Piezoresistive Response Research Articles

Piezoresistive responses of CNT cement composites under different load characteristics

ABSTRACT The piezoresistive response of carbon nanotube-embedded cementitious composites (CNT-CC) can be used to evaluate the stress level and monitor traffic flow on transportation infrastructure. However, the effect of load characteristics on the piezoresistive response of CNT-CC is unclear. In this study, the influence of CNT content and load characteristics on the piezoresistive performance of CNT-CC is investigated by controlling the amplitude and rate of loading. Based on the principles associated with composite volume change, electron hopping and conductive networks, the fractional change in resistivity (FCR) of the composite was calculated theoretically and compared with experimental findings. The results show that the greatest electrical conductivity is achieved when the CNT content is 0.2%, and the addition of steel slag reduces the CNT content for achieving the optimal piezoresistive response but does not improve the piezoresistive performance. The load characteristics have an important influence on the piezoresistive response, FCR is mainly affected by the load amplitude and the increase in the loading rate will deteriorate the piezoresistive characteristics of CNT-CC. The calculated piezoresistive response is basically accurate and verifies the piezoresistive mechanism.

Piezo resistance response prediction in multifunctional hybrid composites under dynamic loading conditions using machine learning methods

Composite materials have garnered significant attention in industries such as automotive, aerospace, and defense due to their advantageous properties. However, concerns about the potential deterioration of mechanical performance over time have led to a growing interest in continuous structural health monitoring (SHM) during load-bearing applications. While there has been extensive research on damage sensing in composite materials under quasi-static conditions, limited attention has been given to dynamic impact loading conditions due to experimental challenges, particularly in measuring electrical resistance. In this study, the piezo-resistance response of hybrid composite materials is investigated under dynamic shear and mode-I fracture loading conditions, and machine learning models are applied to predict the piezo-resistance response. Two data training methods are implemented to train the models, and four widely used machine learning models are chosen for prediction. The results demonstrate that among the models, Gaussian Process Regression (GPR) and Neural Network (NN) models show the lowest Root Mean Square Error (RMSE) values and better prediction accuracy for all composite types under dynamic shear and mode-I fracture loading conditions. This research provides insights into the prediction of piezo-resistance response in multi-functional hybrid composites, offering guidelines for selecting appropriate machine learning models.

The effects of CNT type, alignment and dopants on piezoresistance in CNT fibres

Carbon nanotube fibres (CNTF) are piezoresistive, hence heralded as deformation sensors in applications ranging from flexible touch sensors to artificial skins and robotics. This work studies the piezoresistive behaviour of a wide range of CNT fibres from different sources, processing routes and microstructures. It provides a unifying view of the factors controlling piezoresistance in CNT fibres and related nanocarbon networks. We clarify the role of alignment and concentration of dopants and the constituent CNT type, demonstrating that the origin of piezoresistance in aligned fibres is the direct deformation of the constituent nanotubes, therefore, it is governed by the bulk modulus and thus the degree of CNT alignment. Doping through intercalation, which does not affect modulus or CNT separation, is detrimental to piezoresistive sensing, reducing the gauge factor proportionally to its decrease in resistivity. Aligned fibres show a quasi-linear piezoresistive response, with a positive change in resistance for all deformation modes applied: axial tension, axial or transverse compression. The axial gauge factor is shown to be proportional to fibre Young's modulus, with values of 2–9 for fibres spun from aerogels and above 30 for undoped fibres spun from liquid crystal solutions, respectively. Piezoresistance is attributed to the formation of internal barriers for conduction between metallic regions, which arise from the heterogeneous stress distribution along individual CNTs inherent in shear lag-type stress transfer. Commercial multifilament CNT yarns with a high degree of alignment and a format amenable for integration in large structures have demonstrated the piezoresistive gauge factors of 4 and sufficient sensitivity at strains below 1 % suitable for structural health monitoring of engineering structural composites.

Rubber-based piezoresistive sensing: a new approach based on hydrodynamic flow of material deformation describing nonlinear signals in stretchable sensors

AbstractThe nonlinearity of piezoresistive response is critical in developing strain sensors, various self-monitoring applications and wearable electronics based on filled rubbers. This parameter could change dramatically when scaling up from small-size prototypes to full-scale production. The present work focuses on the nonlinear signals in stretchable rubber-based sensors, their origin and dependence on size of samples. Thus, a set of rectangular, piezoresistive samples differing in width was prepared from natural rubber reinforced with carbon black filler. Their electric resistance was tested under planar strain/recovery conditions at 25 and 50% strain amplitudes. It was found that piezoresistance and the related nonlinear phenomena significantly depended on the size of the samples. For the first time, hydrodynamic flow of deformed material was used to explain the nonlinearities of the piezoresistive signal. The trajectory, velocity, and magnitude of this flow were accounted for by a newly developed empirical equation describing the evolution of local resistivity under the strain/recovery process.

