Helmholtz Institute Freiberg for Resource Technology
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1328 research outputs found
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Data publication: Follow me: Mechanistic insights into Eu(III) uptake, translocation and speciation in hydroponically grown Sand oat (Avena strigosa)
No full textRaw and processed data and graphs for the manuscript "Follow me: Mechanistic insights into Eu(III) uptake, translocation and speciation in hydroponically grown Sand oat (Avena strigosa)
Data publication: Bayesian Analysis of Hybrid Neutron Star EOS Constraints within an Instantaneous Nonlocal Chiral Quark Matter Model
No full textThis dataset contains tabulated hybrid Equations of State (EOS) for neutron star matter, with a phase transition generated by a Maxwell construction between an instantaneous nonlocal chiral quark model for the quark matter phase and the relativistic mean field model "DD2" for the nuclear matter phase. The Bayesian analysis explores the EOS parameter space characterized by the coupling constants \eta_D and \eta_V, which govern diquark and vector interactions, respectively. The provided data includes EOS tables for selected parameter sets. The coupling parameters were selected from the 60% Bayesian credibility region for physically significant cases (see figure Hybrid_EoS_Bayesian_Analysis.png), such as: • the maximum and minimum neutron star masses • the maximum and minimum neutron star onset • the most probable EOS from the Bayesian posterior This dataset enables further analysis of neutron star properties, supporting transparency and reproducibility of the published results.The research was supported by the Polish National Science Center (NCN) under the Polonez-BIS program with grant number 2021/43/P/ST2/03319 and by the Argentinian organizations CONICET, ANPCyT and UNLP under grants Numbers PIP 2022-2024 GI-11220210100150CO, PICT19-00792, PICT22-03-00799 and X960, respectively
Exploring Morphology of Thermoplasmonic Nanoparticles to Synergize Immunotherapeutic FAP-positive Cells Sensitization and Photothermal Therapy
No full textThe precision of photothermal therapy (PTT) is often hindered by the challenge of achieving selective delivery of thermoplasmonic nanostructures to tumors. Key enabler for the specific delivery is so-called active targeting, leveraging synthetic molecular complexes to address receptors overexpressed by malignant cells. The latter one enables combination of the PTT with other anticancer therapy. In this study, we developed thermoplasmonic nanoconjugates designed to selectively sensitize malignant cells to PTT. These nanoconjugates consist of (i) 20 nm spherical gold nanoparticles (AuNPs) or gold nanostars (AuNSs) as nanocarriers, and facilitate heat-generation upon optical irradiation, and (ii) surface-passivated antibody-based FAP targeting modules (anti-FAP TMs), used in adaptive CAR T-cells immunotherapy. The nanoconjugates demonstrated excellent stability and specific binding to FAP-expressing fibrosarcoma HT1080 (hFAP) cells, as confirmed by immunofluorescence and label-free surface plasmon resonance scattering imaging. Moreover, the nanocarriers showed significant photothermal conversion after visible and near-infrared (NIR) irradiation. Quantitative thermal lens spectroscopy (TLS) demonstrated the superior photothermal capability of AuNSs, achieving up to 1.5-fold greater thermal enhancement than AuNPs under identical conditions. This synergistic approach, combining targeted immunotherapy with the thermoplasmonic properties of the nanocarriers not only streamline nanoparticle delivery, increasing photothermal yield and therapeutic efficacy, but also offers a more comprehensive and potent strategy for cancer treatment with the potential for superior outcomes across multiple modalities
Data publication: Methods for energy dispersive x-ray spectroscopy with photon-counting and deconvolution techniques
No full textScripts used to generate and process simulated data; experimental data
Multiphase Python Repository by HZDR
No full textThe python package provides several routines and scripts required to operate the code and cases repositories containing additional code and set-ups for the open-source software released by the OpenFOAM Foundation. This includes among others utilities for pre- and post-processing of simulation cases, utilities to launch virtual environments containing the source code, and utilities to operate the continuous integration and continuous development environment in a self-hosted Gitlab instance
Data publication: Towards Personalized Immunotherapeutic Drug Monitoring with Multiplexed Extended Gate FET Biosensors
No full textMeasurement datasets collected from extended gate Field Effect Transistor biosensor and radioactivity measurement
Data publication: Synthesis of nonadentate ligand diethylene glycol-bis(3-aminopropyl ether)-N,N,N′,N′-tetraacetic acid DEGTA and its complexation behavior towards trivalent lanthanides and actinides
No full textNMR-spectra of the pure title compound and its complexes with La(III), Sm(III), and Eu(III) at different metal-to-ligand ratios as well as pD values. TRLFS spectra of the title compound with Eu(III) as well as Cm(III). Measured and calculated IR spectra of the title compound and EDTA and EGTA with Eu(III). DFT calculations of the ligand with La(III), Eu(III), and Cm(III) as well as EDTA and EGTA comparisons
Test data for MALA
No full textThis repository contains data to test, develop and debug MALA and MALA based runscripts. If you plan to do machine-learning tests ("Does this network implementation work? Is this new data loading strategy working?"), this is the right data to test with. It is NOT production level data
LaserVizTool
No full textPython application using QT5 showing pictures from different cameras (directories) in a grid and a stepwise counter-based scroll functionality original developed for the laser systems at the Department of Laser-driven Particle Acceleration (LPA) at the Institute of Radiation Physics at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
Boron data set for machine learning applications
No full textBoron data set for machine learning applications
This dataset contains DFT inputs, outputs, LDOS data and bispectrum descriptor vectors for an α-rhombohedral boron cell of 144 atoms at room temperature and ambient mass density. All simulations have been performed at an LDOS converged k-grid of 4x4x4 k-points.
This dataset contains one .zip file for each of its five type of data (bispectrum descriptors, LDOS, DFT inputs, DFT outputs and trained models).
Authors:
- Fiedler, Lenz (HZDR / CASUS)
- Cangi, Attila (HZDR / CASUS)
Affiliations:
HZDR - Helmholtz-Zentrum Dresden-Rossendorf
CASUS - Center for Advanced Systems Understanding
Dataset description
- Total size: 26 GB
- System: B144
- Temperature(s): 298K
- Mass density(ies): 2.483 gcc
- Crystal Structure: amorphous (material mp-160 in the materials project)
- Number of atomic snapshots: 15
- Contents:
- ideal crystal structure: no
- MD trajectory: no
- Atomic positions: no
- DFT inputs: yes
- DFT outputs (energies): yes
- SNAP vectors: yes
- dimensions: 108x108x35x94 (last dimension: first three entries are x,y,z coordinates, data size is 91)
- units: a.u.
- LDOS vectors: yes
- dimensions: 108x108x35x241
- units: 1/(eV*Angstrom^3)
- trained networks: yes
Dataset structure
A .zip file is included for each for each of its five type of data:
- ldos.zip: holds the LDOS vectors (one HDF5 file per snapshot)
- bispectrum.zip: holds the bispectrum fingerprint vectors (one HDF5 file per snapshot)
- dft_outputs: holds the outputs from the DFT calculations, i.e. energies and simulation parameters in a .json format (one per snapshot)
- dft_inputs: holds the inputs for the DFT calculations, in the form of a QE input file (one per snapshot)
- models: holds five trained NN models for the data se