291 to 300 of 738 Results
Oct 4, 2023 - SFB 1333 A1+B2 - Buchmeiser group, IPOC-MSF
Groos, Jonas; Koy, Maximilian; Musso, Janis; Neuwirt, Michael; Pham, Thao; Hauser, Philipp Manuel; Frey, Wolfgang; Buchmeiser, Michael, 2023, "Replication data of Buchmeiser group for: "Ligand Variations in Neutral and Cationic Molybdenum Alkylidyne NHC Catalysts"", https://doi.org/10.18419/DARUS-3701, DaRUS, V1
All primary data files related to the publication. Procedures, recation conditions and used analytical equipment is discussed in detail in the experimental section or the supporting information of the paper. Novel complexes were examined via nuclear magnetic resonance (NMR) spectroscopy and the spectra can be found in NMR folder. The NMR folder als... |
Sep 29, 2023 - Dyballa Group
Dyballa, Michael, 2023, "Replication Data for: Accessibility of Reactants and Neighborhood of Mo Species during Methane Aromatization Uncovered by Operando NAP-XPS and MAS NMR", https://doi.org/10.18419/DARUS-3730, DaRUS, V1
Original data (Catalytic measurements, Characterization) of the journal article mentioned under related publications from the Dyballa group can be found here. See File Documentation.txt for a mapping between the figures of the publication and the files in the dataset. |
Sep 27, 2023 - Quantum Computing @IAAS
Mandl, Alexander; Barzen, Johanna; Leymann, Frank; Mangold, Victoria; Riegel, Benedikt; Vietz, Daniel; Winterhalter, Felix, 2023, "Data Repository for: On Reducing the Amount of Samples Required for Training of QNNs", https://doi.org/10.18419/DARUS-3442, DaRUS, V1
Simulation experiment data for training Quantum Neural Networks (QNNs) using entangled datasets. The experiments investigate the validity of the lower bounds for the expected risk after training QNNs given by the extensions to the Quantum No-Free-Lunch theorem presented in the related publication. The QNNs are trained with (i) samples of varying Sc... |
Sep 21, 2023 - Molecular Simulation
Kraus, Hamzeh; Högler, Marc; Hansen, Niels, 2023, "Supplementary material for 'Axial Diffusion in Liquid-Saturated Cylindrical Silica Pore Models'", https://doi.org/10.18419/DARUS-3067, DaRUS, V1
This dataset contains simulation input files in GROMACS format accompanying the mentioned publication. Structure, topology, and simulation parameter files are provided for bulk phase simulations as well as for pore simulations with 14 different compounds. The pore simulations are divided into three steps, an energy-minimization, an NVT equilibratio... |
Sep 21, 2023 - Dyballa Group
Dyballa, Michael, 2023, "Replication Data for: Desilicated ZSM-5 Catalysts: Properties and Ethanol to Aromatics (ETA) Performance", https://doi.org/10.18419/DARUS-3721, DaRUS, V1
Original data (Catalytic measurements, Characterization) of the journal article mentioned under related publications from the Dyballa group can be found here. See File Documentation.txt for a mapping between the figures of the publication and the files in the dataset. |
Sep 15, 2023 - SFB 1333 A1+B2 - Buchmeiser group, IPOC-MSF
Goldstein, Elizabeth L.; Buchmeiser, Michael; Ziegler, Felix; Beurer, Ann-Katrin; Traa, Yvonne; Bruckner, Johanna R., 2023, "Replication Data of Buchmeiser group for: Cationic Molybdenum Imido Alkylidene N‑Heterocyclic Carbene Complexes Confined in Mesoporous Silica: Tuning Transition States Towards Z-Selective Ring-Opening Cross-Metathesis", https://doi.org/10.18419/DARUS-3696, DaRUS, V1
All primary data files of measurements and processed data of the journal article mentioned under related publications from the Buchmeiser group can be found here. The data is structured according to figures and schemes in the research article and contains the following data types: NMR (fid), BET (qps), ICP (pdf), FT-IR (dpt, pdf), GC-MS (D, ms, exc... |
Sep 14, 2023 - SFB 1333 B6 - Peters group, IOC
Peters, Rene, 2023, "Replication Data for: Cooperative Lewis Acid-1,2,3-Triazolium-Aryloxide Catalysis: Pyrazolone Addition to Nitroolefins as Entry to Diaminoamides", https://doi.org/10.18419/DARUS-3614, DaRUS, V1
development and optimization of catalysis, mechanistic experiments, synthetic description of ligand and catalyst synthesis, application of catalysts, synthetic use of products, spectroscopic characterization data of all new products, EPR data of complexes, X-ray data, biological activity of catalysis products and derivatives thereof, for interpreta... |
Sep 12, 2023 - Publication: Microfluidic experiments
Karadimitriou, Nikolaos; Steeb, Holger; Valavanides, Marios, 2022, "Pressure and volumetric flux measurements intended to scale relative permeability under steady state, co-flow conditions, in a PDMS micromodel", https://doi.org/10.18419/DARUS-2816, DaRUS, V2
The current repository contains raw data collected during a systematic laboratory study, examining the flow rate dependency of steady-state, co-injection of two-immiscible fluids within a microfluidic pore network model. The study is presented in the paper by Karadimitriou et al., 2023. The two fluids were the wetting phase (WP), FluorinertTM, FC77... |
Sep 12, 2023 - Estes Group
Maier, Sarah Eleonore, 2023, "Data for Surface Immobilized Cu-1,10-Phenanthroline Complexes with α-Aminophosphonate Groups in the 5-Position as Heterogenous Catalysts for Efficient Atom-Transfer Radical Cyclizations", https://doi.org/10.18419/DARUS-3467, DaRUS, V1
The data set contains the NMR data of the new ligands and immobilized catalyst as well as examplary NMR spectra after the catalysis as used for the determination of the yield. Additionally, it contains the UV/Vis data of the free and immobilized ligand and complex, and crystallographic data of the ligand L1. For experimental details, see the linked... |
Sep 7, 2023 - Holm group
Koppenhöfer, Simon, 2023, "Supplementary videos for "Task oriented algorithms for intelligent self propelled micro-agents"", https://doi.org/10.18419/DARUS-3695, DaRUS, V1
The presented videos visualize chosen aspects of 2D simulations with controlled active propelled agents. Some simulations concern agents that find a point (in maze), rotate a rod, rotate rods arranged in a lattice |