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Accurate and reproducible measurement of the structure and properties of high-value nanoparticles is extremely important for their commercialization. A significant proportion of engineered nanoparticle systems consist of some form of nominally core-shell structure, whether by design or unintentionally. Often, these do not form an ideal core-shell structure, with typical deviations including polydispersity of the core or shell, uneven or incomplete shells, noncentral cores, and others. Such systems may be created with or without intent, and in either case an understanding of the conditions for formation of such particles is desirable. Precise determination of the structure, composition, size, and shell thickness of such particles can prove challenging without the use of a suitable range of characterization techniques. Here, the authors present two such polymer core-shell nanoparticle systems, consisting of polytetrafluoroethylene cores coated with a range of thicknesses of either polymethylmethacrylate or polystyrene. By consideration of surface energy, it is shown that these particles are expected to possess distinctly differing coating structures, with the polystyrene coating being incomplete. A comprehensive characterization of these systems is demonstrated, using a selection of complementary techniques including scanning electron microscopy, scanning transmission electron microscopy, thermogravimetric analysis, dynamic light scattering, differential centrifugal sedimentation, and X-ray photoelectron spectroscopy. By combining the results provided by these techniques, it is possible to achieve superior characterization and understanding of the particle structure than could be obtained by considering results separately.
Imaging and small-spot (small area) XPS have become increasingly important components of surface chemical analysis during the last three decades, and its use is growing. Some ambiguity in the use of terminology, understanding of concepts, and lack of appropriate reference materials leads to confusing and not always reproducible data. In this paper, it is shown that by using existing knowledge, appropriate test specimens, and standardized approaches, problems of comparability and such reproducibility issues recently observed for XPS data reported in the scientific literature can be overcome. The standardized methods of ISO 18516:2019, (i) the straight-edge, (ii) the narrow-line, and (iii) the grating method, can be used to characterize and compare the lateral resolution achieved by imaging XPS instruments and are described by reporting examples. The respective measurements are made using new test specimens. When running an XPS instrument in the small-spot (small area) mode for a quantitative analysis of a feature of interest, the question arises as to what contribution to the intensity originates from outside the analysis area. A valid measurement approach to control the intensity from outside the nominal analysis area is also described. As always, the relevant resolution depends on the specific question that needs to be addressed. The strengths and limitations of methods defining resolution are indicated.
The fluorolytic sol–gel synthesis is applied with the intention to obtain two different types of core–shell nanoparticles, namely, SrF2–CaF2 and CaF2–SrF2. In two separate fluorination steps for core and shell formation, the corresponding metal lactates are reacted with anhydrous HF in ethylene glycol. Scanning transmission electron microscopy (STEM) and dynamic light scattering (DLS) confirm the formation of particles with mean dimensions between 6.4 and 11.5 nm. The overall chemical composition of the particles during the different reaction steps is monitored by quantitative Al Kα excitation X-ray photoelectron spectroscopy (XPS). Here, the formation of stoichiometric metal fluorides (MF2) is confirmed, both for the core and the final core–shell particles. Furthermore, an in-depth analysis by synchrotron radiation XPS (SR-XPS) with tunable excitation energy is performed to confirm the core–Shell character of the nanoparticles. Additionally, Ca2p/Sr3d XPS intensity ratio in-Depth profiles are simulated using the software Simulation of Electron Spectra for Surface Analysis (SESSA). In principle, core–shell like particle morphologies are formed but without a sharp interface between calcium and strontium containing phases.
Surprisingly, the in-depth chemical distribution of the two types of nanoparticles is equal within the error of the experiment. Both comprise a SrF2-rich core domain and CaF2-rich shell domain with an intermixing zone between them. Consequently, the internal morphology of the final nanoparticles seems to be independent from the synthesis chronology.
Influence of oxygen vacancies on core-shell formation in solid solutions of (Na,Bi)TiO3 and SrTiO3
(2021)
Solid solutions of (Na,Bi)TiO3 (NBT) and SrTiO3 (ST) are materials of interest for high-strain or high-energy density capacitor applications. Often, they exhibit chemical heterogeneity and develop core-shell structures during regular solid-state synthesis with an NBT-rich core. In this case, the NBT forms first so that the strontium needs to diffuse into the material to reach chemical homogeneity. Depending on the presence of core-shell structures, the electrical properties can vary drastically. In this work, we rationalize the effect of variations in oxygen vacancy concentration by Fe-acceptor and Nb-donor doping. It can be shown that a diffusion couple of strontium and oxygen is responsible for chemical homogenization and that the oxygen vacancy content can control the formation of a core-shell structure.
