Carbon Nanotubes and Graphene in Polymer Composites for Strain Sensors: Synthesis, Functionalization, and Application

Abstract

This review provides a comprehensive analysis of modern strategies for the synthesis, functionalization, and application of carbon nanotubes (CNTs) and graphene for the development of high-performance polymer composites in the field of strain sensing. The paper systematically organizes key synthesis methods for CNTs and graphene (chemical vapor deposition (CVD), such as arc discharge, laser ablation, microwave synthesis, and flame synthesis, as well as approaches to their chemical and physical modification aimed at enhancing dispersion within polymer matrices and strengthening interfacial adhesion. A detailed examination is presented on the structural features of the nanofillers, such as the CNT aspect ratio, graphene oxide modification, and the formation of hybrid 3D networks and processing techniques, which enable the targeted control of the nanocomposite’s electrical conductivity, mechanical strength, and flexibility. Central focus is placed on the fundamental mechanisms of the piezoresistive response, analyzing the role of percolation thresholds, quantum tunneling effects, and the reconfiguration of conductive networks under mechanical load. The review summarizes the latest advancements in flexible and stretchable sensors capable of detecting both micro- and macro-strains for structural health monitoring, highlighting the achieved improvements in sensitivity, operational range, and durability of the composites. Ultimately, this analysis clarifies the interrelationship between nanofiller structure (CNTs and graphene), processing conditions, and sensor functionality, highlighting key avenues for future innovation in smart materials and wearable devices.

1. Introduction

The advancement of modern materials science is closely linked to the widespread use of conductive polymer composites in fields like robotics and sensor technology, including strain sensing. These composites incorporate various fillers, such as metallic [1,2], ceramic [3], or carbon-based nanomaterials [4,5], notably carbon nanotubes (CNTs) and graphene [6,7]. Conductive polymer composites find applications in a range of fields, including portable electronics and industrial systems [8]. Composites based on carbon nanotubes (CNTs), graphene [9], or their derivatives [10,11] enable a diverse array of technologies. The composites are also valuable in the detection of various gases [12,13] and solid substances, such as bisphenol A [14]. These include electromagnetic interference shielding [7,15], energy storage devices like supercapacitors [16,17], and electricity generators [18] and biomolecules (e.g., glucose) [19]. Further applications span water purification [20], thermoelectric conversion [21], and electrical heaters exhibiting a positive temperature coefficient (PTC) [22]. This represents only a fraction of the potential applications for CNTs and graphene across various technologies [23].

The application of polymer composites in sensing [24,25,26] relies on their functionally tunable properties, which can be customized to meet specific technological requirements [27,28]. The pursuit of diverse functional properties in conductive polymer composites necessitates the synthesis of CNTs, graphene, and their derivatives with tailored characteristics. This demand has therefore spurred the development of various synthesis methods for CNTs [29,30,31], graphene [32,33], and graphene–CNT hybrid structures [34]. The properties of graphene and CNTs can be further modified through functionalization [35], which expands their application range and enables the realization of novel properties within polymer composites [36]. The physical properties of CNTs and graphene are summarized in

Table 1. Physical properties of CNTs and graphene.

Material	Electrical Conductivity, S·m−1	Thermal Conductivity, Wm−1·K−1	Young’s Modulus, GPa	Crystal Lattice Parameters, nm	Band Gap, eV
CNTs	0.17–2.0 × 107	6600	270–950	sp2: 1.7	~0.2
Graphene	1 × 106–107	5000	853.3 ± 0.9	sp2: 0.25	~1.25

.

Furthermore, physical treatment methods, including laser [37,38] and microwave irradiation [39], can be used to modify CNTs, graphene, and their hybrid structures [40]. This combination of graphene and CNTs facilitates the development of intelligent materials with functional electronic properties, making them suitable for use in transistors [41]. The functionalization of CNTs with oxygen-containing groups enables the covalent attachment of nanoparticles to their surface, paving the way for novel functional materials in optoelectronic devices [42].

A highly significant application of polymer-based conductive composites is in strain sensors, which require high sensitivity and a broad measurement range for various deformation modes (tension, compression, torsion) and mechanical stresses [43]. The operating principle of these composites relies on the piezoresistive effect, where mechanical deformation changes the electrical resistance by affecting the conductive carbon nanofiller (CNF) network [44]. This change results from the dynamic breakdown and re-establishment of the percolation network formed by the conductive fillers in the polymer matrix [45]. As functional fillers, CNTs [46] and graphene [47] represent a unique class of materials. Their key advantage over alternatives lies in the fundamental combination of two critically important characteristics: exceptional mechanical strength and superior electrical conductivity. The hybridization strategy, i.e., the targeted combination of CNTs and graphene [48], enables the creation of new materials where the properties of individual components are not merely additive but manifest synergistically, providing superior functional characteristics. To achieve maximum sensor sensitivity, the filler concentration should be near the percolation threshold, where even minor deformations cause significant conductivity changes via the breaking of conductive pathways and the modulation of tunneling resistance between particles [49]. Modern material design strategies include creating hybrid systems, such as combining CNTs with other carbon materials like carbon black, to achieve a synergistic effect and an optimal balance between sensitivity and the composite’s mechanical properties [50]. Recent advances in additive manufacturing, such as 3D printing and the fabrication of filamentary sensors, have enabled the targeted design of complex micro- and macro-structures. One of the advanced additive manufacturing methods, opening new possibilities for creating intelligent materials, is the 4D printing of polymer composites [51]. This technology enables the creation of structures capable of changing their shape or properties under external stimuli over time. Similarly, innovative developments in the field of electrospinning, aimed at refining its principles and strategies, allow for the production of highly efficient piezoelectric nanofibers [52]. Of particular interest for soft robotics are shape memory polymers (SMPs) [53]. They combine low weight, programmability, and the potential for remote control, making them ideal candidates for creating adaptive actuators and drive mechanisms.

This has led to record-breaking performance, including a working strain range of up to 250% and a high gauge factor (GF > 10,000) [54]. As a result, flexible and stretchable strain sensors based on polymer composites are finding applications in diverse fields—from wearable electronics for biomechanical monitoring to structural health monitoring in aviation and tactile sensors for soft robotics [55,56].

In contrast to most review articles, in which the properties of nanomaterials, composite fabrication technologies, and sensor performance characteristics are considered in isolation, this article presents a comprehensive, integrated approach. The primary objective of this review is to establish cause-and-effect relationships between the synthesis method and structure of the carbon filler, its subsequent modification, the properties of the resulting nanocomposite, and the final operational parameters of the strain sensor. Thus, the article is built upon a unified logical chain: synthesis → structure → functionalization/modification → composite properties → device function. The overall schematic of the conducted analysis is presented in Figure 1.

The aim of this review is to analyze synthesis methods for carbon nanotubes and graphene, their functionalization, and the diverse structures used as fillers in polymers for creating various sensors designed to measure physical deformations. To achieve this aim, the following tasks were formulated and addressed:

(1)

Comprehensive analysis of carbon nanomaterials (CNMs).

(2)

Study of methods for obtaining CNMs (CNTs and graphene).

(3)

Analysis of different factors impacting CNM-based polymer composites used in strain sensors.

2. Carbon Nanotubes

2.1. Electrical Properties of CNTs

Carbon atoms can bond with each other through three possible hybridizations: sp, sp², and sp³. These hybrid states lead to various carbon allotropes such as diamond, graphite, graphene, and fullerene [57]. Carbon allotropes, their derivatives, and corresponding hybridizations are schematically illustrated in Figure 2 [45].

CNTs are composed of a graphene sheet rolled into a tubular structure with sp² hybridization of carbon atoms [58,59]. They represent one-dimensional nanostructures with diameters ranging from several nanometers to 100 nm and can extend up to several millimeters in length [60].

Depending on the number of constituent graphene layers, CNTs are classified as either single-walled (SWCNTs) [61] or multi-walled (MWCNTs) [19,20]. Their nanoscale diameters and relatively long lengths result in high aspect ratios and specific surface areas. CNTs exhibit either semiconducting or metallic properties depending on their chirality (Figure 3)—a unique structural characteristic [62]. CNTs with a chirality index of approximately 5.5 demonstrate metallic conductivity [63].

The properties of carbon nanotubes are determined by their molecular structure and diameter (Figure 3). Some CNTs exhibit metallic characteristics with high conductivity (up to 10³ times higher than copper), while others behave as semiconductors with tunable band gaps dependent on both chirality and diameter [64].

