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Silicate bioceramics possess an excellent bioactivity; however, shaping them into complex geometries is still challenging. Therefore, this paper aims to present a new strategy for the shaping of a bioglass-ceramic with controlled geometry and properties starting from a glass powder combined with a preceramic polymer, i.e. a silicon resin, and reactive fillers. The powder-based three-dimensional (3D)-printing of wollastonite (CaSiO3)-based silicate bioceramic parts was demonstrated in this work. The resin plays a dual role, as it not only acts as a non-sacrificial binder for the filler powders in the printing process but it also reacts with the fillers to generate the desired bioceramic phases. The mechanical and physical properties, i.e. ball-on-three-balls test, density, porosity and morphology, were evaluated in 3D-printed discs. These samples possessed a total porosity around 64 vol% and a biaxial flexural strength around 6 MPa. The raw materials used in this work also enabled the 3D-printing of scaffolds possessing a designed multi-scale porosity, suitable bioceramic phase assemblage and a compressive strength of 1 MPa (for cylindrical scaffolds with total porosity ~80 vol%). Solubility in TRIS/HCl and in vitro assays, i.e. viability, cytotoxicity and apoptosis assays, were also performed. In vitro tests indicated good cell viability and no cytotoxicity effect on the cells.
In this study, an Impulse Response Function analysis of pile response to sine-sweep excitation by a low cost, portable Shaker was used to identify defects in piles. In straightforward impact-echo methods, echoes from the pile toe and defects are visible in the time domain measurements. However, these echoes are not present in the time domain records of piles subjected to sine-sweep excitations, due to interactions between the input and output signals. For this reason, the impulse response function in the time domain has been calculated and is able to identify the echoes from pile impedance changes. The proposed methodology has been evaluated both numerically and experimentally. A one-dimensional pile-soil interaction system was developed, and a finite difference method used to calculate the pile response to sine-sweep excitation. The numerical simulations indicate that impulse response measurements with a synthesized logarithmic, sine-sweep excitation could be an effective tool for detecting defects in piles. The methodology was further tested with field trials on 6 cast in situ concrete test piles including 1 intact pile and 5 defective piles subjected to sine-sweep excitations by a shaker. In 5 of the 6 cases the echoes from the pile toe could be identified in the deconvoluted waveforms—the impulse Response functions. Damage detection is more difficult and dependent on the selection of the optimal regularization parameter. Further research and optimization of the deconvolution process is needed to evaluate the effectiveness compared to standard pile integrity testing methods.
Structural health monitoring systems have been widely implemented to provide real-time continuous data support and to ensure structural safety in the context of structural integrity management. However, the quantification of the potential benefits of structural health monitoring systems has not yet attracted widespread attention. At the same time, there is an urgent need to develop strategies, such as optimizing the monitoring period, monitoring variables, and other factors, to maximize the potential benefits of structural health monitoring systems. Considering the continuity of structural health monitoring information, a framework is developed in this article to support decision-making for structural Health monitoring systems arrangement in the context of structural integrity management, which integrates the concepts of value of information and risk-based inspection planning based on an approach which utilizes a conjugate prior probability distribution for updating of the probabilistic models of structural performances based on structural health Monitoring information. An example considering fatigue degradation of steel structures is investigated to illustrate the application of the proposed framework. The considered example shows that the choice of monitoring variables, the Monitoring period, and the monitoring quality may be consistently optimized by the application of the proposed framework and approach. Finally, discussions and conclusions are provided to clarify the potential benefits of the proposed Framework with a special view to practical applications of structural health monitoring systems.
In this study, the optimized fabrication and evolution of the microstructure and magnetic Transition behavior of the melt-extraction LaFe11.2Si1.8 microwires have been studied. After the optimization of extraction technique (heating power 22 KW, feeding rate 30-50 mm/s, rotation velocity 1700 r/min), the content of La Fe,Si)13 phase in the as-extracted microwires was 54 wt% due to the high solidification velocity, which was increased to 85 wt% via annealing at 1373 K for 20 min. The amount of La(Fe,Si)13 phase was increased and the composition of La(Fe,Si)13 phase became more homogenized through peritectic reaction and short-distance diffusion in the microwires during annealing process. The coexistence of the nanocrystalline and amorphous structures contributed to the broad magnetic Transition temperature range of the as-extracted and annealed microwires. The annealed microwires exhibited a second-order magnetic transformation behavior and showed a maximum magnetic entropy Change jDSMjmax of 6.2 J/kgK and working temperature interval of 36.0 K under a magnetic field of 20 kOe.