Three-Dimensional Printed Nanocomposites with Tunable Piezoresistive Response.

This study explores a novel approach to obtaining 3D printed strain sensors, focusing on how changing the printing conditions can produce a different piezoresistive response. Acrylonitrile butadiene styrene (ABS) filled with different weight concentrations of carbon nanotubes (CNTs) was printed in the form of dog bones via fused filament fabrication (FFF) using two different raster angles (0-90°). Scanning electron microscopy (SEM) and atomic force microscopy (AFM) in TUNA mode (TUNA-AFM) were used to study the morphological features and the electrical properties of the 3D printed samples. Tensile tests revealed that sensitivity, measured by the gauge factor (G.F.), decreased with increasing filler content for both raster angles. Notably, the 90° orientation consistently showed higher sensitivity than the 0° orientation for the same filler concentration. Creep and fatigue tests identified permanent damage through residual electrical resistance values. Additionally, a cross-shaped sensor was designed to measure two-dimensional deformations simultaneously, which is applicable in the robotic field. This sensor can monitor small and large deformations in perpendicular directions by tracking electrical resistance variations in its arms, significantly expanding its measuring range.

Mechanically neutral and facile monitoring of thermoset matrices with ultrathin and highly porous carbon nanotube films

We examine how the physical features of single-walled carbon nanotube (SWCNT) thin films can affect monitoring thermoset polymers during their manufacturing and application. Film thickness (23, 37 and 53 nm), electrode materials and configuration, and embedding within or surface application on a polymer were all investigated. During manufacturing monitoring, thicker films provided higher sensitivity than thinner ones (ΔR/R0 = 111% (53 nm) vs. 74% (23 nm)). Electrode material and configuration showed that sputtered gold contacts allow higher sensitivity and reliability compared with conductive silver adhesive, and that parallel electrode placement provides more distinct and discreet stage measurements compared to diagonal one. The opposite trend was noted for mechanical loading, where thinner films provide a higher piezoresistive response and embedded films outperform surface applied sensors (gauge factors between 23-86 and 4-10, respectively). All thin films accurately measured curing stages as well as mechanical changes and are shown to be self-adjusting when the force increases during mechanical loading. We show that manufacturing monitoring should rely on uniformly deposited electrodes and thicker films for identification of polymerization stages, whereas embedding thinner films in the matrix is recommended for highly sensitive deformation monitoring. This work outlines how simple, overlooked parameters can easily be varied to optimize the dual-stage monitoring performance of CNT-based sensors and helps to identify what parameters should be chosen for specific applications.

Anisotropic piezoresistive response of 3D-printed pressure sensor based on ABS/MWCNT nanocomposite

Nanocomposites based on carbon nanotubes (CNTs) are suitable for sensors, due to matrix ability to incorporate nanotube properties. Thus, we developed a low-cost, nanostructured poly(acrylonitrile-butadiene-styrene) (ABS) polymer piezoresistive sensor produced by additive manufacturing. For this, solution layers of acetone, dimethylformamide and CNTs functionalized with carboxylic acid were pulverized on an ABS substrate using an aerograph. Electrical characterization revealed an anisotropic piezoresistive response of the material, induced by the printing lines direction. Field Emission Gun-Scanning Electron Microscopy showed the nanostructured film spreading after five layers of CNTs as well as the random entanglement of nanotubes on parallel and perpendicular 3D-printed ABS substrates. Raman spectroscopy indicated compression and p-type doping of CNTs in interaction with the polymer, as seen mainly by the blueshift of the G and 2D subbands. The results show that the material is promising for pressure sensors, with potential applications in robotic haptic feedback systems.