This structure–property relationship makes CNT materials with tunable characteristics. However, the lack of synthesis and purification methods capable of producing homogeneous batches of nanotubes limits their commercial application in electronics, despite decades of interdisciplinary research. The conductivity of carbon nanotubes is also determined by the number of allowed electronic states per atom per eV. As the tube diameter increases, the density of available electronic states decreases. For instance, a tube with a diameter of 5.3 Å exhibits a significantly higher density of states compared to a 10.6 Å diameter tube, where it measures only 0.035 states per electron-volt per atom [65]. The temperature dependence of conductivity originates from different physical mechanisms in various CNT types. In doped p-type semiconducting MWCNTs, it is governed by the charge carrier activation mechanism, whereas in metallic MWCNTs, it is determined by the temperature-dependent electron relaxation time during intraband transitions [66].

The unique electrical properties of CNTs, particularly their high electrical conductivity and pronounced piezoresistive effect, are directly responsible for the high sensitivity of strain sensors based on them. Mechanical deformations induce substantial and reversible changes in the electrical resistance of both individual CNTs and their composite networks.

Table 2. Influence of the electrical properties of CNTs on sensor characteristics.

№	Mechanism of Influence	Description	Ref.
1	Intrinsic Piezoresistive Effect	In metallic and semiconducting CNTs, deformation changes their band structure, which directly affects electrical conductivity. This forms the basis for the operation of polymer composite sensors using CNTs.	[67]
2	Change in Tunneling Resistance within Composites	In composite materials (CNT/polymer), deformation changes the distance between adjacent CNTs in the conductive network. This changes the tunneling resistance between them, which dominates the piezoresistive response of macroscopic sensors.	[68]
3	Reconfiguration of the Conductive Network	When the composite is stretched, some of the conductive pathways formed by individual CNTs break, leading to a sharp increase in resistance. This provides very high sensitivity (Gauge Factor).	[69]

summarizes the influence of different conduction mechanisms on the performance of polymer composite-based strain sensors.

2.2. Methods for Synthesizing CNTs

The synthesis of CNTs can be achieved through various methods and technologies [70,71,72,73,74,75]. These approaches are based on different physicochemical and mechanical principles, which can be implemented using laser technology, continuous or batch synthesis reactors and catalytic system materials, the arc discharge process in gaseous and liquid media, microwave treatment of carbon precursors, etc. [74]. The key synthesis methods and their characteristics are summarized in

Table 3. CNT synthesis methods.

№	Synthesis Method	Description/Features	Catalysts/Conditions	Type of CNTs	Refs.
1	Chemical Vapor Deposition (CVD)	A highly promising method for large-scale production; allows control over morphology and growth of CNTs on substrates.	Nickel (Ni), cobalt (Co), iron (Fe), or their combinations; temperature 500–1000 °C.	SWCNTs, MWCNTs	[31,34,69,73]
2	Laser Ablation	Pulsed laser vaporizes a graphite target; enables control over CNT diameter, but the method is costly.	Graphite target with metal catalyst particles (Co, Ni); inert gas.	Primarily SWCNTs	[37,38]
3	Arc Discharge	One of the earliest developed methods; synthesis at very high temperatures (~1700–2500 °C); allows for the production of CNTs with a low defect density.	Graphite electrodes, often with catalysts (Fe, Ni, Co, Y); current 50–150 A, inert atmosphere.	SWCNTs, MWCNTs	[8,41,62,71]
4	Microwave Synthesis	Uses microwave radiation to treat carbon-containing catalytic systems.	Catalytic systems based on ferrocene (C10H10Fe)/graphite.	MWCNTs	[74]
5	Flame Synthesis	High-temperature method utilizing controlled flame energy to decompose hydrocarbons; considered a variant of CVD. Distinguished by process continuity, high speed, and potential for industrial scaling. Main challenge: controlling temperature and composition in a turbulent flame.	Fe, Ni, Co (aerosol or on substrate); hydrocarbon fuel (CH4, C2H2, C2H4); temperature in growth zone ~1000–1300 °C.	SWCNTs, MWCNTs	[75]

.

The synthesis of the final CNT product is influenced by various factors, such as pressure, temperature, and the volume of the reaction zone where the synthesis occurs.

The choice of synthesis method directly determines the fundamental properties of CNTs for strain sensors based on polymer composites, as each method predominantly governs structural features, type, and morphology. High-temperature methods [71,74,75], such as arc discharge [62,71], yield CNTs with minimal defects, providing high mechanical strength and stable electrical conductivity essential for sensor durability. In contrast, lower-temperature CVD often results in controlled defect formation, which, while reducing strength, can enhance the piezoresistive effect and sensitivity by modifying the local electronic structure and facilitating electron tunneling. Furthermore, the synthesis method dictates whether SWCNTs, which exhibit the highest piezoresistive coefficient for superior sensitivity, or MWCNTs, characterized by greater rigidity, are produced. It also enables control over material architecture—creating ordered arrays for predictable responses or random networks where performance depends on inter-tube contacts.

Through the sequential removal of carbon atoms, quantitative control over topological defects at the junctions was achieved, enabling the synthesis of T-shaped structures with two distinct atomic configurations [76]. One of these, designated as T-junction 1, contained two heptagonal and two octagonal rings in the corner region (Figure 4).

Thus, the choice of synthesis method—whether CVD for its reproducibility and integration, arc discharge for its stability, or flame synthesis for its scalability—represents a compromise between sensitivity, mechanical stability, and the reproducibility of material properties for sensing applications.

2.3. Flame Synthesis of CNTs

This method is considered promising for large-scale and cost-effective production [77]. In [78], it has been established that the growth process of carbon nanotubes and nanofibers (CNTs/CNFs) in flame involves stages of carbon atom accumulation on the catalyst surface, followed by their diffusion from the catalyst surface, which serves as a key step in initiating subsequent nanostructure growth. The flame synthesis method has successfully enabled the growth of SWCNTs using iron nanoparticles through the optimization of synthesis conditions and parameters, specifically solvent choice, salt concentration, and gas composition, which creates ideal conditions for their growth. It has been determined that an iron catalyst preferentially interacts with CO rather than C₂H₂, attributed to surface reconstruction and chemical interactions during the process [79].

The flame synthesis method offers unique advantages, including the capability for in situ direct deposition of CNTs onto complex substrate surfaces and the formation of structures with controlled defect density, as confirmed by studies of CNT functional properties [80]. Studies show that while catalysts are paramount for defining CNT morphology in flame synthesis [81], growth on copper substrates in counter-flow flames specifically requires mandatory acid pretreatment [82]. Despite the challenges of control in the heterogeneous flame environment, strict maintenance of reaction zone parameters remains essential for effective CNT growth [83], whereas the use of liquefied petroleum gas makes this method an economical alternative to conventional CVD synthesis [84]. Collectively, these factors establish the unique position of flame synthesis in CNM production.

Therefore, flame synthesis holds a unique niche in CNT production, a conclusion supported by its direct comparison with traditional CVD (

Table 4. Comparison of flame synthesis and CVD method for CNT production.

№	Aspect	Flame Synthesis	CVD
1	Process Type	Continuous process	Batch process
2	Scalability	High (industrial)	Moderate (laboratory)/High (industrial)
3	Cost	Low (uses fuel as carbon and energy source)	High (requires energy-intensive furnace)
4	Control/Precision	Moderate (turbulent flame environment)	High (enables controlled synthesis)

).

2.4. CVD Method

The CVD (chemical vapor deposition) method is considered one of the most common and efficient approaches for CNT synthesis. The essence of this method (Figure 5) involves a hydrocarbon gas undergoing a chemical reaction under intense heating, resulting in the formation of high-purity CNTs with a well-defined crystalline structure on the substrate surface.

In the study [85], the synthesized SWCNTs exhibited a stable distribution of chiral indices across a broad temperature range (750–1000 °C). The carbon deposited on the reactor wall was also identified as a key factor for maintaining optimal CNT synthesis conditions (Figure 6), where the blue color corresponds to alcohol droplets with dissolved ferrocene.

The CVD method has demonstrated exceptional efficacy in the controlled synthesis of carbon nanotubes with tailored morphology, structure, and dimensions through precise variation of temperature regimes, pressure, gas composition, and catalytic system materials. Specifically, using a cobalt-containing catalyst Co_xMg_(1−x)MoO₄ in a methane–hydrogen–ammonia gas mixture enabled the production of nitrogen-doped MWCNTs with a yield exceeding the catalyst mass by over 30 times. The nitrogen concentration (0.6–3.2 at.%) was regulated by the ammonia flow rate, while X-ray photoelectron spectroscopy revealed a predominance (95%) of graphitic nitrogen configuration [86].

The application of an alkali–metal–modified copper catalyst facilitated the synthesis of CNTs with copper nanoneedles at their tips, stabilized against oxidation by a carbon coating [87,88]. Meanwhile, a natural manganese mineral catalyzed the formation of helical CNTs, including a unique triple-helix structure [89]. Furthermore, the CVD method showed potential for synthesizing nickel nanoparticles (10–50 nm) on the surface of MWCNTs, where the nanotubes themselves acted as reducing agents at 600 °C [90]. Morphological studies before and after growth on silicon substrates (Si/SiO₂/Co) confirmed the possibility of controlling the nanotube structure [91].

