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Analytical routines for a comprehensive in-depth morphological, structural, and chemical characterization of functionalized TiO2 films by using different state-of-the-art analytical techniques are presented and discussed with the main objective to identify potential reference TiO2 coating parameters able to be certified at a later stage. TiO2 films fabricated by two different synthetic procedures as representative for two main large-scale applications were selected: (i) pulsed d.c. magnetron sputtering for photocatalytic applications and (ii) screen printing from preformed anatase nanoparticles. The screen-printed films were further loaded with a sensitizing dye for application as a dye-sensitized solar cell. Film properties such as microstructure and crystallographic texture of pulsed d.c. magnetron sputtering synthesized films were systematically studied by means of scanning nanobeam electron diffraction in a transmission electron microscope and the surface and inner morphology by scanning electron microscopy. The dye distribution over the depth of screen-printed TiO2 layers was analyzed before and after dye-loading by means of energy dispersive X-ray spectroscopy at scanning electronmicroscope, Auger electron spectroscopy and time-of-flight secondary ion mass spectrometry. The long-term goal of the present study is the improvement of quality of the TiO2 film parameters as measured by using different types of reference TiO2 coatings having specific parameters certified.
The introduction of carbon nanotubes (CNTs) modifies bulk polymer properties, depending on intrinsic quality, dispersion, alignment, interfacial chemistry and mechanical properties of the nanofiller. These effects can be exploited to enhance the matrices of conventional microscale fibre-reinforced polymer composites, by using primary reinforcing fibres grafted with CNTs. This paper presents a methodology that combines atomic force microscopy, polarised Raman spectroscopy, and nanoindentation techniques, to study the distribution, alignment and orientation of CNTs in the vicinity of epoxy-embedded micrometre-scale silica fibres, as well as, the resulting local mechanical properties of the matrix. Raman maps of key features in the CNT spectra clearly show the CNT distribution and orientation, including a parted morphology associated with long grafted CNTs. The hardness and indentation modulus of the epoxy matrix were improved locally by 28% and 24%, respectively, due to the reinforcing effects of CNTs. Moreover, a slower stress relaxation was observed in the epoxy region containing CNTs, which may be due to restricted molecular mobility of the matrix. The proposed methodology is likely to be relevant to further studies of nanocomposites and hierarchical composites.
New possibilities for concrete analysis 4.0 with the Laser-Induced Breakdown Spectroscopy (LIBS)
(2020)
In civil engineering the damage assessment of concrete infrastructures is an important task to monitor and ensure the estimated life-time. The aging of concrete is caused by different damage processes like the chloride induced pitting corrosion of the reinforcement. The penetration depth and the concentration of harmful species are crucial factors in the damage assessment. As a highly cost and time-consuming standard procedure, the analysis of concrete drill cores or drilling by wet-chemistry is widely used. This method provides element concentration to the total mass as aggregates and binder are homogenized. In order to provide a method that is capable to detect the element concentration regarding the cement content only, the laser-induced breakdown spectroscopy (LIBS) will be presented. The LIBS method uses a focused pulsed laser on the sample surface to ablate material. The high-power density and the laser-material interaction causes a laser-induced plasma that emits elemental and molecular line emission due to energy transition of the excited species in the plasma during the cooling phase.
As each element provides element-specific line emission, it is in principle possible to detect any element on the periodic table (spectroscopic fingerprint) with one laser shot. In combination with a translation stage the sample under investigation can be spatially resolved using a scan raster with a resolution up to 100 µm (element mapping). Due to the high spatial resolution, the element distribution and the heterogeneity of the concrete can be evaluated. By using chemometrics the non-relevant aggregates can be excluded from the data set and the element concentration can be quantified and referred to a specific solid phase like the binding matrix (cement) only. In order to analyze transport processes like diffusion and migration the twodimensional element distributions can provide deep insight into the transport through the pore space and local enrichments of elements. As LIBS is a multi-elemental method it is also possible to compare the ingress and transport process of different elements like Cl, Na, K, S, C, and Li simultaneously and evaluate cross-correlations between the different ions. Furthermore, the element mapping allows to visualize the transport along cracks. This work will show the state of the art in terms of hardware and software for an automated LIBS system as well as different application for a concrete analysis 4.0. Focus will be the application of LIBS for a fast concrete analysis.
For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback-Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.
For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback-Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.
For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback-Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.
For mobile robots that operate in complex, uncontrolled environments, estimating air flow models can be of great importance. Aerial robots use air flow models to plan optimal navigation paths and to avoid turbulence-ridden areas. Search and rescue platforms use air flow models to infer the location of gas leaks. Environmental monitoring robots enrich pollution distribution maps by integrating the information conveyed by an air flow model. In this paper, we present an air flow modelling algorithm that uses wind data collected at a sparse number of locations to estimate joint probability distributions over wind speed and direction at given query locations. The algorithm uses a novel extrapolation approach that models the air flow as a linear combination of laminar and turbulent components. We evaluated the prediction capabilities of our algorithm with data collected with an aerial robot during several exploration runs. The results show that our algorithm has a high degree of stability with respect to parameter selection while outperforming conventional extrapolation approaches. In addition, we applied our proposed approach in an industrial application, where the characterization of a ventilation system is supported by a ground mobile robot. We compared multiple air flow maps recorded over several months by estimating stability maps using the Kullback-Leibler divergence between the distributions. The results show that, despite local differences, similar air flow patterns prevail over time. Moreover, we corroborated the validity of our results with knowledge from human experts.
Modifying or controlling surface chemistry is important in new product development, quality control and research. This is particularly true where functionality of surfaces, thin films and interfaces are key to the application, such as organic solar cells and devices for medical diagnostics. Surface chemical analysis aims to provide quantitative elemental, chemical state and functional group information from the surface of materials, but requires comparable test data and improved measurement traceability.