Piezoresistive Nanocomposites Research Articles

A subbands study on the resistivity of field-effect CNT-based piezoresistive nanocomposites

In this paper, an analytical model based on the percolation theory has been developed to predict the subbands effect on the effective electrical resistivity of carbon nanotubes (CNT)-based polymer nanocomposites. The CNTs are considered as randomly distributed or aligned channel material in the polymer transmitting electrons through tunneling. The tunneling effect takes into account the electron transmission between each connected pair of CNTs to evaluate electrical resistivity. The modeling approach contains two steps of primary prediction of resistivity and further calculation of CNTs’ displacements and subsequent change of the resistance. A good agreement is found between the analytical model predictions and experimental data when the tunneling behavior was considered in the percolation transition region. The effect of CNT diameter, orientation state, and subbands on the resistivity has been investigated. The results depict that subbands increment is a collateral benefit to the aspect ratio in decreasing the resistivity. The analytical results demonstrate that a random CNT dispersion leads to a decreased piezoresistivity, while an increased strain range depicts a more non-linear behavior.

Piezoresistive relaxation and creep model of porous polymer nanocomposite supported by experimental data

Porous piezoresistive nanocomposites (PPNs), a blend of conductive nanomaterials and a porous polymer matrix, have garnered significant attention in the realm of flexible pressure sensors. The porous microstructure offers exceptional sensitivity and lightweight characteristics of these sensors, but it also introduces challenges such as relaxation and creep behaviors. Grounded in viscoelastic theory, this paper introduces a mathematical model that provides a quantitative analysis of the resistance-strain relationship of PPN-based piezoresistive sensors, considering both bulk resistance and contact resistance. To elucidate the relaxation and creep behaviors, the model incorporates the conformational change and the slip motion of the polymer macromolecules during the deformation. Utilizing the Adam optimization algorithm, the model can accurately depict the piezoresistive behavior of various PPNs (with different porosities and conductive nanomaterial contents) with a fitting accuracy exceeding 99%. Furthermore, we explored some atypical characteristics of the PPN-based sensor, such as the negative resistance-strain behavior and the overshooting of bulk resistance. This study sets a theoretical basis for the development of sensitive and stable PPN-based sensors.

Tuneable Piezoresistance of Graphene‐Based 2D:2D Nanocomposite Networks

AbstractPiezoresistive nanocomposites are an important class of materials that allow the production of very sensitive strain sensors. Herein, a new class of piezoresistive nanocomposites prepared by mixing different types of 2D nanosheets is explored. In this way, three distinct types of nanocomposite are produced by mixing conducting and insulating nanosheets (graphene, Gr and boron nitride, BN), conducting and semiconducting nanosheets (graphene and tungsten diselenide, WSe2 or tungsten disulfide, WS2) as well as mixing two different types of conducting nanosheets (graphene and silver, Ag). For each nanocomposite type, a different dependence of composite conductivity on filler volume fraction is observed although all behaviors can be fully described by percolation theory. In addition, each composite type shows different piezoresistive properties. Interestingly, while the conductor insulator composites show the standard monotonic relationship between gauge factor and conductivity, both conductor:semi‐conductor and conductor:conductor composites show very unusual behavior, in each case displaying a peak engage factor at the percolation threshold. In each case, percolation theory is used to develop simple equations for gauge factor as a function of both volume fraction and conductivity that fully describes all experimental data. This work expands the understanding of piezoresistive nanocomposites and provides a platform for the engineering of high‐performance strain sensors.

On the economic evaluation of restoration activities of modern monuments of cultural heritage with piezoresistive nanocomposites

Lime based restoration mortars reinforced with multi-walled carbon nanotubes (MWCNTs) exhibit advanced mechanical strength, piezo-resistive properties and probably their main advantage is that they can be used as self-sensors for on-site health monitoring purposes of the restoration areas of traditional / historic structures. The present study assesses their economic viability via a comparison of innovative vs. conventional restoration treatments, based on Activity Based Cost (ABC) modeling and evaluation. ABC was applied to a hypothetical restoration scenario, which includes three restoration intervention ratio sub-cases (expected, optimistic and pessimistic), in relation to the structural part that will require re-treatment in more than 20 years life cycle time frame. The advantages of the application of the innovative material are portrayed in all investigated three-service life intervention sub-cases. In particular, the innovative approach method presents economic risk–benefit advantages from 1 % up to 14.6 % (depending on the sub-case) in comparison to the conventional approach.

