Chemie und Prozesstechnik
Filtern
Erscheinungsjahr
- 2022 (456) (entfernen)
Dokumenttyp
- Zeitschriftenartikel (163)
- Vortrag (149)
- Posterpräsentation (67)
- Beitrag zu einem Tagungsband (36)
- Forschungsbericht (15)
- Forschungsdatensatz (10)
- Sonstiges (7)
- Buchkapitel (3)
- Dissertation (2)
- Video (2)
- Zeitschriftenheft (Herausgeberschaft für das komplette Heft) (2)
Sprache
- Englisch (456) (entfernen)
Schlagworte
- Digitalization (21)
- Certification (19)
- Conformity assessment (19)
- Fluorescence (19)
- Digital transformation (18)
- COVID-19 (17)
- Calibration (17)
- Conformity assessment body (17)
- Non-destructive testing (17)
- Quality infrastructure (17)
Organisationseinheit der BAM
- 1 Analytische Chemie; Referenzmaterialien (165)
- 8 Zerstörungsfreie Prüfung (150)
- 6 Materialchemie (106)
- 8.2 Zerstörungsfreie Prüfmethoden für das Bauwesen (49)
- 1.1 Anorganische Spurenanalytik (44)
- 6.1 Oberflächen- und Dünnschichtanalyse (42)
- 8.5 Röntgenbildgebung (41)
- 6.3 Strukturanalytik (40)
- 8.0 Abteilungsleitung und andere (38)
- 1.2 Biophotonik (35)
Paper des Monats
- ja (2)
Asymmetrical Flow Field-Flow Fractionation (AF4), as a cleaning technique, was combined on-line with the multielemental analytical capabilities of an Inductively Coupled Plasma-Time of Flight-Mass Spectrometer (ICP-ToF-MS). In that manner, the heavy ionic matrix effect of untreated cells' samples can be significantly reduced. As a proof of concept, commercial baker's yeast cells were analysed.
Bisphenol A (BPA) is used in Polycarbonate, Polyacrylic resins, Polysulfones, Epoxy resins, and Polyetherimides. It is also used in recycled Polyvinyl chloride [1–3]. BPA has been classified as a substance of very high concern (SVHC) under REACH [4] due to its endocrine disrupting properties. The German competent authorities want to reduce the content of BPA in the environment. Possible substitutes for BPA are Bisphenol B (BPB), Bisphenol E (BPE),
Bisphenol F (BPF), or Bisphenol S (BPS), as they are similar in structure.
BPA-based materials have a wide area of application especially outside. These outdoor applications are exposed to different external influences, including physical, biological, mechanical and chemical influences. This results in damage and aging of the material with leaching or migration into the environment. There, the substance is transformed by various transformation processes. The emerging metabolites and transformation products (TPs) can have different properties than the parent substance. Understanding the fate and behavior of the emerging pollutants is very important.
Therefore, different transformation products of selected bisphenols will be generated and analyzed:
To investigate the fate of different bisphenols in water treatment plants technical transformation products [5] will be generated by chlorination, ozonization, the Fenton reaction, and UV-radiation.
For the investigation of the fate of Bisphenols in surface water global radiation will be simulated with UVA-lamps
Simulation of phase-I-metabolism with an electrochemical cell coupled to mass spectrometry (EC-MS).
The rails of modern railways face an enormous wear and tear from ever increasing train speeds and loads. This necessitates diligent non-destructive testing for defects of the entire railway system.
Non-destructive testing of rail tracks is carried out by rail inspection trains equipped with ultrasonic and eddy current test devices. However, the evaluation of the gathered data is mainly done manually with a strong focus on ultrasonic data, and defects are checked on-site using hand-held testing equipment. Maintenance measures are derived based on these on-site findings.
The aim of the AIFRI project (Artificial Intelligence For Rail Inspection) is to
- increase the degree of automation of the inspection process, from the evaluation of the data to the planning of maintenance measures,
- increase the accuracy of defect detection,
- automatically classify detected indications into risk classes.
These aims will be achieved by training a neural network for defect detection and classification. Since the current testing data is unbalanced, insufficiently labeled and largely unverified we will supplement fused, simulated eddy current and ultrasonic testing data in form of a configurable digital twin.
