Optimization of fused filament fabrication process parameters for mechanical responses of weather-resistant polymer (acrylonitrile styrene acrylate)

Additively manufactured polymeric products for automotive, aerospace, and biomedical applications are usually intended for service in an outdoor environment with high mechanical loading conditions. The strength and sustainability of the products can be significantly degraded due to the outdoor environmental conditions such as UV light, moisture, heat, and so forth. In this research work, a novel weather-resistant polymer (WRP) material, that is, acrylonitrile styrene acrylate (ASA), has been studied. Furthermore, this work aims to study the effect of process parameters and enhance the strength of WRP (ASA) specimens using the FFF process. The optimized process parameters, that is, build orientation (BO), extrusion temperature (ET), layer thickness (LT), and printing speed (PS), were identified based on the tensile and flexural strength using the Taguchi technique and statistical analysis. The best tensile and flexural strengths for the specimen were achieved at both orientations (XYZ and ZXY) TS: 255°C ET, 0.14 mm LT, 50 mm/s PS; and FS: 245°C ET, 0.28 mm LT, 50 mm/s PS, respectively. Regression model was developed to investigate the correlation between the process parameters with tensile and flexural strength. A validation test confirmed the findings, and the error between the actual and predicted values is less than ±10%.     

Lateral trench oxide Schottky rectifier on SOI for power integrated circuits

In this paper, a Lateral Trench Oxide Schottky (LTOS) rectifier on silicon-on-insulator suitable for power integrated circuits is presented. The proposed structure utilizes a surface Schottky contact with vertical field-plate placed in a trench filled with oxide. The field-plate reduces the electric field on the Schottky contact and suppresses the barrier lowering effect leading to significant improvement in the device performance. Further, the proposed structure folds the drift region in vertical and horizontal directions resulting substantial reduction in pitch length of the device. Two-dimensional numerical simulations have been performed to analyse and optimize the performance of proposed device and results are compared with that of the conventional lateral Schottky rectifier. The LTOS rectifier provides 60 % improvement in breakdown voltage and 50 % reduction in pitch length as compared to the conventional device while maintaining low forward voltage drop and low reverse leakage current.     

Synergy of combining sonolysis and photocatalysis in the degradation and mineralization of chlorinated aromatic compounds

Merits of using advanced oxidation processes such as sonolysis and photocatalysis as well as a combination of the two have been explored using model herbicides such as 2,4-dichlorophenoxy acetic acid and 2,4-dichlorophenoxypropionic acid and the chlorinated phenols 2,4-dichlorophenol and 2,4,6-trichlorophenol. Whereas sonolysis is quite effective in the initial degradation of chlorinated aromatic molecules, complete mineralization is difficult to achieve. Photocatalysis is selective toward the degradation of polar compounds but causes the build up of undesirable chemical intermediates. In contrast to sonolytic degradation, photocatalysis is very effective toward achieving complete mineralization. By simultaneously carrying out high-frequency sonolysis and photocatalysis we have succeeded in achieving faster and complete mineralization with no build up of toxic intermediates even at very low catalyst loadings. The synergy of combining the two advanced oxidation processes is discussed.     

Direct numerical simulation of laminar flame–wall interaction for a novel H2-selective membrane/injector configuration

Direct numerical simulations are performed to investigate the transient processes of laminar flame–wall interaction and quenching near a porous, permeable wall and compared against a reference case of a non-porous impermeable wall. A boundary condition formulation that models species (hydrogen in this case) transport through a permeable wall, driven by the fuel species partial pressure difference between the feed and the permeate side of a selective membrane, has been implemented in a high-order finite difference direct numerical simulation code for reactive flows (S3D) by Chen et al. (2009) [1]. The present results are obtained for lean, stoichiometric and rich initial mixture conditions on the permeate side of the permeable wall and indicate that the characteristic parameters of the flame–wall interaction (wall heat flux, quenching distance) are affected to a large extent by the presence of the membrane hydrogen flux. Concurrently, the hydrogen flux through the membrane is also strongly affected by the presence of the flame during the transient flame–wall interaction process, finally resulting in a strong feedback mechanism between the membrane hydrogen flux and the flame that greatly increases boundary layer flashback speeds at fuel lean conditions.     

On the behavior of thin walled composite beams with stochastic properties under matrix cracking damage

Thin walled composite beam structures are prone to damage which results in change in the performance of these structures. The change in the performance due to damage may get confused with the variation in the performance due uncertainties in the properties of these structures. Here, the performances of the thin walled composite beam under matrix cracking damage having material uncertainties are studied. The cross-sectional stiffness properties are obtained using thin walled beam formulation, which is based on a mixed force and displacement method. The stochastic behaviors of material properties are obtained from previous experimental and analytical studies. The effects of matrix cracking are introduced through the changes in the extension, extension–bending and bending matrices of composites. The effects of matrix cracking on out-of-plane bending, inplane bending and torsion cross-sectional properties are studied at different crack densities for stochastic material properties. Further, the effects of matrix cracking and uncertainties on measurable properties such as deflections and frequencies are studied. Results show that the beam responses at different crack densities get mixed due to the material uncertainties. The estimates of variance obtained for observable system properties due to uncertainty can be used for developing more robust damage detection algorithms.     

