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Data from the figures in the article "Coupling-energy driven pumping through quantum dots: the role of coherences"
No full textWith this dataset, we publish all raw data from the calculations used and presented in arXiv:2602.23099 [cond-mat.mes-hall] (https://doi.org/10.48550/arXiv.2602.23099).
This paper discusses electron pumping through a quantum dot theoretically. Thereby, the focus is on two pumping schemes where lowest-order and off-resonant tunneling processes are suppressed and the pump is exclusively driven by modulations of the coupling energy induced by coupling/decoupling processes or measurements.
This dataset contains the calculated data of the currents through the quantum dot, the energetic efficiency of the pump, the quantum-dot occupation, the energies of the QD and the baths, and the QD-bath coherences.
All calculations are done with Wolfram Mathematica 14, using an exact solution via the Heisenberg equation of motion technique, or by solving the Redfield equation
pyGOLD
No full textA python based API for docking based virtual screening workflow generation.
If extracting for Windows systems: This archive contains symbolic links, which need administrative permission under windows. Extracting the archive with 7-zip and as an Administrator is possible.
Molecular docking is one of the successful approaches in structure based discovery and development of bioactive molecules in chemical biology and medicinal chemistry. Due to the huge amount of computational time that is still required, docking is often the last step in a virtual screening approach. Such screenings are set as workflows spanned over many steps, each aiming at different filtering task. These workflows can be automatized in large parts using python based toolkits except for docking using the docking software GOLD. However, within an automated virtual screening workflow it is not feasible to use the GUI in between every step to change the GOLD configuration file. Thus, a python module called PyGOLD was developed, to parse, edit and write the GOLD configuration file and to automate docking based virtual screening workflows.
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Preregistration for "Trust in Regularization"
No full textIn this work, we investigate the motivators and inhibitors of why data scientists (broadly speaking) do not pick up a certain method - regularizations. In doing so, we test hypotheses related to the unified theory of acceptance and use of technology (UTAUT), and add a special focus on trust in regularizations. Additionally, we experimentally investigate and compare the effects of different recommendations (peer, expert, or journal recommendation) to use this method. This is the updated version of the preregistration, which was originally published at Dec, 11, 2024 on OSF. See the README for further information, also for reasons for the update
Replication Data for: Boosting Process Efficiency Through Concentrate Recycling in Suspension Melt Crystallization
No full text
Project and Dataset Description
PROJECT DESCRIPTION
Suspension melt crystallization is a low-energy purification technology with growing potential in fields such as water and wastewater treatment. To increase melt (water) yield without compromising purity, we studied the impact of internal concentrate recycling on the performance of an integrated suspension melt crystallization pilot plant combining a scraped cooling crystallizer and a mechanical piston-type wash column.
Systematic recycling experiments were conducted for three aqueous model systems NaCl–H₂O, MgSO₄–H₂O, and a mixed NaCl/MgSO₄–H₂O solution under controlled suspension densities and stepwise decreasing operating temperatures. A key observation is that the process concentrate concentration deviates from solid–liquid equilibrium (SLE), primarily due to heat-transfer limitations that limit crystal growth despite near-equilibrium operating temperatures.
Additionally, the study identifies challenges related to ice entrainment in the recycle and demonstrates partial mitigation strategies to improve process stability. Overall, concentrate recycling shifts the process trajectory within the SLE diagram, enabling higher concentrate levels and maximizing water yield while maintaining high purification efficiency (>99%) and stable operation. The results further indicate that the developed operating strategies are transferable across systems with both known and unknown phase equilibria. Overall, the study highlights that controlled concentrate recycling can enhance production capacity while preserving purification efficiency.
Overall, the study highlights that controlled concentrate recycling can enhance production capacity while preserving purification efficiency.
DATASET DESCRIPTION
This dataset provides a comprehensive collection of raw experimental data and visual representations from crystallization experiments. The focus is on investigating the influence of an internal concentrate recycle in a suspension melt crystallization plant on process performance, specifically water yield and purification efficiency.