Carbon-Based Piezoresistive Polymer Nanocomposites by Extrusion Additive Manufacturing: Process, Material Design, and Current Progress.

Advancement in additive manufacturing (AM) allows the production of nanocomposites with complex and custom geometries not typically allowable with conventional manufacturing techniques. The benefits of AM have led to recent interest in producing multifunctional materials capable of being printed with current AM technologies. In this article, piezoresistive composites realized by AM and the matrices and fillers utilized to make such devices are introduced and discussed. Carbon-based nanoparticles (Carbon Nanotubes, Graphene/Graphite, and Carbon Black) are often the filler choice of most researchers and are heavily discussed throughout this review in combination with extrusion AM methods. Piezoresistive applications such as physiological and wearable sensors, structural health monitoring, and soft robotics are presented with an emphasis on material and AM selection to meet the demands of such applications.

The effect of synthetic conditions on piezoresistive properties of ultrasensitive carbon nanotube/PDMS 3D composites

Carbon nanotube polymeric composites are widely developed as piezoresistive sensors. Despite these materials showed improved mechanical stability, they are less sensible than carbon aerogel. Moreover, the piezoresistive properties of these nanocomposites cannot be tuned to adapt the sensor to the different applications. Here we observed as modifying solvent dilution in the hard template synthetic route, allow for the fabrication of porous piezoresistive nanocomposites with different limits of detection, working ranges and sensitivities. More in details three different foams with diverse features were produced. Although all the three materials presented outstanding performances, the porous material prepared at higher dilution showed an incredible limit of detection for displacement (∼50 nm) and pressure (0.2 Pa) that are respectively 2.6 and 10 times lower than that recorded at lower prepolymer dilutions. However lower dilutions allow to produce sensors able to monitor larger pressures ranges (up to ∼150 kPa), which are 3 times higher than that of recorded in composites prepared at higher dilutions. Moreover, all the materials evidenced fast response time and high mechanical and electronics stability. Therefore, we demonstrated as this approach easily allows the synthesis of nanocomposites with improved performances and different sensing properties that can be tuned depending on the needed application with an unprecedent flexibility.

An analytical investigation of self-temperature-compensated stretchable piezoresistive nanocomposites containing hybrid carbon black/carbon nanotube nanofillers

In this study, the temperature-dependent piezoresistivity of hybrid carbon black (CB)/carbon nanotube (CNT) nanocomposites is studied using a percolation model with a Monte Carlo simulation approach. With this approach, the temperature-dependency of electrical resistivity and the strain sensitivity of the nanocomposite are investigated. In addition, other parameters such as the aspect ratio, dispersion state and dimensions’ effect on piezoresistivity of the compensated stretchable nanocomposite are investigated. By developing a temperature-dependent percolation model, the temperature sensitivity response of nanocomposite is investigated based on defining the temperature coefficient of resistance of CNT and thermal expansion coefficient of polymer. It is found that the dimensional aspects and dispersion state affected the percolation threshold and resistivity. The obtained results also indicated that the piezoresistivity increased with the decrease in the Poisson’s ratio and intrinsic electrical conductivity. Moreover, the predicted results showed high prediction accuracy for temperature-dependence resistivity compared with the existing experimental data.

Mechanism of resistance relaxation and hysteresis in viscoelastic piezoresistive polymer nanocomposites

Mechanism of resistance relaxation and hysteresis in viscoelastic piezoresistive polymer nanocomposites

Topological design of strain sensing nanocomposites

High-performance piezoresistive nanocomposites have attracted extensive attention because of their significant potential as next-generation sensing devices for a broad range of applications, such as monitoring structural integrity and human performance. While various piezoresistive nanocomposites have been successfully developed using different material compositions and manufacturing techniques, current development procedures typically involve empirical trial and error that can be laborious, inefficient, and, most importantly, unpredictable. Therefore, this paper proposed and validated a topological design-based methodology to strategically manipulate the piezoresistive effect of nanocomposites to achieve a wide range of strain sensitivities without changing the material system. In particular, patterned nanocomposite thin films with stress-concentrating and stress-releasing topologies were designed. The strain sensing properties of the different topology nanocomposites were characterized and compared via electromechanical experiments. Those results were compared to both linear and nonlinear piezoresistive material model numerical simulations. Both the experimental and simulation results indicated that the stress-concentrating topologies could enhance strain sensitivity, whereas the stress-releasing topologies could significantly suppress bulk film piezoresistivity.

Predicting strain and stress fields in self-sensing nanocomposites using deep learned electrical tomography

Conductive nanocomposites, enabled by their piezoresistivity, have emerged as a new instrument in structural health monitoring. To this end, studies have recently found that electrical resistance tomography (ERT), a non-destructive conductivity imaging technique, can be utilized with piezoresistive nanocomposites to detect and localize damage. Furthermore, by incorporating complementary optimization protocols, the mechanical state of the nanocomposites can also be determined. In many cases, however, such approaches may be associated with high computational cost. To address this, we develop deep learned frameworks using neural networks to directly predict strain and stress distributions—thereby bypassing the need to solve the ERT inverse problem or execute an optimization protocol to assess mechanical state. The feasibility of the learned frameworks is validated using simulated and experimental data considering a carbon nanofiber plate in tension. Results show that the learned frameworks are capable of directly and reliably predicting strain and stress distributions based on ERT voltage measurements.

Toward Automated Tomato Harvesting System: Integration of Haptic Based Piezoresistive Nanocomposite and Machine Learning

Carbon nanotubes (CNT)/polydimethylsiloxane (PDMS) have been investigated as potential materials for tomato-harvesting applications. The current-voltage (I–V) and current time (I–t) properties, as well as tomato hardness measurement and support-vector machine learning, were used to determine the performance of the sensor with respect to sensitivity, response time, accuracy, and detection limit of the nanocomposite. The data suggested an accurate (± 5.2%) measurement in a low-weight region of tomato. Narrowing of the I–V hysteresis curve towards a higher weight region was observed as a result of the increase in electron pathways. The fabricated sensor displayed a higher sensitivity (15 mV <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$/ \mu \text{m}$ </tex-math></inline-formula> ) than the commercial sensor (1 mV <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$/ \mu \text{m}$ </tex-math></inline-formula> ). In addition, machine learning of the resistance–displacement curve data yielded an average accuracy level of 0.67 when tested using acquired data.

Nanoarchitectonics of highly sensitive and with large working range 3D piezoresistive microporous foam based on carbon nanotubes and elastomer

HypothesisNanocarbon/polymeric 3D porous composites have been widely developed as piezoresistive sensors due to their improved performances. Functionalized nanocarbon is usually used to allow its adsorption on the surface of porous polymeric material. However, both the functionalization and the surface localized distribution of the nanomaterial can limit the nanocarbon effect on conductivity and mechanical stability of the material thus affecting piezoresistive performances. ExperimentsA novel nanoarchitectonics strategy to prepare an elastomeric/carbon nanotubes (CNTs) 3D porous piezoresistive nanocomposite is developed. The fabrication route does not require complex apparatus and CNTs chemical functionalization. Moreover, foams of any shape and dimensions can be produced with neither complex machinery and procedures nor wastes production. FindingsThe obtained material is characterized by the presence of well dispersed pristine CNTs on both surface and bulk of the polymeric matrix. The foam exhibited improved piezoresistive properties with excellent compressive stress (>150 kPa), sensitivity at low displacement (29 kPa−1) and limit of detection for both pressure (2 Pa) and extension (130 nm). These excellent features could allow the use of the as prepared nanocomposite in different applications ranging from wearable devices to robotic or infrastructure monitoring with outstanding flexibility.