Normally Oriented Adhesion versus Friction Forces in Bacterial Adhesion to Polymer‐Brush Functionalized Surfaces Under Fluid Flow

Bacterial adhesion is problematic in many diverse applications. Coatings of hydrophilic polymer chains in a brush configuration reduce bacterial adhesion by orders of magnitude, but not to zero. Here, the mechanism by which polymer‐brush functionalized surfaces reduce bacterial adhesion from a flowing carrier fluid by relating bacterial adhesion with normally oriented adhesion and friction forces on polymer (PEG)‐brush coatings of different softness is studied. Softer brush coatings deform more than rigid ones, which yields extensive bond‐maturation and strong, normally oriented adhesion forces, accompanied by irreversible adhesion of bacteria. On rigid brushes, normally oriented adhesion forces remain small, allowing desorption and accordingly lower numbers of adhering bacteria result. Friction forces, generated by fluid flow and normally oriented adhesion forces, are required to oppose fluid shear forces and cause immobile adhesion. Summarizing, inclusion of friction forces and substratum softness provides a more complete mechanism of bacterial adhesion from flowing carrier fluids than available hitherto.     

LIBS based spectroscopic analysis and antidiabetic evaluation of a polyherbal formulation

Although plants have long been a major source of medicine, there is renewed interest in studying the phytochemistry and use of herbal formulations. This paper reports spectroscopic analysis using Laser Induced Breakdown Spectroscopy (LIBS) of a polyherbal formulation, whose antidiabetic activity has also been demonstrated on rat models. LIBS analysis revealed the presence of elements such as Na, K, Mg, Ca, H, O and N. The antidiabetic study showed that amongst the four doses studied (50, 100, 150 and 200 mg/kg bw), the dose of 150 mg/kg bw registered the maximum fall in Blood Glucose Level (BGL) in both normal and diabetic (sub and mild) rats in the Glucose Tolerance Test (GTT) study—normal rats (22 %), sub-diabetic (36.6 %) and mild-diabetic (39 %). The dose of 150 mg/kg also showed the maximum fall of 23.7 % and 22 % in BGL during fasting BGL and GTT studies of normal rats, respectively. The formulation also showed significant antioxidant activity assessed using in vitro assays. The study validates for the first time the therapeutic use of an antidiabetic polyherbal formulation.     

Efficient prediction of drug–drug interaction using deep learning models

A drug–drug interaction or drug synergy is extensively utilised for cancer treatment. However, prediction of drug–drug interaction is defined as an ill‐posed problem, because manual testing is only implementable on small group of drugs. Predicting the drug–drug interaction score has been a popular research topic recently. Recently many machine learning models have proposed in the literature to predict the drug–drug interaction score efficiently. However, these models suffer from the over‐fitting issue. Therefore, these models are not so‐effective for predicting the drug–drug interaction score. In this work, an integrated convolutional mixture density recurrent neural network is proposed and implemented. The proposed model integrates convolutional neural networks, recurrent neural networks and mixture density networks. Extensive comparative analysis reveals that the proposed model significantly outperforms the competitive models.Inspec keywords: cancer, learning (artificial intelligence), drugs, recurrent neural nets, convolutional neural nets, drug delivery systemsOther keywords: drug synergy, drug–drug interaction score, drug–drug interaction prediction, deep learning, cancer treatment, machine learning, convolutional mixture density recurrent neural network     

Synthesis,characterization and comparative OFET behaviour of indenofluorene–bithiophene and terthiophene alternating copolymers

The synthesis of alternating copolymers of tetraalkylindenofluorene with bithiophene and terthiophene using Suzuki polycondensation route is reported. We report on the optical and electrochemical properties of these copolymers. AFM analysis of the microscopic morphology of thin deposits showed that the copolymer with terthiophene units produced the more ordered films, with well-defined fibrillar structures, resulting from highly-regular dense packing due to strong π–π interchain interactions, in contrast to the amorphous bithiophene copolymer. Upon testing these materials in FETs the terthienyl copolymers displayed the higher charge mobilities among the studied compounds, with values of over 10^?4 cm² V^?1 s^?1 being obtained.     

Classification of drug molecules for oxidative stress signalling pathway

In humans, oxidative stress is involved in the development of diabetes, cancer, hypertension, Alzheimers’ disease, and heart failure. One of the mechanisms in the cellular defence against oxidative stress is the activation of the Nrf2‐antioxidant response element (ARE) signalling pathway. Computation of activity, efficacy, and potency score of ARE signalling pathway and to propose a multi‐level prediction scheme for the same is the main aim of the study as it contributes in a big amount to the improvement of oxidative stress in humans. Applying the process of knowledge discovery from data, required knowledge is gathered and then machine learning techniques are applied to propose a multi‐level scheme. The validation of the proposed scheme is done using the K‐fold cross‐validation method and an accuracy of 90% is achieved for prediction of activity score for ARE molecules which determine their power to refine oxidative stress.Inspec keywords: cancer, cellular biophysics, biochemistry, drugs, molecular biophysics, proteins, learning (artificial intelligence), medical computingOther keywords: oxidative stress, Nrf2‐antioxidant response element signalling pathway, ARE signalling pathway, diabetes, cancer, hypertension, Alzheimers’ disease, heart failure, machine learning techniques, K‐fold cross‐validation method, ARE molecules