Poly(lactide)/cellulose nanocrystal nanocomposites by high-shear mixing

There is currently considerable interest in developing stiff, strong, tough, and heat resistant poly(lactide) (PLA) based materials with improved melt elasticity in response to the increasing demand for sustainable plastics. However, simultaneous optimization of stiffness, strength, and toughness is a challenge for any material, and commercial PLA is well-known to be inherently brittle and temperature-sensitive and to show poor melt elasticity. In this study, we report that high-shear mixing with cellulose nanocrystals (CNC) leads to significant improvements in the toughness, heat resistance, and melt elasticity of PLA while further enhancing its already outstanding room temperature stiffness and strength. This is evidenced by (i) one-fold increase in the elastic modulus (6.48 GPa), (ii) 43% increase in the tensile strength (87.1 MPa), (iii) one-fold increase in the strain at break (∼6%), (iv) two-fold increase in the impact strength (44.2 kJ/m²), (v) 113-fold increase in the storage modulus at 90°C (787.8 MPa), and (vi) 10³-fold increase in the melt elasticity at 190°C and 1 rad/s (∼10⁵ Pa) via the addition of 30 wt% CNC. It is hence possible to produce industrially viable, stiff, strong, tough, and heat resistant green materials with improved melt elasticity through high-shear mixing.     

Determination of Electrical and Photoelectrical Properties of Schottky Diodes Made Using New Chitin Derivatives Synthesized as Interface Layer

Silicon - 5-(2,4-dichlorophenyl)-2-furoic acid and anthraquinone-2-carboxylic acid were reacted separately with chitin. The synthesized products were characterized by various spectroscopic methods...     

Stress wave propagation in adhesively bonded functionally graded cylinders: an improved model

This study presents an improved mathematical model to analyse the stress wave propagation in adhesively bonded functionally graded (FG) circular cylinders (butt joint) under an axial impulsive load. The volume fractions of the material constituents in the upper and lower cylinders were functionally tailored through the thickness of each cylinder using a power-law. The effective material properties of both cylinders, which are made of aluminum (Al) and silicon carbide (SiC), at any point were predicted by using the Mori–Tanaka homogenization scheme. In this improved model, the governing equations of the wave propagation include the spatial derivatives of local mechanical properties and were discretized by means of the finite difference method. The influence of these spatial derivatives and the compositional gradient exponent on the displacement and stress distributions of the joint was investigated. The material composition variations of both cylinders affected the displacement and stress fields whereas the compositional gradient exponent had a minor effect. The stress concentrations were alleviated in time, the displacement and stress distributions/variations around/along the upper and lower cylinder-adhesive interfaces were significantly affected by the adhesive layer. The spatial derivatives also affected the temporal histories of the displacement and stress components evaluated at the selected critical points of the upper cylinder, adhesive layer and lower cylinder. The consideration of the spatial local material derivatives provided a more accurate mathematical model of wave propagations through the graded layered structures.     

Designing carbon-supported Fe2O3 anodes for lithium ion batteries

Journal of Applied Electrochemistry - In this study, within the defined orthogonal array of Taguchi design, the hydrothermal process parameters have been optimized for fabricating the smallest...     

Using hybridized ANN-GA prediction method for DOE performed drying experiments

Abstract

Coal is an important component in the energy industry and plays a key role in energy-producing facilities. Moisture is a common condition that has a considerable impact on coal. Coal drying has long been a question of great interest in a wide range of fields. Defining parameters in the coal drying is obtained by experiments. High costs, time constraints, and repetition of an experiment are one of the most frequently stated problems with experimental works. Using qualitative methods with experiments can be more useful for identifying and characterizing the coal drying process. The purpose of this article is finding the effective parameters in the coal drying process by using a hybridized prediction method. Genetic Algorithm (GA) and Artificial Neural Network (ANN) are hybridized with each other to identify and characterize the coal drying process. GA-ANN algorithm is applied to the coal drying process to predict the moisture of coal, but it does not provide a decent result at first. Later, the Design of Experiment (DoE) methodology is performed to determine the main effects of six parameters. Two scenarios are generated because two parameters are not statistically significant. The first scenario excludes the air relative humidity parameter, and the second scenario excludes the air relative humidity and the velocity of air parameters. Following the application of the DoE method, GA-ANN reaches decent results in scenario-2.