Laser Sintering of Nano-Graphite-Reinforced Polyamide Composites for Next-Generation Smart Materials: A Preliminary Investigation of Processability and Electromechanical Properties

Abstract

Multifunctional reinforced polymer composites provide an ideal platform for next-generation smart materials applications, enhancing matrix properties like electrical and thermal conductivity. Reinforcements are usually based on functional metal alloys, inorganic compounds, polymers, and carbon nanomaterials. The latter have drawn significant interest in developing high-performance smart composites due to their exceptional mechanical, electrical, and thermal properties. The increasing demand for highly complex functional structures has led additive manufacturing to become a reference technology for the production of smart material components. In this study, laser sintering technology was adopted to manufacture nano-graphite/nylon-12 composites with a carbon-based particle reinforcement content of up to 10% in weight. The results showed that the addition of the filler led to the fabrication of samples that reached an electrical conductivity of around 4·10⁻⁴ S/cm, in contrast to the insulating behavior of a bare polymeric matrix (i.e., lower than 10⁻¹⁰ S/cm), while maintaining a low production cost, though at the expense of mechanical performance under both tensile and bending loads.

1. Introduction

In recent years, the demand for advanced materials with enhanced mechanical, electrical, and thermal properties has significantly increased, driven by the rapid evolution of industries such as aerospace and automotive [1], healthcare [2], and consumer electronics sectors [3]. Among the many technological innovations, additive manufacturing (AM) has emerged as a key enabler of next-generation material design, allowing the fabrication of complex structures with tailored functionalities [4,5]. One of the most promising AM techniques is laser-based powder bed fusion, defined as laser sintering, according to the ASTM 52900 standard [6], which enables the production of high-performance polymer composites with complete freedom from geometrical constraints compared to traditional manufacturing methods [7]. However, such polymer processing technology still suffers from some limitations [8], mainly related to long process times, low surface quality, reduced print resolution, anisotropy, and the presence of porosity, which still pose a challenge for mass production [9], as well as for its effective scalability from the laboratory to the industrial level. Future technological advances will therefore be of paramount importance to achieve rapid, large-scale printing of reliable, high-performance products, while maintaining a high level of customization [10].

Within this context, polymer composites reinforced with nano-graphite have gained interest due to their unique multifunctional characteristics [11]. Nano-graphite is known for its exceptional electrical conductivity and thermal stability [12] and mechanical strength [13], while maintaining a low production cost compared to other carbon nanoscale materials, such as graphene nanoplatelets and nanotubes [14,15], making it an ideal candidate for enhancing polymer matrices [16]. Smart materials are broadly defined as systems capable of responding to external stimuli, such as mechanical stress, electrical signals, or temperature changes, and often integrate sensing, actuation, or conductivity functions [17]. By incorporating nano-graphite into polyamide (PA) powders, it is possible to develop composite materials with improved thermal conductivity and electrical properties, opening the door to a wide range of smart applications [18]. Among polyamides, grade 12 (PA12), also known as nylon-12, is widely used in laser sintering processes due to its excellent processability, chemical resistance, and durability [19]. However, conventional PA-based materials often exhibit limitations in electrical and thermal conductivity, restricting their use in applications requiring functional integration, such as sensors, lightweight conductive components, and electromagnetic shielding [20]. In fact, electrically conductive polymer composites are considered smart materials that are able to combine insulating polymers and carbon-based nanofillers and are frequently adopted in applications such as structural health monitoring, wearable electronics, and electromagnetic shielding [20,21]. The introduction of nano-graphite reinforcement can effectively address the abovementioned limitations, enhancing a material’s conductive pathways while maintaining the structural integrity and flexibility of the polymer matrix [22].

However, regarding safety, handling fine powders poses a risk of inhalation, ingestion, and contact with skin and eyes, as well as a risk of forming explosive mixtures with air. The greatest risks are related to inhalation toxicity [23], with the possibility of the development of respiratory and cardio-vascular disorders, but also pulmonary diseases [24], such as graphite pneumoconiosis, asthma, and lung cancer. A recent study in Malaysia has verified that during the printing preparation stage of PA12 in laser sintering processes, particulates with a diameter lower than 2.5 µm (i.e., PM 2.5) and CO₂ exceed the acceptable limits recommended [25]. It is therefore necessary to adopt personal protective equipment, such as masks and respirators, gloves, goggles, and coveralls, in addition to exhaust systems and mechanical ventilation of working environments. Such devices are also important to protect against typical air emissions of fine powders from the material being sintered, as well as gases and volatile organic compounds released during polymer melting [26]. In addition, at the environmental level, the risk to aquatic and terrestrial systems is minimal for the disposal of natural graphite powder, while severe risks are associated with improper disposal of PA12 powders, which are highly recyclable [27] but not subject to natural degradation in soils, sewers, and waterways. Although several mechanical, chemical, or thermochemical recycling processes are already available for composite materials [28], research and development in materials science will be critically important to further improve their end-of-life management, increase their recyclability, and safeguard the planet’s resources [29].

