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Paper des Monats
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Pressure-sensitive adhesive tapes are used in a variety of applications such as construction, aircrafts, railway vehicles, and ships, where flame retardancy is essential. Especially in these applications, phosphorus-based flame retardants are often chosen over halogenated ones due to their advantages in terms of toxicity. Although there are pressure-sensitive adhesives with phosphorus flame retardants available on the market, their flame-retardant modes of action and mechanisms are not entirely understood. This research article provides fundamental pyrolysis research of three phosphorus-based flame retardants that exhibit different mechanisms in a pressuresensitive adhesive matrix. The flame-retardants modes of action and mechanisms of a 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide (DOPO) derivate, an aryl phosphate, and a self-synthesized, covalently bonded DOPO derivate (copolymerized) are investigated. The blended DOPO derivate is volatilized at rather low temperatures while the covalently bonded DOPO derivate decomposes together with the polymer matrix at the same temperature. Both DOPO derivates release PO radicals which are known for their flame inhibition. The aryl phosphate decomposes at higher temperatures, releases small amounts of aryl phosphates into the gas phase, and acts predominantly the condensed phase. The aryl phosphate acts as precursor for phosphoric acid and improves the charring of the pressure sensitive adhesive matrix. All flame retardants enhance the flammability of the adhesives depending on their individual mode of action while the covalently bonded flame retardant additionally improves the mechanical properties at elevated temperatures making it a promising future technology for pressure-sensitive adhesives.
This work provides an investigation of the influence of low temperature heat treatments on the fatigue behavior of a PBF-LB AlSi10Mg alloy. Fatigue specimens are produced in form of round bars on a build platform preheated at 200 ◦C. The specimens have been tested in three different conditions: as-built, and after heat treatments at 265 ◦C for 1 h and 300 ◦C for 2 h. Prior to the fatigue testing, the defect distribution is analyzed by means of micro computed tomography. Subsequently, the peak over threshold method is successfully applied to provide a prediction of the size of killer defect. The defect population was of gas porosity type. No clear improvement of the fatigue performance is observed after the heat treatments. The fatigue strength predicted using fracture mechanics-based approaches is in good agreement with the experimental data. Among the studied approaches, short crack models provided the most conservative predictions.
This article presents a method to use the dispersive behavior of ultrasonic guided waves and neural networks to determine the isotropic elastic constants of plate-like structures through dispersion images. Therefore, two different architectures are compared: one using convolutions and transfer learning based on the EfficientNetB7 and a Vision Transformer-like approach. To accomplish this, simulated and measured dispersion images are generated, where the first is applied to design, train, and validate and the second to test the neural networks. During the training of the neural networks, distinct data augmentation layers are employed to introduce artifacts appearing in measurement data into the simulated data. The neural networks can extrapolate from simulated to measured data using these layers. The trained neural networks are assessed using dispersion images from seven known material samples. Multiple variations of the measured dispersion images are tested to guarantee the prediction stability. The study demonstrates that neural networks can learn to predict the isotropic elastic constants from measured dispersion images using only simulated dispersion images for training and validation without needing an initial guess or manual feature extraction, independent of the measurement setup. Furthermore, the suitability of the different architectures for generating information from dispersion images in general is discussed.
A hybrid deterministic-probabilistic framework is presented that combines in-situ measurements and numerical analysis to predict the characteristic size of corrosion pits over extended periods of exposure to seawater. A probabilistic model, informed by corrosion current density and temperature data, applies a sampling method to a range of overpotential values to reflect the surface deterioration in terms of pitting. The model is calibrated and validated with pit size data obtained from corroded coupons. Results reveal a positively skewed truncated lognormal distribution of pit depths which implies a higher prevalence of smaller pit depths, corroborated both experimentally and numerically.
Welded steel structures used in the offshore wind industry are exposed to harsh marine environments, which can result in corrosion-induced fatigue damage. Of particular concern is the heat affected zone (HAZ) of welded joints, a region known for its altered microstructure and mechanical properties, which can significantly influence the initiation and propagation of fatigue cracks. This study investigates the short and long fatigue crack growth rates, and the effect of seawater exposure, for the HAZ in S355 steel weldments. Single-edge notch bend (SENB) specimens are used, with a shallow notch in the HAZ. A series of specimens is immersed in synthetic seawater that is continuously circulated at a controlled temperature to assess the synergistic effects of corrosion and fatigue. The experimental method integrates a novel application of front face strain compliance for monitoring short cracks, alongside an extended back-face strain compliance approach for monitoring long crack propagation. It is concluded that the short fatigue crack growth rate of the HAZ is 2.7 to 3.5 times higher in seawater as compared to air. As the crack propagates and enters into the long crack regime, the ratio decreases to 2.2 times at the transition point of the two-stage crack growth curve and further decreases to 1.5 times when the notch advances towards fracture. The findings indicate that the fatigue crack growth rates documented in standards tend to be on the conservative side. This study significantly enriches the fatigue crack growth data available in literature, which will contribute to a more accurate lifetime assessment offshore wind turbine structures.
