A multistep methodology for calculation of the tensile modulus in polymer/carbon nanotube nanocomposites above the percolation threshold based on the modified rule of mixtures

Influence of Inner Gas Curing Technique on the Development of Thermoplastic Nanocomposite Reinforcement

In this work, a comprehensive shrinkage and tensile strength characterization of unsaturated polyester (UPE-8340) and vinyl ester (VE-922) epoxy matrices and composites reinforced with multiwall carbon nanotubes (MWCNTs) was conducted. The aspect ratio of UPE and VE with methyl ethyl ketone peroxide (MEKP) was kept at 1:16.6; however, the weight of the MWCNTs was varied from 0.03 to 0.3 gm for the doping of the reinforced nanocomposites. Using a dumbbell-shaped mold, samples of the epoxy matrix without MWCNTs and with reinforced UPE/MWCNT and VE/MWCNT nanocomposites were made. The samples were then cured in a typical ambient chamber with air and an inner gas (carbon dioxide). The effect of the MWCNTs on UPE- and VE-reinforced composites was studied by observing the curing kinetics, shrinkage, and tensile properties, as well as the surface free energy of each reinforced sample in confined saline water. The CO2 curing results reveal that the absence of O2 shows a significantly lower shrinkage rate and higher tensile strength and flexural modulus of UPE- and VE-reinforced nanocomposite samples compared with air-cured reinforced nanocomposites. The construction that was air- and CO2-cured produced results in the shape of a dumbbell, and a flawless surface was seen. The results also show that smaller quantities of MWCNTs made the UPET- and VE-reinforced nanocomposites more stable when they were absorbed and adsorbed in concentrated salt water. Perhaps, compared to air-cured nanocomposites, CO2-cured UPE and VE nanocomposites were better at reducing shrinkage, having important mechanical properties, absorbing water, and being resistant to seawater.

Predicting of tunneling resistivity between adjacent nanosheets in graphene-polymer systems

In this work, the tunneling resistivity between neighboring nanosheets in grapheme-polymer nanocomposites is expressed by a simple equation as a function of the characteristics of graphene and tunnels. This expression is obtained by connecting two advanced models for the conductivity of graphene-filled materials reflecting tunneling role and interphase area. The predictions of the applied models are linked to the tested data of several samples. The impressions of all factors on the tunneling resistivity are evaluated and interpreted using the suggested equation. The calculations of tunneling resistivity for the studied examples by the model and suggested equation demonstrate the same levels, which confirm the presented methodology. The results indicate that the tunneling resistivity decreases by super-conductive graphene, small tunneling width, numerous contacts among nanosheets and short tunneling length.

Synthesis and Characterization of Polyhydroxyalkanoate/Graphene Oxide/Nanoclay Bionanocomposites: Experimental Results and Theoretical Predictions via Machine Learning Models

Predicting the mechanical properties of multiscale nanocomposites requires simulations that are costly from a practical viewpoint and time consuming. The use of algorithms for property prediction can reduce the extensive experimental work, saving time and costs. To assess this, ternary poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV)-based bionanocomposites reinforced with graphene oxide (GO) and montmorillonite nanoclay were prepared herein via an environmentally friendly electrochemical process followed by solution casting. The aim was to evaluate the effectiveness of different Machine Learning (ML) models, namely Artificial Neural Network (ANN), Decision Tree (DT), and Support Vector Machine (SVM), in predicting their mechanical properties. The algorithms' input data were the Young's modulus, tensile strength, and elongation at break for various concentrations of the nanofillers (GO and nanoclay). The correlation coefficient (R2), mean absolute error (MAE), and mean square error (MSE) were used as statistical indicators to assess the performance of the models. The results demonstrated that ANN and SVM are useful for estimating the Young's modulus and elongation at break, with MSE values in the range of 0.64-1.0% and 0.14-0.28%, respectively. On the other hand, DT was more suitable for predicting the tensile strength, with the indicated error in the range of 0.02-9.11%. This study paves the way for the application of ML models as confident tools for predicting the mechanical properties of polymeric nanocomposites reinforced with different types of nanofiller, with a view to using them in practical applications such as biomedicine.

