Influence of the addition of carbon nanotube on the physical behavior of a lateritic soil from the southwest Amazon

Abstract

In this work we evaluate the physical-mechanical behavior of lateritic soil with addition of carbon nanotubes. The soil was collected in a commercial deposit located in Rio Branco – AC and later characterized through particle size tests, X-Ray Diffraction, X-Ray Dispersion Spectroscopy and Tropical Compressed Miniature essay. The dispersion of nanotubes in solution was carried out and the size of the nanoparticles was verified using Dynamic Light Spreading - DLS, Zeta Potential and PDI. Three percentages of additions (0.05%, 0.1% and 0.2%) of carbon nanotubes were evaluated and compared with the control group according to the parameters of Atterberg limits, real density, dry density maximum and optimum humidity. The results indicate that the soil has a clayey behavior with a medium texture, with the presence of clay mineral kaolinite in its composition and silicon and iron oxides. The dispersion of carbon nanotubes reached particles with an average hydrodynamic diameter of 68.9 nm and Zeta Potential of -24.87 mV and PDI of 0.231, characterizing a solution as moderately dispersed and kinetically stable. The results of the liquidity limit and plasticity tests showed a reduction of 10 and 13%, respectively, with the addition of carbon nanotubes, while for the parameters of plasticity index, real specific mass, maximum dry density and optimal moisture, they did not show significant variation. Carbon nanotubes interact with soils with lateritic behavior, and further studies are essential to better understand the mechanisms behind this interaction.

Keywords:
Lateritic soil; Carbon nanotubes; Soil improvement; Nanotechnology

1. Introduction

One of the biggest challenges in carrying out infrastructure or paving works is related to finding soil deposits with low quality geotechnical properties, such as low resistance and high deformability, requiring geotechnical engineering to apply soil improvement techniques that minimize the problems of settlement and stability that are typical of these soils (Silva et al., 2020).

Among the fine soils widely used in bases and sub-bases of pavements, soils that present lateritic behavior stand out, where despite their natural state presenting low support capacity, when compacted they acquire high resistance, a factor that, together with its abundance in Brazilian territory, justifies its use in urban paving works (Arruda, 2020).

However, despite its properties enabling its use in the construction of traffic routes, soils with lateritic behavior may require the use of improvement techniques to increase resistance due to the most varied loads that the highway must withstand (Silva & Castro, 2020). Located in the extreme southwest of the Brazilian Amazon, the state of Acre is geologically characterized by a sedimentary basin with a thick horizon of fine material, occupying almost the entire surface of the state, where about 70% of the soil in the region is composed of clayey soils, which has low support capacity and high expansion in the presence of water (Acre, 2010). According to the AcreBioClima Environmental Studies and Services Group (Acre, 2010), the state has monthly rainfall indices above 200 mm, factors that make it difficult to build roads with the minimum resistance indices adopted by conventional methods, such as the CBR.

Soil chemical stabilization is the improvement of soil geotechnical properties, notably strength, durability and deformability, and usually occurs through the addition of cementitious materials to the soil in situ (Corrêa-Silva et al., 2020). Among the main types of stabilizing agents, lime, Portland cement, asphalt, emulsions and acrylic polymers can be mentioned (Machado et al., 2017). However, the significant environmental impacts associated with the production of these elements bring a global concern (Eyo et al., 2020). With this, nanotechnology has been proving to be a promising area, since the use of nanomaterials, in addition to being feasible from a technical point of view, is also proving to be an economical alternative, due to the very low amount of nanomaterials needed in the soil to achieve improvement in the geotechnical parameters (Karumanchi et al., 2020).

Among nanomaterials, carbon nanotubes (CNT) are considered one of the most promising in the construction industry. CNT's have excellent properties, such as high tensile strength, high modulus of elasticity and excellent electricity conductor (Taha et al., 2018). In addition, these nanomaterials are already widely available on the market, with good quality products and relatively low prices, which favors their use for research (Taha & Alsharef, 2018). Compared to other types of cementitious materials used for soil stabilization, CNTs have proven to be an ecological alternative, highlighting their high potential for absorbing biological contaminants or those associated with bodies of water, due to their high specific surface area (Alobaid et al., 2022).