Machine learning for nano-level defect detection in aligned random carbon nanotubes-reinforced electrically conductive nanocomposite

Machine learning allows fast nano-scale defect detection in polymer-impregnated aligned carbon nanotube (CNT) nanocomposites. Digital twins were populated by TEM-validated geometry; considered defects were flat cracks and close-to-spherical voids. Finite-element analysis of piezoresistive response was conducted by embedment of CNT network into matrix. Identification of a defect by change in CNT network piezoresistivity was challenged by: (1) randomness of CNTs’ shapes and placement, ML training happened on random realisations; (2) high strength of CNTs leading to the preservation of conductive paths along CNTs and changes only in conductivities of tunnelling contacts. “Artificial approximation“ was introduced to economise computer time multi-fold: ML was trained on cases with artificially degraded tunnelling conductivities within the defect. Three ML models: XGBoost, fully connected, and convolution neural networks were employed. All models managed the task for near-spherical voids, but performed poorly for flat cracks, due to the limited number of tunnelling contacts in crack volume. When trained on the mixed set of voids and cracks, both neural networks demonstrated the ability to learn the difference and detected even cracks, while XGBoost was not up to the challenge. By metrics, the convolutional neural network demonstrated the highest accuracy of predictions.

Three-Dimensional Printing Limitations of Polymers Reinforced with Continuous Stainless Steel Fibres and Curvature Stiffness

This study investigates the printability limitations of 3D-printed continuous 316L stainless steel fibre-reinforced polymer composites obtained using the Materials Extrusion (MEX) technique. The objective was to better understand the geometric printing limitations of composites fabricated using continuous steel fibres, based on a combination of bending stiffness testing and piezoresistive property studies. The 0.5 mm composite filaments used in this study were obtained by co-extruding polylactic acid (PLA), with a 316 L stainless steel fibre (SSF) bundle. The composite printability limitations were evaluated by the printing of a series of ’teardrop’ shaped geometries with angles in the range from 5° to 90° and radii between 2 and 20 mm. The morphology and dimensional measurements of the resulting PLA-SSF prints were evaluated using μCT scanning, optical microscopy, and calliper measurements. Sample sets were compared and statistically examined to evaluate the repeatability, turning ability, and geometrical print limitations, along with dimensional fluctuations between designed and as-printed structures. Comparisons of the curvature bending stiffness were made with the PLA-only polymer and with 3D-printed nylon-reinforced short and long carbon fibre composites. It was demonstrated that the stainless steel composites exhibited an increase in bending stiffness at smaller radii. The change in piezoresistance response of the PLA-SSF with load applied during the curvature bending stiffness testing demonstrated that the 3D-printed composites may have the potential for use as structural health monitoring sensors.

Evaluation of carbon nanotube (CNT) cement-based traffic sensor incorporating steel slag based on Accelerated Pavement Test (APT)

ABSTRACT In this study, a carbon nanotube (CNT) cement composite containing CNT and steel slag was developed and its application in traffic information sensing was studied. The mix ratio of steel slag cement mortar matrix and CNT cement composite was determined by mechanical properties and electrical conductivity tests. Then the CNT cement composite intelligent sensing device was prepared and an Accelerated Pavement Test equipment was used to test its piezoresistive response curve. The test results show that steel slag and CNT enhance mechanical properties and electrical conductivity. The vehicle speed and wheelbase were calculated based on the piezoresistive response results, and the calculation errors of the wheel speed (0.375 and 0.844 km/h) were less than 10% and 20%, respectively. The wheelbase estimate has a maximum error of approximately 12% and 15% for wheel speeds of 0.375 and 0.844 km/h, respectively. Therefore, the CNT cement composite containing steel slag developed in this study has the potential to serve as a traffic sensor.

Electromechanics of stretchable hybrid response pressure sensors based on porous nanocomposites

Stretchable pressure sensors are a key enabler of human-mimetic e-skin technology, with promising applications in soft robotics, prosthetics, biomimetics, and biosensors. Stretchable hybrid response pressure sensor (SHRPS) is an emerging type of soft pressure sensor that employs hybrid piezoresistive and piezocapacitive responses. A unique feature of SHRPS based on barely conductive porous nanocomposite (PNC) is its exceptional pressure sensitivity which trivializes its sensitivity to lateral stretch or shear. In this work, we experimentally characterize the electromechanical responses of SHRPS under various loading conditions and provide theoretical explanations through an equivalent circuit model. The capacitance and resistance of the PNC are described by a parallel mixing law and Archie’s law, respectively. Our model can reasonably predict the responses of SHRPS. Our findings reveal that SHRPS exhibits minimal sensitivity to stretch and shear because the hybrid response mechanism is activated only under compression. The effects of PNC-electrode contact impedance and fringe effects are discussed.