2.5. Laser Ablation

The laser ablation method, pioneered by Richard Smalley’s research group, is based on the vaporization of a graphite target using a pulsed laser within an inert atmosphere, followed by the condensation of carbon vapors into nanotubes (Figure 7). The vaporized carbon condenses on cooler regions of the reactor, forming CNTs. A separate cooled collector surface is often utilized to collect the final product. The initial approach using a pure graphite target successfully synthesized CNTs [92], while the introduction of catalysts (Co/Ni) into the target enabled the production of SWCNTs with yields up to 70% [93].

The key synthesis parameters are laser intensity and gas environment temperature. Laser intensity determines the evaporation of components (C, Ni, Co) from the target, while the reaction zone temperature critically influences the CNT diameter and uniformity [94]. Comparative studies of laser systems revealed that CO₂ lasers enable SWCNT synthesis at room temperature, whereas Nd:YAG lasers require heating to approximately 1170 K [95].

Recent developments include utilizing alternative carbon sources such as coal, which provides a higher yield and selectivity toward small-diameter SWCNTs [96]. The optimization of laser parameters (wavelength, power, repetition rate) enables control over the nanostructure morphology [97,98]. The method is also employed for synthesizing hybrid materials, including Ag/CNT nanocomposites for photocatalysis [99].

The pulsed laser ablation in liquid (PLAL) method facilitates the synthesis of hybrid nanomaterials. The “CNT/AgNPs” hybrid nanocomposite was produced under laser-induced plasma conditions, which generate plasma shockwaves and streams of high-energy charged nanoparticles. Through electrostatic interactions, positively charged Ag nanoparticles became immobilized on negatively charged active sites of functionalized CNT surfaces [100], leading to the formation of the target structure (Figure 8).

Despite the high quality of the resulting CNTs (primarily SWCNTs), the method remains economically less advantageous compared to CVD and arc discharge, which limits its industrial application [101]. Furthermore, achieving uniform product quality at a large scale presents significant engineering challenges, and the yield rate often lags behind that of more established techniques. Consequently, while invaluable for fundamental research and specialized applications requiring ultra-pure CNTs, its use in mass-produced commercial goods remains limited.

2.6. Arc Discharge Synthesis Method

The arc discharge method is a pioneering and highly efficient technology for the synthesis of CNTs. The process is based on the thermal evaporation of graphite electrodes within the plasma of an electric arc (temperature ~4000 K) in an inert gas atmosphere. The subsequent recombination of sublimated carbon leads to the formation of various carbon nanostructures. This method is historically significant due to its unique capability to produce CNTs with exceptionally low defect density and high structural perfection, making the obtained material a benchmark for fundamental research. A key challenge of this technology has always been the complex control over reaction parameters, which directly determine the yield and selectivity of the target product.

CNT synthesis via arc discharge proceeds in two distinct regimes: active (“synthesis on”) and passive (“synthesis off”). The formation of the primary mass of nanotubes occurs precisely in the active regime, characterized by optimal local conditions of temperature and carbon allotrope concentration [102]. A deep understanding of the process dynamics has enabled the development of targeted strategies to control the morphology, yield, and even electronic properties of the synthesized CNTs.

One of the powerful control tools is the variation of gas composition and pressure. For instance, replacing helium with nitrogen promotes the formation of longer and straighter MWCNTs [103], while adding carbon monoxide (CO) to helium allows for influencing the diameter selectivity of the synthesized tubes [104]. Conducting the synthesis in a liquid medium, such as in electrolyte solutions, has also proven effective for obtaining extended and pure structures [105].

The classical approach using graphite rods has been significantly expanded through the use of alternative carbon sources and novel catalyst forms. For example, filling the anode cavity with a mixture of metal oxides (CuO, Fe) and anthracite enables the production of branched heterogeneous nanostructures [106]. The use of bituminous coal in combination with alkaline catalysts (KOH, K₂CO₃) paves the way for resource-efficient direct CNT synthesis [107] (Figure 9).

Furthermore, an approach utilizing platinum group metal (PGM) catalysts introduced in aerosol form has been developed. This strategy helps to minimize the incorporation of large metal particles of PGM into the final product and facilitates the synthesis of high-quality CNTs [108].

CNTs produced by the arc discharge method exhibit reproducible and distinct properties. Studies on the spatial distribution of synthesis products have revealed that the optimal growth zone for nanotubes is located at a distance of 3–11 mm from the arc center. Within this zone, tube lengths can reach hundreds of nanometers, while their diameter and chirality remain sufficiently uniform throughout the deposition volume [109].

Consequently, the arc discharge method has evolved from an empirical technique into a tool for the directed synthesis of carbon nanostructures with tailored characteristics. A profound understanding of the plasma process physics, combined with a systematic approach to selecting raw materials and catalysts, unlocks new prospects for its application.

3. Graphene and Its Derivatives

3.1. Graphene Synthesis Methods and Properties

Graphene is a material undergoing extensive research and practical application [110]. It is a carbon allotrope with a two-dimensional (2D) structure possessing exceptional mechanical, electrical, surface, and optical properties. Similar to CNTs, various methods exist for producing graphene and its derivatives. The primary synthesis methods for graphene, which can be categorized into three main groups—physical, chemical, and vapor deposition methods—are presented in

Table 5. Methods for the synthesis of graphene and its derivatives.

№	Method	Description	Aspects	Ref.
Physical Methods (Exfoliation)
1	Mechanical Exfoliation	Layer-by-layer separation of graphite using mechanical forces (e.g., scotch tape)	High quality of obtained flakes, but low throughput; suitable for laboratory research	[111]
2	Liquid-Phase Shear Exfoliation	Delamination of graphite in a liquid under high shear stress	Scalability, production of defect-free graphene in large volumes	[112]
Chemical Methods
3	Hummers Method (Oxidation–Reduction)	Oxidation of graphite to form graphene oxide (GO), followed by chemical reduction	High throughput, but introduces a large number of defects into the structure	[113]
CVD
4	CVD on Copper Substrate	Based on surface catalysis and decomposition of carbon-containing gases (e.g., methane) on heated copper foil	The most promising method for producing high-quality, large-area single-layer graphene films	[114]
5	CVD on Nickel Substrate	Based on carbon dissolution in nickel at high temperature, followed by segregation upon cooling	Difficulty in controlling the number of layers; tendency to form multilayer graphene	[115]

.

In the study [115], Liu F. et al. demonstrated a mechanical exfoliation method using adhesive tape to obtain single-layer graphene samples. The CVD method enables direct growth of graphene on insulating substrates, providing uniform coverage, high quality, and compatibility with industrial standards. However, pristine graphene is chemically inert and lacks a bandgap, which limits its functional applications. Nitrogen doping has emerged as an effective solution to this limitation. Unlike complex plasma post-processing methods, which are unsuitable for mass production, researchers have developed a direct single-step synthesis of nitrogen-doped graphene. Using a 4-inch sapphire substrate, graphene was grown via CVD with pyridine serving as both the carbon and nitrogen source, while ethanol was employed as an additive to enhance film quality through its hydroxyl groups [116].

CVD remains the primary method for producing graphene for electronic applications, though its growth mechanisms require further investigation. Experimental studies utilizing Cu-Ni alloys (Figure 10) with varying nickel concentrations—from pure copper (low carbon solubility) to pure nickel (high carbon solubility)—revealed that when the substrate reaches 45 wt% Ni content, a transition from bilayer graphene (BLG) to few-layer graphene (FLG) occurs [117].

The graphene coating applied by CVD onto copper microparticles (Figure 11) provides exceptional corrosion resistance, reducing the corrosion rate by three times compared to untreated powder and achieving performance metrics comparable to CuZn30 alloy [118].

Beyond the methods listed in Table 4, electrochemical exfoliation (ECE) is a promising technique for the large-scale, cost-effective production of graphene. While process parameters are critical, the type of source graphite electrode also significantly influences product quality—an aspect that remains insufficiently studied [119]. For instance, a study utilizing a pulsed voltage method for ECE demonstrated that periodically alternating the voltage between anodic and cathodic polarization achieved high graphene production rates of 0.50 and 0.43 g/min at specific polarization time ratios [120].

The voltage-switching (sV) method for electrochemical graphite exfoliation in a NaOH/KOH melt at 300 °C demonstrated 85% higher efficiency compared to the constant voltage (cV) method, confirming its potential for scalable graphene production. This approach facilitates intensive alternating ion intercalation/deintercalation, promoting effective graphite exfoliation [121]. Owing to its porous structure and high electrical conductivity, 3D graphene not only effectively addresses these challenges but also enables rapid charge transfer, making it a highly promising material for energy storage systems [122].

