Graphene-polymer Nanocomposites Research Articles

A Hierarchical 3D Graphene Nanocomposite Foam for Extremely Tough, Non‐Wettable, and Elastic Conductor

AbstractRecently, stretchable conductors and electrodes with hierarchically 3D foams have been highlighted for the development of simple and cost‐effective systems with enhanced performance in sensors, absorbents, energy storage devices, medical devices, and catalysts. For better versatility, additional properties such as mechanical robustness, water repellency, and thermal resistance are required for stable operation in tough, dry/wet conditions. Herein, a novel strategy is reported to fabricate highly robust and large‐area foam‐type conductor with a multiscale 3D matrix of a graphene/polymer nanocomposite (GNC) using a nickel‐based template. The 3D foam conductor with hierarchically porous GNC structures presents excellent stretchability (<160%), water repellency (contact angle > 126°), fire retardancy (>400 °C), and shock‐absorbing ability (>300 kPa), enabling potential application as versatile conductors working under extreme environments. In addition, it is demonstrated that the 3D GNC foam conductor can be used for healthcare electrode operating in wet environments under mechanical bending strain. It is believed that this work overcomes the lack of versatility of conventional bulk or 3D conductive polymer composites and can pave the way for the development of high‐performance, tough, and non‐wettable conductor that can operate under extreme environments.

Calculating the Electrical Conductivity of Graphene Nanoplatelet Polymer Composites by a Monte Carlo Method.

Electrical conductivity is one of several outstanding features of graphene–polymer nanocomposites, but calculations of this property require the intricate features of the underlying conduction processes to be accounted for. To this end, a novel Monte Carlo method was developed. We first established a randomly distributed graphene nanoplatelet (GNP) network. Then, based on the tunneling effect, the contact conductance between the GNPs was calculated. Coated surfaces (CSs) were next set up to calculate the current flow from the GNPs to the polymer. Using the equipotential approximation, the potentials of the GNPs and CSs met Kirchhoff’s current law, and, based on Laplace equation, the potential of the CSs was obtained from the potential of the GNP by the walk-on-spheres (WoS) method. As such, the potentials of all GNPs were obtained, and the electrical conductivity of the GNP polymer composites was calculated. The barrier heights, polymer conductivity, diameter and thickness of the GNP determining the electrical conductivity of composites were studied in this model. The calculated conductivity and percolation threshold were shown to agree with experimental data.

Nonlinear \u201coddities\u201d at the percolation of 3D hierarchical graphene polymer nanocomposites

The nonlinear rheology of a novel 3D hierarchical graphene polymer nanocomposites was investigated in this study. Based on an isotactic polypropylene, the nanocomposites were prepared using simple melt mixing, which is an industrially relevant and scalable technique. The novel nanocomposites stand out as having an electrical percolation threshold (≈0.94 wt%) comparable to solution mixing graphene-based polymer nanocomposites. Their nonlinear flow behavior was investigated in oscillatory shear via Fourier-transform (FT) rheology and Chebyshev polynomial decomposition. It was shown that in addition to an increase in the magnitude of nonlinearities with filler concentration, the electrical percolation threshold corresponds to a unique nonlinear rheological signature. Thus, in dynamic strain sweep tests, the nonlinearities are dependent on the applied angular frequency, potentially detecting the emergence of a weakly connected network that is being disrupted by the flow. This is valid for both the third relative higher harmonic from Fourier-transform rheology, I3/1, as well as the third relative viscous, v3/1, Chebyshev coefficient. The angular frequency dependency comprised non-quadratic scaling in I3/1 with the applied strain amplitude and a sign change in v3/1. The development of the nonlinear signatures was monitored up to concentrations in the conductor region to reveal the influence of a more robust percolated network.

Non-Destructive Investigation of Dispersion, Bonding, and Thermal Properties of Emerging Polymer Nanocomposites Using Close-Up Lens Assisted Infrared Thermography

AbstractPolymer nanocomposites possess unique sets of properties that make them suitable for different applications, including structural and flame-retardant material, electromagnetic wave reflector, sensors, thin film transistor, flexible display, and many more. The properties of these nanocomposite are dependent on nanofiller dispersion and bonding with polymer matrix (i.e. particle-matrix interaction). Thermography is a non-destructive method that may be used to gain insight into dispersion and particle-matrix interaction. Infrared (IR) radiation emitted from these nanomaterial polymer composite depends on the emissivity of the individual components. In addition, during flash heating and cooling, different thermal conductivity of components in the nanocomposite can influence pixel intensity differently in the IR image or video being captured. We have used an economical mid wavelength IR camera Fluke RSE600 equipped with a close-up macro lens and algorithm based on MATLAB image processing toolbox to analyse dispersion, voids and thermal diffusivity of patented graphene polymer nanocomposite materials (G-PMC) in micro-scale. These G-PMCs can act as a standard material to determine the potential of our IR thermography technique due to their homogeneity and lack of impurity due to unique fabrication process. Thermal diffusivity and dispersion of nanoparticles in our G-PMCs was estimated after irradiation with a xenon flash lamp by spatially mapping transient IR radiations from different G-PMCs using the Fluke RSE600 thermal imager. Results from thermography experiments were compared with scanning electron microscope (SEM) and Raman spectroscopy results. Micro-scale thermography was able to detect millimetre scale thermal diffusivity variation in the injection molded G-PMC samples and relate it to change in dispersion of nanofillers, unlike SEM and Raman, where micro-scale measurements could not determine the reason behind millimetre scale property variation. We believe this low cost, fast, micro-scale, non-destructive technique will provide valuable insight into functional polymer nanocomposite fabrication and corresponding electrical and thermal properties.

Cytotoxicity of Formulated Graphene and Its Natural Rubber Nanocomposite Thin Film in Human Vaginal Epithelial Cells: An Influence of Noncovalent Interaction.

Graphene family materials (GFMs) are extensively explored for various biomedical applications due to their unique physical properties. The prime challenge is to establish a conclusive safety profile of these nanomaterials and their respective products or devices. Formulating GFMs with appropriate ingredients (e.g., surfactant/compatibilizer) will help to disperse them homogeneously (i.e., within the polymer matrix in the case of polymer–graphene nanocomposites) and aid in good interfacial interaction to achieve the desired properties. However, no cytotoxicity report is available on the effects of the additives on graphene and its incorporated materials. Here, we report in vitro cytotoxicity of formulated FLG (FLG-C), i.e., a mixture of FLG, melamine, and sodium poly(naphthalene sulfonate) (SPS), along with natural rubber (NR) latex and FLG-C-included NR latex nanocomposite (FLG-C-NR) thin films on human vaginal epithelial (HVE) cells. FLG-C shows reduced cellular proliferation (∼55%) only at a longer exposure time (72 h) even at a low concentration (50 μg/mL). It also displays significant down- and upregulation in mitochondrial membrane potential (MMP) and reactive oxygen species (ROS), respectively, whereas no changes are observed in lactate dehydrogenase (LDH), propidium iodide (PI), uptake, and cell cycle analysis at 48 h. In vitro experiments on NR latex and FLG-C-NR latex thin films demonstrate that the incorporation of FLG-C does not compromise the biocompatibility of the NR latex. Further substantiation from the in vivo experiments on the thin films recommends that FLG-C could be suitable to prepare a range of biocompatible rubber latex nanocomposites-based products, viz., next-generation condoms (male and female), surgical gloves, catheters, vaginal rings, bladder–rectum spacer balloon, etc.

Wireless Monitoring of Small Strains in Intelligent Robots via a Joule Heating Effect in Stretchable Graphene–Polymer Nanocomposites

AbstractFlexible strain sensors are an important component for future intelligent robotics. However, the majority of current strain sensors must be electrically connected to a corresponding monitoring system via conducting wires, which increases system complexity and restricts the working environment for monitoring strains. Here, stretchable graphene–polymer nanocomposites that act as strain sensors using a Joule heating effect are reported. When the resistance of the sensor changes in response to a strain, the resulting change in temperature is wirelessly detected in an intelligent robot. By engineering and optimizing the surface structure of graphene–polymer nanocomposites, the fabricated strain sensors exhibit excellent stability when subjected to periodic temperature signals over 400 cycles while being periodically strained and deliver a high strain sensitivity of 7.03 × 10−4 °C−1 %−1 for strain levels of 0% to 30%. As a wearable electronic device, the approach provides the capability to wirelessly monitor small strains for intelligent robots at a high strain resolution of ≈0.1%. Moreover, when the strain sensing system operates as a multichannel structure, it allows precise strain detection simultaneously, or in sequence, for each finger of an intelligent robot.

A theory of frequency dependence and sustained high dielectric constant in functionalized graphene-polymer nanocomposites

Functionalized graphene/polymer composites are currently under extensive investigation due to their excellent mechanical and functional properties. Within the framework of electrical properties, many experiments have shown that their electrical conductivity and dielectric permittivity are strongly dependent on the frequency of the applied electric field and the volume concentration of graphene fillers. In this paper we first apply Dyre's hopping function and Debye's relaxation function to build the constitutive equations for the graphene fillers, polymer matrix, and the interface regions. In addition, Cauchy's cumulative probabilistic function is also introduced to account for the drastic increase of electron tunneling and the rapid build-up of Maxwell-Wagner-Sillars polarization at the interface. These functions are then integrated into an effective-medium framework in the complex domain to establish a complete theory for the frequency dependence of these two effective properties as a function of graphene loading. The developed theory is highlighted by a direct comparison with the experimental data of reduced graphene oxide/polypropylene (rGO/PP) nanocomposites. The results show that both the theory and experiment display a general increase of conductivity but a decrease of permittivity as frequency increases, and that, with the graphene loading of 0.2 vol%, there is a sustained, high level of dielectric constant beyond 1,000 covering the frequency range up to 104 Hz. This remarkable feature can be of particular significance to energy storage and conversion.

Studies on the mechanism for the sudden mechanical property drops of graphene/polymer nanocomposites

Graphene/polymer nanocomposites (GPNCs) have gained intense research interest in recent years. Graphene can improve the properties of the nanocomposites at low loadings, but usually causes sudden drops in the mechanical properties of the nanocomposites at similarly low loadings, risking the performance, reproducibility, and batch stability of the nanocomposites. This problem has been troubling the GPNCs field for years, but it is difficult to solve mainly because the mechanism of the sudden mechanical property drops has not been well documented yet. Here, we present a systematic study on this problem. At first, a statistical study was made to provide an overview of the sudden mechanical property drops. It was found that the sudden mechanical property drops were almost independent of the surface modification of graphene, and the in situ polymerization method sometimes leads to lower critical concentration than the solvent blending and melt blending methods. Then, we demonstrated a cutting‐off mechanism which unveiled that the formation of a continuous or semicontinuous network of graphene throughout the polymeric matrix was the main cause of the sudden mechanical property drops, and the low critical concentration of the sudden mechanical property drops was mainly due to the large aspect ratio of graphene. Finally, future research prospects were proposed. Overall, our work has provided new understandings and insights to the mechanical properties of GPNCs.

The effect of temperature and graphene concentration on the electrical conductivity and dielectric permittivity of graphene–polymer nanocomposites

Several recent experiments have revealed the remarkable influence of temperature and graphene concentration on the effective electrical properties of graphene–polymer nanocomposites, but no theory seems to exist at present to quantify such dependence. In this work, we develop a novel micromechanics-based homogenization scheme to connect the microstructural features of constituent phases to the temperature-dependent macroscopic conductivity and permittivity for the nanocomposites. The key microstructural features include the graphene volume concentration, temperature-dependent electrical properties of constituent phases, percolation threshold, imperfect mechanical bonding effect with temperature-degraded interlayer, and the temperature-dependent electron tunneling and Maxwell–Wagner–Sillars polarization. We consider the activation of free electrons and polarization of molecules to write the constitutive equations of polymer, and the collision and vibration probabilities to write those of graphene. We highlight the developed theory with a direct comparison to the experimental data of rGO/epoxy nanocomposites over the temperature range from 293 to 353 K. It shows that before the percolation threshold, the effective electrical conductivity and dielectric permittivity markedly increase with temperature, but after the percolation threshold, the influence of temperature diminishes significantly. In the latter case, the effective permittivity increases only slightly, while the conductivity exhibits an opposite trend.

Microelectrostatic simulation of insulated graphene–polymer nanocomposites using COMSOL Multiphysics

Graphene–polymer nanocomposites have shown promising potential as high dielectric constant and electrostatic energy storage materials. However, such nanocomposites often faced challenges of high energy dissipation and low voltage endurance due to direct contact of graphene nanosheets and localized electric field concentration. Various efforts have been made to address these shortcomings such as the insulative coating of graphene nanosheets in a polymer matrix. In this study, we simulated (using COMSOL Multiphysics) the effects of such insulative coatings on localized voltage concentration around graphene nanosheet in a polymer matrix. The simulation was done by consideration of various insulative materials with different dielectric constant and coating thickness on graphene nanosheets. It was noted that insulative coating can reduce localized voltage concentration around a graphene nanosheet in a polymer matrix, thereby improve the breakdown strength. Further increase in the coating thickness with low dielectric constant coating materials can further enhance breakdown strength. However, high coating thickness resulted in lowering the microcapacitance of the microcapacitor in the polymer matrix. Therefore, for the optimal energy density of such nanocomposites, a compromise dielectric constant of a coating material and coating thickness on graphene nanosheet must be reached. This study showcased the influence of insulative coatings on graphene nanosheets. It will be a guide for further studies on the selection of coating materials for graphene nanosheets for high breakdown strength nanocomposites.

Photo-thermoelectric effect induced electricity in stretchable graphene-polymer nanocomposites for ultrasensitive strain sensing

Stretchable strain sensors play an increasingly important role in artificial intelligent devices. However, high-performance strain sensors have been slowly developed owing to the harsh requirement of self-powered function, long cycle life and high resolution. Here, we report a self-powered stretchable graphene-ecoflex composite strain sensor based on photo-thermoelectric (PTE) effect induced electricity. The device exhibits a high strain sensitivity of -0.056 ln(nA)/% with strains ranged from 0% to 20% under 980 nm light illumination, where the strain sensitivity can be found to increase with increasing light intensity. The strain sensor maintains outstanding dynamic stability under periodic strains ranged from 0 to 100% in 100 cycles. The sensing resolution can be as high as 0.5% with both the response and recovery time of less than 0.6 s. It can precisely monitor human joint motions and stretchable strains by implanting the device in pork.

A review on the mechanical and thermal properties of graphene and graphene-based polymer nanocomposites: understanding of modelling and MD simulation

ABSTRACTSimulation of molecular dynamics (MD) allows mimicking the systematic variation of mechanical properties beyond the capacity of experimental methods. In this review, the status of the molecular modelling, simulation and mechanical and thermal characterisation of graphene and the properties of bulk nanocomposites using MD are extensively examined. The molecular models of graphene (pristine and functionalised), and the most common epoxy matrix with preeminent MD energy minimisation tools are thoroughly examined. The presence of MD’s simulation setup and the computational procedure for different styles of mixing the polymer matrix with graphene as nanofiller are exhaustively reviewed. In particular, in the analysis of several papers on graphene and their composites, it has been noticed that mechanical, thermal and interfacial properties are ubiquitous to most of the engineering applications. In this review, advances in the molecular modelling and simulation of graphene and their composites has been discussed in detail keeping graphene structures, aspect ratio, weight fraction and density effect as major factors. Finally, the research gaps and future scope are discussed, which indeed helps the researchers in scaling up their researches in graphene and their composites, which may further lead to their experimental findings.

A Review on Graphene Polymer Nanocomposites in Harsh Operating Conditions

Development of high-performance composites operating in harsh conditions is gaining tremendous attention due to the broad range of applications such as mass transport, defense, energy, manufacturin...

Sunlight induced catalytic degradation of bromophenol blue and antibacterial performance of graphene nanoplatelets/polypyrrole nanocomposites

Graphene-polymer nanocomposites are innovative materials that have attracted much attention due to their novel features. Herein, we report photocatalytic and antibacterial properties of GNP-PPy nanocomposites. The formation of nanocomposites was confirmed by various characterization techniques. The assembly of GNPs and PPy NPs with optimized GNP content amazingly increased the photocatalytic degradation of bromophenol blue (BPB) up to 93% under solar light irradiation in 120 min. The antibacterial assay revealed that 50% GNPs-PPy nanocomposite showed a best (32.43%) inhibition of growth against a Gram negative bacteria E. coli. The GNP-PPy nanocomposites can possibly be utilized as multifunctional material for wastewater treatment and as antimicrobial agent.

Thermal bridging of graphene nanosheets via covalent molecular junctions: A non-equilibrium Green’s functions–density functional tight-binding study

Despite the uniquely high thermal conductivity of graphene is well known, the exploitation of graphene into thermally conductive nanomaterials and devices is limited by the inefficiency of thermal contacts between the individual nanosheets. A fascinating yet experimentally challenging route to enhance thermal conductance at contacts between graphene nanosheets is through molecular junctions, allowing covalently connecting nanosheets otherwise interacting only via weak Van der Waals forces. Beside the bare existence of covalent connections, the choice of molecular structures to be used as thermal junctions should be guided by their vibrational properties, in terms of phonon transfer through the molecular junction. In this paper, density functional tight-binding combined with Green functions formalism was applied for the calculation of thermal conductance and phonon spectra of several different aliphatic and aromatic molecular junctions between graphene nanosheets. Effect of molecular junction length, conformation, and aromaticity were studied in detail and correlated with phonon tunnelling spectra. The theoretical insight provided by this work can guide future experimental studies to select suitable molecular junctions in order to enhance the thermal transport by suppressing the interfacial thermal resistances, particularly attractive for various systems, including graphene nanopapers and graphene polymer nanocomposites, as well as related devices. In a broader view, the possibility to design molecular junctions to control phonon transport currently appears as an efficient way to produce phononic devices and controlling heat management in nanostructures.

Thermoelectric effect induced electricity in stretchable graphene-polymer nanocomposites for ultrasensitive self-powered strain sensor system

Strain-sensing capabilities are fundamental features for building the next generation of artificial intelligent devices. However, the development of sensors with such functionality has been hindered by the requirements of external power sources and the need for devices with long cycle life and high resolution. Here, we report a self-powered ultrasensitive strain sensor system based on a graphene-ecoflex nanocomposite film, combining good thermoelectric property of graphene and large tensile property of ecoflex elastomer. The device maintains excellent stability with repeatable output signals over 1000 cycles under repetitive strains from 0% to 100%, and exhibits a strain resolution of 0.125% with a response time of less than 0.6 s. As a wearable device attached to the human skin, it can precisely detect subtle human motions based on the temperature gradient between the human skin and ambient environment. A multi-channel 3D strain sensor system was further manufactured to feature the function of strain detection along various directions.

Enhanced dielectric performance and energy storage density of polymer/graphene nanocomposites prepared by dual fabrication

Polymer/graphene nanocomposites (PGNs) have shown great potential as dielectric and energy storage materials. However, insolubility of graphene in most solvents, hydrophobic behaviour and poor dispersion in polymer matrix restrict wider fabrications and applications of PGNs. In this study, we present co-fabricated PGNs engineered by solution blending and melt compounding methods with improved dielectric performance. Further processing of PGNs by melt mixing after solution blending not only improved dispersion of graphene in the matrix but also ensured good interfacial interaction of the composites’ constituents and reduction of oxygen content in PGNs. Graphene nanoplatelets used in this study was slightly functionalized (fGNPs) to enhance dispersion in the polymer matrix. It was later characterized using Fourier transform infrared (FTIR) and Raman spectrometer. Scanning electron microscope (SEM) was used in morphological study of the fabricated composites. Dielectric properties, electrical conductivity, breakdown strength and energy storage capacity of the fabricated composites were investigated. The results obtained showed well-dispersed fGNPs in poly (vinylidene fluoride) (PVDF) matrix and improved dielectric performance. For instance, with 3.34 wt% and 6.67 wt% fGNPs co-fabricated composites, dielectric constant increased from about 9 for neat PVDF to 9930 and 38,418 at 100 Hz, respectively. While 7588 and 12,046 were respectively measured for solution blended-only composites at similar fGNPs content. These resulted to about 176.9% and 376.4% increase in energy storage density with 3.34 wt% and 6.67 wt% fGNPs co-fabricated composites when compared to their counterparts. These results were also credited to strong bonding, reduction of oxygen and recovered graphene structure by melt-mixing approach.

A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites

In this paper, a data-driven-based computational homogenization method based on neural networks is proposed to describe the nonlinear electric conduction in random graphene-polymer nanocomposites. In the proposed technique, the nonlinear effective electric constitutive law is provided by a neural network surrogate model constructed through a learning phase on a set of RVE nonlinear computations. In contrast to multilevel (FE $$^2$$ ) methods where each integration point is associated with a full nonlinear RVE calculation, the nonlinear macroscopic electric field-electric flux relationship is efficiently evaluated by the surrogate neural network model, reducing drastically (by several order of magnitudes) the computational times in multilevel calculations. Several examples are presented, where the RVE contains aligned graphene sheets embedded in a polymer matrix. The nonlinear behavior is due to the modeling of the tunelling effect at the scale of graphene sheets.

Layer-by-layer hybrid chemical doping for high transmittance uniformity in graphene-polymer flexible transparent conductive nanocomposite

A traditional transparent conducting film (TCF) such as indium tin oxide (ITO) exhibits poor mechanical flexibility and inconsistent transmittance throughout the UV-VIS-NIR spectrum. Recent TCFs like graphene films exhibit high sheet resistance (Rs) due to defect induced carrier scattering. Here we show a unique hybrid chemical doping method that results in high transmittance uniformity in a layered graphene-polymer nanocomposite with suppressed defect-induced carrier scattering. This layer-by-layer hybrid chemical doping results in low Rs (15 Ω/sq at >90% transmittance) and 3.6% transmittance uniformity (300–1000 nm) compared with graphene (17%), polymer (8%) and ITO (46%) films. The weak localization effect in our nanocomposite was reduced to 0.5%, compared with pristine (4.25%) and doped graphene films (1.2%). Furthermore, negligible Rs change (1.2 times compared to 12.6 × 103 times in ITO) and nearly unaltered transmittance spectra were observed up to 24 GPa of applied stress highlighting mechanical flexibility of the nanocomposite film.

Cooperative-VBO model for polymer/graphene nanocomposites

The Cooperative–Viscoplasticity theory based on overstress (VBO) theory which is initially developed for polymers is modified to model the mechanical behavior of graphene-polymer nanocomposites. The modifications are proposed for both the elastic and viscoplastic deformation. For the elastic contribution, an effective modulus definition which covers the agglomeration effects is implemented into the Cooperative-VBO theory. A strain rate, temperature, graphene volume fraction and agglomeration rate dependent elasticity modulus is obtained. In order to model the visco-plastic deformation, two scalar material parameters, activation energy and activation volume are defined as functions of graphene volume ratio using Takayanagi averaging approach. The newly defined graphene volume fraction, agglomeration rate, temperature and strain rate dependent elasticity modulus and activation energy and free volume for nanocomposites are incorporated into the Cooperative -VBO model.In this manner, a constitutive model which covers the total viscoelastic-viscoplastic deformation of graphene-polymer nanocomposites is developed. The capabilities of the introduced model are demonstrated by predicting uniaxial compression behavior of pristine epoxy and different graphene content nanocomposites. In addition, storage modulus obtained from a Dynamic Mechanical Analysis (DMA) are compared with predicted values for neat epoxy, 0.1 wt% and 0.5 wt% graphene-epoxy nanocomposite. The simulation results are compared to experimental data from the literature. A fair agreement with experimental data is obtained.