Autonomous Sensing Architected Materials

AbstractIntegrating autonomous sensing materials into future applications necessitates developing advanced multiscale multiphysics predictive models. This study introduces an experimentally informed predictive framework for autonomous sensing architected materials, combining theoretical and computational methodologies. By incorporating stress‐dependent electrical resistivity through anisotropic piezoresistive constitutive effects, alongside considering material, geometric, and contact nonlinearities, the proposed multiscale model captures the architecture‐dependent piezoresistive responses of lattice composites produced via additive manufacturing of polyetherimide (PEI)/carbon nanotube (CNT) nanoengineered feedstock. The PEI/CNT composite exhibits exceptional strength (105 MPa), stiffness (3368 MPa), and strain sensitivity (gauge factor ≈13), translating into remarkable piezoresistive characteristics for the PEI/CNT lattice composites, surpassing existing works (gauge factor ≈3 to 11). This multiscale finite element model accurately predicts both macroscopic piezoresistive responses and the influence of architectural and topological variations on electric current paths, validated via infrared thermography analysis. Additionally, an Ashby chart for the gauge factor of PEI/CNT lattice composites suggests their prediction through a scaling law similar to mechanical properties, underscoring the tunable strain and damage sensitivity of these materials. The combined experimental, theoretical, and numerical findings offer critical insights into optimizing piezoresistive composites through architected design, with profound implications for smart orthopedics, structural health monitoring, sensors, batteries, and other multifunctional applications.

Silk Fabric Functionalized by Nanosilver Enabling the Wearable Sensing for Biomechanics and Biomolecules.

Integrating biomechanical and biomolecular sensing mechanisms into wearable devices is a formidable challenge and key to acquiring personalized health management. To address this, we have developed an innovative multifunctional sensor enabled by plasma functionalized silk fabric, which possesses multimodal sensing capabilities for biomechanics and biomolecules. A seed-mediated in situ growth method was employed to coat silver nanoparticles (AgNPs) onto silk fibers, resulting in silk fibers functionalized with AgNPs (SFs@Ag) that exhibit both piezoresistive response and localized surface plasmon resonance effects. The SFs@Ag membrane enables accurate detection of mechanical pressure and specific biomolecules during wearable sensing, offering a versatile solution for comprehensive personalized health monitoring. Additionally, a machine learning algorithm has been established to specifically recognize muscle strain signals, potentially extending to the diagnosis and monitoring of neuromuscular disorders such as amyotrophic lateral sclerosis (ALS). Unlike electromyography, which detects large muscles in clinical medicine, sensing data for tiny muscles enhance our understanding of muscle coordination using the SFs@Ag sensor. This detection model provides feasibility for the early detection and prevention of neuromuscular diseases. Beyond muscle stress and strain sensing, biomolecular detection is a critical addition to achieving effective health management. In this study, we developed highly sensitive surface-enhanced Raman scattering (SERS) detection for wearable health monitoring. Finite-difference time-domain numerical simulations ware utilized to analyze the efficacy of the SFs@Ag sensor for wearable SERS sensing of biomolecules. Based on the specific SERS spectra, automatic extraction of signals of sweat molecules was also achieved. In summary, the SFs@Ag sensor bridges the gap between biomechanical and biomolecular sensing in wearable applications, providing significant value for personalized health management.

Electrical and piezoresistive properties of ultra-high toughness cementitious composite incorporating multi-walled carbon nanotubes: Testing, analyzing, and phenomenological modeling

This study explores the electrical and piezoresistive properties of ultra-high toughness cementitious composites (UHTCC) enhanced with multi-walled carbon nanotubes (MWCNTs) ranging from 0 to 1 wt% of cementitious binders. The observed polarization behavior is found to be analogous to the charging process of a capacitor. The polarization process and resistivity drift over time in the piezoresistive response are explained using an existing equivalent electrical circuit model incorporating a capacitor. The average results of electrical conductivity initially decrease and subsequently increase with higher MWCNTs concentrations, a phenomenon attributed to increased porosity and reduced matrix conductivity. The percolation threshold is identified at a volume fraction of 0.00387. Notably, even in the absence of MWCNTs, UHTCC materials exhibit piezoresistive properties due to the presence of metal impurities and ionic compounds. The insufficient polarization process results in an increasing trend in fractional change in resistance (FCR). The highest FCR sensitivity to external load occurs within the percolation threshold. Additionally, three equations are proposed to calculate electrical conductivity, incorporating the effects of interfaces, porosity, and matrix conductivity reduction, which align well with the experimental findings. These insights contribute to a deeper understanding of the electrical properties of UHTCC-MWCNTs composites, enabling more precise conductivity measurements and improved sensor sensitivity.