Non-destructive testing of rail tracks is carried out by using rail inspection cars equipped with ultrasonic and eddy current measurement. The evaluation of test data is mainly done manually, supported by a software tool which pre-selects relevant indications shown to the evaluators. The resulting indications have to be checked on-site using hand-held testing equipment. Maintenance interventions are then derived on the basis of these on-site findings.
Overall aim of the AIFRI (Artificial Intelligence For Rail Inspection) project - funded by the German Federal Ministry of Digital and Transport (BMDV) as part of the mFUND programme under funding code 19FS2014 – is to increase the degree of automation of the inspection process from the evaluation of the data to the planning of maintenance interventions. The accuracy of defect detection shall be increased by applying AI methods in order to enable an automated classification of detected indications into risk classes. For this purpose, data from both eddy current inspections and ultrasonic inspections will be used in combination.
Within the framework of this data-driven project, relevant defect patterns and artefacts present in the rail are analysed and implemented into a configurable digital twin. With the help of this digital twin virtual defects can be generated and used to train AI algorithms for detection and classification. With the help of reliability assessment trained AI algorithms will be evaluated with regard to the resulting quality in defect detection and characterisation.
A particular aspect of the development of AI methods is the data fusion of different NDT data sources: Thereby, synergies are used that arise from linking eddy current and ultrasonic inspection data in a combined model.
In the course of the project a demonstrator consisting of the developed IT-tool and an asset management system will be implemented and tested in the field using real-world data.
Structural health monitoring techniques associate strongly with damage detection and characterization. Ultrasonic guided waves (UGW), for such scope, arise as one of the most promising methods for many reasons i.e. UGW are able to travel long distances and they have high sensitivity to damage. In this context, the necessity to model realistic wave-defect interaction occurs to be critical.
Realistic damage scenarios can be modeled through the usage of image-based quadtree meshes. Images, such as the outcome from X-ray scans, C-scans, etc., can be converted into meshes for further integration in a computational domain. Quadtree meshes are created by converting the intensity of the pixels to quadrilateral cells. Homogeneous regions inside one image result in one quad, whereas fine features such as discontinuities can be described with smaller quads.
This contribution proposes an efficient methodology to model wave defect interaction, using as a framework the scaled boundary finite element method (SBFEM) and quadtree meshes. Problems as non-conforming regions in the mesh due to the space tree decomposition can be easily avoided using SBFEM’s polygonal elements. Moreover, the semi-analytical nature of the SBFEM allows the modeling of arbitrarily long prismatic/undamaged regions of the waveguides without an increase in the computational burden.
Natural and synthetic estrogens are key endocrine-disrupting chemicals. Despite occurring at ultra-trace levels (below ng L-1), it is believed that they are contributing to an increase in feminized fish and other endocrine disruptive effects, and hence, their inclusion in the Watch list was not unexpected. One of the main sources ofestrogens to surface waters is wastewater effluent. Once in surface waters, they can partition into different compartments, i.e., water and suspended particulate matter. For this reason, there is an urgent need for a methodology to monitor estrogen levels below the environmental quality standards (EQS) set by the Water Framework Directive requirements.
In this study, a precise and accurate gas chromatography-mass spectrometry method (GC-MS/MS) for the analysis of estrone (E1), 17β-estradiol (17β-E2), 17α-estradiol (17α- E2), 17-alpha-ethinylestradiol (EE2), and estriol (E3) in whole water samples with ng L-1 limit of quantification (LOQ) was developed and validated in accordance with CEN/TS 16800:2020 guidelines.
Monitoring programs should generate high-quality data on the concentrations of substances and other pollutants in the aquatic environment to enable reliable risk assessment. Furthermore, the need for comparability over space and time is critical for analysis of trends and evaluation of restoration of natural environment. Additionally, research work and exercises at the European level have highlighted that reliable measurements of estrogenic substances at the PNEC level are still challenging to achieve.