Despite this scenario, there is still little research related to the use of nanomaterials in soils to verify improvements in geotechnical properties, and even less for the investigation of these materials in soils with lateritic behavior, making this research a pioneer in this area of ​​knowledge. The objective of this work is to verify the influence of the addition of carbon nanotubes in a lateritic soil of the southwest region of the Amazon, aiming at improvements in the physical properties.

2. Materials and Methods

2.1 Description of study place

The State of Acre is in the extreme southwest of the Brazilian Amazon, between latitudes 07°07’S and 11°08’S and longitudes 66°30’W and 74°WGr. Its territorial surface is 164221.36 km², corresponding to 4% of the Brazilian Amazon area and 1.9% of the national territory (Acre, 2010).

Among the geological units, the Solimões Formation occupies about 85% of the territory of Acre, and this formation originated from sediments coming from rivers from the Cretaceous period (Acre, 2010). Dystrophic Red-Yellow Argisols and Latosols dominate the eastern tablelands of the State, with deep, well-drained, leached soils, low natural fertility and the occurrence of lateritic crusts. Such characteristics result from the fact that the mineralogical composition of the Solimões Formation consist of clays with a high expansion content due to moisture variations (Shinzato et al., 2012).

2.2 Collection of samples

The lateritic soil samples come from a commercial deposit located on BR 317, km 15, coordinates 10° 06' 29.37” S and 67° 38' 39.66” W, Rio Branco – AC, Brazil, as shown in Figure 1.

Figure 1
Location of the lateritic soil removal deposit.

After collection, the materials were stored, homogenized and sealed at room temperature at the Soil Laboratory of the Federal University of Acre (UFAC).

2.3 Soil characterization

2.3.1 Granulometric analysis

To determine the dimensions of the lateritic soil particles, as well as their percentages, the granulometry test was carried out using sieves and sedimentation in accordance with NBR 7181/16 (ABNT, 2016). The granulometric analysis by sieves was carried out at the UFAC Soil Laboratory, while the granulometric analysis by sedimentation was carried out at the Technology Foundation of the State of Acre (FUNTAC).

2.3.2 MCT test

To verify the lateritic behavior of the soil, the MCT test was carried out, according to the DNER ME-258/94 and DNER ME-256/94 (DNER, 1994d, 1994e) standards, which are the Mini-MCV and mass loss by immersion tests, respectively. The MCT methodology is used to distinguish the behavior of soils from tropical regions, especially those soils with considerable particle sizes smaller than 2 mm (Lima et al., 2020). The following coefficients are calculated: c' (grading parameter that differentiates sandy and clayey materials), d' (parameter of the compaction curve that is directly related to the maximum density) and Pi (parameter that quantifies the degree of soil laterization). These parameters are necessary to calculate e', which is the parameter that classifies the soil as being lateritic or non-lateritic. All steps were carried out at the FUNTAC Soil Laboratory.

2.3.3 Chemical analysis

The chemical composition of the lateritic soil sample was carried out using an energy dispersive X-ray system (EDX), in the forensic laboratory of the Civil Police of the State of Acre. The equipment used was a Shimadzu EDX 720. EDX is an essential accessory in the study of microscopic characterization of materials, as it allows immediate identification of the chemical composition (Araújo, 2022). When the electron beam falls on a mineral, the outermost electrons of the atoms and the constituent ions are excited, changing energy levels. When they return to their initial position, they release the acquired energy, which is emitted at wavelengths in the X-ray spectrum. A detector installed in the vacuum chamber measures the energy associated with this electron. As the electrons of a given atom have different energies, it is possible, at the point of incidence of the beam, to determine which chemical elements are present in that location and thus instantly identify which mineral is being observed.

