Investigation of PSt-MWCNT concentration on epoxyacrylate photopolymerization and conductivity of polymer films

Photopolymerization kinetics and conductivity changes of epoxyacrylate composites for various loading modified PSt-MWCNT weight fractions changing from 0.0025 to 0.2 wt.% were evaluated by performing photo differential scanning calorimetry (photo-DSC) and four point conductivity measurements. 0.2% PSt-MWCNT additive polymeric films had their electrical conductivity boosted by 6% more than non-additive polymeric films.     

Synthesis,Thermal and Morphological Properties of Polyurethanes Containing Azomethine Linkage

This paper presents synthesis, photophysical, electrochemical, thermal and morphological properties of Schiff bases containing various side-group substitutions and polyurethanes (PUs) containing azomethine linkage. Morphological properties of PUs containing azomethine bonding were investigated by scanning electron microscopy (SEM). SEM images showed that PU containing azomethine consist of semi-crystalline particles. Thermal transitions in PUs containing azomethine units were studied using DSC. The obtained DSC curves showed that PUs containing azomethine are semi-crystalline materials due to they contain both crystallization and melting peaks. Electrochemical properties also investigated by using cyclic voltammetry (CV). According to the cyclic voltommagrams and CV data, PUs containing azomethine have below 2.0 eV electrochemical band gap.     

Power·delay product in COSMOS logic circuits

In this simulation work, we use COSMOS logic devices—a novel single gate CMOS architecture recently announced [1]—in multi-input logic gates, assessing its performance in terms of power·delay product. We consider three different multi-input logic circuits: a two-input NOR gate, a three-input NOR gate, and a three-input composite NOR/NAND (NORAND) gate. For this power·delay analysis, the transient TCAD simulations are employed in a mixed-mode approach where circuit and device simulations are coupled together, culminating in the delay response of the circuits as well as the static/dynamic current components. The analysis shows that all circuits, except the 3-input NOR gate, has acceptable characteristics at low-power applications and static leakage limits all COSMOS circuits at high-bias conditions.     

Synthesis,characterization, thermal stability,conductivity, and band gap of a new aromatic polyether containing an azomethine as a side

In this study, the oxidative polycondensation reaction conditions of 4‐[(4‐methylphenyl)iminomethyl]phenol (4‐MPIMP) were studied by using oxidants such as air O₂, H₂O₂, and NaOCl in an aqueous alkaline medium between 50 and 90°C. The structures of the synthesized monomer and polymer were confirmed by FTIR, UV–vis, ¹H–¹³C‐NMR, and elemental analysis. The characterization was made by TGA‐DTA, size exclusion chromatography (SEC), and solubility tests. At the optimum reaction conditions, the yield of poly‐4‐[(4‐methylphenyl)iminomethyl]phenol (P‐4‐MPIMP) was found to be 28% for air O₂ oxidant, 42% for H₂O₂ oxidant, and 62% for NaOCl oxidant. According to the SEC analysis, the number–average molecular weight (M_n), weight–average molecular weight (M_w), and polydispersity index values of P‐4‐MPIMP were found to be 4400 g mol^?1, 5100 g mol^?1, and 1.159, using H₂O₂, and 4650 g mol^?1, 5200 g mol^?1, and 1.118, using air O₂, and 5100 g mol^?1, 5900 g mol^?1, and 1.157, using NaOCl, respectively. According to TG analysis, the weight losses of 4‐MPIMP and P‐4‐MPIMP were found to be 85.37% and 72.19% at 1000°C, respectively. P‐4‐MPIMP showed higher stability against thermal decomposition. Also, electrical conductivity of the P‐4‐MPIMP was measured, showing that the polymer is a typical semiconductor. The highest occupied molecular orbital and the lowest unoccupied molecular orbital energy levels and electrochemical energy gaps (E) of 4‐MPIMP and P‐4‐MPIMP were found to be ?5.76, ?5.19; ?3.00, ?3.24; 2.76 and 1.95 eV, respectively. According to UV–vis measurements, optical band gaps (E_g) of 4‐MPIMP and P‐4‐MPIMP were found to be 3.34 and 2.82 eV, respectively. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Performance evaluation of a submerged membrane bioreactor for the treatment of brackish oil and natural gas field produced water