Multifunctional wax based conductive and piezoresistive nanocomposites for sensing applications

Carbon nanotube (CNT) and reduced graphene oxide (rGO) filled wax (W) composites were developed and proven to be suitable for screen-printing and molding techniques, allowing simple manufacturing of conductive and multifunctional materials. A homogeneous filler dispersion is essential to obtain smaller filler clusters within the wax. Low electrical percolation thresholds were obtained for CNT/W and rGO/W composites, near 0.3 and 3 wt% filler content, respectively, with maximum electrical conductivity of 2.5 × 10−4 S/m for both fillers. Further, good filler dispersion and mechanical properties have been obtained for all samples. The composites were evaluated as sensing material with good linearity between force and resistance variation. The piezoresistive sensibility is GF < 11 (5 mm of bending) and PS < 0.17 MPa−1 (50 N of pressure), being suitable for the development of sensing applications. Further, the piezoresistive response is stable under cycling solicitations.Finally, the applicability was demonstrated by developing a screen-printed sensor, allowing to detect bending movements, connected to a mobile device through an electronic system.

Self-diagnostic carbon nanocomposites manufactured from industrial epoxy masterbatches

Masterbatches of single wall carbon nanotubes (SWCNTs), multi-wall carbon nanotubes (MWCNTs), graphene (G), reduced graphene oxide (RGO) and nitrogen doped graphene (NDG) were used at 0.5, 1.0, and 2.0 wt% to produce electrically conductive and piezoresistive nanocomposites. The conductivity and piezoresistive response was dependent on the microstructure obtained. A unified processing route incorporating mechanical mixing and ultrasonication was used. Distance between electrode points affected the final measurements and a minimum measurement distance of 2 cm was identified. Digital image correlation (DIC) was employed to measure strain during tensile loading. For all materials, an increase in the wt.% of nanoparticles resulted in lower resistivity. Resistivities for CNT samples ranged between 2 and 106 Ohm⋅cm as compared to G/RGO/NDG samples, 1011–1012 Ohm⋅cm. Strain based gauge factors for CNT samples ranged up to ~ 7 while to G/RGO/NDG masterbatches were found to be unsuitable for self-diagnostic applications.

Piezoresistive nanocomposites for sensing MMOD impact damage in inflatable space structures

Future space exploration requires easy-to-transport, easy-to-build and deployable space habitats. One of the biggest threats faced by the habitat structures is the impact damage caused by micrometeoroid orbital debris (MMOD) traveling at high velocities of few km/s. This work presents fabrication and testing of hybrid nanocomposites with carbon nanotubes (CNTs) and graphite nanoplatelets (GNPs) as fillers in a flexible epoxy matrix, that are proposed to be used for sensing the impact damage by MMOD on inflatable space structures. CNTs and GNPs are chosen as fillers owing to their excellent electrical properties and piezoresistivity. A hypervelocity impact is conducted on the nanocomposite sensors to study their functionality in the space environment. The results prove that the piezoresistive nanocomposites can be utilized as a part of damage detection system, which would not only detect the impact damage caused by MMOD of 3 mm diameter traveling at 7 km/s but also discern its location and depth within the inflatable structural layers.

Failure prediction in self-sensing nanocomposites via genetic algorithm-enabled piezoresistive inversion

Conductive nanocomposites have been explored extensively for structural health monitoring (SHM) due to their self-sensing nature via the piezoresistive effect. Combined with a non-invasive conductivity imaging modality such as electrical impedance tomography (EIT), piezoresistivity is a powerful tool for SHM. To date, however, the combination of the piezoresistive effect and EIT has been limited to just damage detection. From a SHM perspective, it may be more beneficial to pre-emptively predict failure before it occurs. To that end, we propose a novel methodology for failure prediction in nanocomposites using piezoresistive inversion. Our approach makes use of a genetic algorithm (GA) to determine the mechanical state of the structure using conductivity changes observed via EIT. First, a rectangular nanocomposite specimen with a central hole is manufactured. Second, the specimen is loaded in tension to induce stress concentrations near the hole. Third, EIT is used to image the resulting stress concentration-induced conductivity changes near the hole. Fourth, GA-enabled piezoresistive inversion is implemented to determine the underlying displacements from the observed conductivity changes. The strains are then determined from kinematic relations and the stresses from constitutive relations. Lastly, a failure criterion is used to predict failure. By validating our results with finite element analysis and digital image correlation, we demonstrate that the proposed approach can accurately predict the onset of failure and therefore enable unprecedented SHM capabilities in piezoresistive nanocomposites.