One of the major challenges in the fabrication of nano-graphite-reinforced polyamide composites using laser sintering is achieving uniform dispersion and strong interfacial bonding between the polymer and the nanofiller [18]. Proper dispersion of nano-graphite is crucial to prevent agglomeration, which could compromise the overall performance of the composite. Additionally, optimizing the laser processing parameters, such as laser power, scanning speed, and energy density, is essential to ensure efficient sintering, minimize porosity, and achieve desirable mechanical and thermo-electrical properties [30].

To date, several studies have been conducted to investigate the effect of raw material nature (i.e., type, geometry, supplier, etc.), filler nature and concentration, process parameters, laser sintering equipment, and post-processing operations on the resulting flowability, processability, and electrical and mechanical properties. Karevan et al. [31] determined the effect of a processing method on the mechanical, thermo-mechanical, and electrical properties of graphite nanoplatelet-reinforced PA12 composites, demonstrating improved tensile, flexural, impact, and electrical properties compared to a sample fabricated through the traditional injection molding technique. However, for weight percentages greater than 5%, the property enhancement was compromised due to the filler agglomeration. Ho et al. [32] prepared high-performance composites based on a thermoplastic polyamide matrix containing well-dispersed graphene nanoplatelets through melt compounding, improving the electrical, thermal, and mechanical properties at a filler volume fraction of around 15%. Lupone et al. [33] developed electrically conductive polymer composites made of PA12 reinforced with carbon fibers and graphite, mechanically mixing the raw materials in different relative amounts. They verified that the hybrid composites allow great enhancements in electrical conductivity with respect to the neat PA12 up to the anti-static and conductive range, while no synergistic effect between the two fillers was observed. Chen et al. [22] fabricated nanocomposite powders based on a core–shell structure, where graphene nanoplatelets were encapsulated on the surface of polymeric particles of PA12 in a thin layer of polyvinyl alcohol (PVA). They showed that the presence of the PVA and the nanofiller accelerated the crystallization of the nanocomposite powders compared to plain PA12, impacting the laser sintering process parameters, leading to modest improvements in the mechanical properties.

From the literature, it emerges that numerous combinations of PA powders with different fillers, mixing methods, and fabrication parameters are currently being studied to identify the optimal components that meet the stringent requirements of modern industry. Graphite nanoplatelets could represent a well-balanced trade-off between performance enhancement and material cost, making their utilization a more economical alternative to pure graphene reinforcement [34]. However, despite the promising characteristics of PA12/carbon-based composite powders processed through laser sintering, further research is required to thoroughly investigate their mechanical, electrical, and thermal properties under various operating conditions [35].

This study aimed to investigate the fabrication and characterization of nano-graphite-reinforced PA composites using laser sintering technology. The starting polymeric material and the processing machine were both commercially available products. The mixing technique was a simple yet efficient process based on mechanical roto-translation mixing. A comprehensive analysis of the material properties, including flowability, sintering window, electrical conductivity, and mechanical strength, was conducted to evaluate their suitability for next-generation smart applications. The potential applications of these advanced composites range from wearable electronics and biomedical devices to aerospace components and structural health monitoring systems, where lightweight, durable, and multifunctional materials are highly desirable [29]. By leveraging the advantages of laser sintering and nano-graphite reinforcement, this research contributes to the ongoing development of high-performance, multifunctional materials that bridge the gap between traditional polymer composites and advanced smart materials. The findings from this study provide insights into the processing–structure–property relationships of these novel composites, paving the way for future innovations in the field of AM and materials science by offering a streamlined technological solution for producing smart materials based on conductive polymer composites.

