Electron Microscopy Images using Artificial Neural Networks, DOI: 10.1038/s41598-021-84287-6
It contains electron microscopy micrographs of TiO2 particles, the corresponding segmentation masks, and their classifications into different categories depending on their visibility/occlusion. Please refer to the publication and its supporting information for more details on the acquisition and contents of the dataset, as well as the GitHub repository at https://github.com/BAMresearch/automatic-sem-image-segmentation
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.