1. General Structure
The dataset is organized into two main categories:
Figures
.opju files (OriginLab project files) containing experimental data and corresponding plots.
Raw_data
Excel files with raw experimental data for Test Series 1 and 2 (RAW_DATA_TS1_TS2), including two subfolders:
RAW_DATA_TS1 (Test Series 1)
RAW_DATA_TS2 (Test Series 2)
Particle videos with analyzed particle size distributions for Test Series 1 (RAW_DATA_PSD_TS1_NaCl_1_3.5), including:
Subfolders containing videos for each individual experiment
One subfolder containing PSD results for all experiments
2. Substance Systems
The experiments were conducted using different substance systems in Test Series 1 and 2:
Sodium chloride (NaCl) as impurity in aqueous solution
Feed concentrations: 1 wt% and 3.5 wt%
Magnesium sulfate (MgSO4) as impurity in aqueous solution
Feed concentration: 3.5 wt%
NaCl and MgSO4 in equal proportion as impurities in aqueous solution
Feed concentration: 1 wt%
Due to internal recycling, the operating temperature was reduced on each experimental day in order to decrease the solution concentration.
3. File Contents and Data Description
3.1 opju Files (OriginLab Project Files)
These files contain processed experimental data and visualizations:
Concentration trajectories and suspension densities
Three subfolders for each substance system
Each contains two worksheets and corresponding graphs:
Concentration trajectories in the solid–liquid equilibrium (SLE) diagram
Suspension densities
Data provided for both test series
Particle Size Distribution (PSD)
One worksheet and graph containing cumulative volume distributions
Includes all NaCl experiments from Test Series 1 and both feed concentrations
Purification efficiency
One graph showing purification efficiency
Covers all three substance systems for experiments conducted in Test Series 1
Yield
One graph showing water yield
Covers all three substance systems for experiments conducted in Test Series 1
3.2 RAW_DATA_TS1_TS2 Excel Files
File naming convention:
Test Series 1 (TS1):
Impurity_Date_Feed concentration_Set point temperature for recycling
Test Series 2 (TS2):
TS2_Impurity_Date_Start concentration of experiment day
Test Series 1 contains:
Raw data from the human–machine interface (HMI):
Temperature data (process medium, melt loop, cooling medium)
Light transmission value
Scraper blade energy input
Raw data from LabVIEW:
Inline conductivity data
Pressure data from the wash column melt loop
Offline conductivity measurements:
Conductivity data for each sample taken during the process used to calculate salt concentration
Test Series 2 contains:
Concentrations:
Conductivity data for each sample taken during the process used to calculate salt concentration
Results:
Raw data from the human–machine interface (HMI):
Temperature data (process medium, melt loop, cooling medium)
Light transmission value
Scraper blade energy input
Raw data from LabVIEW:
Inline conductivity data
Pressure data from the wash column melt loop
3.3 RAW_DATA_PSD_TS1_NaCl_1_3.5
Particle Size Distribution (PSD) results for the NaCl–water system at both feed concentrations.