2. Materials and Methods

The nano-graphite-reinforced PA composite used in this study was processed through laser sintering and was made of a polyamide powder matrix, specifically nylon-12 (PA12), mixed with exfoliated graphite nanoplatelets (xGNPs) at varying weight percentages. The matrix used was a commercial smooth powder supplied by Sinterit (Nad Drwiną 10/bud. B3, Kraków, Poland), while the reinforcement was supplied by Nanesa S.r.l. (Via Calcutta 8, Rome, Italy).

Table 1. Main characteristics of the composite matrix (PA12) and reinforcement (xGNPs), as declared by the suppliers.

Characteristic	PA12	xGNPs
Average particle size—D50 [µm]	38	25
Average particle size—D90 [µm]	-	30
Mean thickness [µm]	-	14
Specific surface area [m2/g]	-	30
Density [g/cm3]	0.92	0.31
Melting point [°C]	185	-
Carbon content [%]	-	>97
C:O ratio [-]	-	44:1

presents the main characteristics of the two constituents.

To prepare the composite, the starting PA12 powder was mechanically mixed with the xGNPs using a roto-vibrational sieve, which allowed the original morphology of the powder to be retained [36]. It is worth noting that no surface treatment was applied to the materials to keep the process as streamlined and straightforward as possible. In addition, it is worth specifying how, in the experimental development, all the necessary procedures to ensure the safety of the operators and the protection of the environment were followed, in terms of personal protective equipment, exhaust systems and mechanical ventilation, and proper disposal of process waste.

To investigate the effect of the reinforcement on the mechanical and electrical properties of the composite, five weight percentages of fillers were added to the matrix material, i.e., 2%, 4%, 6%, 8%, and 10%. This range was chosen according to preliminary tests aimed at determining the threshold beyond which the fabrication process was compromised, as detailed in Section 3. To ensure homogeneity, the mixing process was repeated 10 times. Before the laser sintering, the mixture was dried for 24 h in a static oven at a temperature of 80 °C.

Particle shape and size distribution are determining factors influencing the flow properties of the powder, which are crucial for high-quality fabricated parts in the laser sintering process [37]. Since the mixed powder is not commercially available, a preliminary campaign aimed at evaluating the flowability and the processing window was carried out to verify/adjust the process parameters to be adopted for the fabrication of the samples. After preparing the specimens, the electrical conductivity was estimated. Among the five xGNP concentrations, the most distinctive were further investigated in terms of mechanical properties through tensile and three-point bending tests.

2.1. Flowability and Sintering Window

Mixing different materials can potentially alter the flowability of a powder and therefore the properties of the final product [38]. In fact, good flowability is necessary to properly deposit the layers of raw material during the laser sintering process [39]. To measure the flowability, the compressibility index ($CPR$) and the Hausner ratio ($HR$) were determined using the bulk density (${\rho }_b$) and the tapped density (${\rho }_t$), according to Equations (1) and (2):

$$CPR={\displaystyle \frac{{\rho }_t-{\rho }_b}{{\rho }_t}}100,$$

(1)

$$HR={\displaystyle \frac{{\rho }_t}{{\rho }_b}}.$$

(2)

A compressibility index lower than 10 or a Hausner ratio lower than 1.11 is considered an excellent flow, whereas a compressibility index greater than 38 or a Hausner ratio greater than 1.60 is considered a very, very poor flow [40]. The densities were measured by marking a small cuvette with known volumes, then adding a small mass of powder to determine the bulk density. The cuvette was then gently tapped vertically against a padded benchtop 50 times to calculate the tapped density [41]. This process was repeated three times to ensure the repeatability of the results.

The addition of xGNPs could alter also the sintering window of the powder, i.e., the temperature interval between the onset melting point ($T_{m,On}$) and the onset recrystallization temperature ($T_{r,On}$). In fact, it represents the temperature range within which the bed temperature of a semi-crystalline polymer is typically set to reduce eventual thermal stress following consolidation [42]. To evaluate any effect, a Differential Scanning Calorimetric (DSC) analysis was carried out using the DSC Q2000 supplied by TA Instruments (159 Lukens Dr, New Castle, DE, USA).

Table 2. DSC parameters adopted to perform the tests for the evaluation of the sintering window.

Parameter	Value
Heating/cooling rate [°C/min]	10
Nitrogen flow [mL/min]	20
Temperature interval [°C]	From 20 to 225
Sample weight [mg]	Between 5 and 10

presents the DSC parameters adopted to perform the tests. The resulting DSC curves, given as temperature vs. heat flow, were finally analyzed with the TA Universal Analysis app according to the ASTM D3418 standard [43], allowing the definition of the onset temperatures by extrapolating the baseline and the leading side of the melting (or recrystallization) peak to their intersection [44], as schematized in Figure 1.

