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Paper des Monats
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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.
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.
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.
The chemical composition of surfaces functionalized with self-assembled monolayers (SAMs) is an important parameter that determines their performance in a broad range of applications, from immobilizing molecular machines to initiation and growth control of MOFs (Metal-Organic Frameworks). In this article, a critical Survey of XPS (X-ray photoelectron spectroscopy) and NEXAFS (near edge X-ray absorption fine structure) spectroscopy data for pyridine-functionalized monolayers on gold surfaces is presented to compile correlations that have been indiscernible before. Monolayers with aromatic backbones are compared to monolayers with aliphatic backbones.
Monolayers with pyridine end-groups are compared to mixed monolayers formed terpyridine-functionalized by molecules end-groups and non-functionalized molecules. Thiol-oxidation during ageing of the SAMs in air is addressed. And finally, the addressability of the SAMs for the deposition of metal-ions and organic molecules is investigated and compared. This work consequently delivers a comprehensive set of spectroscopic data of (ter-)pyridine-terminated SAMs and their performance as template for the preparation of functional multilayers of macrocycles exhibiting a preferential orientation.
Biomedical applications, including functional biomaterials, carbohydrate-arrays, and glycan-based biosensors.
The chemistry of glycan immobilization plays an essential role in the bioavailability and function of surface bound carbohydrate moieties. For biomedical applications the stability over time (shelf life) of glycan arrays is a crucial factor. Herein we report on approaches for surface and interface characterization relevant to the needs of production of glycan microarrays which were tested using model carbohydrate surfaces. For detailed characterization of glycan model surfaces we used a combination of X-ray photoelectron spectroscopy (XPS), near edge X-ray absorption fine structure spectroscopy (NEXAFS) and ToF SIMS which are complementary techniques of surface chemical analysis. Links to fluorescence spectroscopy often used for characterization in the microarray community were established as well. In detail, amine-reactive silicon oxide and glass surfaces were used for anchoring oligosaccharides with an amino linker. The amount of surface bound carbohydrates was estimated by X-ray photoelectron spectroscopy (XPS). Glycan immobilization was investigated using lectins, which are glycan-binding molecules. A shelf life study of model glycan microarrays on epoxy-coated glass surfaces was done over a period of 160 days under different storage conditions utilizing fluorescence, ToF-SIMS and XPS analysis. It was shown that glycan activity of the models used can be maintained at least for half a year of storage at 4 °C.
Low temperature functionalization of two-dimensional boron nitride for electrochemical sensing
(2019)
Two-dimensional hexagonal boron nitride(h-BN)as an emerging nanomaterial exhibits uniquephysicochemical properties, making it suitable candidate for a wide spectrum of applications.However, due to its poor functionality, the processability of this nanomaterial is low. In this work, wereport on a straightforward and scalable approach for the functionalization of h-BN by nitrene[2+1]cycloaddition at room temperature. The triazine-functionalized h-BN(Trz-BNs)showed ahigh reactivity toward nucleophiles, through which post-modifications are performable. The post-modification of Trz-BNs by L-cysteine was studied using cyclic voltammetry and differential pulsevoltammetry. Taking advantage of the scalable and straightforward functionalization as well as abilityof triazine functional groups for the controlled post-modifications, Trz-BNs is a promisingnanoplatform for a wide range of future applications.
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.
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.