PSD Results folder
Excel files named:
PSDResults_Impurity_Date_Feed concentration_Set point temperature recycling
Cumulative distribution function (Qi)
Characteristic variables and summation function
Characteristic parameters:
X10, X50 (median), X90 (percentile values)
Agglomeration degree (Ag)
Probability density function (qi):
Size distribution data for single crystals and agglomerates
Other folder
Folder name format:
Impurity_Date_Feed concentration_Set point temperature recycling_QICPIC
Particle videos for each individual experiment
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ProSPECCTs
No full textThe automated comparison of protein-ligand binding sites provides useful insights into yet unexplored site similarities. Various stages of computational and chemical biology research can benefit from this knowledge. The search for putative off-targets and the establishment of polypharmacological effects by comparing binding sites led to promising results for numerous projects. Although many cavity comparison methods are available, a comprehensive analysis to guide the choice of a tool for a specific application is wanting. Moreover, the broad variety of binding site modeling approaches, comparison algorithms, and scoring metrics impedes this choice. Herein, we aim to elucidate strengths and weaknesses of binding site comparison methodologies. A detailed benchmark study is the only possibility to rationalize the selection of appropriate tools for different scenarios. Specific evaluation data sets were developed to shed light on multiple aspects of binding site comparison. An assembly of all applied benchmark sets (ProSPECCTs - Protein Site Pairs for the Evaluation of Cavity Comparison Tools) is made available for the evaluation and optimization of further and still emerging methods. The results indicate the importance of such analyses to facilitate the choice of a methodology that complies with the requirements of a specific scientific challenge
Association Between eHealth Literacy and Health Outcomes in German Athletes using the GR-eHEALS Questionnaire
No full textThis data set was collected to examine the eHealth Literacy Scale as a means of assessing eHealth literacy among German athletes. In this context, we pursued two objectives with this study: first, to test the factorial structure of the GR-eHEALS and assess its construct validity by examining both convergent and discriminant validity; and second, to examine the associations between eHealth literacy and health-related outcomes (i.e., substance use and injuries). This digital survey was conducted as a cross-sectional study, adhering to the approval guidelines of the Ethics Committee of the Faculty of Medicine at the University of Duisburg-Essen (19-8947-BO). Prior to the survey, each participant provided electronic informed consent. Participation was both anonymous and voluntary, without any form of reimbursement. We utilized the Unipark software (Tivian XI GmbH) that was distributed through social media, sports clubs (involving athletes competing in regional and nationwide tournaments), and sports associations (both regional and nationwide) from December 2021 to December 2022.
Methods
This study collected sociodemographic data from participants through self-report measures, including information on their sex, age, marital status, education level, occupation, and financial situation. Furthermore, sports-related data (i.e., type of sports and whether they do individual or team sports) was assessed. eHealth literacy of participants was assessed using the GR-eHEALS (1), which is based on the eHEALS by Norman and Skinner (2). The GR-eHEALS consists of eight items that are rated on a 5-point Likert scale (1 = do not agree at all; 5 = fully agree).
To test the convergent validity of the GR-eHEALS, established scales measuring confidence in using digital media (3, 4) were administered. Furthermore, the length of daily internet use for personal and professional purposes was evaluated using a single self-developed item rated on a 5-point Likert scale (1 = not at all; 5 = more than 5 hours). In addition, three items each were inquired after internet anxiety and digital overload (3, 5), and they were rated on a 5-point Likert scale (1 = totally disagree; 5 = totally agree). These measures were expected to correlate significantly with the GR-eHEALS, as per Campbell and Fiske (6) guidelines. To evaluate the discriminant validity of the GR-eHEALS, we used the 8-item Impulsive Behavior–8 Scale (7) to measure impulsivity as a personal trait that was expected to be independent of eHealth literacy. These items were also rated on a 5-point Likert scale.
Additionally, participants provided medical data through self-report measures. It was assessed how often the following substances were consumed on a 5-point Likert scale (1 = never, 5 = daily): Cannabis, nicotine, sedatives prescribed by physicians (e.g. benzodiazepines), painkillers prescribed by physicians (e.g. tramadol), sedatives not prescribed by physicians / over-the-counter sedatives, painkillers not prescribed by physicians / over-the-counter painkillers (e.g. ibuprofen, diclofenac). Moreover, number and severity of injuries was assessed. For this purpose, athletes indicated on a 5-point Likert scale (1 = not at all, 5 = more than 20 times a year) how often they had suffered minor, moderate and severe injuries within the last year and how often surgery had been necessary.