2.2. Sample Preparation

The samples were fabricated using the Sinterit Lisa Pro laser sintering machine supplied by Sinterit (Nad Drwiną 10/bud. B3, Kraków, Poland). The main features and process parameters adopted for production are listed in

Table 3. Main features of the laser sintering machine and standard process parameters for bulk PA12 sample fabrication.

Feature/Parameter	Type/Value
Laser source [-]	Diode
Laser wavelength [nm]	808
Laser spot diameter [µm]	400
Laser nominal power [W]	5
Laser scanning speed [mm/s]	80
Maximum volume [mm3]	110 × 160 × 230
Layer thickness [µm]	125

. The operational parameters were chosen as a proper compromise between quality and production rate, as suggested by the supplier of the laser sintering machine.

The samples were fabricated parallel to the building plate to speed up the process while ensuring the best condition to fabricate homogenous samples [45]. The software Sinterit Studio 2019 Advanced provided together with the laser sintering machine was adopted to design the process. Each test required a proper sample geometry, as detailed in the following sections. After fabrication, the excess powder was removed manually to recover as much material as possible for reuse, and then the samples were sandblasted to eliminate the excess powder from their surface.

In addition, a qualitative inspection of the samples’ surfaces and cross-sections was carried out using the SNE ALPHA Scanning Electron Microscope (SEM) supplied by SEC Co., Ltd. (111 Saneop-ro, Gwonseon-gu, Suwon-si, Gyeonggi-do, Republic of Korea) to analyze the adhesion and dispersion of the filler within the PA12 matrix after the fabrication process.

2.3. Electrical Characterization

To investigate the influence of xGNPs on the electrical properties of the composite samples, which consisted of 30 × 30 × 3 mm³ square plates, their electrical resistance was measured using the DC series 2840 resistance meter supplied by B&K Precision (22820 Savi Ranch Pkwy, Yorba Linda, CA, USA) with a 4-point probe configuration Kelvin test lead. To avoid any unwanted effects due to the presence of porosity, the measurements were carried out while the samples underwent compressive loading to promote the closing of the pores. With this aim, the tests were performed using the MTS Insight 5 Universal Electromechanical System supplied by MTS S.r.l. (Strada Pianezza 289, Torino, Italy) equipped with a load cell of 5 kN, as schematized in Figure 2. To achieve a proper identification of the effect of the applied load on the electrical resistance, 10 different loads, ranging from 0.1 kN to 1 kN, were investigated.

The resulting electrical conductivity ($\sigma$) of the samples was then calculated as the inverse of the resistivity ($\rho$), according to Equation (3):

$$\sigma ={\displaystyle \frac{1}{\rho }}={\displaystyle \frac{l}{RA}},$$

(3)

where $l$ is the thickness of the sample, $A$ is its cross-section, and $R$ is the measured resistance.

2.4. Mechanical Characterization

The mechanical characterization concerned two types of investigation, i.e., under quasi-static tension and quasi-static bending conditions. The MTS Insight 50 Universal Electromechanical System supplied by MTS S.r.l. (Strada Pianezza 289, Torino, Italy) equipped with a load cell of 50 kN was used to perform both types of tests, with the parameters set through the proprietary software TestWorks 4, the sample geometry (see Figure 3) designed through the software Autodesk Fusion 360, and all the procedures defined according to the respective standards, i.e., ASTM D638 [46] for tensile tests and ASTM D790 [47] for three-point bending tests. Specifically, the control parameter of the tests was the crosshead speed, set at 5 mm/min for the tensile tests and at 1.28 mm/min for the bending tests.

The acquired data were applied load ($F$) and crosshead displacement ($d$) in both types of tests, which allowed the reconstruction of stress–strain diagrams, after their proper calculation according to the respective standards for tensile tests and three-point bending tests, as described by Equations (4)–(7):

$${\sigma }_T={\displaystyle \frac{F}{S}},$$

(4)

$${\epsilon }_T={\displaystyle \frac{d}{g}},$$

(5)

$${\sigma }_F={\displaystyle \frac{3FL}{2W{T}^2}},$$

(6)

$${\epsilon }_T={\displaystyle \frac{6dT}{L^2}},$$

(7)

where ${\sigma }_T$ is the tensile stress, $S$ is the cross-section of the tensile sample, ${\epsilon }_T$ is the tensile strain, $g$ is the gauge length of the tensile sample, $L$ is the span of the bending sample (i.e., the minimum distance between supports, equal to 16 times the thickness according to the standard), ${\sigma }_F$ is the bending stress, $W$ is the width of the bending sample, $T$ is the thickness of the bending sample, and ${\epsilon }_F$ is the bending strain.

