Excitation with a focused, pulsed optical beam in scattering media: diffraction effects

To gain a better understanding of the spatiotemporal problems that are encountered in two-photon excitation fluorescence imaging through highly scattering media, we investigate how diffraction affects the three-dimensional intensity distribution of a focused, pulsed optical beam propagating inside a scattering medium. In practice, the full potential of the two-photon excitation fluorescence imaging is unrealized at long scattering depths, owing to the unwanted temporal and spatial broadening of the femtosecond excitation light pulse that reduces the energy density at the geometric focus while it increases the excitation energy density in the out-of-focus regions. To analyze the excitation intensity distribution, we modify the Monte Carlo-based photon-transport model to a semi-quantum-mechanical representation that combines the wave properties of light with the particle behavior of the propagating photons. In our model the propagating photon is represented by a plane wave with its propagation direction in the scattering medium determined by the Monte Carlo technique. The intensity distribution in the focal region is given by the square of the linear superposition of the various plane waves that arrive at different incident angles and optical path lengths. In the absence of scattering, the propagation model yields the intensity distribution that is predicted by the Huygens-Fresnel principle. We quantify the decrease of the energy density delivered at the geometric focus as a function of the optical depth to the mean-free-path ratio that yields the average number of scattering events that a photon encounters as it propagates toward the focus. Both isotropic and anisotropic scattering media are considered. Three values for the numerical aperture (NA) of the focusing lens are considered: NA = 0.25, 0.5, 0.75.     

Design and operation of pilot plant for CO2 capture from IGCC flue gases by combined cryogenic and hydrate method

This project is a trial conducted under contract with CO₂CRC, Australia of a new CO₂ capture technology that can be applied to integrated gasification combined cycle power plants and other industrial gasification facilities. The technology is based on combination of two low temperature processes, namely cryogenic condensation and the formation of hydrates, to remove CO₂ from the gas stream. The first stage of this technology is condensation at −55 °C where CO₂ concentration is expected to be reduced by up to 75 mol%. Remaining CO₂ is captured in the form of solid hydrate at about 1 °C reducing CO₂ concentration down to 7 mol% using hydrate promoters. This integrated cryogenic condensation and CO₂ hydrate capture technology hold promise for greater reduction of CO₂ emissions at lower cost and energy demand. Overall, the process produced gas with a hydrogen content better than 90 mol%. The concentrated CO₂ stream was produced with 95-97 mol% purity in liquid form at high pressure and is available for re-use or sequestration. The enhancement of carbon dioxide hydrate formation and separation in the presence of new hydrate promoter is also discussed. A laboratory scale flow system for the continuous production of condensed CO₂ and carbon dioxide hydrates is also described and operational details are identified.     

Photooxidation processes for an azo dye in aqueous media: modeling of degradation kinetic and ecological parameters evaluation

Three photooxidation processes, UV/H(2)O(2), UV/S(2)O(8)(2-) and UV/O(3) were applied to the treatment of model wastewater containing non-biodegradable organic pollutant, azo dye Acid Orange 7 (AO7). Dye degradation was monitored using UV/VIS and total organic carbon (TOC) analysis, determining decolorization, the degradation/formation of naphthalene and benzene structured AO7 by-products, and the mineralization of model wastewater. The water quality during the treatment was evaluated on the bases of ecological parameters: chemical (COD) and biochemical (BOD(5)) oxygen demand and toxicity on Vibrio fischeri determining the EC(50) value. The main goals of the study were to develop an appropriate mathematic model (MM) predicting the behavior of the systems under investigation, and to evaluate the toxicity and biodegradability of the model wastewater during treatments. MM developed showed a high accuracy in predicting the degradation of AO7 when considering the following observed parameters: decolorization, formation/degradation of by-products and mineralization. Good agreement of the data predicted and the empirically obtained was confirmed by calculated standard deviations. The biodegradability of model wastewater was significantly improved by three processes after mineralizing a half of the initially present organic content. The toxicity AO7 model wastewater was decreased as well. The differences in monitored ecological parameters during the treatment indicated the formation of different by-products of dye degradation regarding the oxidant type applied.     

Dynamic component chemiluminescent sensor for assessing circulating polymorphonuclear leukocyte activity of peritoneal dialysis patients

Recurrent bacterial peritonitis is a major complication in peritoneal dialysis (PD) patients, which is associated with polymorphonuclear leukocyte (PMN) functional changes and can be assessed by a chemiluminescent (CL) reaction. We applied a new approach of a dynamic component chemiluminescence sensor for the assessment of functional states of PMNs in a luminol-amplified whole-blood system. This method is based on the evaluation of CL kinetic patterns of stimulated PMNs, while the parallel measurements of intracellular and extracellular production of reactive oxygen species (ROS) from the same sample can be conducted. Blood was drawn from diabetic and nondiabetic patients during follow-up, and during peritonitis. Healthy medical personnel served as the control group. Chemiluminescence curves were recorded and presented as a sum of three biological components. CL kinetic parameters were calculated, and functional states of PMNs were assessed. Data mining algorithms were used to build decision tree models that can distinguish between different clinical groups. The induced classification models were used afterward for differentiating and classifying new blind cases and demonstrated good correlation with medical diagnosis (84.6% predictive accuracy). In conclusion, this novel method shows a high predictive diagnostic value and may assist in detection of PD-associated clinical states.     