The project EDC-WFD Metrology for monitoring endocrine disrupting compounds under the EU Water Framework Directive aims to develop traceable analytical methods for determining endocrine disrupting compounds and their effects, with a specific focus on three estrogens of the first watch list (17-beta-estradiol (17βE2), 17-alpha-ethinylestradiol (EE2), and estrone (E1)). Estrogens 17-alpha-estradiol (17E2) and estriol (E3) will be included to demonstrate the reliability of the developed methods and to support the requirements of Directive 2013/39/EC, Directive 2009/90/EC and Commission Implementation Decision (EU) 2018/840, hence improving the comparability and compatibility of measurement results within Europe. During the EDC-WFD project four selected effect-based methods (EBM) will be deeply investigated in order to improve their rationale use and their support in water quality assessment. In particular, the EBM sensitivity, specificity and accuracy on reference materials with single or mixture solutions of the five substances at a concentration of EQS values will be explored.
This contribution will present the objectives and methods applied within the EDC-WFD project.
Chromium(III) complexes can show phosphorescence from the spin-flip excited doublet states 2E/2T1 in the near-infrared with high photoluminescence quantum yields and extremely long lifetimes in the absence of dioxygen. The prototype molecular ruby, [Cr(ddpd)2]3+ (ddpd = N,N’-dimethyl-N,N’-dipyridine-2-ylpyridine-2,6-diamine), has a photoluminescence quantum yield and a luminescence lifetime of 13.7% and 1.1 ms in deaerated acetonitrile, respectively. However, its luminescence is strongly quenched by 3O2 via an efficient Dexter-type energy transfer process. To enable luminescence applications of molecular rubies in solution under aerobic conditions, we explored the potential of sterically demanding ddpd ligands to shield the chromium(III) center from O2 using steady state and time-resolved photoluminescence spectroscopy. The structures of the novel complexes with sterically demanding ligands were investigated by single crystal X-ray diffraction and quantum chemically by density functional theory calculations. The O2 sensitivity of the photoluminescence was derived from absolutely measured photoluminescence quantum yields and excited state lifetimes under inert and aerobic conditions and by Stern–Volmer analyses of these data. Optimal sterically shielded chromium(III) complexes revealed photoluminescence quantum yields of up to 5.1% and excited state lifetimes of 518 μs in air-saturated acetonitrile, underlining the large potential of this ligand design approach to broaden the applicability of highly emissive chromium(III) complexes.
pH and oxygen are amongst the most important and frequently measured analytes in the life and material sciences, indicating, e.g., diseases and corrosion processes. This includes the optical monitoring of pH in living cells for studying cellular internalization pathways, such as phagocytosis, endocytosis, and receptor ligand internalization with the aid of molecular and nanoscale fluorescent sensors. Nanoparticle (NP)-based sensors, that are labeled or stained with a multitude of sensor dyes, have several advantages as compare to conventional molecular probes like enhanced brightness, i.e., amplified signals, ease of designing ratiometric systems by combining analyte sensitive and inert reference dyes, and increased photostability. Moreover, this can enable the use of hydrophobic dyes in aqueous environments. Versatile templates and carriers for the fabrication of nanosensors by the staining and/or labelling with different fluorophores and sensor molecules or surface functionalized NP like silica (SiO2-NP) and polystyrene (PS-NP) particles provide. Here we present the design of a versatile platform of color emissive nanosensors and stimuli-responsive microparticles for the measurement of pH, oxygen, and other targets utilizing both types of matrices and sets of spectrally distinguishable sensor and reference dyes and their characterization and demonstrate the applicability of representative sensor particle for cellular studies.
Bioanalytical, diagnostic, and security applications require the fast and sensitive determination of a steadily increasing number of analytes or events in parallel in a broad variety of detection formats and increased sensitivities. This – flanked by recent technical advancements and the availability of simple to use, commercial time-resolved photoluminescence measuring devices at reasonable costs - calls for the exploitation of the species- and environment-specific photoluminescence parameter luminescence lifetime. In this context, time-resolved photoluminescence measurements of different classes of molecular and nanocrystalline emitter and luminescent particles in different time windows are presented and examples for applications such as lifetime multiplexing and barcoding in conjunction with fluorescence lifetime imaging microscopy (FLIM) and flow cytometry are given.