The reconstructed diagrams allowed the evaluation of the elastic modulus ($E_T$ and $E_F$), the yield stress (${\sigma }_{T,Y}$ and ${\sigma }_{F,Y}$), the ultimate tensile strength ($UTS$), the ultimate flexural strength ($UFS$), and the elongation at break (${\epsilon }_{T,R}$ and ${\epsilon }_{F,R}$). The elastic modulus was calculated by linear regression as the slope of the stress–strain curve for deformation within the range of 0.05% to 0.25% [48], while the yield stress was estimated through Considere’s graphical method [49]. For each condition, 5 replicas were carried out and conducted 24 h after the fabrication to allow the samples to cool down to room temperature.

3. Results and Discussion

The experimental campaign was aimed at investigating the influence of the addition of exfoliated graphite nanoplatelets within nylon powder for the laser sintering fabrication of smart composite components. A preliminary campaign concerned the evaluation of the printability range for different weight percentages of the mixture and the effect of the reinforcements on the flowability and processability of the powder through the determination of the compressibility index, the Hausner ratio, and the sintering window. Then, electrical and mechanical tests were performed to highlight any variation induced by the addition of the xGNPs in the polymeric matrix.

3.1. Filler Content, Flowability, and Sintering Window

The filler percentage was chosen according to preliminary fabrication test trials that enabled the definition of the threshold beyond which the occurrence of defects would compromise the integrity of the component. For weight percentages higher than 10%, during the sintering process, printing defects began to occur, i.e., the initiation of cracks along the perimeter areas of the printed components and warping of the edges, as highlighted in Figure 4. This figure compares samples produced with different nano-graphite contents during the fabrication process, i.e., successfully produced specimens with 10% xGNPs, including dog-bone-shaped samples for tensile testing and square samples for electrical conductivity measurements, alongside samples with 12% xGNPs, which exhibited deformation that compromised the laser sintering process. It is worth noting that square samples are not shown for the formulations at higher concentrations, as the number of layers that could be fabricated was insufficient to allow photographic documentation. These defects could have stemmed from enhanced light absorption and the thermal conductivity of nano-graphite compared to the surrounding nylon-12 powder [50], creating perimeter zones with an excessively high thermal gradient, which, due to residual stresses, led to cracks and discontinuities in the powder.

The compressibility index and the Hausner ratio are crucial in understanding the flow properties of powders. Low values, i.e., $CPR$ < 20% and $HR$ < 1.25 [51], promote uniform mixing, i.e., ensuring consistent distribution of constituents; efficient processing, i.e., powders move smoothly under the action of the recoater during the deposition, thus minimizing blockages; and densification, i.e., powders compress easily. As can be seen in Figure 5, all the mixtures present $CPR$s < 15% and $HR$s < 1.18, thus assuring a good flowability. It is worth noting that the addition of the xGNPs promoted reductions in both indices, which were lower the higher the percentage added, i.e., from $CPR$ ≅ 13.8% and $HR$ ≅ 1.16 for the starting powder to $CPR$ ≅ 10.7% and $HR$ ≅ 1.12 for the highest percentage of xGNPs. This finding can be attributed to the small thickness of the nanoplatelets, which would occupy the gaps created by the larger PA12 particles [52].

The addition of xGNPs can also affect the sintering window, shifting the characteristics peaks of the DSC curve and therefore the onset temperatures of melting and recrystallization, as shown in Figure 6 and

Table 4. DSC results: melting ($T_m$), onset melting ($T_{m,On}$), recrystallization ($T_r$), and onset recrystallization ($T_{r,On}$) temperatures for the identification of the sintering window (${∆}_{Onset}$).

xGNPs [%]	${\mathit{T}}_{\mathit{m}}$ [°C]	${\mathit{T}}_{\mathit{m},\mathit{O}\mathit{n}}$ [°C]	${\mathit{T}}_{\mathit{r}}$ [°C]	${\mathit{T}}_{\mathit{r},\mathit{O}\mathit{n}}$ [°C]	${∆}_{\mathit{O}\mathit{n}\mathit{s}\mathit{e}\mathit{t}}$ [°C]
0	180.7	173.7	155.6	157.8	15.9
2	180.3	173.6	155.7	157.8	15.9
4	179.0	174.0	156.0	158.1	16.0
6	180.4	174.0	156.2	158.4	15.6
8	180.4	174.4	156.2	158.6	15.8
10	180.5	174.6	156.2	158.4	16.2