Effect of contact number among graphene nanosheets on the conductivities of tunnels and polymer composites

Simple equations are expressed for tunnel conductivity, tunnel resistance and conductivity of a graphene-filled composite by the number of contacts and interphase part. More specially, the active filler amount is suggested by interphase depth, which changes the contact number. The conductivity of nanocomposite is presented by filler content, filler dimensions, tunneling length and interphase depth. The innovative model is surveyed by the experimented conductivity of real examples. Too, the impacts of numerous issues on the tunnel resistance, tunnel conductivity and conductivity of nanocomposite are discussed to validate the novel equations. The estimates agree with the experimented data and the impacts of several terms on the tunnel resistance, tunnel conductivity and conductivity of system are sensible. Thin and big nanosheets positively affect the nanocomposite's conductivity, but thick nanosheets improve the tunnel conductivity. High conductivity is found at short tunnels, while the nanocomposite's conductivity directly depends on the tunneling length. The dissimilar effects of these features on the tunneling properties and conductivity are described.

Simulating of effective conductivity for grapheme-polymer nanocomposites

The efficient conductivity of graphene-polymer systems is expressed supposing graphene, tunneling and interphase components. The volume shares and inherent resistances of the mentioned components are used to define the efficient conductivity. Besides, the percolation start and the share of graphene and interphase pieces in the nets are formulated by simple equations. Also, the resistances of tunneling and interphase parts are correlated to graphene conductivity and their specifications. Suitable arrangements among experimented data and model's estimates as well as the proper trends between efficient conductivity and model's parameters validate the correctness of the novel model. The calculations disclose that the efficient conductivity improves by low percolation level, dense interphase, short tunnel, large tunneling pieces and poor polymer tunnel resistivity. Furthermore, only the tunneling resistance can govern the electron transportation between nanosheets and efficient conductivity, while the big amounts of graphene and interphase conductivity cannot play a role in the efficient conductivity.

Progressing of a power model for electrical conductivity of graphene-based composites

This work presents a power equation for the conductivity of graphene-based polymer composites by the tunneling length, interphase deepness and filler size. The impressions of these factors on the effective concentration and percolation beginning of graphene nano-sheets in nanocomposites are also expressed. The developed equations for percolation beginning and conductivity are examined by the experimented data of some examples, which can guesstimate the interphase depth, tunneling size and percolation exponent. Besides, the impacts of numerous factors on the percolation beginning and conductivity are designed. The developed equation for percolation beginning shows the formation of thick interphase and large tunnels in the reported samples. So, disregarding of tunneling and interphase spaces in polymer graphene nanocomposites overpredicts the percolation beginning. Additionally, the developed model presents the acceptable calculations for the conductivity of samples. Among the mentioned parameters, the concentration and graphene conductivity in addition to the interphase depth induce the strongest effects on the conductivity of composites.

SENSOR FOR PREDNISOLONE DETECTION IN SPORTS DOPING

ABSTRACT Introduction: Prednisolone causes pro-inflammatory impulses to be inhibited and anti-inflammatory signals to be promoted. As a result, it alters how the body's immune system reacts to certain diseases. The World Anti-Doping Agency, however, has banned SNP and other glucocorticosteroids. An electrochemical sensor can be developed using a gold nanocomposite, polypyrrole nanoparticles and synthesized carbon nanotubes (Au-PPy NPs@CNTs). Objective: Develop an electrochemical sensor to detect prednisolone. Method: Au-PPy NPs@CNTs nanocomposite was chemically synthesized with a modified glassy carbon electrode (GCE) surface. Results: According to SEM data, the nanocomposite was composed of amorphous Au NPs, and PPy NPs deposited in tubes strongly entangled in a CNTs network. The wide linear range and low detection limit of the Au-PPy NPs@CNTs/GCE as prednisolone sensors were attributed to the combined catalytic performance of the Au and PPy NPs@CNTs nanostructures. Conclusion: The results of prednisolone detection in each specimen using the amperometric method indicated good accuracy. The accuracy and precision of Au-PPy NPs@CNTs/GCE for prednisolone detection were explored in blood samples from 5 young athletes aged 20-24 years who used prednisolone tablets (RSD less than 4.25%). In addition to monitoring prednisolone concentrations in athletes’ serum, Au-PPy NPs@CNTs/GCE can be used as a reliable prednisolone sensor. Level of evidence II; Therapeutic studies - investigating treatment outcomes.

Multilayer assemblies of cellulose nanocrystal - polyvinyl alcohol films featuring excellent physical integrity and multi-functional properties

A simplistic approach to cellulose nanocrystals (CNCs) percolation at low concentrations with multifold increases in properties, through the development of multilayered film assemblies was employed. CNC networks combined with polyvinyl alcohol (PVOH) thin films were fabricated leading to multilayer assembly of randomly aligned CNC nanorod bundles, similar to those found in biological structures. Oxygen impermeable barrier property of the films was achieved. The optical clarity remained mostly pristine while exhibiting improved UV absorbance. These films can be applied towards shielding UV sensitive materials that require optical transparency. A 415 and 2300% increase in strength and modulus, respectively, were observed for multilayered film with 10 wt% CNC loading as compared to the baseline neat PVOH film. The multilayers and networks formed through strong hydrogen bonds and structural alignment of the CNCs make this strategy effective in achieving enhanced properties at low CNCs loadings, which can be applied to other polymer films with property limitations.

Model Progress for Tensile Power of Polymer Nanocomposites Reinforced with Carbon Nanotubes by Percolating Interphase Zone and Network Aspects

In the present work, a simple simulation is advanced based on a Callister equation considering the impacts of interphase and carbon nanotube (CNT) nets on the strength of nanocomposites after percolation onset. The advanced model can analyze the strength of nanocomposite by filler aspect ratio (α), percolation beginning (φp), interphase depth (t), interphase power (σi), net density (N), and net power (σN). The empirical consequences of several samples agree with the estimations of the industrialised model. The nanocomposite strength straightly depends on "α", "t", "σi", "N", and "σN", while the radius and percolation onset of CNT play the inverse characters. The reasonable impacts of net and interphase possessions on the nanocomposite strength rationalise the accurate progress of the Callister equation.

Analysis of the Connecting Effectiveness of the Interphase Zone on the Tensile Properties of Carbon Nanotubes (CNT) Reinforced Nanocomposite

The establishment of interphase region around nanoparticles accelerates the percolating of carbon nanotubes (CNT) in polymer nanocomposites reinforced with CNT (PCNT), due to the linking productivity of interphase district before the physical connecting of nanoparticles. Therefore, the interphase is an important character in the networks of CNT in PCNT. Here, a simulation study is presented to investigate the interphase connection in the mechanical possessions of PCNT including tensile modulus and strength. A number of models comprising Takayanagi, Ouali, Pukanszky and Callister are developed by the assumption of an interphase district in the CNT excluded volume. The advanced models depict the optimistic influences of reedy and lengthy CNT besides dense interphase on the stiffness and tensile power of nanocomposites. The Pukanszky calculations depict that the interphase strength plays a more noteworthy role in the nanocomposites strength compared to the CNT length.

Calculation of the Electrical Conductivity of Polymer Nanocomposites Assuming the Interphase Layer Surrounding Carbon Nanotubes

The interphase layer surrounding nanoparticles can reflect the tunneling effect as the main mechanism of charge transferring in polymer/carbon nanotube (CNT) nanocomposites (PCNT). In this paper, the percolation threshold, effective volume fraction of CNT, and the portion of percolated filler after percolation are expressed by interphase and CNT waviness. Moreover, the developed terms are used to suggest the influences of CNT dimensions, interphase thickness, and waviness on the electrical conductivity of PCNT by conventional and developed models. Thin and long CNT, thick interphase, and low waviness obtain a high fraction of percolated CNT. However, the highest level of effective filler fraction is only calculated by the thinnest CNT and the thickest interphase. Furthermore, both models show that the thinnest and the longest CNT as well as the thickest interphase and the least CNT waviness cause the highest conductivity in PCNT, because they positively contribute to the formation and properties of the conductive network.

