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Paper des Monats
- ja (72)
Quantum dots (QDs) possess unique optoelectronic properties, which make them very attractive to be used as optical probes in biosensing and bioimaging applications. The strong absorbance and light scattering of biological compounds like tissue and blood in the visible range pose a problem. However, if optical probes emitting in the near-infrared (NIR) range are used, scattering, absorption, and autofluorescence of biological components are strongly reduced. This allows for an increased light penetration depth and higher spatial and temporal resolution for the investigation of biological processes. The synthesis and application of NIR emitting QDs is a fast-growing research field and the benefits of using QDs were demonstrated for a variety of applications, such as photoelectrochemical biosensor, in vivo vascular imaging, and fluorescence-guided surgery. This article reviews the state-of-the-art developments in the preparation of NIR/IR QDs and highlights the latest research about their utilization in biosensing and bioimaging applications.
In addition to sensitivity, selectivity, and portability, chemical sensing systems must generate reliable signals and offer modular configurability to address various small molecule targets, particularly in environmental applications. We present a versatile, modular strategy utilizing ratiometric molecularly imprinted particle probes based on BODIPY indicators and dyes for recognition and internal referencing. Our approach employs polystyrene core particles doped with a red fluorescent BODIPY as an internal standard, providing built-in reference for environmental influences. A molecularly imprinted polymer (MIP) recognition shell, incorporating a green-fluorescent BODIPY indicator monomer with a thiourea binding site for carboxylate containing analytes, is grafted from the core particles in the presence of the analyte as the template. The dual-fluorescent MIP probe detects fexofenadine as the model analyte with a change in green emission signal referenced against a stable red signal, achieving a detection limit of 0.13 μM and a broad dynamic range from 0.16 μM to 1.2 mM, with good discrimination against other antibiotics in acetonitrile. By selecting a versatile dye scaffold and recognition element, this approach can be extended to other carboxylate-containing analytes and/or wavelength combinations, potentially serving as a robust multiplexing platform.
Performance Prediction of Anchors in SFRC using Minimally Invasive and Non‐Destructive Techniques
(2023)
AbstractFastenings are crucial for the upgrade of existing structures, by either enhancing structural components or modernising and expanding structures and structural systems. Whilst concrete is prevailing in the existing building stock, new types of concrete composites are increasingly used to improve the sustainability and overall structural performance of built assets. The addition of short dispersed fibres is a material technology with several applications. This paper addresses the synergy of fastening elements, introducing localised loads in concrete substrates, and of fibres, particularly in terms of the fibre orientation and density in the load application area, which is a significant quality aspect. The possibility to forecast this synergy based on non‐destructive or minimally invasive testing methods is an efficient tool to increase confidence in the design load‐bearing performance of the anchorages. Initially, previous literature and own investigations on such techniques are presented. Furthermore, a methodology is presented on associating such information to predict a single anchor's resistance on the basis of sophisticated non‐linear analyses and testing with engineered fibres alignments.
Advanced ceramics printed with photon-based additive manufacturing deals with anisotropic mechanical properties from the layer-by-layer manufacturing. Motivated by the success in using highly filled transparent slurries containing nanoparticles for powder-based two-photon-polymerization (2PP) for advanced ceramic printing, this works approach is the transfer to Xolography, a volumetric additive manufacturing technology based on linear two-photon excitation and without recoating steps. This paper reports the results of a preliminary investigation optimizing the photocurable slurry to the requirements of Xolography in terms of transparency, over a significantly larger mean free path, compared to 2PP. A feedstock filled with 70 % weight fraction of ceramic particles (∼30 vol%) exhibiting an exceptionally high degree of transparency in the relevant wavelength range of 400–800 nm was prepared from 5 nm zirconia nanoparticles. The high transparency of the photocurable slurry is attributed to the near-monomodal particle size distribution of the zirconia nanoparticles used.
AbstractTwo‐photon polymerization (2PP) additive manufacturing (AM) utilizes feedstocks of ceramic nanoparticles of a few nanometers in diameter, enabling the fabrication of highly accurate technical ceramic design with structural details as small as 500 nm. The performance of these materials is expected to differ from conventional AM ceramics, as nanoparticles and three‐dimensional printing at high resolution introduce new microstructural aspects. This study applies 2PP‐AM of yttria‐stabilized zirconia to investigate the mechanical response behavior under compressive load, probing the influence of smallest structural units induced by the line packing during the printing process, design of sintered microblocks, and sintering temperature and thereby microstructure. We find a dissipative mechanical response enhanced by sintering at lower temperatures than conventional. The pursued 2PP‐AM approach yields a microstructured material with an increased number of grain boundaries that proposedly play a major role in facilitating energy dissipation within the here printed ceramic material. This microplastic response is further triggered by the filigree structures induced by hollow line packing at the order of the critical defect size of ceramics. Together, these unique aspects made accessible by the 2PP‐AM approach contribute to a heterogeneous nano‐ and microstructure, and hint toward opportunities for tailoring the mechanical response in future ceramic applications.