Metastasis is the main cause of death from colorectal cancer (CRC). About 20% of stage II CRC patients develop metastasis during the course of disease. We performed metabolic profiling of plasma samples from non-metastasized and metachronously metastasized stage II CRC patients to assess the potential of plasma metabolites to serve as biomarkers for stratification of stage II CRC patients according to metastasis risk. We compared the metabolic profiles of plasma samples prospectively obtained prior to metastasis formation from non-metastasized vs. metachronously metastasized stage II CRC patients of the German population-based case–control multicenter DACHS study retrospectively. Plasma samples were analyzed from stage II CRC patients for whom follow-up data including the information on metachronous metastasis were available. To identify metabolites distinguishing non-metastasized from metachronously metastasized stage II CRC patients robust supervised classifications using decision trees and support vector machines were performed and verified by 10-fold cross-validation, by nested cross-validation and by traditional validation using training and test sets. We found that metabolic profiles distinguish non-metastasized from metachronously metastasized stage II CRC patients. Classification models from decision trees and support vector machines with 10-fold cross-validation gave average accuracy of 0.75 (sensitivity 0.79, specificity 0.7) and 0.82 (sensitivity 0.85, specificity 0.77), respectively, correctly predicting metachronous metastasis in stage II CRC patients. Taken together, plasma metabolic profiles distinguished non-metastasized and metachronously metastasized stage II CRC patients. The classification models consisting of few metabolites stratify non-invasively stage II CRC patients according to their risk for metachronous metastasis.
Water is generally considered to be an undesirable substance in fuel system, which may lead to microbial contamination. The antibacterial strategies that can turn water into things of value with high disinfection efficacy have been urgently needed for fuel system. Here, we reveal a water-fueled autocatalytic bactericidal pathway comprised by bi-metal micro-electrode system, which can spontaneously produce reactive oxygen species (mainly H2O2 and O2•–) by the electron Fenton-like reaction in water medium without external energy., The respiratory chain component of bacteria and the galvanic corrosion on the coated metals were two electron sources in the system. The specific model of Ag-Ru water-fueled autocatalytic (WFA) microelectrode particles presents extremely high disinfection efficiency (>99.9999%) in less than one hour for three aerobic bacteria (Escherichia coli, Pseudomonas aeruginosa and Bacillus subtilis) in LB media and high disinfection efficiency for the anaerobic bacteria (Desulfovibrio alaskensis) in Postgate E media without natural light irradiation. Overall, the novel WFA Ag-Ru antibacterial material explored in this study has a high potential for sterilizing applications in fuel system and this work provides the potential for the development of non-chemical and water-based antibacterial materials, such as WFA Ag-Ru antibacterial coating on stainless steel.
Uncertainty quantification of multimodal surface wave inversion using artificial neural networks
(2023)
An inversion of surface waves dispersion curves is a non-unique and ill-conditioned problem. The inversion result has a probabilistic nature, which becomes apparent when simultaneously restoring the shear wave (S-wave) velocity and layer thickness. Therefore, the problem of uncertainty quantification is relevant. Existing methods through deterministic or global optimization approaches of uncertainty quantification via posterior probability density (PPD) of the model parameters are not computationally efficient since they demand multiple solutions of the inverse problem. We present an alternative method based on a multi-layer fully connected artificial neural network (ANN). We improve the current uni-modal approach, which is known from publications, to multi-modal inversion. The learned ANN maps the phase velocity dispersion curves to values of the S-wave velocity and layers thickness. To estimate the uncertainties, we adapt the Monte-Carlo simulation strategy and project onto the resulting velocity model both frequency-dependent data noise and inverse operator errors, which are evaluated by the prediction of the training data set. The proposed combination of surface waves data processing methods, configured with each other, provides a novel surface waves multi-modal dispersion data inversion and uncertainty quantification approach. We first test our approach on synthetic experiments for various velocity models: a positive velocity gradient, a low-velocity layer and a high-velocity layer. This is done considering uni-modal inversion at first and then compared to the multi-modal inversion. Afterwards, we apply our approach to field data and compare resulting models with the body S-wave processing by the generalized reciprocal method (GRM). The experiments show high-potential results using ANN yields the possibility to accurately estimate PPD of restored model parameters without a significant computational effort. The PPD-based comparison demonstrates advantages of a multi-modal inversion over uni-modal inversion. The trained ANN provides reasonable model parameters predictions and related uncertainties in real-time.
