Evolving an accurate model based on machine learning approach for prediction of dew-point pressure in gas condensate reservoirs

Over the years, accurate prediction of dew-point pressure of gas condensate has been a vital importance in reservoir evaluation. Although various scientists and researchers have proposed correlations for this purpose since 1942, but most of these models fail to provide the desired accuracy in prediction of dew-point pressure. Therefore, further improvement is still needed. The objective of this study is to present an improved artificial neural network (ANN) method to predict dew-point pressures in gas condensate reservoirs. The model was developed and tested using a total set of 562 experimental data point from different gas condensate fluids covering a wide range of variables. After a series of optimization processes by monitoring the networks performance, the best network structure was selected. This study also presents a detailed comparison between the results predicted by this ANN model and those of other universal empirical correlations for estimation dew-point pressure. The results showed that the developed model outperforms all the existing methods and provides predictions in acceptable agreement with experimental data. Also it is shown that the improved ANN model is capable of simulating the actual physical trend of the dew-point pressure versus temperature between the cricondenbar and cricondenterm on the phase envelope. Finally, an outlier diagnosis was performed on the whole data set to detect the erroneous measurements from experimental data.     

Use of artificial neural networks to determine parameters controlling the nanofibers diameter in electrospinning of nylon‐6,6

This study aimed to find out the primary factors influencing the diameter of electrospun nanofibers of nylon‐6,6 using artificial neural networks (ANNs). Four variables, namely, polymer concentration, working distance, injection rate, and applied voltage were considered as input parameters and the nanofibers diameter measured by scanning electron microscopy was taken as the output. The data were modeled and validated against a set of unseen data. The generated model was used to study the interactions occurring between the input variables and their effect on the diameter. Results show that the injection rate and the polymer concentration are major factors affecting the nanofibers diameter with inverse and direct relations with the diameter, respectively, while the working distance and the applied voltage have direct but minor effects on nanofibers diameter. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci 124:1589–1597, 2011     

Electrode Effects on Microstructure Formation During FLASH Sintering of Yttrium‐Stabilized Zirconia

Systematic microstructural statistics for 3 mol% yttria‐stabilized zirconia synthesized by both conventional sintering and flash sintering with AC and DC current were obtained. Within the gage section, flash sintered microstructures were indistinguishable from those synthesized by conventional sintering procedures. With both techniques, full densification was obtained. However, from both AC and DC flash sintered specimens, heterogeneous grain size distributions and residual porosity were observed in the proximity of the electrodes. After DC sintering, an almost 400 times increased average grain size was observed near cathode compared to the gage section, unlike areas close to the anode. Concepts of Joule heating alone were not sufficient to explain the experimental observations. Instead, the activation energy for grain growth close to the cathode is lowered considerably during flash sintering, hence suggesting that electrode effects can cause significant heterogeneities in microstructure evolution during flash sintering. Microstructural characterization further indicated that microfracturing during green‐pressing and variations in contact resistance between the electrodes and the ceramic may also contribute to grain size gradients and hence local variations of physical properties.     

Quasi-Binary Transition Metal Dichalcogenide Alloys: Thermodynamic Stability Prediction,Scalable Synthesis,and Application

Transition metal dichalcogenide (TMDCs) alloys could have a wide range of physical and chemical properties, ranging from charge density waves to superconductivity and electrochemical activities. While many exciting behaviors of unary TMDCs have been demonstrated, the vast compositional space of TMDC alloys has remained largely unexplored due to the lack of understanding regarding their stability when accommodating different cations or chalcogens in a single-phase. Here, a theory-guided synthesis approach is reported to achieve unexplored quasi-binary TMDC alloys through computationally predicted stability maps. Equilibrium temperature–composition phase diagrams using first-principles calculations are generated to identify the stability of 25 quasi-binary TMDC alloys, including some involving non-isovalent cations and are verified experimentally through the synthesis of a subset of 12 predicted alloys using a scalable chemical vapor transport method. It is demonstrated that the synthesized alloys can be exfoliated into 2D structures, and some of them exhibit: i) outstanding thermal stability tested up to 1230 K, ii) exceptionally high electrochemical activity for the CO₂ reduction reaction in a kinetically limited regime with near zero overpotential for CO formation, iii) excellent energy efficiency in a high rate Li–air battery, and iv) high break-down current density for interconnect applications. This framework can be extended to accelerate the discovery of other TMDC alloys for various applications.     

Gallium-Based Liquid–Solid Biphasic Conductors for Soft Electronics

Soft and stretchable electronics have diverse applications in the fields of compliant bioelectronics, textile-integrated wearables, novel forms of mechanical sensors, electronics skins, and soft robotics. In recent years, multiple material architectures have been proposed for highly deformable circuits that can undergo large tensile strains without losing electronic functionality. Among them, gallium-based liquid metals benefit from fluidic deformability, high electrical conductivity, and self-healing property. However, their deposition and patterning is challenging. Biphasic material architectures are recently proposed as a method to address this problem, by combining advantages of solid-phase materials and composites, with liquid deformability and self-healing of liquid phase conductors, thus moving toward scalable fabrication of reliable stretchable circuits. This article reviews recent biphasic conductor architectures that combine gallium-based liquid-phase conductors, with solid-phase particles and polymers, and their application in fabrication of soft electronic systems. In particular, various material combinations for the solid and liquid phases in the biphasic conductor, as well as methods used to print and pattern biphasic conductive compounds, are discussed. Finally, some applications that benefit from biphasic architectures are reviewed.     

Peptide mass mapping constrained with stable isotope-tagged peptides for identification of protein mixtures.

Through proteolysis and peptide mass determination using mass spectrometry, a peptide mass map (PMM) can be generated for protein identification. However, insufficient peptide mass accuracy and protein sequence coverage limit the potential of the PMM approach for high-throughput, large-scale analysis of proteins. In our novel approach, nonlabile protons in particular amino acid residues were replaced with deuteriums to mass-tag proteins of the S. cerevisiae proteome in a sequence-specific manner. The resulting mass-tagged proteolytic peptides with characteristic mass-split patterns can be identified in the data search using constraints of both amino acid composition and mass-to-charge ratio. More importantly, the mass-tagged peptides can further act as internal calibrants with high confidence in a PMM to identify the parent proteins at modest mass accuracy and low sequence coverage. As a result, the specificity and accuracy of a PMM was greatly enhanced without the need for peptide sequencing or instrumental improvements to obtain increased mass accuracy. The power of PMM has been extended to the unambiguous identification of multiple proteins in a 1D SDS gel band including the identification of a membrane protein.     

Effect of gas diffusion electrode pre-treatment by ultrasonic bath cleaning technique on proton exchange membrane fuel cell performance

Ultrasonic bath cleaning technique was successfully applied for the pre-treatment of the gas diffusion electrode (GDE) before membrane-electrode assembly (MEA). The results show that ultrasonic bath pre-treatment significantly improves MEA performance in all current density regions, especially at the high current density region. This technique also reduces the time of the MEA conditioning, at least 30%. Electrochemical impedance spectroscopy (EIS) results demonstrate that pre-treatment of GDEs by ultrasonic bath technique leads to a decrease in the cell impedance especially in the ohmic resistance, which is very well due to the GDE cleaning process.