Produced water, which is co-produced during oil and gas manufacturing, represents one of the largest sources of oily wastewaters. Therefore, treatment of this produced water may improve the economic viability and lead to a new source of water for beneficial use. In this study a submerged hollow fiber membrane bioreactor (MBR) has been studied experimentally for the treatment of brackish oil and natural gas field produced water. This type of wastewater is also characterized with relatively moderate to high amount of salt, oil and total petroleum hydrocarbons (TPH). However, the bacteria which are growing in conventional activated sludge and MBR cannot survive at these strict conditions, therefore acclimation of the bacteria is of vital importance. The performance of the biological system, membrane permeability, the rate and extent of TPH biodegradability have been investigated under different sludge age and F/M ratios. The results obtained by gas chromatography analyses showed that the MBR system could be very effective in the removal of TPH from produced water and a significant improvement in the effluent quality was achieved.     

Synthesis and characterization of polyphenols derived from 4-fluorobenzaldeyde: The effect of electron-donating group on some physical properties

In this study, we proposed to investigate how the effect of electron-donating methyl ( CH₃) group at p-position of amin ( NH₂) group in aminophenol (AP) compound affected thermal stability, optical, electrochemical properties, and conductivity measurement. For this reason, we choice 2-AP and 2-amino-4-methylphenol compounds and synthesized phenolic monomers by condensation reactions 4-fluorobenzaldehyde with aromatic amino phenols. Then, these monomers were converted to their polyphenol derivatives by oxidative polycondensation reactions in an aqueous alkaline medium. Structural characterizations were carried out by FTIR, NMR, and size exclusion chromatography. Cyclic voltammetry was used to determine the electrochemical oxidation-reduction characteristics. Optical properties were investigated by UV–vis and fluorescence analyses. Solid state electrical conductivities were measured on polymer films by four-point probe technique using an electrometer. Thermal data of monomer/polymer and polymers were obtained by TG-DTA and DSC techniques, respectively. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Cost-conscious comparison of supervised learning algorithms over multiple data sets

In the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi²Test, a generalization of our previous work, for ordering multiple learning algorithms on multiple data sets from “best” to “worst” where our goodness measure is composed of a prior cost term additional to generalization error. Our simulations show that Multi²Test generates orderings using pairwise tests on error and different types of cost using time and space complexity of the learning algorithms.     

Integrated optimal topology design and shape optimization using neural networks

In this paper, neural network- and feature-based approaches are introduced to overcome current shortcomings in the automated integration of topology design and shape optimization. The topology optimization results are reconstructed in terms of features, which consist of attributes required for automation and integration in subsequent applications. Features are defined as cost-efficient simple shapes for manufacturing. A neural network-based image-processing technique is presented to match the arbitrarily shaped holes inside the structure with predefined features. The effectiveness of the proposed approach in integrating topology design and shape optimization is demonstrated with several experimental examples.     

Molecular dynamics simulation of compressible hot/cold moving lid-driven microcavity flow

Hard-sphere molecular dynamics simulations of lid-driven microcavity gas flow with various subsonic speeds and lid temperatures are conducted. Simulations with faster and colder lids show streamlines of stronger primary vortices. Variations of mass and energy centers with respect to lid speed and temperature are examined. Center of energy is less sensitive to employed lid conditions than center of gravity is. Although moving lid imparts energy into fluid, due to change of impingement rates on the walls of fixed temperature, average energy within the cavity seems quite insensitive to the subsonic lid speed. Behavior of compressibility at both top corners is observed even at low Mach numbers widely considered within incompressible flow region. While high Knudsen number causes considerable property slips near the lid, two-dimensional pressure, density, and temperature plots of excellent quality are generated. Results are promising in use of molecular dynamics simulations for compressible vortex flow analyses while providing insights for understanding microfluidics and nanofluidics in context of molecular mass, momentum and heat transfer in microscale and nanoscale systems.     

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.