Quantifying the mechanical properties of polymeric tubing and scaffold using atomic force microscopy and nanoindentation

Measurement of mechanical parameters of polymeric scaffolds presents a significant challenge due to their intricate shape and small characteristics dimensions of their elements—around 100 μm. In this study, mechanical properties of polymeric tubing and scaffold, made of biodegradable poly(l ‐lactic) acid (PLLA), were characterized using atomic force microscopy (AFM) and nanoindentation, complemented with tensile testing. AFM was employed to assess the properties of the tube and scaffold locally, while nanoindentation produced results with a dependency on the depth of indentation. As a result, the AFM‐measured elastic modulus differs from the nanoindentation data due to a substantial difference in indentation depth between the two methods. With AFM, a modulus between 2 and 2.5 GPa was measured, while a wide range was obtained from nanoindentation on both the tube and scaffold, depending on the indentation scale. Changes in the elastic modulus with in‐vitro degradation and aging were observed over the 1‐year period. To complement the indentation measurements, tensile testing was used to study the structural behavior of the tube, demonstrating the yielding, hardening and fracture properties of the material. POLYM. ENG. SCI., 59:1084–1091, 2019. © 2019 Society of Plastics Engineers     

Efficient conversion of light paraffinic naphtha to aromatics over metal-modified Mo/MFI catalysts

Journal of Porous Materials - The transformation of light naphtha to value-added aromatic compounds is gaining momentum in the petrochemical industry. In this work, a series of metal modified...     

Heat transfer enhancement in oscillatory two-phase flow of Casson and ferrofluid comprising differently shaped nanoparticles in a horizontal composite channel

The most commonly discussed topic at the present time is the fluid flow in a channel having a porous area, as it is of practical importance for petroleum extraction, frequently isolated irrigation, coolant circulation, biofluid transportation in living organisms, and industrial cleaning systems. An investigation of heat transfer characteristics of unsteady magnetohydrodynamics oscillatory two-immiscible fluid flow of Casson fluid (CF) and ferrofluid (FF) in a long-infinite horizontal composite channel is performed analytically. The channel is divided into two regions. Region I is occupied by a porous region with CF, while Region II is a clear region filled with FF. The mathematical system of coupled partial differential equations is solved analytically considering the two-term periodic and nonperiodic functions. The influences of physical parameters such as CF parameter, porosity parameter, nanoparticles volume fraction, Hartmann number, periodic frequency parameter, oscillations amplitude, and pressure on momentum as well as heat transfer are presented through graphical illustrations (two-dimensional along with three-dimensional) and in tabular form using the MATHEMATICA program. Four different shaped nano-size ferroparticles are used in this study. The investigation of four different nanosized ferroparticles exhibits that the momentum transfer is higher when brick-shaped nanosized ferroparticles are added to the base fluid, water. It is also observed that thermal performance enhances in the case of brick-shaped nanosized ferroparticles compared to the blade, cylinder, and platelet-shaped nanosized ferroparticles. It is observed that the dispersion of brick-shaped nanosized ferroparticles is recommended in base fluid water for greater thermal performance through a horizontal channel.     

Microwave assisted synthesis and antimicrobial activity of Fe3O4-doped ZrO2 nanoparticles

Composites play important role in dental filling by controlling shrinkage along with correction in teeth's shape and position. Rehabilitation of severely worn dentition can be achieved using mechanically strong composites. This study aims to synthesize zirconia-based composites to be used as dental fillers. Effect of microwave powers (100–900?W) along with Fe₃O₄ doping are studied on the structural, mechanical and magnetic properties of stabilized zirconia. SEM and TEM reveal formation of spherical nanoparticles with diameter of ～30?nm. XRD results shows phase pure tetragonal zirconia (t-ZrO₂) at microwave power of 500?W without any post heat treatment. Crystallite size calculated from XRD data (～23?nm) matches well with the previously reported value for stabilization of t-ZrO₂. Microwave energy dissipation results in stresses causing volume shrinkage leading to monoclinic to tetragonal phase transformation with higher X-ray density and hardness of ～1347HV. VSM results show ferromagnetic response with low coercivity (600Oe) value and saturation magnetization (～2emu/g). It is worth mentioning here that this is one of its kind study reporting synthesis of room temperature stabilized Fe₃O₄ doped zirconia composites at microwave power of 500?W. Antibacterial studies reveal inhibition zone of ～32?mm against bacillus bacteria suggesting their potential use as dental filler.     

