TY - GEN A1 - Pauw, Brian Richard A1 - Szymoniak, Paulina T1 - 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" N2 - 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. KW - Small angle scattering KW - X-ray scattering KW - Nanocomposite KW - Polymer nanocomposite KW - Boehmite KW - Analysis KW - SAXS/WAXS PY - 2020 DO - https://doi.org/10.5281/zenodo.4321087 PB - Zenodo CY - Geneva AN - OPUS4-51829 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Dittmann, Daniel T1 - Experimental raw data for "Specific adsorption sites and conditions derived by thermal decomposition of activated carbons and adsorbed carbamazepine" N2 - 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 KW - Adsorption KW - Aktivkohle KW - TED-GC/MS KW - Thermoanalytik PY - 2020 DO - https://doi.org/10.5281/zenodo.3716316 PB - Zenodo CY - Geneva AN - OPUS4-51902 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Léonard, Fabien T1 - Overview of XCT data processing workflow for ammonium nitrate prills quantitative analysis N2 - This video presents the data processing workflow that was developed to perform the quantitative structural and morphological analysis of ammonium nitrate prills by x-ray computed tomography. KW - ANFO KW - Data processing KW - Explosives KW - Porosity KW - Surface area KW - XCT PY - 2020 DO - https://doi.org/10.5281/zenodo.3611339 PB - Zenodo CY - Geneva AN - OPUS4-51897 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Naik, Aakash A1 - Ertural, Christina A1 - Dhamrait, Nidal A1 - Benner, Philipp A1 - George, Janine T1 - Quantum-Chemical Bonding Database (Unprocessed data : Part 8) N2 - This data is associated with the manuscript "A Quantum-Chemical Bonding Database for Solid-State Materials." Refer to mpids.txt to see data related to which compounds are available in the tar file. (mp-xxx refer to Materials Project ID) KW - Bonding analysis KW - Automation KW - Materials Informatics KW - Computational Chemistry KW - Database PY - 2023 DO - https://doi.org/10.5281/zenodo.7852823 PB - Zenodo CY - Geneva AN - OPUS4-57448 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Schilling, Markus T1 - Tensile Test Ontology (TTO) N2 - 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. KW - Ontology KW - Tensile Test KW - Digitalization KW - Plattform MaterialDigital KW - Structured Data PY - 2023 UR - https://github.com/MarkusSchilling/application-ontologies/blob/479311832819af695a2c64fa8eb772f2da398061/tensile_test_ontology_TTO/pmd_tto.ttl UR - https://github.com/materialdigital/core-ontology/blob/59f5727b0437ceea5e3d9fcb8fcd0ac211e92cc3/pmd_tto.ttl PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-57935 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Pauw, Brian Richard T1 - The SPONGE v.1.0.0: Modeling scattering of shapes by STL input, with absolute intensities and size distribution. N2 - 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. KW - Software KW - Scattering pattern analysis KW - Scattering pattern simulation KW - X-ray KW - Neutron KW - Command-line interface KW - Scattering pattern KW - STL file input KW - Python KW - Arbitrary shapes PY - 2023 DO - https://doi.org/10.6084/m9.figshare.21857130.v1 PB - Digital Science CY - Cambridge, MA, USA AN - OPUS4-56815 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Pauw, Brian Richard T1 - Trinamic TMCL IOC for exposing Trinamics motor controllers to EPICS CA N2 - 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). KW - Instrumentation KW - Motor controller KW - EPICS KW - Channel access KW - Instrument control KW - Laboratory automation PY - 2024 DO - https://doi.org/10.5281/zenodo.10792593 PB - Zenodo CY - Geneva AN - OPUS4-59624 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Schilling, Markus A1 - Bayerlein, Bernd A1 - Birkholz, H. A1 - Fliegener, S. A1 - Grundmann, J. A1 - Hanke, T. A1 - von Hartrott, P. A1 - Waitelonis, J. T1 - PMD Core Ontology (PMDco) N2 - 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 KW - Ontology KW - Semantic Web technologies KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology PY - 2023 UR - https://github.com/materialdigital/core-ontology/blob/f2bd420348b276583fad6fa0fb4225f17b893c78/pmd_core.ttl PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-59352 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Völker, Christoph A1 - Moreno Torres, Benjami T1 - SLAMD-FIB-Case-Study N2 - 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. KW - Alkali activated concrete KW - Concrete PY - 2022 UR - https://github.com/BAMcvoelker/SLAMD-FIB-Case-Study#slamd-fib-case-study PB - GitHub CY - San Francisco, CA, USA AN - OPUS4-56637 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Friedrich, Alexander T1 - Simulation data for eddy current rail testing - simulation accuracy and evaluation uncertainty quantification N2 - This dataset serve to quantify the simulation error and the evaluation uncertainties in the context of eddy current rail testing. It was obtained during the AIFRI project (Artificial Intelligence for Rail Inspection) with the Faraday software by INTEGRATED Engineering Software, using its BEM Solver. KW - Non-destructive testing KW - Eddy current testing KW - Eddy current simulstion PY - 2024 DO - https://doi.org/10.5281/zenodo.13970374 PB - Zenodo CY - Geneva AN - OPUS4-61470 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -