Optimization and Prediction of Mechanical and Thermal Properties of Graphene/LLDPE Nanocomposites by Using Artificial Neural Networks

The focus of this work is to develop the knowledge of prediction of the physical and chemical properties of processed linear low density polyethylene (LLDPE)/graphene nanoplatelets composites. Composites made from LLDPE reinforced with 1, 2, 4, 6, 8, and 10 wt% grade C graphene nanoplatelets (C-GNP) were processed in a twin screw extruder with three different screw speeds and feeder speeds (50, 100, and 150 rpm). These applied conditions are used to optimize the following properties: thermal conductivity, crystallization temperature, degradation temperature, and tensile strength while prediction of these properties was done through artificial neural network (ANN). The three first properties increased with increase in both screw speed and C-GNP content. The tensile strength reached a maximum value at 4 wt% C-GNP and a speed of 150 rpm as this represented the optimum condition for the stress transfer through the amorphous chains of the matrix to the C-GNP. ANN can be confidently used as a tool to predict the above material properties before investing in development programs and actual manufacturing, thus significantly saving money, time, and effort.

Exploring the joint compositional variability of major components and trace elements in the Tellus soil geochemistry survey (Northern Ireland)

The complexity of modern geochemical data sets is increasing in several aspects (number of available samples, number of elements measured, number of matrices analysed, geological-environmental variability covered, etc), hence it is becoming increasingly necessary to apply statistical methods to elucidate their structure. This paper presents an exploratory analysis of one such complex data set, the Tellus geochemical soil survey of Northern Ireland (NI). This exploratory analysis is based on one of the most fundamental exploratory tools, principal component analysis (PCA) and its graphical representation as a biplot, albeit in several variations: the set of elements included (only major oxides vs. all observed elements), the prior transformation applied to the data (none, a standardization or a logratio transformation) and the way the covariance matrix between components is estimated (classical estimation vs. robust estimation). Results show that a log-ratio PCA (robust or classical) of all available elements is the most powerful exploratory setting, providing the following insights: the first two processes controlling the whole geochemical variation in NI soils are peat coverage and a contrast between “mafic” and “felsic” background lithologies; peat covered areas are detected as outliers by a robust analysis, and can be then filtered out if required for further modelling; and peat coverage intensity can be quantified with the %Br in the subcomposition (Br, Rb, Ni).

Dynamically mechanical and nano-impact (fatigue) analysis of touch screen thin films deposited on polyethylene terephthalate substrate

Nano-scale touch screen thin film have not been thoroughly investigated in terms of dynamic impact analysis under various strain rates. This research is focused on two different thin films, Zinc Oxide (ZnO) film and Indium Tin Oxide (ITO) film, deposited on Polyethylene Terephthalate (PET) substrate for the standard touch screen panels. Dynamic Mechanical Analysis (DMA) was performed on the ZnO film coated PET substrates. Nano-impact (fatigue) testing was performed on ITO film coated PET substrates. Other analysis includes hardness and the elastic modulus measurements, atomic force microscopy (AFM), Fourier Transform Infrared Spectroscopy (FTIR) and the Scanning Electron Microscopy (SEM) of the film surface.
Ten delta of DMA is described as the ratio of loss modulus (viscous properties) and storage modulus (elastic properties) of the material and its peak against time identifies the glass transition temperature (Tg). Thus, in essence the Tg recognizes changes from glassy to rubber state of the material and for our sample ZnO film, Tg was found as 388.3 K. The DMA results also showed that the Ten delta curve for Tg increases monotonically in the viscoelastic state (before Tg) and decreases sharply in the rubber state (after Tg) until recrystallization of ZnO takes place. This led to an interpretation that enhanced ductility can be achieved by negating the strength of the material.
For the nano-impact testing using the ITO coated PET, the damage started with the crack initiation and propagation. The interpretation of the nano-impact results depended on the characteristics of the loading history. Under the nano-impact loading, the surface structure of ITO film suffered from several forms of failure damages that range from deformation to catastrophic failures. It is concluded that in such type of application, the films should have low residual stress to prevent deformation, good adhesive strength, durable and good resistance to wear.

Mechanical properties and cellular response of novel electrospun nanofibers for ligament tissue engineering: Effects of orientation and geometry

Electrospun nanofibers are a promising material for ligamentous tissue engineering, however weak mechanical properties of fibers to date have limited their clinical usage. The goal of this work was to modify electrospun nanofibers to create a robust structure that mimics the complex hierarchy of native tendons and ligaments. The scaffolds that were fabricated in this study consisted of either random or aligned nanofibers in flat sheets or rolled nanofiber bundles that mimic the size scale of fascicle units in primarily tensile load bearing soft musculoskeletal tissues. Altering nanofiber orientation and geometry significantly affected mechanical properties; most notably aligned nanofiber sheets had the greatest modulus; 125% higher than that of random nanofiber sheets; and 45% higher than aligned nanofiber bundles. Modifying aligned nanofiber sheets to form aligned nanofiber bundles also resulted in approximately 107% higher yield stresses and 140% higher yield strains. The mechanical properties of aligned nanofiber bundles were in the range of the mechanical properties of the native ACL: modulus=158±32MPa, yield stress=57±23MPa and yield strain=0.38±0.08. Adipose derived stem cells cultured on all surfaces remained viable and proliferated extensively over a 7 day culture period and cells elongated on nanofiber bundles. The results of the study suggest that aligned nanofiber bundles may be useful for ligament and tendon tissue engineering based on their mechanical properties and ability to support cell adhesion, proliferation, and elongation.