Strategies to reduce photobleaching, dark state transitions and phototoxicity in subdiffraction optical microscopy

In all subdiffraction fluorescence microscopy techniques, the theoretically infinite attainable resolution is, in practice, limited by the photobleaching of fluorophores. Repetitive scans of the sample required for e.g. three dimensional recordings, increase photobleaching, dark state transitions and, in case of living cells, phototoxicity. To advance such experiments all possibilities to reduce the photobleaching must be explored. In this thesis, various chemical and physical approaches to tackle photobleaching are studied within the context of stimulated emission depletion (STED) microscopy, which is the first and most prominent method for subdiffraction imaging. The STED setup constructed for this purpose allows for the fast adaptation to new fluorescent dyes, and relies on a novel adaptive spectral and phase filter technique. Furthermore, the optical setup facilitates a gentle exposure strategy, in which the time that the dye is irradiated is significantly reduced. Three-dimensional images can therefore be recorded, and the palette of applicable dyes can be expanded to the blue-green regime by the so far unemployed coumarin derivatives, which are known to be prone to photobleaching. The label itself is another vantage point from which photobleaching limitations in subdiffraction microscopy can be circumvented. For the first time, light-driven modulation of the fluorescence from Mn-doped ZnSe quantum nanocrystals has been established through excited-state absorption (ESA). This enables a new type of far-field fluorescence microscopy with diffraction-unlimited resolution based on quantum dots, which are well known for their superior photostability. The correct sample embedding in the refractive index matching is also of high importance, if spherical aberrations and light scattering are to be minimized to optimize the fluorescence collection. For this purpose, an embedding medium, 2,2´-thiodiethanol (TDE) is introduced, which, by being miscible with water at any ratio, allows for refractive index matching up to that of immersion oil and making high resolution recordings deep within the sample feasible

Microstructural Classification of Bainitic Subclasses in Low-Carbon Multi-Phase Steels Using Machine Learning Techniques

With its excellent property combinations and ability to specifically adjust tailor-made microstructures, steel is still the world’s most important engineering and construction material. To fulfill ever-increasing demands and tighter tolerances in today’s steel industry, steel research remains indispensable. The continuous material development leads to more and more complex microstruc tures, which is especially true for steel designs that include bainitic structures. This poses new challenges for the classification and quantification of these microstructures. Machine learning (ML) based microstructure classification offers exciting potentials in this context. This paper is concerned with the automated, objective, and reproducible classification of the carbon-rich second phase objects in multi-phase steels by using machine learning techniques. For successful applications of ML-based classifications, a holistic approach combining computer science expertise and material science domain knowledge is necessary. Seven microstructure classes are considered: pearlite, martensite, and the bainitic subclasses degenerate pearlite, debris of cementite, incomplete transformation product, and upper and lower bainite, which can all be present simultaneously in one micrograph. Based on SEM images, textural features (Haralick parameters and local binary pattern) and morphological parame ters are calculated and classified with a support vector machine. Of all second phase objects, 82.9% are classified correctly. Regarding the total area of these objects, 89.2% are classified correctly. The reported classification can be the basis for an improved, sophisticated microstructure quantification, enabling process–microstructure–property correlations to be established and thereby forming the backbone of further, microstructure-centered material development

Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques

Bainite is an essential constituent of modern high strength steels. In addition to the still great challenge of characterization, the classification of bainite poses difficulties. Challenges when dealing with bainite are the variety and amount of involved phases, the fineness and complexity of the structures and that there is often no consensus among human experts in labeling and classifying those. Therefore, an objective and reproducible characterization and classification is crucial. To achieve this, it is necessary to analyze the substructure of bainite using scanning electron microscope (SEM). This work will present how textural parameters (Haralick features and local binary pattern) calculated from SEM images, taken from specifically produced benchmark samples with defined structures, can be used to distinguish different bainitic microstructures by using machine learning techniques (support vector machine). For the classification task of distinguishing pearlite, granular, degenerate upper, upper and lower bainite as well as martensite a classification accuracy of 91.80% was achieved, by combining Haralick features and local binary pattern

Reproducible Quantification of the Microstructure of Complex Quenched and Quenched and Tempered Steels Using Modern Methods of Machine Learning

Current conventional methods of evaluating microstructures are characterized by a high degree of subjectivity and a lack of reproducibility. Modern machine learning (ML) approaches have already shown great potential in overcoming these challenges. Once trained with representative data in combination with objective ground truth, the ML model is able to perform a task properly in a reproducible and automated manner. However, in highly complex use cases, it is often not possible to create a definite ground truth. This study addresses this problem using the underlying showcase of microstructures of highly complex quenched and quenched and tempered (Q/QT) steels. A patch-wise classification approach combined with a sliding window technique provides a solution for segmenting entire microphotographs where pixel-wise segmentation is not applicable since it is hardly feasible to create reproducible training masks. Using correlative microscopy, consisting of light optical microscope (LOM) and scanning electron microscope (SEM) micrographs, as well as corresponding data from electron backscatter diffraction (EBSD), a training dataset of reference states that covers a wide range of microstructures was acquired in order to train accurate and robust ML models in order to classify LOM or SEM images. Despite the enormous complexity associated with the steels treated here, classification accuracies of 88.8% in the case of LOM images and 93.7% for high-resolution SEM images were achieved. These high accuracies are close to super-human performance, especially in consideration of the reproducibility of the automated ML approaches compared to conventional methods based on subjective evaluations through experts

Colloidal Analysis of Particles Extracted from Microalloyed Steels

Different colloidal particle characterization methods are examined for their suitability to determine the particle size distribution of particles extracted from steels. Microalloyed steels are dissolved to extract niobium and titanium carbonitride particles that are important for the mechanical properties of these steels. Such particles have sizes ranging from several nanometers to hundreds of nanometers depending on the precipitation stage during the thermomechanically controlled rolling process. The size distribution of the particles is analyzed by dynamic light scattering (DLS), analytical ultracentrifugation (AUC), and hollow fiber flow field-flow fractionation (HF5) and compared to data obtained for reference particles as well as data from electron microscopy, the standard sizing technique used in metallurgy today. AUC and HF5 provide high-quality size distributions, average over large particle numbers that enables statistical analysis, and yield useful insights for alloy design; however, DLS fails due to a lack of resolution. Important aspects in the conversion and comparison of size distributions obtained for broadly distributed particle systems with different measurement principles and the role of surfactants used in sample preparation are discussed

Numerical simulation of dual-phase steel based on real and virtual three-dimensional microstructures

Dual-phase steel shows a strong connection between its microstructure and its mechanical properties. This structure–property correlation is caused by the composition of the microstructure of a soft ferritic matrix with embedded hard martensite areas, leading to a simultaneous increase in strength and ductility. As a result, dual-phase steels are widely used especially for strength-relevant and energy-absorbing sheet metal structures. However, their use as heavy plate steel is also desirable. Therefore, a better understanding of the structure–property correlation is of great interest. Microstructure-based simulation is essential for a realistic simulation of the mechanical properties of dual-phase steel. This paper describes the entire process route of such a simulation, from the extraction of the microstructure by 3D tomography and the determination of the properties of the individual phases by nanoindentation, to the implementation of a simulation model and its validation by experiments. In addition to simulations based on real microstructures, simulations based on virtual microstructures are also of great importance. Thus, a model for the generation of virtual microstructures is presented, allowing for the same statistical properties as real microstructures. With the help of these structures and the aforementioned simulation model, it is then possible to predict the mechanical properties of a dual-phase steel, whose three-dimensional (3D) microstructure is not yet known with high accuracy. This will enable future investigations of new dual-phase steel microstructures within a virtual laboratory even before their production

RVE-size Estimation and Efficient Microstructure-based Simulation of Dual-Phase Steel

Dual-phase steel shows a pronounced structure-property correlation, caused by its internal structure consisting of asoft ferrite matrix and embedded hard martensite regions. Due to its high strength combined with high ductility, dual-phasesteel is particularly suitable for energy-absorbing and strength-relevant sheet metal applications, but its use as heavy plate isalso desirable. Due to the complex microstructure, microstructure-based simulation is essential for a realistic simulation of themechanical properties of dual-phase steel. This paper describes two important points for the microstructure-based simulation ofdual-phase steel. First a method for the straightforward experimental estimation of the RVE size based on hardness measurementsprior to tomography preparation is presented and evaluated. Secondly, a method for the efficient meshing of these microstructures,based on material definition at the integration points of a finite element model, is developed