A long-term study was conducted on double-lap spruce wood-concrete joints to investigate their shear strength and stiffness over a 12-month period. These joints were manufactured using both wet and dry processes, each incorporating two adhesive types for bonding the wood to the concrete: a brittle epoxy and a ductile polyurethane (PUR). The experimental design exposed the joints to three specific long-term environments: (1) outdoor exposure, (2) indoor conditions with applied load, and (3) outdoor conditions with applied load. The wood concrete joints exposed to outdoor conditions were subjected to destructive shear testing at intervals of 0 (serving as the reference sample), 2, 4, 6, and 12 months, respectively. For joints subjected to both indoor and outdoor conditions with shear loading, the shear deformation of joints was monitored continuously over the 12 months before performing the destructive tests. A gradual reduction in the shear stiffness and strength of dry joints (produced using the dry bond method) exposed to outdoor conditions was observed over a 12-month period, primarily due to bond failure at the concrete-adhesive interface. The wet joints exhibited no degradation in shear stiffness and strength across long-term conditions over the same period. The bond failure observed in dry joints was predominantly associated with stresses arising from dimensional changes in the wood. No degradation was found in the cross-linking density of the adhesive or in the concrete’s compressive stiffness and strength.
AbstractUnderstanding the origin of low thermal conductivities in ionic conductors is essential for improving their thermoelectric efficiency, although accompanying high ionic conduction may present challenges for maintaining thermoelectric device integrity. This study investigates the thermal and ionic transport in Cu7PSe6, aiming to elucidate their fundamental origins and correlation with the structural and dynamic properties. Through a comprehensive approach including various characterization techniques and computational analyses, it is demonstrated that the low thermal conductivity in Cu7PSe6 arises from structural complexity, variations in bond strengths, and high lattice anharmonicity, leading to pronounced diffuson transport of heat and fast ionic conduction. It is found that upon increasing the temperature, the ionic conductivity increases significantly in Cu7PSe6, whereas the thermal conductivity remains nearly constant, revealing no direct correlation between ionic and thermal transport. This absence of direct influence suggests innovative design strategies in thermoelectric applications to enhance stability by diminishing ionic conduction, while maintaining low thermal conductivity, thereby linking the domains of solid‐state ionics and thermoelectrics. Thus, this study attempts to clarify the fundamental principles governing thermal and ionic transport in Cu+‐superionic conductors, similar to recent findings in Ag+ argyrodites.
Mechanochemistry is drawing attention from the pharmaceutical industry given its potential for sustainable material synthesis and manufacture. Scaling mechanochemical processes to industrial level remains a challenge due to an incomplete understanding of their underlying mechanisms. We here show how time-resolved in situ powder X-ray diffraction data, coupled with analytical kinetic modelling, provides a powerful approach to gain mechanistic insight into mechanochemical reactions. By using the ibuprofen–nicotinamide co-crystal mechanosynthesis as a benchmark system, we investigate the behaviour of the solids involved and identify the factors that promote the reaction. As mechanochemical mechanisms become increasingly clear, it promises to become a breakthrough in the industrial preparation of advanced pharmaceuticals.
The ultrasonic echo technique is broadly applied in non‐destructive testing (NDT) of concrete structures involving tasks such as measuring thickness, determining geometry and locating built‐in elements. To address the challenge of enhancing ultrasonic imaging for complex concrete constructions, we adapted a seismic imaging algorithm – reverse time migration (RTM) – for NDT in civil engineering. Unlike the traditionally applied synthetic aperture focusing technique (SAFT), RTM takes into account the full wavefield including primary and reflected arrivals as well as multiples. This capability enables RTM to effectively handle all wave phenomena, unlimited by changes in velocity and reflector inclinations. This paper concentrates on applying and evaluating a two‐dimensional elastic RTM algorithm that specifically addresses horizontally polarized shear (SH) waves only, as these are predominantly used in ultrasonic NDT of concrete structures. The elastic SH RTM algorithm was deployed for imaging real ultrasonic echo SH‐wave data obtained at a concrete specimen exhibiting a complex back wall geometry and containing four tendon ducts. As these features are frequently encountered in practical NDT scenarios, their precise imaging holds significant importance. By applying the elastic SH RTM algorithm, we successfully reproduced nearly all reflectors within the concrete specimen. In particular, we were capable of accurately reconstructing all vertically oriented reflectors as well as the circular cross sections of three tendon ducts, which was not achievable with traditional SAFT imaging. These findings demonstrate that elastic SH RTM holds the ability to considerably improve the imaging of complex concrete geometries, marking a crucial advancement for accurate, high‐quality ultrasonic NDT in civil engineering.
Non‐targeted screenings (NTS) are essential tools in different fields, such as forensics, health and environmental sciences. NTSs often employ mass spectrometry (MS) methods due to their high throughput and sensitivity in comparison to, for example, nuclear magnetic resonance–based methods. As the identification of mass spectral signals, called annotation, is labour intensive, it has been used for developing supporting tools based on machine learning (ML). However, both the diversity of mass spectral signals and the sheer quantity of different ML tools developed for compound annotation present a challenge for researchers in maintaining a comprehensive overview of the field.In this work, we illustrate which ML‐based methods are available for compound annotation in non‐targeted MS experiments and provide a nuanced comparison of the ML models used in MS data analysis, unravelling their unique features and performance metrics. Through this overview we support researchers to judiciously apply these tools in their daily research. This review also offers a detailed exploration of methods and datasets to show gaps in current methods, and promising target areas, offering a starting point for developers intending to improve existing methodologies.