A Flexible Graphene-Polydimethylsiloxane Nanocomposite Force Sensor with Linear Response Across a Wide Pressure Detection Range.

As a flexible force sensor operating in the pressure range covering the entire general human pressure detection range, we developed a piezoresistive nanocomposite using graphene flakes as the conducting filler with polydimethylsiloxane (PDMS) as the polymer matrix. The homogeneous dispersion of graphene flakes allows their uniform distribution in the PDMS matrix with a low percolation threshold owing to their geometrically high aspect ratio, thus resulting in a linear piezoresistive response across a wide pressure detection range (100 Pa-1,020 kPa), when static forces are externally applied. Furthermore, the sensor shows sensitive piezoresistive responses to dynamically applied forces. Based on the characteristics demonstrated and described in this study, graphene- PDMS nanocomposites can be considered promising materials for flexible force sensors capable of describing human pressure perception ability.

Investigation of Lightweight and Flexible Carbon Nanofiber/Poly Dimethylsiloxane Nanocomposite Sponge for Piezoresistive Sensor Application

In this paper, highly flexible and compressible piezoresistive nanocomposites consisting of carbon nanofibers (CNFs) and polydimethylsiloxane (PDMS) are developed for sensing applications. Porous PDMS with a porosity of 74.7% is manufactured using sugar templates, and CNFs are infiltrated into the porous structures to tailor the material's electrical resistivity. The highly flexible pressure sensors show piezoresistive characteristics up to 70% compressive strains due to the conductive CNF network and the porous microstructures of the fabricated nanocomposites. The reorganization of CNF conductive network is characterized using in‐situ micromechanical tests within a scanning electron microscope, validating the piezoresistive sensing mechanism. The sensing performance under various maximum applied strains, elevated load rates, and long‐term repeatability is characterized. The developed porous nanocomposite sponge is employed as a tactile wearable sensor.

Optimized silk fibroin piezoresistive nanocomposites for pressure sensing applications based on natural polymers.

Environmental issues promote the development of sensors based on natural polymers which are becoming an area of increasing interest. Piezoresistive sensors based on silk fibroin with carbon nanotubes (CNTs) as fillers were produced by solvent-casting in order to tune their electrical conductivity and electromechanical responses. It is shown that the carbonaceous fillers are well dispersed in the polymer matrix and the thermal and mechanical properties are independent of the CNT content. On the other hand, the inclusion of CNTs reduces the β-sheet content of silk fibroin and the electrical properties of the composite strongly depend on the filler content, the percolation threshold being around 1 wt% CNTs. The piezoresistive response demonstrates good reproducibility during cyclic loading without hysteresis with a piezoresistive sensitivity of ∼4 MPa−1, regardless of the CNT content. Overall, the results confirm that polymer composites based on natural polymers exhibit excellent piezoresistive responses, also demonstrated by the implementation and testing of a pressure sensor with the corresponding readout electronics. Thus, it is shown that natural polymers such as silk fibroin will allow the development of a new generation of multifunctional force and deformation sensors.

Polydimethylsiloxane nanocomposite filled with 3D carbon nanosheet frameworks for tensile and compressive strain sensors

Elastomeric matrix filled with carbon nanotubes or graphene has been focused for making resistive-type strain sensors. Here, we report the low-cost synthesis of 3D carbon nanosheet frameworks by exploiting the chemical reaction between magnesium powder and carbon disulfide vapor, and prepared piezoresistive nanocomposites of as-prepared carbon nanomaterials and polydimethylsiloxane elastomer for tensile and compressive strain sensors with different device configurations. The optimal carbon content in PDMS for achieving high sensitivity is obtained. The tensile strain sensor shows fast response and high gauge factor in the order of 102. The compressive strain sensor that is elaborated designed as in-plane and miniature concept also shows striking response to external loads.