Ingenieurwissenschaften und zugeordnete Tätigkeiten
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A new “green” and mild synthesis of highly stable microcrystalline Cs2AgxNa1-xBiyIn1-yCl6 (CANBIC) perovskites under ambient conditions was developed that is scalable to the multi-gram production. Under UV illumination, the CANBIC perovskites emit intense broadband photoluminescence (PL) with a quantum yield (QY) of 92% observed for x = 0.35 and y = 0.01-0.02. The combination of strong UV absorbance and broadband visible emission, high PL QY, and long PL lifetimes of up to 1.4 μs, along with an outstanding stability makes these CANBICs a promising material class for many optical applications.
Directed energy deposition (DED) has been in industrial use as a coating process for many years. Modern applications include the repair of existing components and additive manufacturing. The main advantages of DED are high deposition rates and low energy input. However, the process is influenced by a variety of parameters affecting the component quality. Artificial neural networks (ANNs) offer the possibility of mapping complex processes such as DED. They can serve as a tool for predicting optimal process parameters and quality characteristics. Previous research only refers to weld beads: a transferability to additively manufactured three-dimensional components has not been investigated. In the context of this work, an ANN is generated based on 86 weld beads. Quality categories (poor, medium, and good) are chosen as target variables to combine several quality features. The applicability of this categorization compared to conventional characteristics is discussed in detail. The ANN predicts the quality category of weld beads with an average accuracy of 81.5%. Two randomly generated parameter sets predicted as “good” by the network are then used to build tracks, coatings,walls, and cubes. It is shown that ANN trained with weld beads are suitable for complex parameter predictions in a limited way.
Current trends in materials and life sciences are flanked by the need to push detection limits to single molecules or single cells, enable the characterization of increasingly complex matrices or sophisticated nanostructures, speed up the time of analysis, reduce instrument complexity and costs, and improve the reliability of data. This requires suitable analytical tools such as spectroscopic, separation and imaging techniques, mass spectrometry, and hyphenated techniques as well as sensors and their adaptation to application-specific challenges in the environmental, food, consumer product, health sector, nanotechnology, and bioanalysis. Increasing concerns about health threatening known or emerging pollutants in drinking water, consumer products, and food and about the safety of nanomaterials led to a new awareness of the importance of analytical sciences. Another important driver in this direction is the increasing demand by legislation, particularly in view of the 17 sustainable development goals by the United Nations addressing clean energy, industry, and innovation, sustainable cities, clean water, and responsible consumption and production. In this respect, also the development of analytical methods that enable the characterization of material flows in production processes and support recycling concepts of precious raw materials becomes more and more relevant. In the future, this will provide the basis for greener production in the chemical industry utilizing recycled or sustainable starting materials.
This makes analytical chemistry an essential player in terms of the circular economy helping to increase the sustainability of production processes. In the life sciences sector, products based on proteins, such as therapeutic and diagnostic antibodies, increase in importance. These increasingly biotechnologically produced functional biomolecules pose a high level of complexity of matrix and structural features that can be met only by highly advanced methods for separation, characterization, and detection. In addition, metrological traceability and target definition are still significant challenges for the future, particularly in the life sciences.
However, innovative reference materials as required for the health and food sector and the characterization of advanced materials can only be developed when suitable analytical protocols are available. The so-called reproducibility crisis in sciences underlines the importance of improved measures of quality control for all kinds of measurements and material characterization. This calls for thorough method validation concepts, suitable reference materials, and regular interlaboratory comparisons of measurements as well as better training of scientists in analytical sciences.
The important contribution of analytical sciences to these developments is highlighted by a broad collection of research papers, trend articles, and critical reviews from these different application fields. Special emphasis is dedicated to often-overlooked quality assurance and reference materials.
The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the
quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries.
The Directed Energy Deposition process is used in a wide range of applications including the repair, coating or modification of existing structures and the additive manufacturing of individual parts. As the process is frequently applied in the aerospace industry, the requirements for quality assurance are extremely high. Therefore, more and more sensor systems are being implemented for process monitoring. To evaluate the generated data, suitable methods must be developed. A solution, in this context, was the application of artificial neural networks (ANNs). This article demonstrates how measurement data can be used as input data for ANNs. The measurement data were generated using a pyrometer, an emission spectrometer, a camera (Charge-Coupled Device) and a laser scanner. First, a concept for the extraction of relevant features from dynamic measurement data series was presented. The developed method was then applied to generate a data set for the quality prediction of various geometries, including weld beads, coatings and cubes. The results were compared to ANNs trained with process parameters such as laser power, scan speed and powder mass flow. It was shown that the use of measurement data provides additional value. Neural networks trained with measurement data achieve significantly higher prediction accuracy, especially for more complex geometries.