. The observed trend suggests a widening of the sintering window from 15.9 °C for the PA12 matrix up to 16.2 °C for 10% of xGNPs. In particular, the shift of the recrystallization temperature to higher values can be attributed to the nucleating effect of the xGNPs that prevents the mobility of polymer chains [22]. The same can be said for the melting point, since it requires a greater amount of heat of fusion to increase the temperature of the sample, resulting in a more pronounced melting peak [53]. However, the change in the sintering window extent is very small and the same process parameters of the pure PA12 can be adopted to fabricate the composite samples at varying xGNP concentrations (see Table 3).

3.2. Electrical Characterization

Due to the limitations of the instrumentation used for electrical measurements, the resistance of pure PA12 and 2% xGNP samples could not be detected under any of the applied compressive loads, leading to their classification as electrical insulators. Figure 7 shows the results of the samples with a content of xGNPs from 4% to 10%. For better visualization, the $y$-axis is represented in the logarithmic scale. As can be seen, there is an abrupt increase in the electrical conductivity with a filler content greater than 4% of xGNPs, from, e.g., at 1 kN of compressive load, around 1.07·10⁻⁹ S/cm up to 2.02·10⁻⁶ S/cm for 6% (i.e., similar to silicon [54]), 6.22·10⁻⁵ S/cm for 8% (i.e., similar to boron [55]), and 3.93·10⁻⁴ S/cm for 10% (i.e., similar to germanium [56]). This finding can be ascribed to the higher number of conductive paths developed at the nanoscale between xGNPs and PA12 for increasing filler content [57]. It is worth noting that the electrical conductivity increases linearly with the applied load. Such a phenomenon is due to the compacting effect induced by the compressive load, which closes porosities and promotes an interconnected path between the nanoplatelets, thus increasing the electrical conductivity [58].

Thanks to the distribution of the electrically conductive nanoplatelets within the insulating matrix, the resulting composite behaves as a conductor. It is therefore possible to define a percolation threshold, where the critical amount of conducting particles for the onset of electrical conduction is reached and the electrical properties of the material typically exhibit nonlinear behavior [59], as shown in Figure 8.

The relation between the composite conductivity (${\sigma }_C$) and the filler concentration (${\phi }_f$) can be found through the percolation model described by Equation (8) [60]:

$${\sigma }_C={\sigma }_f{({\phi }_f-{\phi }_{cr})}^t,$$

(8)

where ${\sigma }_f$ is the filler conductivity, ${\phi }_{cr}$ is the percolation threshold, and $t$ is the critical exponent. The values of ${\sigma }_f$, ${\phi }_{cr}$, and $t$ were determined through the fitting of the experimental data. It is worth noting that the volume fraction of xGNPs was evaluated by weighing the samples after fabrication with a precision balance and measuring the actual dimensions with a digital caliper to determine their volumes. Therefore, since the weight percentage was known, together with the density, it was possible to calculate the volume fraction of the reinforcement according to the rule of mixture for composites [61], described by Equation (9):

$${\rho }_C={\rho }_f{\phi }_f+{\rho }_m(1-{\phi }_f),$$

(9)

where ${\rho }_C$ is the density of the composite, considered equal to ${\rho }_b$, measured for the evaluation of the flowability characteristics (see Section 2.1); ${\rho }_f$ is the density of the filler; and ${\rho }_m$ is the density of the PA12 matrix, the latter two given by the technical data sheets provided by the suppliers (see Table 1). In this way, the weight percentages of 4%, 6%, 8%, and 10% correspond to 0.092, 0.146, 0.195, and 0.241 volume fractions.