Experimental investigation revealed the substantial influence of the liquid medium’s nature on the morpho-structural characteristics of graphene nanoplates synthesized via laser ablation of a graphite target using a pulsed Nd:YAG laser (λ = 1064 nm, τ = 7 ns) in various environments, including distilled water, liquid nitrogen, and organic solvents [123]. Comparative analysis demonstrated that parameters of the synthesized nanoplates, such as size, number of layers, and defect density, varied statistically significantly depending on the dielectric constant and surface tension of the receiving liquid.

Plasma-based methods demonstrate high efficiency in the synthesis and modification of graphene materials. Direct sulfur doping of FLG in microwave plasma using diethyl sulfide as a precursor confirmed the possibility of controlled heteroatom incorporation [124]. A more complex architecture was realized via a co-doping method using S and N in a magnetically rotating arc plasma, which demonstrated a synergistic effect, achieving record doping concentrations—up to 15.0 at.% S and 6.9 at.% N [125]. The morphology of carbon nanoparticles synthesized in a plasma reactor substantially depends on the nature of the carbon precursor: using ethanol or toluene at different concentrations allows for the targeted production of either few-layer graphene or soot-like particles [126].

3.2. A Comparative Analysis of Graphene Synthesis Methods for Piezoresistive Sensors

The CVD method enables the production of high-quality graphene with minimal defects. When integrated into a polymer matrix, such graphene forms a continuous conductive network, which provides the foundation for high sensitivity and a stable piezoresistive response under cyclic deformation. A notable example is a sensor based on a graphene/Ecoflex composite, demonstrating a high GF (up to 138), low detection limit (0.1% strain), and reliability exceeding 1500 cycles [127]. The main challenges limiting reproducibility remain the difficulty of achieving uniform flake distribution within the polymer and aggregation processes [128].

ECE, particularly using pulsed voltage, yields graphene with a developed surface area and a high concentration of functional groups. These groups enhance chemical compatibility with the polymer matrix, promoting more homogeneous filler distribution and lowering the percolation threshold. However, the method inherently introduces a high density of defects in the graphene crystal structure, which can reduce the mechanical strength of the entire composite and lead to hysteresis in the sensor response. This necessitates further optimization of functionalization and synthesis processes [129].

Plasma synthesis methods, such as plasma-enhanced chemical vapor deposition (PECVD), offer possibilities for direct graphene growth on dielectric substrates and its doping with heteroatoms. Doping, with sulfur and nitrogen, for instance, intentionally modifies the electronic structure of graphene by creating localized energy states within the band gap. This enhances the dependence of resistance on mechanical deformation, thereby increasing sensor sensitivity. Research confirms that controlling parameters such as precursor gas ratios and reactor pressure allow for management of morphology and doping level, which is a key factor in ensuring the reproducibility of the final material’s sensing characteristics [130].

3.3. Graphene Oxide

The most common method for synthesizing graphene oxide (GO) is the modified Hummers’ method, which is valued for its relatively simple apparatus requirements, controlled and deep oxidation level of graphite, reproducible synthesis, and the absence of toxic gaseous byproducts compared to classical approaches [131,132,133,134]. Figure 12 illustrates three distinct types of modified graphene oxide derived from graphite using H₂SO₄ and KMnO₄ as reagents, yielding GO with different ratios of functional groups. By varying the reaction conditions, the resulting GO can be tailored to be enriched with either hydroxyl or epoxide groups on its basal planes or carboxyl groups at its edges.

Figure 13 shows transmission electron microscopy (TEM) images of GO; the majority of the graphene flakes were thin and transparent enough to see the network through them [135]. The samples were suspended in absolute ethanol (≥99%) and applied to a CuTEM grid.

The high electrical resistance of GO, resulting from the disruption of the sp²-hybridized structure, indeed limits its application in strain sensors. To address this limitation, the following approaches are employed: partial reduction of GO to rGO (reduced graphene oxide), which significantly enhances the material’s conductivity while preserving functional groups—this method has been extensively studied in [136]. Another effective solution involves creating hybrid materials where graphene nanosheets are combined with carbon nanotubes, forming a synergistic conductive network as demonstrated in research [137]. The fundamental operational mechanism of such sensors is based on controlling the percolation network, where deformation causes reversible changes in the tunneling resistance between conductive particles, as detailed in [85].

4. Functionalization of CNTs and Graphene

4.1. Special Functionalization Methods

The abundance of oxygen-containing groups (hydroxyl, epoxide, carboxyl, carbonyl, etc.) in the structure of GO enables the modification of graphene particles through various chemical and physical treatments, allowing controlled alteration of their functional group composition [137]. To date, the family of functionalized graphenes includes aminated, carbonylated, carboxylated, brominated, fluorinated, and other chemical derivatives of graphene [138]. This diversity of forms facilitates the development of composite materials with tailored electronic, thermophysical, chemical, and mechanical properties [139].

When CNTs, graphene, and other related nanoscale carbon materials interact with ozone, acids, air, or oxygen-containing acids, in addition to opening and oxidation processes that generate adsorbed or gaseous CO and CO₂, the attachment of surface functional groups occurs [140]. These oxygen-containing groups (Figure 14) can react with various reagents, enabling the attachment of numerous other functional groups to CNTs [141].

Covalent modification of CNT surfaces is primarily achieved through chemical and electrochemical reactions. When CNTs are utilized as electrodes in an electrochemical cell, controlled reduction or oxidation of surface molecules can be performed, leading to the formation of covalent bonds between generated radicals and the carbon lattice [142,143]. The most prevalent chemical functionalization approaches include oxidation, fluorination, and amidation reactions.

Oxidative treatment results in the formation of various oxygen-containing functional groups. Interaction with acids predominantly yields carboxyl (–COOH), carbonyl (>C=O), and hydroxyl (≡C–OH) groups [144]. The resulting oxidized CNTs exhibit improved dispersibility in polar solvents without requiring additional stabilizers [145].

The type and density of functional groups (such as –COOH, –OH) introduced onto the filler surface determine its chemical affinity to the polymer matrix. This factor is critical for achieving homogeneous dispersion and forming a strong interfacial boundary, which, in turn, directly influences the properties of the final composite.

Functionalization of carbon nanomaterials represents a key strategy for optimizing polymer nanocomposites. Covalent attachment of functional groups or polymer chains to the filler surface—for example, grafting polystyrene (PS) onto carbon nanotubes (CNTs)—significantly improves dispersion in a compatible polymer matrix and enhances interfacial adhesion, leading to a substantial increase in the mechanical strength of the material [146]. A similar effect is observed in other systems: the introduction of just 0.05% functionalized graphene into poly(methyl methacrylate) (PMMA) allows the modulus of elasticity and tensile strength to be increased to levels comparable to those of composites reinforced with single-walled carbon nanotubes (SWCNTs) [147]. In the case of epoxy resins, functionalization of graphene can increase strength by 57% and Young’s modulus by 70% [148].

The choice of the optimal functionalization method is determined by the chemical nature of the polymer matrix, and no universal approach exists. For polar polymers, such as epoxy resins or polyacrylonitrile, oxidation with the formation of carboxyl groups (–COOH) is most effective, providing high chemical affinity and strong adhesion at the interface [149]. For non-polar matrices (e.g., polystyrene or polyolefins), strategies that minimize disruption of the filler’s conductive structure are preferable, such as non-covalent functionalization or grafting of compatible polymer chains. The improvement of the mechanical parameters of nanocomposites through controlled functionalization directly enhances their strain-sensing characteristics, paving the way for the creation of highly efficient functional materials.

Ozone serves as a powerful oxidizing agent capable of not only creating functional groups but also completely oxidizing CNTs to CO and CO₂. A standard protocol involves treating a colloidal solution of CNTs in acetic acid with an ozone stream, followed by adjustment of the surface group composition [150].

The efficacy of ozone treatment has been demonstrated in regeneration processes of magnetic CNTs after adsorption of organic contaminants from water, employing a cyclic process: magnetic separation → ozone oxidation → ethanol and water washing for atrazine desorption [151].

Acid treatment, particularly with concentrated H₂SO₄ and oleum, provides a high degree of protonation with the formation of CH_x groups [152,153]. The maximum concentration of hydroxyl groups is achieved through mechanochemical treatment with molten KOH, where the reagent ratio serves as the key efficiency parameter [154]. Thermal treatment of functionalized CNTs at temperatures above 350 °C leads to defunctionalization—the decomposition of surface groups. This process is accompanied by the formation and “opening” of structural defects, which increases the specific surface area and sorption capacity of the nanotubes [155].

Various methods for covalent functionalization have been developed [156], typically involving the formation of covalent bonds between functional groups and the graphene nanoparticle surface through reactions such as 1,3-dipolar cycloaddition, azide reactions, Friedel–Crafts reactions, or click chemistry (Figure 15).

Recent advances have led to increasingly sophisticated and diverse methods for the surface functionalization of CNTs, enabling the targeted modification of their properties. Common techniques include wet oxidation (e.g., with nitric acid, hydrogen peroxide, or potassium permanganate), dry oxidation (using air, ozone, or plasma), amidation, silanization, silylation, polymer grafting, polymer wrapping, and fluorination. These methods are crucial for enhancing CNT dispersibility and improving their compatibility with various polymer matrices [156].

4.2. CNTs and Graphene with Acidic Functional Groups

The functionalization of CNTs and graphene with acidic groups enables a diverse range of chemical modifications and applications. Large molecules, such as Vaska’s complex (trans-[IrCl(CO)(PPh₃)₂]), can attach to CNT surfaces. This reaction exhibits a specificity towards small-diameter nanotubes, presenting a viable route for their size-selective separation [157,158]. A well-established amidation procedure using long-chain amines leads to a significant increase in CNT volume [159,160].

Oxygen-containing functional groups on the CNT surface facilitate interactions with various organosilicon compounds, such as 3-mercaptopropyltrimethoxysilane and amic acid oligomers containing alkoxysilane groups. Furthermore, terminal carboxyl groups on CNTs can undergo condensation reactions to form integrated polymeric structures [161].

Self-assembled layers based on graphene or CNTs can form via the deprotonation of surface carboxyl groups upon contact with metal oxides (e.g., Ag, Cu, Al), with the resulting structures stabilized by electrostatic interactions [162,163,164]. A particularly promising direction involves the decoration of CNTs with gold nanoparticles (AuNPs), creating hybrid materials with significant potential in photocatalysis and the development of advanced biosensors [165].

Enhanced interfacial interaction between graphene and the polymer matrix promotes its uniform distribution and ultimately leads to improved overall performance of the nanocomposite. Thus, graphene functionalization serves as a key tool for enhancing adhesion at the phase interface, preserving the unique properties of the nanofiller and preventing its agglomeration [166].

4.3. Modern Functionalization Methods

In [167], an efficient method for dispersing graphene nanoparticles (GNPs) in water using 1-pyrenecarboxylic acid under ultrasonic treatment was developed. The monolayer nature of the obtained graphene is confirmed by the Lorentzian shape of the phonon peak in the Raman spectrum, while the π-π interaction with pyrenecarboxylic acid ensures dispersion stability and enables the creation of sensors for alcohol detection. A promising approach is laser ablation for the synthesis of hybrid nanomaterials. Decorating graphene nanolayers with TiO₂ nanoparticles demonstrates enhanced optical absorption and potential for photocatalytic applications [168]. Similarly, graphene/CuO composites, synthesized by pulsed-laser ablation (PLA), show morphology dependence on the radiation flux density: increased flux leads to smaller nanoparticle sizes and improved adhesion [169]. A one-step in situ method for synthesizing “gold–graphene” composites without surfactants or linkers has been developed, preserving the surface catalytic activity [170]. For Au-Ag core-shell structures deposited on GO, TEM analysis confirmed the successful formation of heterostructures [171].

Figure 16 demonstrates that the morphology of xCu₂O·yMnO nanocomposite films, deposited on graphene sensors via PLA, is highly dependent on the substrate temperature. It transitions from uniform layers at room temperature to distinct nanograins (~7 nm) at 300 °C, enabling tunability of their functional properties [172].

The synthesized and functionalized carbon nanomaterials provide more than just conductivity; their electrophysical and morphological traits are central to converting mechanical strain into a measurable electrical signal. A key requirement is the formation of a conductive network within the insulating polymer, governed by percolation theory. The percolation threshold depends strongly on filler morphology: the high aspect ratio of CNTs and the 2D geometry of graphene enable network formation at very low loadings, while their intrinsic conductivity ensures low baseline resistance.

The piezoresistive effect emerges from three interdependent mechanisms:

• Altered interparticle tunneling: Strain changes nanoscale gaps, exponentially affecting tunneling resistance—a primary source of high sensitivity.
• Reconfiguration of the conductive network: Under large deformations, contacts break and reform, influenced by the filler’s mechanical flexibility, which defines the sensor’s working range and hysteresis.
• Change in the filler’s intrinsic resistance: Reversible lattice distortion in high-quality CNTs or graphene can modulate their own conductivity.

Therefore, strain sensor design involves optimizing filler type, morphology, dispersion, and functionalization to balance these mechanisms. This enables the creation of tailored sensors, from highly sensitive detectors for micro-strains to robust sensors for monitoring large deformations.

4.4. Impact of Functionalization Techniques on Composite Properties and Sensor Performance

The choice of functionalization method directly determines the type and density of functional groups introduced onto the surface of carbon nanomaterials, which, in turn, has a cascading effect on the key stages of sensing element formation: dispersion, interfacial adhesion, formation of the conductive network, and its response to deformation.

Oxidative Treatment (HNO₃, H₂SO₄, H₂O₂). Leads primarily to the formation of carboxyl (–COOH) and hydroxyl (–OH) groups at defects and the ends of CNTs [143]. This increases surface energy and affinity for polar polymer matrices (e.g., epoxy resins, polyvinyl alcohol). Improved dispersibility and adhesion prevent filler agglomeration and promote the formation of a more uniform and stable conductive network with a low percolation threshold. However, intense oxidation disrupts the sp²-hybridized lattice, increasing defect density, which can reduce the intrinsic electrical conductivity of the nanoparticles and, consequently, the composite’s baseline conductivity [144]. For strain sensors, this creates a compromise: improved dispersion enhances reproducibility and sensitivity due to a greater number of tunneling contacts, but excessive oxidation can limit the maximum gauge factor (GF).

Ozonation. Represents a controlled method of “mild” oxidation. Ozone selectively attacks sites of high reactivity, creating oxygen-containing groups without the large-scale framework destruction characteristic of treatment with strong acids [150]. This allows for targeted surface modification while preserving high baseline electrical conductivity. In the context of sensors, such particles form a conductive network with an optimal balance between good dispersion (due to functional groups) and high intrinsic conductivity of the pathways. An additional advantage is the possibility of regenerating the material’s sorption properties, which is potentially useful for creating rechargeable or self-cleaning sensor platforms [151].

Acid Treatment (conc. H₂SO₄, oleum). Provides deep functionalization, including sulfonation and the formation of C–H groups, radically changing the chemical nature of the surface [152,153]. Such treatment is most effective for creating chemically active sites for the subsequent grafting of complex molecules or polymers. In composites, this enables covalent “stitching” of the filler with the matrix, maximally enhancing interfacial adhesion and stress transfer. For strain sensors under high cyclic loads, this is critically important, as it prevents delamination and resistance drift, thereby increasing durability.

Mechanochemical Treatment (e.g., with molten KOH). Aims at the maximal introduction of hydroxyl groups [154]. This is highly effective for the subsequent synthesis of hybrid materials and the creation of hierarchical structures. In sensors, this can be used to create porous or layered structures with an increased specific surface area, enhancing sensitivity to small deformations due to a larger number of variable contacts.

Thermal Treatment (>350 °C) of Functionalized CNTs. Induces defunctionalization—the removal of introduced groups [155]. This process is accompanied by the “healing” of some defects, but can also create new pores. As a result, the specific surface area increases and electrical conductivity rises due to the restoration of the π-conjugation system. For sensors, the strategy of “functionalization → dispersion in polymer → controlled thermal treatment” represents a powerful tool for post-synthesis optimization of the conductive network: uniform distribution is first achieved, and then the conductivity of the nanotubes themselves is partially restored.

Table 6. Comparative analysis of carbon nanomaterial functionalization methods for polymer composite strain sensors.

