Chemische Charakterisierung und Spurenanalytik
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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_1 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.
Mikroplastik Detektion mit Thermoanalytischen Methoden: Analytik, Referenzmaterial, Ringversuche
(2024)
Ich dem Vortrag geht es um die Vorstellung von thermoanalytischen Methoden für die Mikroplastik-Detektion. Verschiedene Kopplungsmöglichkeiten werden gezeigt und die Funktionsweise der TED-GC/MS wird erklärt. Im zweiten Teil werden Referenzmaterialien für die Mikroplastik-Analytik diskutiert. PET -Tabletten des PlasticTrace Projektes werden vorgestellt. Am Ende wird der VAMAS Ringversuch zur Mikroplastik-Detektion gezeigt.
This report contains detailed information on the preparation of the reference material as well as on homogeneity and stability investigations of the property of interest, the equivalent particle diameter, and on the analytical methods to determine the additional properties. The values for particle size distribution are based on the results from laser diffraction measurements of 3 different devices with at least 3 replicate measurements of 4 randomly chosen units of the reference material P206.
The talk summarizes challenges in microplastic analysis. It shows the preparation of microplastic reference materials as well as the testing on homogeneity and stability. The reference material is used in an international laboratory comparison to compare different detection methods used for microplastic analysis. The methods used were µ-IR (FTIR+LDIR) for number-based methods and TED-GC/MS and Py-GC/MS for mass-based methods. The ILC was done under the umbrella of VAMAS TWA 45. Results of the participants are presented.
Preliminary results of an interlaboratory comparison on microplastics organised by plasticsfate
(2023)
Microplastics are everywhere in the environment, but analytics is challenging. Since harmonisation is missing as well es suitable reference materials, BAM did under th umbrella of VAMAS funded by the EU Horizon 2020 project PlasticsFate a ILC for microplastic detection methods. Methods adressed were IR, Raman, Py-GC/MS and TED-GC/MS. The talk gives a first presentation and evaluation on the results.
Die Probenpräparation ist ein wichtiger Schritt für die Qualität der Massenspektren in MALDI-TOF MS. Es werden Polymerproben, die lösungsmittelbasiert und lösungsmittelfrei präpariert worden sind, verglichen. Dabei werden die Intensitätsverhältnisse von ternären Mischungen von PEG-, PS- und PMMA-Polymeren beachtet.
The presentation gives an overview of BAM's activities on processing influences and application properties of MPEAs in the form of joined and machined high and medium entropy alloys (CoCrFeMnNi and CoCrNi). In the case of welding, the focus is on defect-free welded joints with sufficient mechanical properties. In the case of machining, the focus is on the possible influence on the surface quality of the materials through adequate milling parameters. In addition, the hydrogen absorption and diffusion properties as well as the electrochemical corrosion behavior are fundamentally examined.
Solvent-free sample preparation offers some advantages over solvent-based techniques, such as improved accuracy, reproducibility and sensitivity, for matrix-assisted laser desorption/ionization (MALDI) analysis. However, little or no information is available on the application of solvent-free techniques for the MALDI analysis of polymer blends. Solvent-free sample preparation by ball milling was applied with varying sample-to-matrix ratios for MALDI time-of-flight mass spectrometry analysis of various polymers, including polystyrenes, poly(methyl methacrylate)s and poly(ethylene glycol)s. The peak intensity ratios were compared with those obtained after using the conventional dried droplet sample preparation method. In addition, solvent-assisted milling was also applied to improve sample homogeneities.
Depending on the sample preparation method used, different peak intensity ratios were found, showing varying degrees of suppression of the signal intensities of higher mass polymers. Ball milling for up to 30 min was required to achieve constant intensity ratios indicating homogeneous mixtures. The use of wet-assisted grinding to improve the homogeneity of the blends was found to be disadvantageous as it caused partial degradation and mass-dependent segregation of the polymers in the vials.The results clearly show that solvent-free sample preparation must be carefully considered when applied to synthetic polymer blends, as it may cause additional problems with regard to homogeneity and stability of the blends.
This is a set of use examples for the HDF5Translator framework. This framework lets you translate measurement files into a different (e.g. NeXus-compatible) structure, with some optional checks and conversions on the way. For an in-depth look at what it does, there is a blog post here.
The use examples provided herein are each accompanied by the measurement data necessary to test and replicate the conversion. The README.md files in each example show the steps necessary to do the conversion for each.
We encourage those who have used or adapted one or more of these exampes to create their own conversion, to get in touch with us so we may add your example to the set.
A poly(L-lactide) with a trifluoroethyl ester end group and an average degree of polymerization (DP) of 50 was synthesized by ROP of L-lactide initiated with trifluoroethanol. Small-angle X-ray scattering (SAXS) in combination with differential scanning calorimetry (DSC) measurements revealed an average crystal thickness of 13 nm, corresponding to 45 repeat units. This suggests that most crystallites were formed by extended PLA chains, and both flat surfaces were covered by CF3 groups. The crystalline PLAs were annealed at 140 or 160 °C in the presence of two catalysts: tin(II) 2-ethylhexanoate, (SnOct2) or dibutyltin bis(pentafluorophenoxide) (BuSnPhF). The chemical reactions, such as polycondensation and cyclization, proceeded in the solid state and were monitored by matrix-assisted laser desorption/ionization time-offlight (MALDI TOF) mass spectrometry and gel permeation chromatography (GPC) measurements. Under optimal conditions a large fraction of linear chains was transformed into crystallites composed of extended cycles. Additionally, MALDI TOF MS analysis of GPC fractions from samples annealed for 28 or 42 days detected chain elongation of the linear species up to a factor of 20.