In this paper, the imperialist competitive optimization algorithm is improved by damage functions to detect damage in a model steel frame test structure for offshore applications. A finite element model of the test structure is developed, validated and updated using the proposed method. As there are much more design variables, which are related to the stiffness of each finite element than the measured mode shapes, the problem is underdetermined. Therefore, damage functions are used to regularize the problem and decrease the number of design variables. A new objective function is proposed for the algorithm using the mode shapes and their l1 norm. The first ten measured mode shapes are used to solve the problem. It is shown that the proposed method is capable of predicting the damage locations with acceptable accuracy.
Software-driven scientific workflows are often characterized by a complex interplay of various pieces of software executed in a particular order. The output of a computational step may serve as input to a subsequent computation, which requires them to be processed sequentially with a proper mapping of outputs to inputs. Other computations are independent of each other and can be executed in parallel. Thus, one of the main tasks of a workflow tool is a proper and efficient scheduling of the individual processing steps.
Each processing step, just as the workflow itself, typically processes some input and produces output data. Apart from changing the input data to operate on, processing steps can usually be configured by a set of parameters to change their behavior. Moreover, the behavior of a processing step is determined by its source code and/or executable binaries/packages that are called within it. Beyond this, the computation environment not only has a significant influence on its behavior, but is also crucial in order for the processing step to work at all. The environment includes the versions of the interpreters or compilers, as well as all third-party libraries and packages that contribute to the computations carried out in a processing step.
Numerical models built as virtual-twins of a real structure (digital-twins) are considered the future ofmonitoring systems. Their setup requires the estimation of unknown parameters, which are not directly measurable. Stochastic model identification is then essential, which can be computationally costly and even unfeasible when it comes to real applications. Efficient surrogate models, such as reduced-order method, can be used to overcome this limitation and provide real time model identification. Since their numerical accuracy influences the identification process, the optimal surrogate not only has to be computationally efficient, but also accurate with respect to the identified parameters. This work aims at automatically controlling the Proper Generalized Decomposition (PGD) surrogate’s numerical accuracy for parameter identification. For this purpose, a sequence of Bayesian model identification problems, in which the surrogate’s accuracy is iteratively increased, is solved with a variational Bayesian inference procedure. The effect of the numerical accuracy on the resulting posteriors probability density functions is analyzed through two metrics, the Bayes Factor (BF) and a criterion based on the Kullback-Leibler (KL) divergence. The approach is demonstrated by a simple test example and by two structural problems. The latter aims to identify spatially distributed damage, modeled with a PGD surrogate extended for log-normal random fields, in two different structures: a truss with synthetic data and a small, reinforced bridge with real measurement data. For all examples, the evolution of the KL-based and BF criteria for increased accuracy is shown and their convergence indicates when model refinement no longer affects the identification results.
In recent years, the use of simulation-based digital twins for monitoring and assessment of complex mechanical systems has greatly expanded. Their potential to increase the information obtained from limited data makes them an invaluable tool for a broad range of real-world applications. Nonetheless, there usually exists a discrepancy between the predicted response and the measurements of the system once built. One of the main contributors to this difference in addition to miscalibrated model parameters is the model error. Quantifying this socalled model bias (as well as proper values for the model parameters) is critical for the reliable performance of digital twins. Model bias identification is ultimately an inverse problem where information from measurements is used to update the original model. Bayesian formulations can tackle this task. Including the model bias as a parameter to be inferred enables the use of a Bayesian framework to obtain a probability distribution that represents the uncertainty between the measurements and the model. Simultaneously, this procedure can be combined with a classic parameter updating scheme to account for the trainable parameters in the original model.
This study evaluates the effectiveness of different model bias identification approaches based on Bayesian inference methods. This includes more classical approaches such as direct parameter estimation using MCMC in a Bayesian setup, as well as more recent proposals such as stat-FEM or orthogonal Gaussian Processes. Their potential use in digital twins, generalization capabilities, and computational cost is extensively analyzed.