Method/Ref.	Main Functional Groups/Surface Change	Dispersion in Polymer	Interfacial Adhesion	Network Conductivity	Key Sensor Effect	Optimal Application
Oxidative treatment (HNO3, H2SO4, H2O2) [143,144]	–COOH, –OH at defects/ends	Greatly improves (polar matrix affinity)	Enhances (polar interactions)	Reduces (sp2 damage → defects)	Trade-off: Better dispersion → more tunneling → higher GF, but excessive oxidation limits max GF; improves reproducibility	Polar matrices (epoxy, PVA); baseline method
Ozonation [150,151]	Oxygen groups (selective, “soft”)	Moderately improves (controlled)	Moderately enhances	Preserves high (minimal damage)	Balance: Optimal dispersion + intrinsic conductivity; regeneration capability	Systems requiring balanced sensitivity and baseline stability
Acid treatment (conc. H2SO4, oleum) [152,153]	Deep oxidation, sulfonation, C–H groups	Strongly improves (active sites for grafting)	Maximizes (covalent cross-linking)	Significantly reduces (severe structure damage)	Durability: critical for drift/peeling prevention under high cyclic loads; long-term stability	Extreme conditions, high-cycling sensors
Mechanochemical (e.g., molten KOH) [154]	High –OH concentration	Effective for hierarchical/hybrid structures	Enhances	May reduce (degree-dependent)	Architecture: porous/layered high-surface structures → high sensitivity to micro-strains	Micro-strain detection (pulse, vibration)
Thermal treatment (>350 °C) of func. CNTs [155]	Defunctionalization, defect healing, pore formation	May degrade (reversal)	May decrease (loss of polar groups)	Restores/increases (π-system recovery)	Post-processing: strategy “functionalize → disperse → anneal” for uniform distribution + restored conductivity	Final tuning of composite electrophysical properties

presents a comparative analysis of functionalization methods for carbon nanomaterials and their impact on the properties of composites for strain sensors.

Thus, no universal “best” functionalization method exists. Oxidation serves as a fundamental route for improving compatibility with polar polymers. Ozonation provides a more controlled oxidative approach. Deep acid treatment and mechanochemistry are tools for creating robust covalent interfaces or complex hybrid structures. Thermal treatment is used for final tuning of the electronic properties of the formed network. The choice of strategy depends on the target polymer matrix and the required sensor performance profile, such as high gauge factor (GF), wide operating range, or low hysteresis.

5. Polymer Composites with CNTs or Graphene

5.1. Classification of Polymer Composites

CNTs are widely used as highly effective reinforcing fillers in polymer composite materials. Due to their exceptional combination of mechanical strength, flexibility, and high specific surface area, CNTs significantly enhance the structural integrity of composites while simultaneously improving their electrical and thermal conductivity. Depending on the architecture of the nanofiller and the target properties of the final material, CNT-based composites can be systematically categorized into four main classes (Figure 17), which differ both in the distribution morphology of the nanotubes and their interaction mechanism with the polymer matrix.

Composites with a dispersed filler are systems in which CNTs are introduced directly into the polymer matrix as a discrete dispersed phase, serving as the primary or sole functional filler [172].

Research on electrically conductive polymer composites demonstrates diverse strategies for designing effective materials. For instance, using a straightforward laboratory method involving vacuum filtration, ultra-thin films of single-walled carbon nanotubes (SWCNTs) and their composites with poly(3,4-ethylenedioxythiophene)/polystyrene sulfonate (PEDOT:PSS) have been produced. These films exhibited exceptional electromagnetic interference shielding effectiveness in the X-band frequency range: with a thickness of only 2.12 µm, they achieved a shielding efficiency of 55.53 dB [173].

To impart additional functional properties, such as impact resistance and chemical stability, an effective approach is the formation of hybrid composites—multicomponent systems in which CNTs are combined with other nanofillers like graphene [174]. Adding graphene can not only reduce impact energy and enhance mechanical reliability, but also, due to its high chemical resistance, expand the material’s applicability in harsh environments [175]. The synergistic effect of simultaneously incorporating different carbon fillers, such as CNTs and GO, into a polymer matrix has been experimentally confirmed, showing improved thermomechanical properties compared to systems containing only one filler type [176].

Therefore, the choice between using dispersed filler composites and creating complex hybrid systems [177] is fundamental, as it dictates the architecture of the conductive network and, ultimately, the material’s suitability for targeted applications—ranging from single-function sensors to sophisticated multifunctional devices.

The GF was determined by studying the dependence of electrical resistance on deformation (tension/bending) [178] using the setup shown in Figure 18.

Regarding the gauge factor, studies of resistance on deformation dependence (tension/bending) have been carried out using an installation (Figure 19). The installation consisted of a displacement module (1) containing a moving head, in which fasteners are provided for fixing the strain sensor (2), a multimeter (3) connected to a strain sensor for recording electrical resistance values, and a personal computer (4) for controlling the displacement module. The movement trajectory was set using the movement module software. The strain-sensitive material was fixed in mountings of the installation, the multimeter was connected to it, and the readings of electrical resistance were recorded with an extension every 5 mm [179].

Table 7. Research methods for nanomaterials and nanocomposites.

№	Analysis Category	Method/Equipment	Target	Application Examples for Polymers with CNTs/Graphene
1	Morphology and structure	Scanning electron microscopy (SEM)	Surface morphology, filler distribution, agglomerates, and fractures.	Visualization of CNT dispersion in the matrix and study of the composite structure after deformation [180]
		Transmission electron microscopy (TEM, HR-TEM)	Internal structure at the atomic level, diameter and walls of CNTs, defects, and packing of graphene layers.	Determination of the diameter and number of CNT walls and study of interfacial boundaries in the composite [181]
		Atomic force microscopy (AFM)	Surface topography with nanometer resolution, thickness of graphene flakes, and mechanical properties (Young’s modulus).	Measurement of the thickness of graphene/graphene oxide flakes and mapping of the elastic properties of the composite [182]
2	Structural and phase analysis	Raman spectroscopy (Raman)	Crystallinity, defectiveness, number of graphene layers, type of CNT conductivity (metal/semiconductor), and load in the polymer.	Quality control of CNTs (D/G-band ratio), determination of the number of graphene layers, and analysis of stresses in the composite [183]
		X-ray diffraction analysis (XRD)	Phase composition, interlayer distance (in graphite/graphene), and degree of polymer crystallinity.	Determination of the degree of graphite intercalation/exfoliation and the effect of the filler on the crystallinity of the polymer matrix [184]
3	Thermal analysis	Thermogravimetric analysis (TGA)	Thermal stability, filler content, decomposition temperature, and residue (ash content).	Determination of the exact mass content of CNT/graphene in the composite and evaluation of its thermo-oxidative stability [185]
		Differential scanning calorimetry (DSC/DSK)	Temperatures of phase transitions (glass formation, melting, crystallization), degree of crystallinity, and heat of reactions.	Studying the effect of the filler on the polymer’s glass transition temperature and crystallization kinetics [186]

presents the characterization techniques for nanomaterials and nanocomposites.

The analysis flowchart for nanoscale fillers and their resulting nanocomposites is presented in Figure 20.

5.2. Electrical Resistance of Polymer Composites

By filling voids and suppressing aggregation, CNTs impart new properties to the composite that are promising for tensometry [187]. The sharp increase in electrical conductivity upon the addition of CNTs typically signals the attainment of the percolation threshold [188], classically described by the power-law equation:

σ = σ₀ (φ − φ_c)^t,

(1)

where

• σ is the electrical conductivity of the composite, S·m⁻¹;
• σ₀ is the electrical conductivity of the CNTs or the conductivity of an ideal network, S·m⁻¹;
• φ is the volume fraction (concentration) of CNTs in the composite, vol. %;
• φ_c is the critical volume fraction at which an infinite conducting cluster forms in the system and conductivity increases sharply, vol. %;
• t is the critical exponent, a dimensionless parameter that depends on the filler geometry and system dimensionality.

Equation (1) enables the prediction of the composite’s electrical conductivity and allows for the extraction of key parameters of the CNT network—namely, the percolation threshold and the critical exponent—from experimental data. The operation of many highly sensitive strain and stress sensors is based on conductive polymers, whose electrical resistance changes predictably under mechanical stress. The working principle of these sensors involves converting geometric deformation caused by external forces (tension, compression, etc.) into a change in electrical resistance [189].

Figure 21 schematically illustrates the percolation behavior in CNT–polymer composites, showing the logarithmic conductivity as a function of CNT volume fraction. This relationship can be divided into three distinct regions:

(1)

Dielectric Zone: at low CNT content, the material behaves as a dielectric.

(2)

Percolation Threshold: conductivity increases sharply when CNTs begin to form a continuous network.

(3)

Conduction Zone: at high CNT content, the material reaches its maximum conductivity.

The fundamental mechanism relies on the increase in electrical resistance when the polymer is stretched (due to increased length or reduced cross-sectional area), and conversely, its decrease upon compression. This property is precisely what enables their application in flexible and wearable electronics: the polymer transduces mechanical deformations from body movements (such as joint flexion or muscle contraction) into measurable changes in electrical resistance [190].

Although the conduction mechanism in these materials can be either electronic or ionic, the signal readout principle remains universal—it is based on the dependence of electrical resistance on the applied strain [191]. Consequently, sensors based on conductive polymers serve as highly effective tools for dynamic monitoring of human biomechanics [192] and for tracking mechanical stresses [193].