On the interaction of Mg with the (111) and (110) surfaces of ceria

The catalytic activity of cerium dioxide can be modified by deposition of alkaline earth oxide layers or nanoparticles or by substitutional doping of metal cations at the Ce site in ceria. In order to understand the effect of Mg oxide deposition and doping, a combination of experiment and first principles simulations is a powerful tool. In this paper, we examine the interaction of Mg with the ceria (111) surface using both angle resolved X-ray (ARXPS) and resonant (RPES) photoelectron spectroscopy measurements and density functional theory (DFT) corrected for on-site Coulomb interactions (DFT + U). With DFT + U, we also examine the interaction of Mg with the ceria (110) surface. The experiments show that upon deposition of Mg, Ce ions are reduced to Ce3+, while Mg is oxidised. When Mg is incorporated into ceria, no reduced Ce3+ ions are found and oxygen vacancies are present. The DFT + U simulations show that each Mg that is introduced leads to formation of two reduced Ce3+ ions. When Mg is incorporated at a Ce site in the (111) surface, one oxygen vacancy is formed for each Mg to compensate the different valencies, so that all Ce ions are oxidised. The behaviour of Mg upon interaction with the (110) surface is the same as with the (111) surface. The combined results provide a basis for deeper insights into the catalytic behaviour of ceria-based mixed oxide catalysts

Efficient reconstruction of prior austenite grains in steel from etched light optical micrographs using deep learning and annotations from correlative microscopy

The high-temperature austenite phase is the initial state of practically all technologically relevant hot forming and heat treatment operations in steel processing. The phenomena occurring in austenite, such as recrystallization or grain growth, can have a decisive influence on the subsequent properties of the material. After the hot forming or heat treatment process, however, the austenite transforms into other microstructural constituents and information on the prior austenite morphology are no longer directly accessible. There are established methods available for reconstructing former austenite grain boundaries via metallographic etching or electron backscatter diffraction (EBSD) which both exhibit shortcomings. While etching is often difficult to reproduce and strongly depend on the investigated steel’s alloying concept, EBSD acquisition and reconstruction is rather time-consuming. But in fact, though, light optical micrographs of steels contrasted with conventional Nital etchant also contain information about the former austenite grains. However, relevant features are not directly apparent or accessible with conventional segmentation approaches. This work presents a deep learning (DL) segmentation of prior austenite grains (PAG) from Nital etched light optical micrographs. The basis for successful segmentation is a correlative characterization from EBSD, light and scanning electron microscopy to specify the ground truth required for supervised learning. The DL model shows good and robust segmentation results. While the intersection over union of 70% does not fully reflect the model performance due to the inherent uncertainty in PAG estimation, a mean error of 6.1% in mean grain size derived from the segmentation clearly shows the high quality of the result

Fertility education for adolescent cancer patients: Gaps in current clinical practice in Europe

Objective: As adolescent cancer patients may suffer from infertility following treatment, fertility counselling is essential. Our aim was to explore the current situation in four European countries in terms of (I) education about the risk for infertility, (II) counselling on fertility preservation, (III) patients' knowledge on fertility, (IV) sufficiency of information and (V) uptake of cryopreservation. Methods: In total, 113 patients (13–20 years) at 11 study centres completed a self-report questionnaire three and six months after cancer diagnosis. Multivariate logistic regression was used to estimate odds ratios (OR) with 95% confidence intervals (CI). Results: As many as 80.2% of participants reported having received education about the risk for infertility prior to treatment, 73.2% recalled counselling on fertility preservation. Only 52.3% stated they felt sufficiently informed to make a decision. Inability to recall counselling on fertility preservation (OR = 0.03, CI: 0.00–0.47) and female gender (OR = 0.11, CI: 0.03–0.48) was associated with lower use of cryopreservation, whereas older age was associated with higher use. Conclusion: Fertility counselling was available to a relatively high proportion of patients, and it did influence the utilisation of cryopreservation. However, many patients did not feel sufficiently informed. Further improvement is needed to enable adolescent cancer patients to make an informed decision on fertility preservation