Focusing attention on the results obtained for a compressive load of 1 kN, shown in blue in Figure 8, the percolation threshold was estimated as 0.091, corresponding to a weight percentage of xGNPs of around 3.9%; the electrical conductivity of the filler as 0.0143 S/cm; and the critical exponent as 1.971. Percolation is induced when the dispersed filler establishes a conductive pathway within the insulating matrix, promoting exponential growth of the conductivity [32]. The value of the percolation threshold obtained in this study is in line with the pertinent literature [62], which highlights a variation between 2.5% and 7.5%, typically given for exfoliated graphite nanoplatelets with a large lateral size compared to their thickness and which are therefore expected to form a more extensive and uniformly connected network throughout the matrix. Also, the percolation coefficient lies between 1.65 and 2 [13], while the electrical conductivity of the filler is around three orders of magnitude lower than the expected one, which theoretically should approach the electrical resistivity of the conductive nanofiller, i.e., between 10 S/cm and 10³ S/cm [63]. This can be attributed to two main synergistic factors which lead to the formation of an imperfect pathway among the nanofillers: (i) a thin layer of polymeric matrix surrounds the nanoplatelets, offering resistance at the junctions of the network, and/or (ii) the clustering of the particles [64]. This effect is more evident with a low filler content and a low applied compressive load, as can be seen from the black and red lines in Figure 8, e.g., for 0.1 kN of applied load, the percolation coefficient reduced to 1.841, while the conductivity of the fillers was 0.0022 S/cm. This may be due to a lack of physical or direct contact between particles allowing the formation of conductive paths, as Figure 9 suggests. These results, although promising, highlight the need to further investigate the optimal xGNP content to properly tailor electrical conductivity depending on the final application, e.g., for electrostatic discharge applications, a value between 10⁻² S/cm and 10 S/cm is usually required, and for signal transmission applications, a value of 10 S/cm or more is needed [65]. Moreover, it is worth noting that, beyond a certain threshold, the geometry and continuity of the filler network may govern performance more than the filler content alone [66]. In fact, factors like filler type, concentration, and matrix material play crucial roles in achieving the desired quality.

3.3. Mechanical Characterization

The electrical characterization highlighted a percolation threshold very close to a 4% content of xGNPs; therefore, the investigation of the mechanical properties concerned only two mixture compositions, i.e., 6% and 10%, which were compared to the pure powder samples. Figure 10 shows representative trends, over five replicas, of the stress–strain diagrams obtained during the tensile and the three-point bending tests, while

Table 5. Mechanical characterization results: elastic modulus ($E_T$ and $E_F$), yield stress (${\sigma }_{T,Y}$ and ${\sigma }_{F,Y}$), ultimate tensile strength ($UTS$), ultimate flexural strength ($UFS$), and elongation at break (${\epsilon }_{T,R}$ and ${\epsilon }_{F,R}$). $T$ indicates tensile, while $F$ indicates flexural. The ± symbol indicates the standard deviation.

xGNPs [%]	${\mathit{E}}_{\mathit{T}}$ [GPa]	${\mathit{E}}_{\mathit{F}}$ [GPa]	${\mathit{\sigma }}_{\mathit{T},\mathit{Y}}$ [MPa]	${\mathit{\sigma }}_{\mathit{F},\mathit{Y}}$ [MPa]	$\mathit{U}\mathit{T}\mathit{S}$ [MPa]	$\mathit{U}\mathit{F}\mathit{S}$ [MPa]	${\mathit{\epsilon }}_{\mathit{T},\mathit{R}}$ [%]	${\mathit{\epsilon }}_{\mathit{F},\mathit{R}}$ [%]
0	1.21 ± 0.11	0.65 ± 0.05	19.40 ± 0.84	23.05 ± 2.68	30.71 ± 0.72	45.16 ± 2.73	7.19 ± 0.60	12.1 ± 0.43
6	0.86 ± 0.05	0.55 ± 0.06	10.72 ± 1.24	16.19 ± 2.43	19.48 ± 2.25	28.66 ± 2.74	6.60 ± 0.59	8.90 ± 0.51
10	0.79 ± 0.07	0.39 ± 0.21	8.75 ± 0.94	13.14 ± 1.96	13.21 ± 0.99	20.78 ± 2.29	4.11 ± 0.70	8.41 ± 0.14

collects the resulting properties.