Development of efficient portable sensors for accurately detecting biomarkers is crucial for early disease diagnosis, yet remains a significant challenge. To address this need, we introduce the enhanced luminescence lateral-flow assay, which leverages highly luminescent upconverting nanoparticles (UCNPs) alongside a portable reader and a smartphone app. The sensor’s efficiency and versatility were shown for kidney health monitoring as a proof of concept. We engineered Er3+- and Tm3+-doped UCNPs coated with multiple layers, including an undoped
inert matrix shell, a mesoporous silica shell, and an outer layer of
gold (UCNP@mSiO2@Au). These coatings synergistically enhance emission by over 40-fold and facilitate biomolecule conjugation, rendering UCNP@mSiO2@Au easy to use and suitable for a broad range of bioapplications. Employing these optimized nanoparticles in lateral-flow assays, we successfully detected two acute kidney injury-related biomarkers-kidney injury molecule-1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL)-in urine samples. Using our sensor platform, KIM-1 and NGAL can be accurately detected and quantified
within the range of 0.1 to 20 ng/mL, boasting impressively low limits of detection at 0.28 and 0.23 ng/mL, respectively. Validating our approach, we analyzed clinical urine samples, achieving biomarker concentrations that closely correlated with results obtained via ELISA. Importantly, our system enables biomarker quantification in less than 15 min, underscoring the performance of our novel UCNP-based approach and its potential as reliable, rapid, and user-friendly diagnostics.
Reducing the size of upconversion nanoparticles (UCNPs) down to a few nm yields luminescent materials containing a very small number of emitters.
Considering the bottom limit of one activator per particle ultrasmall UCNPs offer an unprecedented platform to study the contributions of the energy transfers at play in upconversion luminescence. Maintaining detectable emission despite the limited number of emitting ions and the high surface-to-volume ratio requires suitable particle architectures.
Na(Gd-Yb)F4:Tm3+ emissive sub-3 nm diameter 𝜷-phase UCNPs are prepared using a gadolinium-rich composition in situ mixing of the precursors and a microwave high-temperature cycling sequence allowing precise control of the particle size and dispersity. These cores are coated with a NaGdF4 inert shell to minimize the deleterious influence of surface quenching (SQ).
Time-resolved luminescence measurements combining standard NIR excitation of the Yb3+ sensitizer and direct UV excitation of the Tm3+ activator are performed to quantify cross relaxation and surface quenching processes.
The fine tuning of the number of activators per particle via an optimized synthesis pathway along with the use of an appropriate excitation scheme enabled to provide an accurate analysis of the different mechanisms at play in these model nanoparticles and to characterize the structure of the core-shell architecture.
The hydrogen economy is one of the most important solutions to achieve climate neutrality in Europe. It involves the production, storage, transport and use of large quantities of hydrogen in existing and new infrastructures. Components along this supply chain, such as pipelines and storage tanks, are made of various metallic materials, with steel being the most common construction material. The rapid introduction of hydrogen therefore brings with it major challenges, in particular the need for comprehensive qualification of components and materials to ensure the sustainable and safe use of hydrogen technologies. This article provides an overview of the state of the art in the testing of materials and components as well as corresponding future trends and developments for a successful transition to a hydrogen economy.
Most high capacity anode materials for lithium-ion batteries (LiB) require a carbonaceous matrix. In this context one promising material is reduced graphene oxide (rGO). Herein, we present the influence of different reduction degrees of rGO on its physico-chemical properties, such as crystallinity, specific surface area, electrical conductivity and electrochemical lithiation/delithiation behavior. It is found that a heat treatment under inert and reducing atmospheres increases the long-range order of rGO up to a temperature of 700 °C. At temperatures around 1000 °C, the crystallinity decreases. With decreasing oxygen content, a linear decrease in irreversible capacity during cycle 1 can be observed, along with a significant increase in electrical conductivity. This decrease in irreversible capacity can be observed despite an increase in specific surface area indicating the more significant influence of the oxygen content on the capacity loss. Consequently, the reversible capacity increases continuously up to a carbon content of 84.4 at% due to the thermal reduction. Contrary to expectations, the capacity decreases with further reduction. This can be explained by the loss of functional groups that will be lithiated reversibly, and a simultaneous reduction of long-range order, as concluded from dq/dU analysis in combination with XRD analysis.
AbstractRecent studies have hypothesized that the reinforcing effects of boehmite nanoparticles (BNPs) in polymer nanocomposites (PNCs) are partly related to the particles themselves and partly to the water released from the BNP during curing. In this work, PNCs made from dried BNP (dBNP) with concentrations up to 15 wt% are investigated to differentiate particle and water related effects. The observed trend of the storage modulus in dynamic mechanical thermal analysis measurements was found to be independent of the drying procedure. Stiffness maps from intermodulation atomic force microscopy showed that dBNP leads to a stiffening of the interphase surrounding the particles compared with the unaffected epoxy matrix, while a softer interphase was reported for PNCs with as received BNP. A slight decrease in the glass transition temperature was observed by broadband dielectric spectroscopy related to a lowered crosslink density due to the particles. A significantly higher decrease was reported for PNCs with BNP, attributed to water influencing the curing process. In conclusion, the stiffening of PNC with BNP is related to the particles themselves, while the release of water causes the formation of a soft interphase in the vicinity of the particles and a significant decrease in crosslink density.