Influence of reduced graphene oxide on structural,optical, thermal and dielectric properties of SnO2 nanoparticles

The present work has been carried out with the aim to synthesize tin oxide decorated reduce graphene oxide nanocomposite (SnO₂/RGO-Nc) via in-situ synthesis process and the influence of RGO loading on structural, optical, thermal and dielectric properties of SnO₂ has been discussed. The XRD, FESEM coupled with EDX elemental mapping, TEM, FTIR, Raman and XPS results reveal that the SnO₂ nanoparticles have been successfully incorporated onto the RGO sheets. The reduction in the energy gap of the composite sample as compared to SnO₂ measured from the Tauc’s relation can be attributed to strong coupling between RGO and SnO₂ NPs. Thermogravimetric analysis (TGA) shows improved thermal stability of the SnO₂/RGO-Nc. From the dielectric measurements, it is observed that the dielectric constant and dielectric loss decreases as frequency of applied field increases. AC conductivity of all samples increases as applied frequency increases which follows Jonscher’s power law. All composite samples show better conductivity as compared to SnO₂. This is due to the formation of continuous conductive pathway between SnO₂ and RGO sheets. Further high dielectric constant, low loss and high ac conductivity have been observed at optimum loading of RGO in SnO₂/RGO2-Nc as compared to other composite samples which is due the percolation effects. The impedance analysis exhibits only one semicircle for SnO₂ and SnO₂/RGO composite which suggests that the involvement of grain boundaries dominated over the grain contribution.     

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this sub-band image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.     

EMI Shielding Characteristics of Electrically Conductive Polymer Blends of PS/PANI in Microwave and IR Region

Conductive polymeric blends (CPBs) of polystyrene and polyaniline (PS/PANI) were prepared by solution casting method in various compositions. Film thickness of CPBs was achieved?～?250 micron. PS/PANI blend films were analyzed for electromagnetic interference (EMI) shielding characteristics in microwave and near-infrared (NIR) regions. PS/PANI blends showed remarkable features. Most mobile telecommunications use GHz frequency range and shielding effectiveness was observed in 9 GHz to 18 GHz. In 9 GHz to 18 GHz frequency range, 45 dB shielding effectiveness was measured. CPBs were also analyzed in the NIR region and showed transmittance of <1%. Microwaves and NIR radiation are the most abundant in the environment and cause damage to human health. Both types of radiation causes serious damage to electronic devices as well.

     

An effective method for classification with missing values

Classification is one of the most important tasks in machine learning with a huge number of real-life applications. In many practical classification problems, the available information for making object classification is partial or incomplete because some attribute values can be missing due to various reasons. These missing values can significantly affect the efficacy of the classification model. So it is crucial to develop effective techniques to impute these missing values. A number of methods have been introduced for solving classification problem with missing values. However they have various problems. So, we introduce an effective method for imputing missing values using the correlation among the attributes. Other methods which consider correlation for imputing missing values works better either for categorical or numeric data, or designed for a particular application only. Moreover they will not work if all the records have at least one missing attribute. Our method, Model based Missing value Imputation using Correlation (MMIC), can effectively impute both categorical and numeric data. It uses an effective model based technique for filling the missing values attribute wise and reusing then effectively using the model. Extensive performance analyzes show that our proposed approach achieves high performance in imputing missing values and thus increases the efficacy of the classifier. The experimental results also show that our method outperforms various existing methods for handling missing data in classification.