Investigation on the loading rate dependence of electromechanical properties of graphene-cement composites under compressive loading

In this paper, the dependence of the electromechanical properties of graphene-cement composites on the loading rate is studied by combining experiment and theory. The graphene-cement composites are prepared using a wet dispersion technique combined with surfactant assist and ultrasonication treatment. Both monotonic and cyclic compressive experiments with different loading rates are conducted on the graphene-cement composites with different graphene contents to measure their mechanical, electrical and piezoresistive properties. The optimum dosage of graphene in cement matrix is about 0.05 wt%, which can increase the compressive strength by 35.7 % and 34.1 %, and decrease the electrical resistivity to 1.43 and 3.48 × 105 Ω•cm, respectively, after 14 and 28 d curing periods. The uniform dispersion of graphene with less layers endows the graphene-cement composites with very low percolation threshold of electrical conductivity (0.007 wt%), stable piezoresistive response, and good reproducibility. Furthermore, the mechanism of the loading rate on the strain sensing behavior is discussed, and a rate dependent theoretical model of resistance change is proposed to quantitatively predict the electromechanical responses. This study provides guidelines for the fabrication of highly strain-sensitive graphene-cement composites and the rate-dependent evaluation of their electromechanical properties in the fields of intelligent construction.

Piezoresistive response and self-sensing properties of UHPC reinforced with recycled carbon fibers

This research explores Ultra-High-Performance Concrete (UHPC) reinforced with recycled carbon fibers to verify its potential application in self-sensing concrete for structural health monitoring. The improved electrical conductivity due to the inclusion of carbon fibers enables the emergence of piezoresistivity, transforming UHPC into a sensitive sensor for detecting mechanical stresses. By incorporating various characterization tests alongside investigations into fiber type (loose versus fibrillated sheets) and content variations, the study aims to establish a correlation between the electrical properties and the resulting piezoresistive response. Resistivity will be measured using impedance scanning, while piezoresistivity will be determined through loading and unloading cycles with a datalogger recording the variations in electrical resistance. The inclusion of carbon fibers suggests an improvement in mechanical performance, up to 42 % in flexural strength and 26 % in compression compared to UHPC without fibers. Resistivity ranges from 1 to 2 Ω⋅m and gauge factor reach 38. These results are comparable to those reported in other studies on UHPC with carbonaceous additions, highlighting the potential of recycled carbon fibers as a sustainable and effective reinforcement strategy.

Origami inspired dual matrix intelligent shape memory polymer composite folds for deployable structures

Abstract It remains a challenge to develop an intelligent, programmable multifunctional material system capable of recovering shape, withstanding high loads, and detecting folding extent remotely for self-deployable structures used in aerospace, robotics, and medical devices. In this work, our objective is to develop intelligent shape memory polymer composite (iSMPC) folds embedded with reduced graphene oxide (rGO)-coated self-sensing fabric. This will enable remote sensing of the fold state based on resistance changes and achieve higher strength and modulus. Firstly, we demonstrate the ability to sense the extent of folding and establish the relationship between piezoresistivity and fold state change by conducting cyclic compression analysis on folds with different gap sizes (6mm, 9mm, and 12mm) at temperatures of 25°C, 35°C, and 45°C. The iSMPC fold with a 6mm gap exhibited the highest bending stiffness (650.3Nmm-1) and curvature (0.55mm-1), resulting in a higher change in fractional change in resistance (FCR). Subsequently, the shape memory cycles of the 6mm iSMPC fold were demonstrated through localized controlled heating. Its shape recovery process exhibited repeatable behavior with a high recovery ratio of 95%. Lastly, a two-fold iSMPC structure was developed, and its performance was analyzed during a complete shape memory cycle. The piezoresistive response during higher-temperature cyclic loading resembled that of the single fold, exhibiting an FCR range between -9% and 5%, thereby demonstrating the repeatability of the iSMPC fold response.

