Enhancement of glass-ceramic performance by TiO2 doping: In vitro cell viability,proliferation, and differentiation

A new TiO₂-containing bioactive glass and glass-ceramics based on 50SiO₂-(45-X)CaO-(XTiO₂)-5P₂O₅ system was designed using a sol–gel technique (where X = 5, 7.5 and 10 wt %). The roles of the crystallization behavior and physicochemical characteristics of the designed glass and glass-ceramics which were played in the introduction of TiO₂ substitutions were investigated. Moreover, cell proliferation and differentiation were evaluated against human osteosarcoma cells (Saos-2). The TiO₂/CaO replacements led to the formation of a stronger glass structure and thus increased thermal parameters and the chemical stabilization of the designed materials. The FTIR data confirmed the existence of Ti within the glass and glass-ceramics samples, and no remarkable effect on their chemical integrity was observed. The XRD patterns indicated that calcium-containing minerals, including Ca₂SiO₄,Ca₃(PO₄)₂, Ca(Ti,Si)O₅, CaTiSiO₅, and Ca₁₅(PO₄)₂·(SiO₄)₆ phases were developed as a role of structure/texture under the applied heat-treatment. The results of the cytotoxicity test proved that a safe sample dose is 12–50 μg/ml, at which cell viability is ≥ 85%. The cell differentiation determined by ALP test proved the superiority of glass-ceramics compared with their native glasses. Therefore, the obtained materials could be safely used as novel biocompatible materials for the regeneration of bone tissue.     

Morphological evolution on the surface of hydrothermally synthesized hydroxyapatite microspheres in the presence of EDTMP

Well-ordered and surface engineered hierarchical hydroxyapatite microspheres (HAM) were prepared via a template free hydrothermal process. Ethylene diamine tetra (methylene phosphonic acid) (EDTMP) was used as chelating or regulating agent for the first time in this study. The results indicated the formation of sheet-like particles in the absence of EDTMP. On the other hand, microspheres with radially grown nanorods (HAMNR) or nanosheets (HAMNS) on the surface were obtained (with average diameter of 5?µm) in the presence of EDTMP. X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy were used to characterize the crystalline phases in the synthesized samples. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) showed that EDTMP concentration played an important part in regulating the morphology to form well organized microspheres with nanosheets or nanorods on the surface. Brunauer-Emmett-Teller (BET) revealed an increase in the specific surface area with the change in morphology from the HAMNS to HAMNR. Possible mechanisms are proposed to account for the formation of different morphologies based upon thermodynamic and kinetic theories.     

A Simple Quasi-2D Numerical Model of a Thermogage Furnace

A simple quasi-2D model for the temperature distribution in a graphite tube furnace is presented. The model is used to estimate the temperature gradients in the furnace at temperatures above which contact sensors can be used, and to assist in the redesign of the furnace heater element to improve the temperature gradients. The Thermogage graphite tube furnace is commonly used in many NMIs as a blackbody source for radiation thermometer calibration and as a spectral irradiance standard. Although the design is robust, easy to operate and can change temperature rapidly, it is limited by its effective emissivity of typically 99.5–99.8%. At NMIA, the temperature gradient along the tube is assessed using thermocouples up to about 1,500°C, and the blackbody emissivity is calculated from this. However, at higher operating temperatures (up to 2,900°C), it is impractical to measure the gradient, and we propose to numerically model the temperature distributions used to calculate emissivity. In another paper at this conference, the model is used to design an optimized heater tube with improved temperature gradients. In the model presented here, the 2-D temperature distribution is simplified to separate the axial and radial temperature distributions within the heater tube and the surrounding insulation. Literature data for the temperature dependence of the electrical and thermal conductivities of the graphite tube were coupled to models for the thermal conductivity of the felt insulation, particularly including the effects of allowing for a gas mixture in the insulation. Experimental measurements of the temperature profile up to 1,500°C and radial heat fluxes up to 2,200°C were compared to the theoretical predictions of the model and good agreement was obtained.     

An adaptive region segmentation combining surrogate model applied to correlate design variables and performance parameters in a transonic axial compressor

Surrogate models have been widely applied to correlate design variables and performance parameters in turbomachinery optimization applications. With more design variables and uncertain factors taken into account in an optimization design problem, the mathematical relations between the design variables and the performance parameters might present linear, low-order nonlinear or even high-order nonlinear characteristics, and are usually analytically unknown. Therefore, it is required that surrogate models have high adaptability and prediction accuracy for both the linear and nonlinear characteristics. The paper mainly investigates the effectiveness of an adaptive region segmentation combining surrogate model based on support vector regression and kriging model applied to a transonic axial compressor to approximate the complicated relationships between geometrical variables and objective performance outputs with different sampling methods and sizes. The purpose is to explore the prediction accuracy and computational efficiency of this adaptive surrogate model in real turbomachinery applications. Three different sampling techniques are studied: (1) uniform design; (2) Latin hypercube sampling method; (3) Sobol quasi-random design. For the low dimensional case with five variables, the adaptive region segmentation combining surrogate model performs better (not worse) than the single component surrogate in terms of prediction accuracy and computational efficiency. In the meanwhile, it is also noted that the uniform design applied to the adaptive surrogate model has more advantages over the Latin hypercube sampling method especially for the small sample size cases, both performing better than the Sobol quasi-random design. Moreover, a high dimensional case with 12 variables is also utilized to further validate the prediction advantage of the adaptive region segmentation combining surrogate model over the single component surrogate, and the computational results favor it. Overall, the adaptive region segmentation combining surrogate model has produced acceptable to high prediction accuracy in presenting complex relationships between the geometrical variables and the objective performance outputs and performed robustly for a transonic axial compressor problem.