Statistical analyses
Statistical analyses were conducted with R version 4.2.2.2 and R Studio 2023.06.1+524. A confirmatory factor analysis (CFA) was performed in order to affirm the factor structure of the GR-eHEALS scale in the present sample. Results were interpreted according to Hu and Bentler (8) assuming the comparative fit index (CFI) and Tucker Lewis index (TLI) of at least 0.95 and root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR) of below 0.06 and 0.08, respectively (8). As the GR-eHEALS consists of items on ordinal scale, a robust likelihood estimator (WLSMV; 9) was chosen to avoid biases in the model. Internal consistencies (reliability) of the convergent and discriminant validity scales, the GR-eHEALS and its two subscales were examined. Subsequently, two-tailed Pearson correlations were conducted between the validity scales, the outcome measurements, and sociodemographic variables with the GR-eHEALS. Sex differences on GR-eHEALS were assessed by a two-tailed independent t-Test. Results were considered as significant with p = .05. Incomplete data was deleted list wise.</p
Digital Appendix of the dissertation "Accessing PC-SAFT parameters using an integrated Machine Learning framework" (J. Habicht, 2026)
No full textThis publication contains the digital appendix to the RESOLV dissertation "Accessing PC-SAFT parameters using an integrated Machine Learning framework" (J. Habicht, 2026) at the TU Dortmund University.
Project description:
Exploring new, efficient, and environmentally friendly chemical or biotechnological processes is one of the major challenges of modern industry. Precise process simulation techniques established as a cornerstone for this purpose providing fast reliable results in all stages of process development. Those simulators are either guided by key experiments or are used to assist experimental work and significantly reduce the material consumption. Within the last decades, the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) emerged as reliable thermodynamic theory to provide the physical base for such simulators in various fields including reaction engineering, design of separation techniques, or the design of pharmaceutical formulations.
Although providing excellent modeling results, the application of PC-SAFT for calculating complex mixtures requires fitting the pure-component parameter sets for all molecules involved, as well as determining the binary interaction parameters for all binary combinations of these molecules, which is requiring a substantial experimental effort. Especially in the development of pharmaceutical processes, minimizing material consumption is a major cost factor. Thus, the development of predictive tools to obtain the PC-SAFT parameters for any molecular system of interest is an important challenge to ensure thermodynamic-based calculations even with minimal experimental input.
Therefore, an integrated Machine Learning (ML) framework using neural network-ensembles was developed to predict the PC-SAFT pure-component parameters and binary interaction parameters of mixtures in this work. Particular focus was put on the flexibility of the approach with regard to already existing parameters and to potentially available experimental data. To meet this requirement, the ML framework was designed with a two-level approach, while the first level is used to predict PC-SAFT pure-component parameters from the molecular structure of the pure components and the second level is used to predict the binary interaction parameter using the two PC-SAFT pure-component parameters of the respective binary system as input.
This approach was successfully trained and validated for various examples including the prediction of physical pure component properties (vapor pressure, liquid density) using the first level and the prediction of phase equilibria (Vapor-liquid-equilibrium, Solid-liquid-equilibrium, Liquid-liquid-equilibrium) using the complete ML framework.
Dataset description:
The dataset contains eight subdatasets DA_1-DA_8, as described below.
DA_1:
Contains the calculations (training and test set) of PC-SAFT pure-component parameter sets for non-associating molecules using three different Neural Networks (NN1-NN3) trained in this work compared to the parameters published in literature (fitted to experimental data). The initial bit length of the Extended-Connectivity fingerprints used as input was varied for the three Neural Networks (NN1: 2e10, NN2: 2e11, NN3:2e12). Columns E-G contain the literature values (no new generated data), all other columns contain newly generated data of this work.
DA_2:
Contains the dataset of PC-SAFT pure-component parameter sets retrieved from literature or fitted to experimental data in this work for associating and non-associating molecules. For all molecules having association interactions, the 2B association scheme is used. No new data is shown in this dataset.
DA_3:
Contains the details of the dataset used for the training of Neural Networks to predict the PC-SAFT binary interaction parameter (kij) using PC-SAFT pure-component parameter sets as input. kij values from the literature are given alongside with the corresponding publications as well as the corresponding pure-component parameter sets for the respective binary systems. If a kij value has been fitted in this work, the corresponding publication providing the VLE data used for parameter regression is given. If "this work" is referenced for a kij value in "kij_Lit_dataset.csv", new data is presented based on a Regression of the kij to the corresponding Vapor-liquid-equilibrium (VLE), otherwise no new data is shown.