A series of NaYF4:Yb3+/Tm3+@NaYF4:Yb3+/Er3+ nanoparticles doped with Tm3+ and Er3+ were successfully prepared by the solvothermal method. Under 980 nm laser excitation, intense upconversion emission peaks of Tm3+ and Er3+ were observed for all samples. By doping Tm3+ and Er3+ with core–shell partitioning, not only a significant increase in fluorescence intensity could be achieved, but also simultaneous temperature measurements on multiple thermocouple energy levels could be realised. In addition, the temperature sensing performance of different thermocouple energy levels was also investigated, and it was found that the 3 F3 → 3 H6 and 1 G4 → 3 F4 thermocouple energy level pairs of Tm3+ were the best, with maximum absolute sensitivity and maximum relative sensitivity of up to 0.0250 K−1 and 2.155% K−1 respectively, higher than the sensitivity of other thermocouple energy levels. It has a temperature resolution of less than 0.0139 K, which is lower than that of most materials available today. By using this material as a probe to build a fiber optic temperature sensor platform, it was found to have reliable temperature measurement performance.
A first tricolor fluorescent pH nanosensor is presented, which was rationally designed from biocompatible carboxylated polystyrene nanoparticles and two analyte-responsive molecular fluorophores. Its fabrication involved particle staining with a blue-red-emissive dyad, consisting of a rhodamine moiety responsive to acidic pH values and a pH-inert quinoline fluorophore, followed by the covalent attachment of a fluorescein dye to the particle surface that signals neutral and basic pH values with a green fluorescence. These sensor particles change their fluorescence from blue to red and green, depending on the pH and excitation wavelength, and enable ratiometric pH measurements in the pH range of 3.0−9.0. The localization of the different sensor dyes in the particle core and at the particle surface was confirmed with fluorescence microscopy utilizing analogously prepared polystyrene microparticles. To show the application potential of these polystyrene-based multicolor sensor particles, fluorescence microscopy studies with a human A549 cell line were performed, which revealed the cellular uptake of the pH nanosensor and the differently colored emissions in different cell organelles, that is, compartments of the endosomal-lysosomal pathway. Our results demonstrate the underexplored potential of biocompatible polystyrene particles for multicolor and multianalyte sensing and bioimaging utilizing hydrophobic and/or hydrophilic stimuli-responsive luminophores.
NaYF4:Yb3+/Tm3+@NaGdF4:Nd3+/Yb3+ upconversion nanoparticles (UCNPs) were prepared using a solvothermal method, and the effects of key factors such as the content of sensitiser Nd 3+ and Yb3+ on their luminescence properties were investigated. The nanoparticles are homogeneous in size and well dispersed. Under 808 nm excitation, it can produce strong upconversion fluorescence. At the same time, the nanoparticles have good temperature-sensing properties at the thermally coupled energy levels of 700 nm and 646 nm for Tm3+. Using its fluorescence intensity ratio (FIR), accurate temperature measurements can be performed, and it has been found that it exhibits different temperature sensing properties in low and high-temperature regions. The maximum relative sensitivity was found to be 0.88% K-1 and 1.89% K-1 for the lowtemperature region of 285 K-345 K and the high-temperature region of 345 K-495 K. The nanoparticles were applied to the internal temperature measurement of lithium batteries and the actual high-temperature environment, respectively, and were found to have good temperature measurementt performance.
Current trends in materials and life sciences are flanked by the need to push detection limits to single molecules or single cells, enable the characterization of increasingly complex matrices or sophisticated nanostructures, speed up the time of analysis, reduce instrument complexity and costs, and improve the reliability of data. This requires suitable analytical tools such as spectroscopic, separation and imaging techniques, mass spectrometry, and hyphenated techniques as well as sensors and their adaptation to application-specific challenges in the environmental, food, consumer product, health sector, nanotechnology, and bioanalysis. Increasing concerns about health threatening known or emerging pollutants in drinking water, consumer products, and food and about the safety of nanomaterials led to a new awareness of the importance of analytical sciences. Another important driver in this direction is the increasing demand by legislation, particularly in view of the 17 sustainable development goals by the United Nations addressing clean energy, industry, and innovation, sustainable cities, clean water, and responsible consumption and production. In this respect, also the development of analytical methods that enable the characterization of material flows in production processes and support recycling concepts of precious raw materials becomes more and more relevant. In the future, this will provide the basis for greener production in the chemical industry utilizing recycled or sustainable starting materials.
This makes analytical chemistry an essential player in terms of the circular economy helping to increase the sustainability of production processes. In the life sciences sector, products based on proteins, such as therapeutic and diagnostic antibodies, increase in importance. These increasingly biotechnologically produced functional biomolecules pose a high level of complexity of matrix and structural features that can be met only by highly advanced methods for separation, characterization, and detection. In addition, metrological traceability and target definition are still significant challenges for the future, particularly in the life sciences.
However, innovative reference materials as required for the health and food sector and the characterization of advanced materials can only be developed when suitable analytical protocols are available. The so-called reproducibility crisis in sciences underlines the importance of improved measures of quality control for all kinds of measurements and material characterization. This calls for thorough method validation concepts, suitable reference materials, and regular interlaboratory comparisons of measurements as well as better training of scientists in analytical sciences.
The important contribution of analytical sciences to these developments is highlighted by a broad collection of research papers, trend articles, and critical reviews from these different application fields. Special emphasis is dedicated to often-overlooked quality assurance and reference materials.
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.