Filtern
Dokumenttyp
- Zeitschriftenartikel (10)
Sprache
- Englisch (10)
Referierte Publikation
- ja (10) (entfernen)
Schlagworte
Organisationseinheit der BAM
We demonstrate the use of a 3D printed radial collimator in X-ray powder diffraction and surface sensitive grazing incidence X-ray diffraction. We find a significant improvement in the overall Signal to background ratio of up to 100 and a suppression of more than a factor 3⋅10⁵ for undesirable Bragg reflections generated by the X-ray “transparent” windows of the sample environment.
The background reduction and the removal of the high intensity signals from the windows, which limit the detector’s dynamic range, enable significantly higher sensitivity in experiments within sample environments such as vacuum chambers and gas- or liquid-cells. Details of the additively manufactured steel collimator geometry, alignment strategies using X-ray fluorescence, and data analysis are also briefly discussed. The flexibility and affordability of 3D prints enable designs optimized for specific detectors and sample environments, without compromising the degrees of freedom of the diffractometer.
Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars, The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods, The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h, The analysis methods presented here will help to establish routines for near real-time Train tracking and train integrity analysis.
X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study Shows how a simple artificial neural network model can be used to determine the thickness, roughness and density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and alpha-sexithiophene] on silica from their XRR data with millisecond computation time and with minimal user input or a priori knowledge. For a large experimental data set of 372 XRR curves, it is shown that a simple fully connected model can provide good results with a mean absolute percentage error of 8–18% when compared with the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed.
The influence of size effects on the thermoelectric properties of thin platinum films is investigated and compared to the bulk. Structural properties, like the film thickness and the grain size, are varied. We correlate the electron mean free path with the temperature dependence of the electrical conductivity and the absolute Seebeck coefficient SPt of platinum. A measurement platform was developed as a standardized method to determine SPt and show that SPt,film is reduced compared to SPt,bulk. Boundary and surface scattering reduce the thermodiffusion and the phonon drag contribution to SPt,film by nearly the same factor. We discuss in detail on behalf of a model, which describes the temperature dependence of the absolute Seebeck coefficient, the influence of size effects of electron-phonon and phonon-phonon interaction on SPt.
We propose to use artificial neural networks (ANNs) for raw measurement data interpolation and signal shift computation and to demonstrate advantages for wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) and dynamic strain distribution measurement along optical fibers. The ANNs are trained with synthetic data to predict signal shifts from wavelength scans. Domain adaptation to measurement data is achieved, and standard correlation algorithms are outperformed. First and foremost, the ANN reduces the data analysis time by more than two orders of magnitude, making it possible for the first time to predict strain in real-time applications using the WS-COTDR approach. Further, strain noise and linearity of the sensor response are improved, resulting in more accurate measurements. ANNs also perform better for low signal-to-noise measurement data, for a reduced length of correlation input (i.e., extended distance range), and for coarser sampling settings (i.e., extended strain scanning range). The general applicability is demonstrated for distributed measurement of ground movement along a dark fiber in a telecom cable. The presented ANN-based techniques can be employed to improve the performance of a wide range of correlation or interpolation problems in fiber sensing data analysis and beyond.
Multiple timescales in the photoswitching kinetics of crystalline thin films of azobenzene-trimers
(2017)
Functional materials that exhibit photoinduced structural phase transitions are highly interesting for applications in optomechanics and mechanochemistry. It is, however, still not fully understood how photochemical reactions, which are often accompanied by molecular motion, proceed in confined and crystalline environments. Here we show that thin films of azobenzene trimers exhibit high structural order and determine the crystallographic unit cell. We demonstrate that thin film can be switched partially reversibly between a crystalline and an amorphous phase. The time constant of the photoinduced amorphisation as measured with real-time x-ray diffraction ($\approx $ 220 s) lies between the two time constants (120 s and 2870 s) of the ensemble photoisomerisation processes that are measured via optical spectroscopy. Our observation of a photoinduced shrinking of the crystalline domains indicates a cascading process, in which photoisomerisation starts at the surface of the thin film and propagates deeper into the crystalline layer by introducing disorder and generating free volume. This finding is important for the rapidly evolving research field of photoresponsive thin films and smart crystalline materials in general.
We present a combined experimental and theoretical study to solve the unit-cell and molecular arrangement of the tetracene thin film (TF) phase. TF phases, also known as substrate induced phases (SIP), are polymorphs that exist at interfaces and decisively impact the functionality of organic thin films, e.g., in a transistor channel, but also change the optical spectra due to the different molecular packing. As SIPs only exist in textured ultrathin films, their structure determination remains challenging compared to bulk materials. Here, we use grazing incidence Xray diffraction and atomistic simulations to extract the TF unit-cell parameters of tetracene together with the atomic positions within the unit-cell.
In this work, two novel donor/acceptor (D/A) complexes, namely, diindenoperylene (DIP)/1,3,4,5,7,8-hexafluoro-tetracyanonaphthoquinodimethane (F6TCNNQ) and alpha-sexithiophene (6T)/F6TCNNQ, are studied. The D/A complexes segregate in form of π−π stacked D/A cocrystals and can be observed by X-ray scattering. The different conformational degrees of freedom of the donor molecules, respectively, seem to affect the thin-film crystalline texture and composition of the D/A mixtures significantly. In equimolar mixtures, for DIP/F6TCNNQ, the crystallites are mostly uniaxially oriented and homogeneous, whereas for 6T/F6TCNNQ, a mostly 3D (isotropic) orientation of the crystallites and coexistence of domains of pristine compounds and D/A complex, respectively, are observed. Using optical absorption spectroscopy, we observe for each of the two mixed systems a set of new, strong transitions located in the near-IR range below the gap of the pristine compounds: such transitions are related to charge-transfer (CT) interactions between donor and acceptor. The optical anisotropy of domains of the D/A complexes with associated new electronic states is studied by ellipsometry. We infer that the
CT-related transition dipole moment is perpendicular to the respective π-conjugated planes in the D/A complex.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.