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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.
The DACHS (Database for Automation, Characterization and Holistic Synthesis) project aims to create completely traceable experimental data, covering syntheses, measurements, analyses, and interpretations. DACHS_MOFs focuses on the synthesis and characterisation of metal-organic frameworks, across multiple, automation-assisted experimental series (AutoMOFs), with the overall goal of producing reproducible MOF samples through tracking of the synthesis parameters.
DACHS_MOFs is simultaneously used to test the DACHS principles.
This upload contain synthesis data from AutoMOFs_5 in HDF5 format (.h5). Each .h5 file contains detailed information on the chemical, experimenal, and synthesis parameters used during the synthesis of a single AutoMOF sample.
The DACHS (Database for Automation, Characterization and Holistic Synthesis) project aims to create completely traceable experimental data, covering syntheses, measurements, analyses, and interpretations. DACHS_MOFs focuses on the synthesis and characterisation of metal-organic frameworks, across multiple, automation-assisted experimental series (AutoMOFs), with the overall goal of producing reproducible MOF samples through tracking of the synthesis parameters.
DACHS_MOFs is simultaneously used to test the DACHS principles.
This upload contain synthesis data from AutoMOFs_4 in HDF5 format (.h5). Each .h5 file contains detailed information on the chemical, experimenal, and synthesis parameters used during the synthesis of a single AutoMOF sample.
With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading-edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie – Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTP) that result from such surface contamination or damage. To compliment the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise the turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.
The RILEM TC 304-ADC has set up a large interlaboratory study on the mechanical properties of 3D printed concrete (ILS-mech). The study was prepared in 2022 by a preparation group leading to a Study Plan which the TC approved on 29 November 2022 (https://doi.org/10.14459/2023mp1705940). The ILS-mech was performed in 2023. The data was collected using a pre-prepared spreadsheet template. For data management, a database was derived and set-up in openBIS. The underlying Postgres database of openBIS was exported to the here-published SQLite database for sharing without maintaining a server. The structure of the database is described in (doi). The results are discussed in three associated papers focusing on the overall outcomes and evaluation of the procedures (doi), the compressive test results (doi), and the tensile test results (doi).
These data sets serve as models for calculating the specific surface area (BET method) using gas sorption in accordance with ISO 9277.
The present measurements were carried out with nitrogen at 77 Kelvin and argon at 87 Kelvin.
It is recommended to use the following requirements for the molecular cross-sectional area:
Nitrogen: 0.1620 nm²
Argon: 0.1420 nm²
Expected specific surface area for nitrogen (BET): 140 to 154 m²/g
Expected specific surface area for argon (BET): 129 to 135 m²/g
Titanium dioxides certified with nitrogen sorption and additionally measured with argon for research purposes were used as sample material.
The resulting data sets are intended to serve as comparative data for own measurements and show the differences in sorption behaviour and evaluations between nitrogen and argon.
These data are stored in the universal AIF format (adsorption information format), which allows flexible use of the data.
These data sets serve as models for calculating the specific surface area (BET method) using gas sorption in accordance with ISO 9277.
The present measurements were carried out with nitrogen at 77 Kelvin and argon at 87 Kelvin.
It is recommended to use the following requirements for the molecular cross-sectional area:
Nitrogen: 0.1620 nm²
Argon: 0.1420 nm²
Expected specific surface area for nitrogen (BET): 24 to 25 m²/g
Expected specific surface area for argon (BET): 20 m²/g
Titanium dioxides certified with nitrogen sorption and additionally measured with argon for research purposes were used as sample material.
The resulting data sets are intended to serve as comparative data for own measurements and show the differences in sorption behaviour and evaluations between nitrogen and argon.
These data are stored in the universal AIF format (adsorption information format), which allows flexible use of the data.
This dataset accompanies the following publication:
Hülagü, D., Tobias, C., Dao, R., Komarov, P., Rurack, K., Hodoroaba, V.-D., Towards 3D determination of the surface roughness of core-shell microparticles as a routine quality control procedure by scanning electron microscopy. Sci.Rep, 14, 17936 (2024), https://doi.org/10.1038/s41598-024-68797-7.
It contains SEM and AFM-in-SEM images of polystyrene (PS) core particles, polystyrene-iron oxide (PS/Fe3O4) core-shell particles, and polystyrene-iron oxide-silica (PS/Fe3O4/SiO2) core-shell-shell particles. 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/denizhulagu/roughness-analysis-by-electron-microscopy.
The investigated particles were produced at BAM laboratories as previously described in:
Hülagü, D. et al. Generalized analysis approach of the profile roughness by electron microscopy with the example of hierarchically grown polystyrene–iron oxide–silica core–shell–shell particles. Adv. Eng. Mater. 24, 2101344, https://doi.org/10.1002/adem.202101344 (2022).
Tobias, C., Climent, E., Gawlitza, K. & Rurack, K. Polystyrene microparticles with convergently grown mesoporous silica shells as a promising tool for multiplexed bioanalytical assays. ACS Appl. Mater. Interfaces 13, 207, https://dx.doi.org/10.1021/acsami.0c17940 (2020).
Spectroscopic ellipsometry was used to determine the thickness and dielectric function of a Aluminium Nitride (AlN) layer on a Si wafer. The layer was determined to be 170 nm thick. The layer was provided by AIXTRON and manufactured by means of MOVPE.
The data was created using a M2000DI spectroscopic ellipsometer from Woollam Co. Inc. Analysis was done using the CompleteEASE software. The model used is a multi-peak oscillator model for the AlN layer.
The data resembles common database values for the material AlN.