     

A robust critical path in an environment with hybrid uncertainty

In this article, we consider the project critical path problem in an environment with hybrid uncertainty. In this environment, the duration of activities are considered as random fuzzy variables that have probability and fuzzy natures, simultaneously. To obtain a robust critical path with this kind of uncertainty a chance constraints programming model is used. This model is converted to a deterministic model in two stages. In the first stage, the uncertain model is converted to a model with interval parameters by alpha-cut method and distribution function concepts. In the second stage, the interval model is converted to a deterministic model by robust optimization and min-max regret criterion and ultimately a genetic algorithm with a proposed exact algorithm are applied to solve the final model. Finally, some numerical examples are given to show the efficiency of the solution procedure.     

A potential trackable bone filler: Preparation and characterization

This study reported the synthesis of fluorescent hydroxyapatite/alginate/carbon quantum dots (HA/Alg/CQDs) nanocomposites via the co-precipitation technique. The N-doped CQDs as a new class of fluorescent materials were prepared by the citric acid pyrolysis method, with an average size around 4 nm. Physical, chemical, and optical properties of the synthesized nanocomposites were investigated by X-ray diffraction (XRD), Fourier-transformed infrared spectroscopy (FTIR), atomic force microscopy (AFM), field-emission scanning electron microscopy (FESEM), UV–visible spectroscopy, and photoluminescence (PL) spectroscopy, respectively. The PL spectroscopy data verified the favorable in vitro luminescent emission of the HA/Alg/CQDs nanocomposites in comparison with HA/Alg and HA samples. The XRD patterns of the prepared samples confirmed the formation of crystalline HA in all composites, possessing a Ca/P ratio around 1.5 as obtained by EDX elemental analysis. The FESEM analysis exhibited HA nanoplates that homogeneously distributed throughout the alginate matrix. Therefore, the synthesized nanocomposites could be regarded as potential trackable drug carriers for hard tissue engineering applications.     

Effect of Extraordinary Large Floods on at-site Flood Frequency

The peak flow of extraordinary large floods that occur during a period of systematic record is a controversial problem for flood frequency analysis (FFA) using traditional methods. The present study suggests that such floods be treated as historic flood data even though their historical period is unknown. In this paper, the extraordinary large flood peak was first identified using statistical outlier tests and normal probability plots. FFA was then applied with and without the extraordinary large floods. In this step, two goodness-of-fit tests including mean absolute relative deviation and mean squared relative deviation were used to identify the best-fit probability distributions. Next, the generalized extreme value (GEV), three-parameter lognormal (LN3), log-Pearson type III (LP3), and Wakeby (WAK) probability distributions were used to incorporate and adjust the extraordinary large floods with other systematic data. Finally, procedures with and without historical adjustment were compared for the extraordinary large floods in terms of goodness-of-fit and flood return-period quantiles. The results of this comparison indicate that historical adjustment from an operational perspective was more viable than without adjustment procedure. Furthermore, the results without adjustment were unreasonable (subject to over- and under-estimation) and produced physically unrealistic estimates that were not compatible with the study area. The proposed approach substantially improved the probability estimation of rare floods for efficient design of hydraulic structures, risk analysis, and floodplain management.     

The effect of feedstock and process conditions on the synthesis of high purity CNTs from aromatic hydrocarbons

High purity multi-walled carbon nanotubes were synthesized from aromatic hydrocarbons (benzene, toluene, xylene and trimethyl benzene) using ferrocene as the source of Fe catalyst. Screening studies of aromatic feeds at 675 °C, residence time of 14 s and Fe/C atom ratio of 1.07%, resulted in feedstock carbon conversion of 20-31%, CNT yield of 19.8-30.5%, and catalyst yield of 5.3-8.3 (g CNT/g catalyst). While the quality of the CNTs as determined by TGA, SEM, TEM and Raman spectroscopy, were high and comparable for different feedstocks; their carbon conversion, CNT yield and catalyst yield differed noticeably. A process optimization study for toluene feed showed that carbon conversion of more than 39%, CNT yield of 38.7% and catalyst yield of 18.3 can be achieved at temperature of 800 °C, Fe/C atom ratio of 0.47%, and residence time of 10-20 s.     

Robust Unknown Input Observer Design for Linear Uncertain Time Delay Systems with Application to Fault Detection

In this paper, a novel approach is proposed to design a robust fault detection observer for uncertain linear time delay systems. The system is composed of both norm‐bounded uncertainties and exogenous signals (noise, disturbance, and fault) which are considered to be unknown. The main contribution of this paper is to present unknown input observer (UIO)‐based fault detection system which shows the maximum sensitivity to fault signals and the minimum sensitivity to other signals. Since the system contains uncertainty terms, an H_∞ model‐matching approach is used in design procedure. The reference residual signal generator system is designed so that the fault signal has maximum sensitivity while the exogenous signals have minimum sensitivity on the residual signal. Then, the fault detection system is designed by minimizing the estimation error between the reference residual signal and the UIO residual signal in the sense of H_∞ norm. A sufficient condition for the existence of such a filter is exploited in terms of certain linear matrix inequalities (LMIs). Application of the proposed method in a numerical example and an engineering process are simulated to demonstrate the effectiveness of the proposed algorithm. Simulation results show the validity of the proposed approach to detect the occurrence of faults in the presence of modeling errors, disturbances, and noise.