5.3. PEDOT:PSS-Modified CNT/Polymer Nanocomposites for Piezoresistive Sensors

Promising piezoresistive nanocomposites have been developed using polycarbonate incorporated with CNTs that were pre-modified with the conductive polymer poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). This approach achieved high electrical conductivity and a significant piezoresistive response due to the synergistic effect between the components [194]. A critical parameter for forming the conductive network in such systems is the filler particle size, which directly determines the percolation threshold value and influences the stability of the material’s electrophysical characteristics [195]. The resulting composites are successfully used in piezoelectric sensors of the carbon hybrid-flexible wireless sensor (CH-FWS) type. Combining high sensitivity, mechanical flexibility, and stability, these sensors have become key elements in structural health monitoring systems for civil engineering structures, the aerospace industry, and robotics [196]. The mechanism of the piezoresistive effect in these materials is attributed to the reversible change in contacts between CNTs and the disruption of conductive pathways under strain, leading to a reproducible and linear change in electrical resistance in response to the applied mechanical load.

5.4. Influence of Composite Structure and Composition on Properties

The electromechanical properties of the composites (Figure 22) are intrinsically governed by their internal hierarchical architecture.

This structure–property relationship manifests distinctly under different loading conditions. For instance, the linear increase in conductivity observed under compressive strain is attributed to the progressive reduction of inter-tube distances. This compression facilitates the formation of new percolation pathways and enhances the number of conductive contacts between adjacent CNTs, effectively lowering the overall electrical resistance [197]. Conversely, under tensile strain, the internal structure experiences a reversible disassembly; the conductive network is disrupted as nanotubes separate and tunneling gaps widen, leading to a measurable increase in resistance. This reversible and predictable response to mechanical deformation is the fundamental mechanism that enables the use of these composites as highly sensitive and reliable piezoresistive sensors.

As evident from Figure 23, the McCullough model provides accurate fitting of the experimental DC conductivity data at and above the percolation threshold, corresponding to the filler loading range of 0.5–1.5 wt% CNTs, where the characteristic conductivity surge occurs [198].

Even a minimal addition of graphene to a polymer matrix can critically enhance its mechanical performance. For example, with uniform dispersion—a key factor for efficient stress transfer—a low graphene content (0.6 wt.%) significantly reinforces a polystyrene matrix [199]. The resulting nanostructured composite exhibits a record improvement in key properties compared to the neat polymer: ultimate tensile strength (UTS) increases by 97.36%, ductility (elongation at break) by 82.70%, and stiffness (Young’s modulus, E) by 174.08%. Moreover, such a dispersed system can be further augmented with another type of nanofiller to form advanced hybrid composites [200].

5.5. Conductive Polymer Composites for Strain Sensors

The development of highly sensitive and reliable strain sensors based on polymer composites requires a thorough understanding of the role played by each component and its synergistic interaction. This section details the functions of the polymer matrix and carbon nanofillers, the physical mechanisms underlying strain sensitivity, and the key factors determining sensor performance.

Polymer Matrix: the mechanical backbone and deformation mediator. The polymer serves as the foundation, defining the composite’s macroscopic mechanical properties: elasticity, tensile strength, fatigue durability, and the operational strain range. It is the matrix that bears the external mechanical load and transmits it to the dispersed nanofillers. Properties such as the polymer’s Young’s modulus and its behavior under cyclic loading (hysteresis) directly influence the reversibility and stability of the sensor’s response. The choice of matrix (e.g., elastomers like PDMS or TPU for flexible sensors, epoxy resins for rigid ones) is the primary step in the targeted sensor design.

CNTs and graphene fulfill two key roles:

(1)

They form an electrically conductive network within the insulating polymer matrix via the percolation effect;

(2)

Their intrinsic properties and morphology dictate the mechanism of conductivity change under strain. The high aspect ratio of CNTs promotes a low percolation threshold, while graphene’s large specific surface area provides numerous interparticle contacts that are critical for sensitivity.

5.6. Microscopic Mechanisms of Strain Sensitivity (Piezoresistive Effect)

The piezoresistive effect in composites arises from a combination of micro- and nanoscale mechanisms: the dominant change in tunneling resistance between neighboring nanoparticles, where deformation exponentially alters gap widths providing high sensitivity to micro-strains; the reconfiguration of the conductive network through contact breakage and reformation at larger deformations, governing hysteresis and the operational limit; and, to a lesser extent in dispersed systems, the change in the intrinsic resistance of highly oriented fillers like aligned CNTs or graphene films [201,202]. Conductive polymer systems are broadly categorized as intrinsically conductive polymers or composites with dispersed conductive fillers like CNTs and graphene, which form charge-transfer pathways but face challenges of aggregation that can be mitigated by ultrasonic dispersion [203,204,205,206,207]. For a comparative evaluation of sensor performance, a Sensitivity Index (SI = GF × ε_max), integrating the GF and the maximum operational strain (ε_max), can be utilized, with representative GF values for various materials provided in

Table 8. Parameters of polymer strain sensors.

№	Polymer	Conductive Fillers	GF	Working Strain, %	Sensitivity index (SI) = GF × εmax	Ref.
1	Fluoroelastomer	CNTs	1.36 × 105	85–100	1.36 × 107	[208]
2	TPU	MWCNT	5200	5–50	2.60 × 105	[209]
3	TPU	SWCNT/RGO	114.7	200–300	3.44 × 104	[210]
4	TPU	CNTs+ GNP	136,327.4	250	545	[211]
5	TPU	RGO	79	10–100	7.90 × 103	[212]
6	TPU	RGO	79	10–100	7.90 × 103	[213]
7	Epoxy resin	CNTs	0.6–14	6	8.40 × 101	[214]
8	PMVS	CNTAS/CB	10	0–60	6.00 × 102	[215]
9	Nitrile elastomers	Graphite nanoflakes	868.12 ± 56.90	30	2.60 × 104	[216]
10	PU	MWCNTs	62.37	80	4.99 × 103	[217]
11	TPU	CNT	1.41	10–100	1.41 × 102	[218]
12	Silicone rubber	MWCNT/carbon black	11.4	211	2.41 × 103	[219]
13	PDMS	MWCNT/μ-SiO2	62.9	30	1.89 × 103	[220]
14	TPU	CNTs	6.8	135	9.18 × 102	[221]
15	PEBAX	MWCNT	4.5	5	2.25 × 10	[222]
16	TPU	Graphene/CNTs hybrid	210	112	2.35 × 104	[223]
17	TPU@SBS	CNT	32,411	100	3.24 × 106	[224]
18	TPU	GNP	4047.5	350	1.42 × 106	[225]
19	TPU	MXene/rGO	84,326	220	1.86 × 107	[226]
20	PDMS/TPU	CB + CNTs	49,863.5	437.9	2.18 × 107	[227]
21	TPU	Graphene/CNTs	217	172	3.73 × 104	[228]

.

Analysis of the data in Table 8 leads to the following conclusions about polymer-based composite materials filled with conductive particles for use in strain sensors: the predominant polymer matrix is TPU, found in 12 out of 21 studies, and the most common conductive fillers are various forms of carbon nanomaterials, including carbon nanotubes (CNT, MWCNT), graphene, and RGO. The GF shows an extremely wide range of values—from single digits to nearly 10⁵—while the maximum working strain varies from 5% to 437.9%. The SI, defined as the product of GF and maximum strain, also varies by several orders of magnitude, reaching maximum values (~10⁷) for composites based on fluoroelastomer with CNTs, TPU with MXene/rGO, and a PDMS/TPU hybrid with CB + CNTs. It is observed that high SI values depend both on an extremely high GF (e.g., sample №1) and on a combination of high GF with significant strain capability (e.g., samples №19 and 20). These data highlight that varying the combination of polymer matrix, filler type, and composite morphology allows for targeted tuning of material properties for sensor applications across different strain ranges.

Polymer composites represent a promising platform for creating flexible strain sensors due to the ability to fine-tune their mechanical and electrical properties. Key criteria for selecting a polymer matrix include elasticity, strength, fatigue durability, and stability over a wide range of temperatures and cyclic loads. However, for applications in biosensing and wearable electronics, challenges related to biocompatibility and flexibility retention must be addressed. Significant progress can be achieved through modern processing methods that enable controlled dispersion of fillers and the formation of optimal conductive networks. For example, uniform distribution of CNTs lowers the percolation threshold and enhances strain sensitivity. The use of hybrid fillers, such as combinations of CNTs with graphene or rGO, leads to a synergistic effect—improving electrical conductivity, sensitivity, and the functional properties of the composite.

The mechanical and damping characteristics of the material critically depend on its microstructure: they are influenced by filler volume fraction, particle geometry, orientation, and interphase properties. Controlling these parameters enables the creation of materials with programmable responses.