The data center needs more and more electricity due to the explosive growth of IT servers and it could cause electricity power shortage and huge carbon emission. It is an attractive and promising solution to power the data center with hydrogen energy source. The present work aims to conduct an economic analysis on the hydrogen-powered data center. Configurations of hydrogen-powered and traditional data centers are compared and the differences focus on backup power system, converter/inverter, fuel cell subsystem, carbon emission, hydrogen and electricity consumptions. Economic analysis is conducted to evaluate the feasibility to power the data center with hydrogen energy source. Results show that electricity price increasing rate and hydrogen cost are the main factors to influence economic feasibility of hydrogen-powered data center. When the electricity price keeps constant in the coming two decades, the critical hydrogen price is about 2.8 U.S. dollar per kilogram. If the electricity price could increase 5% annually due to explosive growth of electric vehicles and economy, critical hydrogen price will become 6.4 U.S. dollar per kilogram. Hydrogen sources and transportation determine the hydrogen price together. Hydrogen production cost varies greatly with hydrogen sources and production technologies. Hydrogen transport cost is greatly influenced by distances and H2 consumptions to consumers. It could be summarized that the hydrogen-powered data center is economic if hydrogen could be produced from natural gas or H2-rich industrial waste streams in chemical plant and data center could not be built too far away from hydrogen sources. In addition, large-scale hydrogen-powered data center is more likely to be economic. Solar hydrogen powered data center has entered into a critical stage in the economic feasibility. Solar hydrogen production cost has restrained the H2 utilization in data center power systems now, since it could be competitive only when more strict carbon emission regulation is employed, hydrogen production cost reduces greatly and electricity price is increasing greatly in the future. However, it could be expected solar hydrogen-powered system will be adopted as the power source of data centers in the next few years.
Stainless steel AISI 304 surfaces were studied after a mild anodic polarization for oxide growth in the presence and absence of two derivatives of vitamin B2 (riboflavin and flavin mononucleotide) that can be secreted by metal‐reducing bacteria and act as a chelating agent for iron species. The alterations in oxide chemistry were studied by means of surface‐sensitive techniques such as X‐ray photoelectron spectroscopy and time‐of‐flight secondary ion mass spectrometry analysis. The complementary electrochemical characterization revealed a preferential growth of an oxide/hydroxide iron‐rich film that is responsible for an altered pit initiation and nucleation behavior. These findings suggest that as the corrosion behavior is determined by the interplay of the chemical and electronic properties, only a mild anodic polarization in the presence of redox‐active molecules is able to alter the chemical and electronic structure of the passive film formed on stainless steel AISI 304. This helps to achieve a profound understanding of the mechanisms of microbially influenced corrosion (MIC) and especially the possible effects of the redox‐active biomolecules, as they may play an important role in the corrosion susceptibility of stainless steel surfaces.
Biofilm formation and microbially influenced corrosion of the iron-reducing microorganism Shewanella putrefaciens were investigated on stainless steel surfaces preconditioned in the absence and presence of flavin molecules by means of XANES (X-ray absorption near-edge structure) analysis and electrochemical methods. The results indicate that biofilm formation was promoted on samples preconditioned in electrolytes containing minute amounts of flavins. On the basis of the XANES results, the corrosion processes are controlled by the iron-rich outer layer of the passive film. Biofilm formation resulted in a cathodic shift of the open circuit potential and a protective effect in terms of pitting corrosion. The samples preconditioned in the absence of flavins have shown delayed pitting and the samples preconditioned in the presence of flavins did not show any pitting in a window of −0.3- to +0.0-V overpotential in the bacterial medium. The results indicate that changes in the passive film chemistry induced by the presence of minute amounts of flavins during a mild anodic polarization can change the susceptibility of stainless steel surfaces to microbially influenced corrosion.