Development of Expanded Takayanagi Model for Tensile Modulus of Carbon Nanotubes Reinforced Nanocomposites Assuming Interphase Regions Surrounding the Dispersed and Networked Nanoparticles

In this paper, we consider the interphase regions surrounding the dispersed and networked carbon nanotubes (CNT) to develop and simplify the expanded Takayanagi model for tensile modulus of polymer CNT nanocomposites (PCNT). The moduli and volume fractions of dispersed and networked CNT and the surrounding interphase regions are considered. Since the modulus of interphase region around the dispersed CNT insignificantly changes the modulus of nanocomposites, this parameter is removed from the developed model. The developed model shows acceptable agreement with the experimental results of several samples. "ER" as nanocomposite modulus per the modulus of neat matrix changes from 1.4 to 7.7 at dissimilar levels of "f" (CNT fraction in the network) and network modulus. Moreover, the lowest relative modulus of 2.2 is observed at the smallest levels of interphase volume fraction ( ϕ i < 0.017), while the highest " ϕ i " as 0.07 obtains the highest relative modulus of 11.8. Also, the variation of CNT size (radius and length) significantly changes the relative modulus from 2 to 20.

Study on the Effects of the Interphase Region on the Network Properties in Polymer Carbon Nanotube Nanocomposites

The interphase region around nanoparticles changes the percolation threshold of long and thin nanoparticles, such as carbon nanotubes (CNT) in polymer nanocomposites. In this paper, the effects of the interphase region on the percolation threshold of nanoparticles and the network fraction are studied. New percolation threshold (φP) is defined by the role of the interphase in the excluded volume of nanoparticles (Vex). Moreover, the influences of filler and interphase size on the percolation volume fraction, the fraction of nanoparticles in the network as well as the volume fraction and relative density of the filler network are investigated. The least ranges of "φP" are obtained by thin and long CNT. Similarly, a thick interphase increases the "Vex" parameter, which causes a positive role in the percolation occurrence. Also, thin CNT and a thick interphase cause the high fraction of the filler network in the nanocomposites.

Simulation of Percolation Threshold, Tunneling Distance, and Conductivity for Carbon Nanotube (CNT)-Reinforced Nanocomposites Assuming Effective CNT Concentration

This article suggests simple and new equations for the percolation threshold of nanoparticles, the tunneling distance between nanoparticles, and the tunneling conductivity of polymer carbon nanotubes (CNTs) nanocomposites (PCNT), assuming an effective filler concentration. The developed equations correlate the conductivity, tunneling distance, and percolation threshold to CNT waviness, interphase thickness, CNT dimensions, and CNT concentration. The developed model for conductivity is applied for some samples and the predictions are evaluated by experimental measurements. In addition, the impacts of various parameters on the mentioned terms are discussed to confirm the developed equations. Comparisons between the calculations and the experimental results demonstrate the validity of the developed model for tunneling conductivity. High levels of CNT concentration, CNT length, and interphase thickness, as well as the straightness and thinness of CNTs increase the nanocomposite conductivity. The developed formulations can substitute for the conventional equations for determining the conductivity and percolation threshold in CNT-reinforced nanocomposites.

Effects of critical interfacial shear strength between a polymer matrix and carbon nanotubes on the interphase strength and Pukanszky's “B” interphase parameter

In this paper, the “B” interphase parameter in the Pukanszky model and interphase strength for polymer carbon nanotube (CNT) nanocomposites are expressed by the critical interfacial shear strength (τ_c) and interfacial shear strength (τ) between a polymer matrix and CNTs. A suggested model and a developed Pukanszky model for tensile strength of nanocomposites are combined to develop the equations for “B” and interphase strength. Many experimental data for various samples confirm the models. The impacts of all parameters on the “B” and interphase strength are explained to approve the developed equations. The contour plots display the same trends for the roles of all parameters in the “B” and interphase strength. Low “τ_c”, high “τ”, thin and large CNTs as well as a dense interphase are ideal to obtain the high levels for “B” and interphase strength. Among the studied parameters, CNT size largely controls the “B” and interphase strength, while the waviness and strength of CNTs play insignificant roles.

In this paper, the “B” interphase parameter in the Pukanszky model and interphase strength for polymer carbon nanotube (CNT) nanocomposites are expressed by the critical interfacial shear strength (τ_c) and interfacial shear strength (τ) between a polymer matrix and CNTs.