A particularly promising direction involves sensors based on carbon and graphene quantum dots, which combine high biocompatibility, multifunctionality, and unique detection mechanisms—not only piezoresistive but also optical (mechanofluorescent). Such materials enable operation over an ultra-wide strain range and open possibilities for developing stimulus-responsive and biologically active sensing systems. Their integration into polymer matrices is technologically straightforward and cost-effective, facilitating the development of next-generation intelligent sensors [69,228,229,230,231,232,233,234,235,236,237,238,239].

5.7. AI and Machine Learning for Developing Strain Sensors from Nanomodified Composites

Artificial intelligence and machine learning (AI/ML) methods are transforming the development of materials for strain sensors, enabling virtual optimization of composite composition—polymer matrix, type, concentration, and ratio of nanofillers—to achieve target characteristics such as high GF, broad working strain range, low hysteresis, and thermal stability [45,46,47]. Neural networks also help optimize manufacturing parameters (temperature, pressure, curing time) to obtain the desired microstructure. During operation, ML algorithms compensate in real time for the influence of external factors (temperature, humidity) and internal nonlinearities, improving measurement accuracy. The use of recurrent neural networks (RNNs) allows for the analysis of not only the magnitude but also the dynamics of deformation, which is important for predictive maintenance of structures and monitoring of complex motions [240].

In materials science, a key emerging direction is the use of ML for the purpose-driven design of conductive polymers. This approach is based on transforming chemical structures into machine-readable descriptors and addresses tasks such as property prediction and optimization of parameters like electrical conductivity and mechanical strength. Despite challenges related to data scarcity and the complexity of inverse design, AI methods demonstrate high effectiveness. For example, combining molecular dynamics and ML enabled the identification of polymers with enhanced thermal conductivity (>0.300 W/m·K), and a specialized Bayesian model was developed to assess prediction reliability [241]. Another example is a hybrid strategy integrating machine learning and inkjet printing to optimize conductive polymer films. Algorithms determined optimal printing and composition parameters, resulting in materials with a sharp thermoresistive effect (~10⁶–10⁷ at 100 °C), high strain sensitivity, and improved stiffness, opening new possibilities for additive manufacturing of functional sensors [242].

A promising application of AI is also the development of ready-to-use devices, such as a highly sensitive temperature sensor for electronic skin based on a polyurethane/carbon black composite. Using ML for signal processing achieved 99.8% accuracy, and the designed architecture enabled the formation of an efficient conductive network at low filler content, which is important for integration into medical systems and robotics [243].

5.8. Multiphysics Computer Modeling

It should be emphasized that alongside experimental research, theoretical and computational methods make a significant contribution to the design and optimization of composites for strain sensors. With advances in computing power, numerical modeling of the structure and properties of nanocomposites has become an important tool for linking the micro- and nanoscale characteristics of a material to its macroscopic behavior within the framework of multiphysics simulations that account for interrelated electrical, thermal, and mechanical fields.

One key approach is the Monte Carlo method, which is used to model the formation of conductive networks. In this method, objects (e.g., rods simulating CNTs) are randomly distributed within a virtual volume, after which the emergence of a percolation cluster is analyzed, and the effective electrical conductivity of the composite is calculated using network algorithms. This method is effective for predicting the percolation threshold and studying the influence of filler geometry and spatial distribution on electrical properties.

For analyzing mechanical response and the spatial distribution of deformations and stresses in complex microstructures, finite element analysis (FEA) is widely used. FEA allows the evaluation of localized deformations arising in the composite under load and links them to changes in the conductive network, which is crucial for understanding and predicting the piezoresistive response.

• Despite their high predictive capability, these computational methods require separate in-depth examination. In practice, they are most effective when used in conjunction with experimental studies—for hypothesis verification, model refinement, and reducing the number of resource-intensive trial syntheses. Within the scope of this review, which focuses on the practical aspects of material production and application, a detailed discussion of these models lies beyond the established framework. However, the development and integration of multiphysics computer modeling, especially in combination with machine learning methods, remain the most promising directions for future research in the field of intelligent design of next-generation polymer composites.

5.9. Engineering Requirements for Strain Sensors Based on Polymer Nanocomposites

The development of competitive strain sensors based on polymer composites requires meeting a set of interrelated requirements that determine their practical applicability. These requirements can be divided into three key groups:

• Functional and Operational Characteristics. The core of the sensor lies in its sensing properties. A high and stable GF enables the detection of both minor deformations (low activation threshold) and a reliable response across a broad working range—from microdeformations (<1%) to significant elongations (50–500%). Linearity of response and minimal hysteresis are critically important, ensuring that the change in resistance accurately follows the applied deformation and quickly returns to its initial value after the load is removed. Long-term cyclic stability is also essential—the ability to withstand thousands of cycles without resistance drift or loss of sensitivity.
• Mechanical and Dynamic Properties. The sensor must be integrable into flexible systems. The material should possess mechanical strength, flexibility, and stretchability to withstand loads and conform to curved surfaces. Fast response and low relaxation time are necessary for accurately tracking dynamic processes and vibrations.
• Manufacturing and Reliability. For widespread adoption, reproducibility and cost-effectiveness are crucial. Key factors include the simplicity and scalability of manufacturing methods, such as 3D printing, casting, or screen printing. Environmental stability refers to maintaining performance under variations in temperature, humidity, and exposure to sweat or oils, which is particularly important for wearable electronics.

Table 9. Key engineering parameters.

№	Parameter/Properties	Description/Comments
1	High and stable sensitivity (GF)	Ability to detect minor deformations with a clear, reproducible signal
2	Wide working strain range	Capability to operate under both micro-deformations (<1%) and large strains (>50%, often up to 100–500%)
3	Good linearity and low hysteresis	The change in resistance should follow the deformation linearly and quickly return to the baseline after unloading
4	Cyclic stability and long-term reliability	Ability to withstand thousands of cycles without baseline resistance drift or loss of sensitivity
5	Fast response and short relaxation time	The sensor must respond rapidly to changes in strain and stabilize quickly
6	Mechanical strength and flexibility	The material must withstand loads without failure and be integrable into flexible systems
7	Ease of fabrication and scalability	The technology should be compatible with industrial processes (printing, molding) to reduce cost
8	Environmental stability	Resistance to temperature, humidity, exposure to oils, and other environmental factors

summarizes the key parameters of nanocomposite-based strain sensors.

Thus, the key engineering challenge lies not in maximizing a single parameter, but in finding an optimal balance among high sensitivity, a wide operational range, mechanical reliability, and technological feasibility to create sensors suitable for commercialization.

6. Conclusions and Perspectives

This review has systematically analyzed modern synthesis methods for CNTs and graphene and their functionalization strategies, which provide broad morphological and structural diversity for advanced technical applications. The main conclusions of the work are as follows:

(1)

CNTs and graphene, with their outstanding electrophysical and mechanical properties, serve as exceptionally effective functional fillers for creating a versatile class of electrically conductive polymer composites, which are particularly suitable for advanced sensing applications.

(2)

Precise control over nanomaterial synthesis (via methods like CVD) combined with surface functionalization strategies is crucial. These processes enable the fine-tuning of nanofiller properties, enhance their dispersion within the polymer matrix, and allow for the reduction of the electrical percolation threshold to very low levels.

(3)

The resulting new generation of conductive polymer composite-based strain sensors achieves unprecedented performance metrics, including gauge factors (GFs) exceeding 80,000 and operational strain ranges spanning from micro-deformations (<0.1%) to several hundred percent (up to ~440%). Their high sensitivity is governed by synergistic piezoresistive, tunneling, and dynamic network restructuring mechanisms.

(4)

Microstructural engineering, particularly the design of hybrid conductive networks (e.g., combining CNTs and graphene/rGO), creates a synergistic effect that significantly boosts sensor response, improving both sensitivity (GF) and electrical conductivity compared to composites with single-type fillers.

(5)

Surface functionalization of CNTs and graphene (e.g., via oxidation or ozonolysis) is a critical step that directly enhances the compatibility and interfacial adhesion between the nanofillers and the polymer matrix, which is essential for achieving a uniform dispersion and preventing agglomeration.

(6)

Beyond improving dispersibility, chemical functionalization tailors the surface chemistry of nanomaterials, enabling precise modulation of the composite’s electronic properties, optimizing charge transport, and facilitating the creation of more stable and efficient conductive networks within the polymer.

(7)

Functionalization strategies are key to developing advanced hybrid systems. By selectively modifying the surface properties of different nanofillers (e.g., CNTs vs. graphene), their synergistic integration is enhanced, leading to composites with superior and multifunctional performance characteristics.

(8)

Future progress in the field is directed toward the development of complex hybrid/hierarchical systems, the integration of additive manufacturing (3D/4D printing) for fabricating programmable microstructures, and the application of AI/ML for the inverse design of composites with tailored properties. These advancements pave the way for intelligent, flexible, and durable next-generation sensors for wearable electronics, soft robotics, and health/structural monitoring.