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Paper des Monats
- ja (68)
Cryoadsorption on the inner surface of porous materials is a promising solution for safe, fast, and reversible hydrogen storage. Within the class of highly porous metal−organic frameworks, zeolitic imidazolate frameworks (ZIFs) show high thermal, chemical, and mechanical stability. In this study, we selected ZIF-8 synthesized mechanochemically by twin-screw extrusion as powder and pellets. The hydrogen storage capacity at 77 K and up to 100 bar has been analyzed in two laboratories applying three different measurement setups showing a high reproducibility. Pelletizing ZIF-8 increases the packing density close to the corresponding value for a single crystal without loss of porosity, resulting in an improved volumetric hydrogen storage capacity close to the upper limit for a single crystal. The high volumetric uptake combined with a low and constant heat of adsorption provides ca. 31 g of usable hydrogen per liter of pellet assuming a temperature−pressure swing adsorption process between 77 K − 100 bar and 117 K − 5 bar. Cycling experiments do not indicate any degradation in storage capacity. The excellent stability during preparation, handling, and operation of ZIF-8 pellets demonstrates its potential as a robust adsorbent material for technical application in pilot- and full-scale adsorption vessel prototypes.
Data science (digitalisation and artificial intelligence) became more than an important facilitator for many domains in fundamental and applied sciences as well as industry and is disrupting the way of research already to a large extent. Originally, data sciences were viewed to be well-suited, especially, for data-intensive applications such as image processing, pattern recognition, etc. In the recent past, particularly, data-driven and physics-inspired machine learning methods have been developed to an extent that they accelerate numerical simulations and became directly usable for applications related to the nuclear waste management cycle. In addition to process-based approaches for creating surrogate models, other disciplines such as virtual reality methods and high-performance computing are leveraging the potential of data sciences more and more. The present challenge is utilising the best models, input data and monitoring information to integrate multi-chemical-physical, coupled processes, multi-scale and probabilistic simulations in Digital Twins (DTw) able to mirror or predict the performance of its corresponding physical twins. Therefore, the main target of the Topical Collection is exploring how the development of DTw can benefit the development of safe, efficient solutions for the pre-disposal and disposal of radioactive waste. A particular challenge for DTw in radioactive waste management is the combination of concepts from geological modelling and underground construction which will be addressed by linking structural and multi-physics/chemistry process models to building or tunnel information models. As for technical systems, engineered structures a variety of DTw approaches already exist, the development of DTw concepts for geological systems poses a particular challenge when taking the complexities (structures and processes) and uncertainties at extremely varying time and spatial scales of subsurface environments into account.
The 18O/16O ratio of cherts (δ18Ochert) increases nearly monotonically by ~15‰ from the Archean to present. Two end-member explanations have emerged: cooling seawater temperature (TSW) and increasing seawater δ18O (δ18Osw). Yet despite decades of work, there is no consensus, leading some to view the δ18Ochert record as pervasively altered.
Here, we demonstrate that cherts are a robust archive of diagenetic temperatures, despite metamorphism and exposure to meteoric fluids, and show that the timing and temperature of quartz precipitation and thus δ18Ochert are determined by the kinetics of silica diagenesis. A diagenetic model shows that δ18Ochert is influenced by heat flow through the sediment column. Heat flow has decreased over time as planetary heat is dissipated, and reasonable Archean-modern heat flow changes account for ~5‰ of the increase in δ18Ochert, obviating the need for extreme TSW or δ18Osw reconstructions. The seawater oxygen isotope budget is also influenced by solid Earth cooling, with a recent reconstruction placing Archean δ18OSW 5 to 10‰ lower than today. Together, this provides an internally consistent view of the δ18Ochert record as driven by solid Earth cooling over billion-year timescales that is compatible with Precambrian glaciations and biological.