DA_4:
Contains the predictions of the PC-SAFT binary interaction Parameter made with three different Neural Network Ensembles trained in this work. The Neural Network Ensembles have been varied by applying three different strategies to account for the symmetry of the binary interaction Parameter. NNe0: direct Training using a Standard loss function; NNe1: Standard training with a symmetric dataset; NNe2: Adjustment of the Loss function to force symmetry + using a symmetric dataset. Except column G "kij_Lit", this dataset presents newly generated data of this work.
DA_5:
Contains the Errors of VLE calculations for 100 binary Systems compared to experimental data using the ML Framework developed in this work.
Scenario 1: Literature derived PC-SAFT pure-component parameter sets and ML-predicted kij values
Scenario 2: One literature derived and one ML-predicted PC-SAFT pure-component parameter set and ML-predicted kij values
Scenario 3: Fully ML-predicted parameters (pure-component parameter sets and binary interaction parameter)
Scenario 4: Literature derived PC-SAFT pure-component parameter sets and the kij value set to zero
All calculations are newly generated data of this work.
DA_6:
Contains the predictions of Active pharmaceutical ingredient (API) solubility made with the ML Framework developed in this work and the respective literature values and related publications to this data.
Columns D and G Show literature data (respective source given at the end), all other columns present newly generated data.
DA_7:
Contains the minima in AARDmean for discrete radii given for the complete dataset of PC-SAFT pure-component Parameter sets in the relative Parameter space (as defined in this work). The absolute values of AARDmean are given with the literature Parameter sets, the two minimal Parameter sets and the corresponding spherical angles and cartesian coordinates.
Columns O-Q Show existing literature data. All other columns present newly generated data of this work.
DA_8:
Contains the Performances of the three different loss functions, which have been used to Train neural network Ensembles in this work to predict PC-SAFT pure-component Parameter sets in this work. The prediction of the Parameter sets ("..._PC-SAFT-Parameters_...") is given for all loss functions for the Training and test set as well as the corresponding Errors ("..._pvt_...") in the calculation of physical properties (vapor pressures, saturated liquid densities). The Errors were obtained by comparing the calculations of physical properties using the ML-predicted Parameter sets vs. the Parameter sets published in literature.
In the files of type "..._PC-SAFT-Parameters_...", columns F-H refer to literature data, all other columns Show newly generated data of this work. All other files Show newly generated data of this work
Fracture mechanical estimation of the influence of frequency and process conditions on the fatigue behavior of PBF-LB/M manufactured Ti6Al4V alloy
No full textThe dataset links data for the publication "Fracture mechanical estimation of the influence of frequency and process conditions on the fatigue behavior of PBF-LB/M manufactured Ti6Al4V alloy". The data consists of mechanical characterization for additively manufactured Ti6Al4V alloy
3D finite element model for simulating the BTA deep hole drilling process - supplementary videos
No full textSupplementary videos (18) for research paper titled:
3D finite element model for simulating the BTA deep hole
drilling process
Transient Current Technique (TCT) measurement data for a pcCVD diamond detector using an Am-241 alpha source
No full textThis dataset contains experimental Transient Current Technique (TCT) measurement data acquired from a pcCVD diamond detector using an Am-241 alpha source. The data consist of time-resolved current waveforms recorded under electron and hole transport configurations, for both pumped and unpumped detector states, at applied bias voltages from 150 V to 500 V in 50 V steps. The dataset is organized into condition-specific and voltage-specific subfolders, each containing the corresponding waveform data in CSV format. Its purpose is to support the analysis of charge-carrier transport, pulse-shape evolution, timing characteristics, and electric-field-dependent behavior in diamond detectors, and to provide the experimental basis for comparison with the associated simulation and modelling results reported in the related publication