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).
This presentation highlights ongoing scientific misconduct as found in academic literature. This includes data- and image manipulation, and paper mills. Starting with an expose of examples, it delves deeper into the causes and metrics driving this phenomenon. Finally a range of possible tools is presented, that the young researcher can use to prevent themselves from sliding into the dark scientific methods.
By automatically recording as much information as possible in automated laboratory setups, reproducibility and traceability of experiments are vastly improved. This presentation shows what such an approach means for the quality of experiments in an X-ray scattering laboratory and an automated synthesis set-up.
The principles of (Hard) X-ray photoelectron spectroscopy and some application in the field of (core-shell) nanoparticles will be presented. The presentation should answer hoe to get reliable results. Furthermore, examples of the correlation between physical-chemical measurments and toxicological results are given which are crucial for the risk assessment of nanoparticles.
The usefulness of seven different Tin catalysts, Bismuth subsalicylate and Titan tetra(ethoxide) for the polycondensation of ethyl L-lactate (ELA) was examined at 150 °C/6 d. Dibutyltin bis(phenoxides) proved to be particularly effective. Despite the low reactivity of ELA, weight average molecular masses (Mw) up to 12 500 were found along with partial crystallization. Furthermore, polylactides (PLAs) of similar molecular masses were prepared via ELA-initiated ROPs of L-lactide by means of the four most effective polycondensation catalysts. The crystalline linear PLAs were annealed at 140 or 160 °C in the presence of these catalysts. The consequences of the transesterification reactions in the solid PLAs were studied by means of matrix-assisted laser desorption/ionization (MALDI TOF) mass spectrometry, gel permeation chromatography (GPC) and small-angle X-ray scattering (SAXS). The results indicate that polycondensation and formation of cycles proceed in the solid state via formation of loops on the surface of the crystallites. In summary, five different transesterification reactions are required to explain all results.
Inputs of plastic impurities into the environment via the application of fertilizers are regulated in Germany and the EU by means of ordinances. Robust and fast analytical methods are the basis of legal regulations. Currently, only macro- and large microplastic contents (>1 mm) are measured. Microplastics (1–1,000 µm), are not yet monitored. Thermal analytical methods are suitable for this purpose, which can determine the mass content and can also be operated fully automatically in routine mode. Thermal extraction desorption-gas chromatography/mass spectrometry (TED-GC/MS) allows the identification of polymers and the determination of mass contents in solid samples from natural environments. In accordance with the German or European Commission (EC) Fertiliser Ordinance, composting plants should be monitored for microplastic particles with this method in the future. In this context a compost plant was sampled. At the end of the rotting process, the compost was sieved and separated in a coarse (>1 mm) and a fine fraction (<1 mm). The fine fraction was processed using density separation comparing NaCl and NaI as possible salt alternative and screened for microplastic masses by TED-GC/MS with additional validation and quality assurance experiments. With TED-GC/MS total microplastics mass contents of 1.1–3.0 μg/mg in finished compost could be detected with polyethylene mainly. What differs much to the total mass of plastics in the coarse fraction with up to 60 μg/mg, which were visually searched, identified via ATR-FTIR and gravimetrically weighted.
This contribution provides an overview of the BAMline synchrotron radiation beamline, which specializes in hard X-ray spectroscopy techniques for materials research. The BAMline offers X-ray absorption spectroscopy (XAS), x-ray fluorescence spectroscopy (XRF), and tomography to study materials' electronic structure, chemical composition, and structure. Key capabilities include standard and dispersive XAS for electronic structure, micro-XRF for elemental mapping, coded aperture imaging, and depth-resolved grazing exit XAS. The BAMline enables in situ characterization during materials synthesis and functions for energy, catalysis, corrosion, biology, and cultural heritage applications.
Ongoing developments like the implementation of machine learning techniques for experiment optimization and data analysis will be discussed. For instance, Bayesian optimization is being used to improve beamline alignment and scanning. An outlook to the future, where the BAMline will continue pioneering dynamic and multi-scale characterization, aided by advanced data science methods, to provide unique insights into materials research, will be given.
The structure and composition of ancient gold objects retain information about their long history of manufacture, from the exploitation of the ore to the finishing touches, as well as evidence of their use, deposition, and degradation. By developing an efficient analytical strategy, it is possible to retrieve that information. This chapter sets the necessary foundation 131to explore fully the analytical results presented in the following chapters of this volume. The techniques employed in the analyses of the Egyptian jewellery are described and the analytical parameters provided. For more established techniques, only brief introductions are presented, while more recent developments are presented in greater detail.
Enhancing efficiency at bamline: employing data science and machine learning for x-ray research
(2023)
This talk discusses how data science and machine learning techniques are being applied at the BAM Federal Institute for Materials Research and Testing to enhance efficiency and automation at the BAMLine synchrotron facility. The methods presented include Gaussian processes and Bayesian optimization for beamline adjustment and optimization of X-ray measurements. These statistical techniques allow automated alignment of beamline components and active learning scanning to reduce measurement time.
Additional machine learning methods covered are neural networks for quantification of X-ray fluorescence (XRF) data and decoding coded apertures.