The reliable identification and quantitation of phosphorylated amino acids, peptides and proteins is one of the key challenges in contemporary bioanalytical research, an area of particular interest when attempting to diagnose and treat diseases at an early stage. We have developed a synthetic probe for targeting phosphorylated amino acids, based on core–shell submicron-sized particles consisting of a silica core, coated with a molecularly imprinted polymer (MIP) shell. The MIP layer contains a fluorescent probe crosslinker which binds selectively to phosphorylated tyrosine (pY) moieties with a significant imprinting factor (IF) and responds with a "light-up” fluorescence signal. The bead-based ratiometric detection scheme has been successfully transferred to a microfluidic chip format and its applicability to rapid assays has been exemplarily shown by discriminating a pY-terminating oligopeptide against its nonphosphorylated counterpart. Such miniaturised devices could lead to an automated pY or pY N-terminated peptide measurement system in the future. The setup combines a modular microfluidic system for amino acid derivatisation, extraction (by micropillar co-flow) and selective adsorption and detection with the fluorescent MIP core–shell particle probes. A miniaturised optical assembly for low-light fluorescence measurements was also developed, based on miniaturised opto-electronic parts and optical fibres. The emission from the MIP particles upon binding of pY or pY N-terminated peptides could be monitored in real-time.
Ratiometric green–red fluorescent nanosensors for fluorometrically monitoring pH in the acidic range were designed from 80 nm-sized polystyrene (PS) and silica (SiO2) nanoparticles (NPs), red emissive reference dyes, and a green emissive naphthalimide pH probe, analytically and spectroscopically characterized, and compared regarding their sensing performance in aqueous dispersion and in cellular uptake studies. Preparation of these optical probes, which are excitable by 405 nm laser or LED light sources, involved the encapsulation of the pH-inert red-fuorescent dye Nile Red (NR) in the core of self-made carboxylated PSNPs by a simple swelling procedure and the fabrication of rhodamine B (RhB)-stained SiO2-NPs from a silane derivative of pH-insensitive RhB. Subsequently, the custom-made naphthalimide pH probe, that utilizes a protonation-controlled photoinduced electron transfer process, was covalently attached to the carboxylic acid groups at the surface of both types of NPs. Fluorescence microscopy studies with the molecular and nanoscale optical probes and A549 lung cancer cells confirmed the cellular uptake of all probes and their penetration into acidic cell compartments, i.e., the lysosomes, indicated by the switching ON of the green naphthalimide fluorescence. This underlines their suitability for intracellular pH sensing, with the SiO2-based nanosensor revealing the best performance regarding uptake speed and stability.
We report, to our knowledge for the first time on simultaneous distributed temperature and strain sensing in a standard telecom optical fiber using a machine learning assisted Brillouin frequency domain analysis (BOFDA) system. The well-known temperature and strain cross-sensitivity problem is addressed by developing a BOFDA system with a high signal-to-noise ratio and applying machine learning. The spectrum consists of four highly resolved peaks, whose Brillouin frequency shifts are extracted and serve as features for the machine learning algorithms. The spectra result from a 450-m standard SMF-28 optical fiber, and particularly from a segment of 30 m. This fiber segment is coiled around a stretcher and placed in a climate chamber. The applied temperature and strain values range from 20 °C to 40 °C and from 0 µɛ to 1380 µɛ, respectively. The total measurement time to achieve a high SNR and resolve four peaks with a spatial resolution of 6 m is 16 min. To discriminate temperature and strain effects, simple frequentist and more sophisticated Bayesian-based algorithms are employed with the powerful Gaussian process regression (GPR) delivering the best performance in terms of temperature and strain errors, which are found to be 2 °C and 45 µɛ, respectively. These errors are calculated using leave-one-out cross-validation, so that an unbiased estimation of the sensor’s performance is provided.
Metastable austenitic stainless steel (304L) samples with a rectangular cross-section were plastically deformed in torsion during which they experienced multiaxial stresses that led to a complex martensitic phase distribution owing to the transformation induced plasticity effect. A three-dimensional characterization of the phase distributions in these cm-sized samples was carried out by wavelength-selective neutron tomography. It was found that quantitatively correct results are obtained as long as the samples do not exhibit any considerable preferential grain orientation. Optical microscopy, electron backscatter diffraction, and finite element modeling were used to verify and explain the results obtained by neutron tomography. Altogether, neutron tomography was shown to extend the range of microstructure characterization methods towards the meso- and macroscale.