Oxidative coupling of methane over strontium-doped neodymium oxide: Parametric evaluations

Since its discovery in 1982, oxidative coupling of methane (OCM) has been considered one of the most promising approaches for the on-purpose synthesis of ethylene. The development of more selective catalysts is essential to improve process economics. In this work, undoped neodymium oxide as well as neodymium oxide doped with high (20%) and low (2.5%) levels of strontium were tested in a high-throughput fashion covering a wide range of operating conditions. The catalysts were shown to be able to achieve greater than 18% C₂+ yield. Space velocity was shown to play a significant role in C₂+ selectivity. For a methane to oxygen feed ratio of 3.5, selectivity increased with increasing space velocity, reaching a maximum of 62% at a methane conversion of 30% at an optimal space velocity of ~250,000 ml/h/g. The difference in activity between the three samples was linked to the contribution of different oxygen centers.     

An accurate approach to real-time machine-readable zone detection with mobile devices

International Journal on Document Analysis and Recognition (IJDAR) - In this article we consider a problem of machine-readable zone (MRZ) detection in document images on mobile devices. MRZ...     

Hydrophobized Hydrogels: Construction Strategies,Properties, and Biomedical Applications

Hydrogels, as 3D networks containing huge amount of water, display similarity to soft tissues, and thus they are of wide interest in tissue engineering. Hydrogels, due to biocompatibility and porous structure, are valuable therapeutic platforms for hydrophilic drugs. Over the last decade, there has been a strong emphasis on the development of hydrogel platforms with the ability to increase the solubility of hydrophobic drugs. However, the pronounced discrepancy between the hydrophilic character of hydrogels and the hydrophobic nature of numerous pharmacologically active compounds is problematic. In recent years, different strategies are applied using special polymer constructs or composite materials exploiting the advanced scientific knowledge in the area of polymer and lipid-based nano- and microcarriers hydrophobization of the hydrogel turns out to be not only valuable in terms of achieving the ability to dissolve poorly soluble drugs in water, but also proves to be crucial in obtaining bioadhesion in wet conditions, but also, unexpected abnormal water swelling behavior, as well as in mechanical properties such as the dissipation mechanism and self-healable hydrogel properties. This review is mainly focused on recent advances in the usage of hydrophobized hydrogels in biomedical applications.     

Acridine/Acridone–Carborane Conjugates as Strong DNA-Binding Agents with Anticancer Potential

Synthesis of acridine derivatives that act as DNA-targeting anticancer agents is an evolving field and has resulted in the introduction of several drugs into clinical trials. Carboranes can be of importance in designing biologically active compounds due to their specific properties. Therefore, a series of novel acridine analogs modified with carborane clusters were synthesized. The DNA-binding ability of these analogs was evaluated on calf thymus DNA (ct-DNA). Results of these analyses showed that 9-[(1,7-dicarba-closo-dodecaborane-1-yl)propylamino]acridine ( 30 ) interacted strongly with ct-DNA, indicating its ability to intercalate into DNA, whereas 9-[(1,7-dicarba-closo-dodecaborane-1-yl)propanamido]acridine ( 29 ) changed the B-form of ct-DNA to the Z form. Compound 30 demonstrated cytotoxicity, was able to inhibit cell proliferation, arrest the cell cycle in the S phase in the HeLa cancer cell line, and induced the production of reactive oxygen species (ROS). In addition, it was specifically localized in lysosomes and was a weak inhibitor of Topo IIα.     

Artificial neural network to predict the power number of agitated tanks fed by CFD simulations

The power consumption of the agitator is a critical variable to consider in the design of a mixing system. It is generally evaluated through a dimensionless number known as the power number $N_p$. Multiple empirical equations exist to calculate the power number based on the Reynolds number $Re$ and dimensionless geometrical variables that characterize the tank, the impeller, and the height of the fluid. However, correlations perform poorly outside of the conditions in which they were established. We create a rich database of 100 k computational fluid dynamics (CFD) simulations. We simulate paddle and pitched blade turbines in unbaffled tanks from $Re$ 1 to 100 and use an artificial neural network (ANN) to create a robust and accurate predictor of the power number. We perform a mesh sensitivity analysis to verify the precision of the $N_p$ values given by the CFD simulations. To sample the 100 k mixers by their geometrical and physical properties, we use the Latin hypercube sampling (LHS) method. We then normalize the data with a MinMax transformation to put all features in the same scale and thus avoid bias during the ANN's training. Using a grid search cross-validation, we find the best architecture of the ANN that prevents overfitting and underfitting. Finally, we quantify the performance of the ANN by extracting 30% of the database, predicting the $N_p$ using the ANN, and evaluating the mean absolute percentage error. The mean absolute error in the ANN prediction is 0.5%, and its accuracy surpasses correlations even for untrained geometries.