The results highlight that the addition of the nanoplatelets to the pure material negatively affected the mechanical properties of the fabricated samples, and the effect was more pronounced the higher the weight percentage of the additive, with an overall average deterioration of around 28% for 6% xGNPs and 40% for 10% xGNPs, with a major effect on the ultimate strength, which was somewhat more than halved at 10% if compared to the starting material, while the other properties decreased by an average of around 41%. These findings can be attributed to the sintering process, which is not able to guarantee the melting and complete incorporation of the nanoplatelets within and between the PA12 powder particles, due to their thermal inertia, promoting pore formation, as can be clearly seen in Figure 11, which constitute defects and trigger breakage of the samples with lower applied loads [31]. In fact, graphite and polyamide, while not inherently incompatible, do present challenges in achieving optimal composite properties due to differences in their surface energies and structures. Graphite’s layered structure and low surface energy can lead to poor dispersion and adhesion within the polyamide matrix [67]. The relatively low surface energy of graphite compared to the polyamide matrix can lead to weak interfacial bonding, resulting in poor load transfer between the filler and the polymer [68]. However, modifying the graphite surface with functional groups can increase its surface energy and polarity, promoting better interactions with the polyamide [69]. The establishment of a uniform and homogeneous incorporation of the nanofillers within the polymeric matrix is therefore essential to ensure the development of composite materials for laser sintering with improved properties. The results suggest that the xGNPs may not have been properly dispersed and distributed within the nylon matrix, thus requiring further investigation to better understand the interaction between the two constituents both in terms of mixture preparation and process parameter combination. Moreover, although the material shows promise at moderate nano-graphite contents, the brittleness observed at higher concentrations could potentially be mitigated by tailoring the filler geometry or employing hybrid filler systems, as complex input–output relationships often result in nonlinear performance limitations [70]. However, it is worth noting that all specimen failures occurred within the gauge length, and no edge or premature failures due to visible surface defects were observed, thus suggesting appropriate repeatability.

The proposed mixtures exhibit promising characteristics that make them suitable for the development of smart materials. One of the key advantages of the proposed mixtures is their tunable mechanical and electrical properties, which can be leveraged to create self-adaptive structures. For instance, in aerospace and automotive industries, these smart materials can be employed in components that adapt to varying environmental conditions. Moreover, the conductive nature of the mixtures can allow their integration into sensor technologies, both for the electronic and biomedical industries. Finally, the compatibility of the proposed mixtures with additive manufacturing techniques enables the production of customized, multifunctional smart components. This facilitates the design and fabrication of next-generation intelligent materials tailored to specific industry needs.

4. Conclusions

The analysis presented in this study aims to investigate the suitability of nano-graphite fillers for the fabrication of polyamide-based smart composite materials through the laser sintering process. In general, the incorporation of exfoliated graphite nanoplatelets (xGNPs) into the polymeric matrix (PA12) enhances flowability and electrical properties but at the expense of the mechanical performance under both tensile and bending loading.

The addition of xGNPs does not significantly alter the sintering window of the powder at any concentration; therefore, the fabrication parameters can remain unchanged. In fact, the proposed powder composite shows good processability and compatibility with laser sintering, as confirmed by flowability indicators such as a compressibility index below 15% and a Hausner ratio below 1.18 across all mixtures. The addition of xGNPs significantly improves electrical conductivity, raising it to ~4·10⁻⁴ S/cm compared to the insulating base material. This conductive behavior, with a percolation threshold around 3.9 wt%, is consistent with well-dispersed nanoplatelets having a high aspect ratio. Conversely, mechanical properties degrade with increasing xGNP content. Ultimate tensile and flexural strength drop by over 50%, likely due to poor nanoparticle–matrix integration, which leads to porosity and structural defects. Despite this, the material’s improved electrical performance and processability make it a promising candidate for functional applications where conductivity is prioritized over mechanical strength. Moreover, the proposed manufacturing solution offers the ability to tune material conductivity in response to mechanical compression and compatibility with low-cost and scalable processing, fulfilling key requirements for a smart material. The developed composite indeed exhibits characteristics widely accepted for smart material systems, including conductivity, a low percolation threshold, and the possibility of being processed through additive manufacturing. These traits enable potential deployment in next-generation devices in electronic, biomedical, and aerospace sectors, where smart functions are increasingly required.

Future research should focus on optimizing the dispersion of nano-graphite within the polymer matrix to maximize performance benefits. Additionally, exploring alternative filler materials and hybrid composites could further enhance the multifunctionality of PA12-based materials. The economic, energy, and environmental sustainability aspects of the technological solution here proposed require a deeper investigation, with a view to the possible industrialization of the process, highlighting the role of major traditional limitations and the ongoing technological advances on the effective scalability of the process, from laboratory to industrial level. In summary, the proposed mixtures present a promising avenue as polymeric materials capable of reading external electrical signals, increasing their field of application as transmitters of signals in highly corrosive environments not subjected to high external stresses, contributing to technological advancements across multiple industries.