The flammable hydrogen-blended methane–air and natural gas–air mixtures raise specific safety and environmental issues in the industry and transportation; therefore, their explosion characteristics such as the explosion limits, explosion pressures, and rates of pressure rise have significant importance from a safety point of view. At the same time, the laminar burning velocities are the most useful parameters for practical applications and in basic studies for the validation of reaction mechanisms and modeling turbulent combustion. In the present study, an experimental and numerical study of the effect of hydrogen addition on the laminar burning velocity (LBV) of methane–air and natural gas–air mixtures was conducted, using mixtures with equivalence ratios within 0.90 and 1.30 and various hydrogen fractions rH within 0.0 and 0.5. The experiments were performed in a 14 L spherical vessel with central ignition at ambient initial conditions. The LBVs were calculated from p(t) data, determined in accordance with EN 15967, by using only the early stage of flame propagation. The results show that hydrogen addition determines an increase in LBV for all examined binary flammable mixtures. The LBV variation versus the fraction of added hydrogen, rH, follows a linear trend only at moderate hydrogen fractions. The further increase in rH results in a stronger variation in LBV, as shown by both experimental and computed LBVs. Hydrogen addition significantly changes the thermal diffusivity of flammable CH4–air or NG–air mixtures, the rate of heat release, and the concentration of active radical species in the flame front and contribute, thus, to LBV variation.
Performance of calcium phosphate cements in the augmentation of sheep vertebrae - An ex vivo study
(2021)
Oil-based calcium phosphate cement (Paste-CPC) shows not only prolonged shelf life and injection times, but also improved cohesion and reproducibility during application, while retaining the advantages of fast setting, mechanical strength, and biocompatibility. In addition, poly(L-lactideco-glycolide) (PLGA) fiber reinforcement may decrease the risk for local extrusion. Bone defects (diameter 5 mm; depth 15 mm) generated ex vivo in lumbar (L) spines of female Merino sheep (2–4 years) were augmented using: (i) water-based CPC with 10% PLGA fiber reinforcement (L3); (ii) Paste-CPC (L4); or (iii) clinically established polymethylmethacrylate (PMMA) bone cement (L5). Untouched (L1) and empty vertebrae (L2) served as controls. Cement performance was analyzed using micro-computed tomography, histology, and biomechanical testing. Extrusion was comparable for Paste-CPC(-PLGA) and PMMA, but significantly lower for CPC + PLGA. Compressive strength and Young’s modulus were similar for Paste-CPC and PMMA, but significantly higher compared to those for empty defects and/or CPC + PLGA. Expectedly, all experimental groups showed significantly or numerically lower compressive strength and Young’s modulus than those of untouched controls. Ready-to-use Paste-CPC demonstrates a performance similar to that of PMMA, but improved biomechanics compared to those of water-based CPC + PLGA, expanding the therapeutic arsenal for bone defects. O, significantly lower extrusion of CPC + PLGA fibers into adjacent lumbar spongiosa may help to reduce the risk of local extrusion in spinal surgery.
In this paper, we present a collection of machine learning assisted distributed fiber optic sensors (DFOS) for applications in the field of infrastructure monitoring. We employ advanced signal processing based on artificial neural networks (ANNs) to enhance the performance of the dynamic DFOS for strain and vibration sensing. Specifically, ANNs in comparison to conventional and computationally expensive correlation and linearization algorithms, deliver lower strain errors and speed up the signal processing allowing real time strain monitoring. Furthermore, convolutional neural networks (CNNs) are used to denoise the dynamic DFOS signal and enable useable sensing lengths of up to 100 km. Applications of the machine learning assisted dynamic DFOS in road traffic and railway infrastructure monitoring are demonstrated. In the field of static DFOS, machine learning is applied to the well-known Brillouin optical frequency domain analysis (BOFDA) system. Specifically, CNN are shown to be very tolerant against noisy spectra and contribute towards significantly shorter measurement times. Furthermore, different machine learning algorithms (linear and polynomial regression, decision trees, ANNs) are applied to solve the well-known problem of cross-sensitivity in cases when temperature and humidity are measured simultaneously. The presented machine learning assisted DFOS can potentially contribute towards enhanced, cost effective and reliable monitoring of infrastructures.