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Understanding the chemistry and nature of individual chemical bonds is essential for materials design. Bonding analysis via the LOBSTER software package has provided valuable insights into the properties of materials for thermoelectric and catalysis applications. Thus, the data generated from
bonding analysis becomes an invaluable asset that could be utilized as features in large-scale data analysis and machine learning of material properties. However, no systematic studies exist that conducted high-throughput materials simulations to curate and validate bonding data obtained from LOBSTER. Here we present an approach to constructing such a large database consisting of quantum-chemical bonding information.
A deep insight into the chemistry and nature of individual chemical bonds is essential for understanding materials. Bonding analysis is expected to provide important features for large-scale data analysis and machine learning of material properties. Such information on chemical bonds can be calculated using the LOBSTER (www.cohp.de) software package, which post-processes data from modern density functional theory computations by projecting plane wave-based wave functions onto a local atomic orbital basis. We have performed bonding analysis on 1520 compounds (insulators and semiconductors) using a fully automated workflow combining the VASP and LOBSTER software packages. We then automatically evaluated the data with LobsterPy (https://github.com/jageo/lobsterpy) and provide results as a database. The projected densities of states and bonding indicators are benchmarked on VASP projections and available heuristics, respectively. Lastly, we illustrate the predictive power of bonding descriptors by constructing a machine-learning model for phononic properties, which shows an increase in prediction accuracies by 27 % (mean absolute errors) compared to a benchmark model differing only by not relying on any quantum-chemical bonding features.
The presentation describes a novel approach to dynamically adjusting the weaving motion of the electrode in narrow gap GMAW.
An event driven arc sensor is used to dynamically adjust the weaving angle to variations in gap width by detecting each groove sidewall independently and in real-time. The approach presented requires only minimal user configuration for spray-arc or pulsed-arc transfer modes and can effectively be used in double- and single-sided weaving applications. Furthermore displacements of the welding torch with regards to the groove center line or contact-tip to workpiece distance are compensated.
Turbine blades for gas turbines are exposed to extreme working conditions in a demanding environment. In-service inspection, maintenance and refurbishment of the heavily stressed parts is necessary to ensure both safety and efficiency, e.g. based on immersion ultrasound testing (UT).
In the course of NDE 4.0, the European project MRO 2.0 aims to innovate the maintenance, repair and overhaul of turbine blades by linking these with modern digital methods. For this, the goal of this project is to go beyond conventional automated and manual UT testing techniques.
The aim is to measure the actual geometry and wall thickness of the complex shaped parts by applying an adaptive TFM that takes into account the refraction of the ultrasonic waves at the transition from the coupling material (water) to the inspected part (steel). In this setup the phased array probe is held by a robotic arm that allows the part to be scanned while remaining mainly perpendicular to the inspected surface. In this way, even complex geometries can be inspected and a 3D model of the actual condition of the part can be created.
The laboratory setup is equipped with a Vantage 64 phased array instrument from Verasonics Inc. and an industrial robot from ABB. A 64 element linear array probe operating at 10 MHz is attached to the robot.
The focus is on optimizing resolution, reliability and inspection speed, as the reconstructed model will be fed to the digital twin at a later stage of the project and used for targeted repairs. In addition to enhancing the reconstruction algorithms, required probe geometry and the parameters needed to inspect turbine blades with partially thin walls and anisotropic materials will also be investigated.
This talk will describe the 3-year project and present the results of the first year. The main focus will be on the development of the reconstruction algorithms used and the experimental setup.
The improvement of immunoanalytical methods for the determination of pharmaceuticals in wastewaters is a crucial yet challenging endeavor. In this work, the development of an automated miniaturized ELISA based on micro-Bead Injection Spectroscopy (μ-BIS) [1] for the determination of carbamazepine, a widely employed anti-epileptic drug and emergent pollutant [2], was pursued.
The experimental workflow comprised the offline functionalization of Sepharose beads with specific anti-CBZ antibodies via affinity immobilization using protein G, and 3 online steps inside the microfluidic analyzer lab-on-valve (LOV): I) packing of the bead column into the detection unit; II) sequential percolation of sample and a CBZ competitor- labeled with horseradish peroxidase (tracer) through the bead column; and III) on-column colorimetric detection employing the enzyme substrate 3,3’,5,5’-tetramethylbenzidine. After each analysis, the bead column was discarded, and the flow cell was washed before receiving new beads.
The elimination of manual washing steps is a novel feature compared to batch-wise ELISA, making the method less error-prone and therefore more robust. The replacement of the solid support prevents memory effects and cross-contamination between runs. The use of microparticles as solid support for the molecular recognition elements accounts for high area-to-volume ratios, and low molecular diffusion distances. For that reason, time-to-result was reduced from several hours to less than 10 min. The consumption of reagents was also very low. For instance, only ca. 200 μg of solid support and 900 ng of anti-CBZ antibody were required per determination. At last, the versatility of the LOV platform offers the possibility of adapting the assay to other relevant pharmaceuticals and anthropogenic markers in water.
Acknowledgements: Inês I. Ramos thanks FCT (Fundação para a Ciência e a Tecnologia) and POPH (Programa Operacional Potencial Humano) for her grant (SFRH/BD/97540/2013). This work received financial support from the European Union (FEDER funds POCI/01/0145/FEDER/007265) and National Funds (FCT/MEC - Ministério da Educação e Ciência) under the Partnership Agreement PT2020 UID/QUI/50006/2013. Financial support from Deutscher Akademischer Austauschdienst and from Fundação das Universidades Portuguesas under the protocol CRUP-DAAD (Ações Integradas Luso-Alemãs nºE-20/16) is also acknowledged.
[1] Gutzman, Y.; Carrol, A. D. Analyst 2006, 131, 809.
[2] Murray, K. E.; Thomas, S. M.; Bodour, A. A. Environ. Pollut. 2010, 158, 3462.
We created a workflow that fully automates bonding analysis using Crystal Orbital Hamilton Populations, which are bond-weighted densities of states. This enables understanding of crystalline material properties based on chemical bonding information. To facilitate data analysis and machine-learning research, our tools include automatic plots, automated text output, and output in machine-readable format.
The overall interest in nanotoxicity, triggered by the increasing use of nanomaterials in the material and life sciences, and the synthesis of an ever increasing number of new functional nanoparticles calls for standardized test procedures1,2 and for efficient approaches to screen the potential genotoxicity of these materials. Aiming at the development of fast and easy to use, automated microscopic methods for the determination of the genotoxicity of different types of nanoparticles, we assess the potential of the fluorometric γH2AX assay for this purpose. This assay, which can be run on an automated microscopic detection system, relies on the detection of DNA double strand breaks as a sign for genotoxicity3. Here, we provide first results obtained with broadly used nanomaterials like CdSe/CdS and InP/ZnS quantum dots as well as iron oxide, gold, and polymer particles of different surface chemistry with previously tested colloidal stability and different cell lines like Hep-2 and 8E11 cells, which reveal a dependence of the genotoxicity on the chemical composition as well as the surface chemistry of these nanomaterials. These studies will be also used to establish nanomaterials as positive and negative genotoxicity controls or standards for assay performance validation for users of this fluorometric genotoxicity assay. In the future, after proper validation, this microscopic platform technology will be expanded to other typical toxicity assays.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, other recent workflow contributions to the Materials Project software infrastructure (pymatgen, atomate2) related to phonons and machine-learning potentials will be discussed.