Mechanical and piezoresistive performance of polymethyl methacrylate modified with carbon nanotubes for sensitive road surface

Piezoresistive materials, known for their mechanical adaptability and sensing capacity, hold the potential to be used as the materials for the sensitive surface layer in the digital twin of road systems. This study aims to investigate the mechanical and piezoresistive performance of a novel pavement material, a composite that comprises Polymethyl methacrylate (PMMA) as the matrix and carbon nanotubes (CNTs) as the conductive filler. Firstly, the composition and preparation process of CNTs-modified PMMA (CMP) were optimized via investigation on curing time, dispersion methods, and initiator content. Subsequently, the piezoresistive responses of CMP were analyzed within a loading range of 0 to 10 MPa by conducting linear increase compressive stress tests. Additionally, dynamic modulus tests and dynamic creep tests were employed to assess the mechanical and piezoresistive responses of CMP under equivalent vehicle loads. Results show that optimal properties are achieved when using ultrasonic methods to disperse CNTs, with a curing period of 72 h and an initiator content of 1.6 wt%. PMMA exhibits a lower modulus, reduced elasticity recovery and temperature sensitivity after modification with CNTs. And varying the CNTs content from 0.8 wt% to 1.2 wt% does not significantly affect the viscoelastic properties of CMP. The piezoresistive response of CMP under equivalent vehicle loads occurs in the first stage of the fractional change in resistance curve and is exclusively correlated with the tunneling resistance of adjacent CNTs. In this stage, CMP demonstrates higher sensitivity to heavy loads at lower CNTs content. The developed piezoresistive model based on tunneling resistance aligns with the experimental results. This study provides a basis for the performance study of CMP as a piezoresistive material in pavements, contributing to the design and development of sensitive surface layers in road systems.

Electromechanical response of multilayer graphene sheet/polypropylene nanocomposites and its relationship with the graphene sheet physicochemical properties

Abstract The mechanical, electrical, and piezoresistive responses of multilayer graphene sheet (GS)/polypropylene (PP) nanocomposites are investigated using four GSs of distinctive physicochemical properties. It is found that the morphology of the interconnected network of GS agglomerates at the mesoscale governs the mechanical, electrical, and electro-mechanical (piezoresistive) properties of the PP nanocomposites. The morphology of the mesoscale network of electroconductive fillers governs the effective properties of the nanocomposite. This network morphology strongly depends on the GS lateral size, dispersion, agglomeration, and, to a lesser extent, the specific surface area of the GSs. Within the range of lateral sizes investigated herein (1 - 21 m), larger GSs yields nanocomposites with higher electrical conductivity. On the other hand, GSs of moderate lateral size (~ 6.5 m) and specific surface area of ~ 141 m2/g render GS/PP nanocomposites with a more dispersed and more sparsely interconnected network. This better dispersed network with agglomerates of smaller dimensions is concomitant with improved stiffness and strength, and higher gauge factors (~ 18.2) for this GS/PP nanocomposites. Excellent capabilities for detection of human motion were proved for these nanocomposites.

(Invited) On the Optical Response of Layered Materials Exfoliated in Liquid Phase: Evidence of Functional Groups and Size-Dependent Features

Liquid phase exfoliation of layered materials has proven successful for a range of applications [1]. However, the range of organic solvents needed, and the broad distribution of thicknesses [2] can be aided by applying intercalation steps such as electrochemical or solvothermal expansion which leads to thinner crystals in the dispersion [2, 3]. In this talk I will present the effect of electrochemical exfoliation on the basal functionalization of graphene and a proposal for a more accurate determination of the optical extinction coefficient based on oxidation levels for standardization [4]. I will also present a few examples of MoS2 and WS2 exfoliated via ultrasonication which display non-linear optical effects in fluorescence spectroscopy [5] and the effect of crystal-size selection on the piezoresistive and photoconduction response of MoS2 and WS2 exfoliated via lithium intercalation.[1] Yenny Hernandez, et al, Nature Nanotechnology, 3, 563–568 (2008)[2] Jonathan N. Coleman et al. Science 331, 568-571 (2011)[3] Zhongfen Ding, et al, J. Mater. Chem., 2009, 19, 2588-2592 (2009)[4] Khaled Parvez, et al, ACS Nano 2013, 7, 4, 3598–3606 (2013)[5] J Rico, et al, J. Phys.: Condens. Matter 34 105701 (2022)[6] Gabriel Cardenas-Chirivi, et al, Optical Materials, 133,112890 (2022)