2.3.4 Mineralogical analysis

To carry out the mineralogical analysis of the existing clay fraction in the lateritic soil studied (fraction less than 0.002 mm), the X-ray diffraction technique was used, using the X-Ray Diffractometer equipment. The test was carried out at the Department of Geoscience of the University Federal of Amazonas, with equipment model Shimadzu XRD 6000®, using X-ray tube with Cu Kα radiation, wavelength of 0.15418 nm. The configuration used was a monochromator with slits (1, 1, 0.30), at 40kV and a current of 30 mA and a speed of 2000 deg/min. The identification of clay minerals present in the soil was performed using the Match! software. When characterizing clay minerals, the use of the X-ray diffraction technique becomes even more indicated, since the high quartz content of the sample and its ease of orientation result in well-defined peaks of great intensity in this crystalline phase, which helps in the identification of phases (Albers et al., 2002).

2.4 Preparation and characterization of carbon nanotubes

2.4.1 Obtaining carbon nanotubes

The CNTs used in the research are of the multiple wall type (MWCNT), and were commercially obtained from the company CNTCompany, located in South Korea, and the product in question is called CTube120.

2.4.2 Dispersion of carbon nanotubes

Carbon nanotubes are very small particles with a very large specific surface, which makes them tend to attract and agglomerate (Cwirzen et al., 2008). To take real benefits from its potential, it is necessary to make a good dispersion of the particles. The methodology used in this work was adapted from Taha et al. (2018) and Rafael (2017).

First, a 0.15% Tween 80 surfactant solution was prepared to promote better solubilization of the nanotubes in the aqueous medium. The use of the surfactant is justified because it has the property of reducing the interfacial tension between two components of different affinities (Leite, 2020), in the example water and nanotubes, forming a homogeneous mixture. The solution was placed on a magnetic stirrer for 2 h, and then the carbon nanotubes were added at a concentration of 0.01% in relation to the solution, as shown in Figure 2.

Figure 2
Solution for carbon nanotubes dispersion. (A) Solution with Tween 80 surfactant (before the shaker); (B) Solution during magnetic stirrer; (C) Solution after 2 h period on shaker; (D) Nanotubes used in the solution.

However, just inserting the nanotubes into the solution is not enough, as the nanotubes tend to agglomerate in the dispersion. Therefore, an ultrasonic homogenizer brand Sonoplus Bandelin, model UW 2070, equipped with a titanium rod was used. Rafael (2017) verified that the increase in temperature produces negative effects on the dispersion of MWCNT, and with that, the solution was stored in a container containing crushed ice, to control the temperature during the test. Ultrasound was used with a frequency of 20 kHz and power of 500 W for 10 minutes, as shown in Figure 3.

Figure 3
Dispersion process of carbon nanotubes. (A) Addition of the nanotubes to the solution before the ultrasound; (B) Solution in a temperature-controlled container for use in ultrasound; (C) Solution after 10 minutes in the ultrasonic homogenizer.

After the process of dispersing the nanotubes through the ultrasonic homogenizer, the amount of soil necessary for carrying out each test was added inside the container with the solution, the magnetic stirrer was used for 15 minutes and then the material was taken to an oven at 100 °C, for complete drying of the water. Finally, the dry material was slightly crumbled in a mortar with the aid of a mortar hand and separated into identified flasks referring to the test to be carried out with the appropriate percentages of carbon nanotubes for this research, as shown in Figure 4.

Figure 4
Process of preparation of samples used for physical and mechanical tests. (A) Addition of soil to the solution, before the agitator; (B) Soil solution + nanotubes after the shaker; (C) drying the solution in an oven; (D) Sample to be used in physical.

2.4.3 Average size, Polydispersity Index (PDI) and Zeta Potential

The particle size after the carbon nanotube dispersion process, the polydispersity index and the Zeta Potential were determined with Zetasizer Nano ZS 90 equipment (Malvern Instruments; Malvern, UK), performed using the Dynamic Light Scattering (EDL) technique. This technique is widely used for evaluating the dimensions of particles in suspension, because when the particle is illuminated by a light source, it will scatter light in all directions, and this technique consists of analyzing the intensity fluctuations of the scattered light at a certain angle, which provides information about the particle movement and, consequently, its mean hydrodynamic diameter (Nomura et al., 2014).

In addition, through the PDI and the Zeta Potential, it is possible to evaluate the quality and stability of the solutions. In an Eppendorf tube, a solution was prepared with 100 μL of each sample together with 2 mL of ultrapure water, and the solution was stirred for approximately 2 min. 900 μL of each solution were removed for analysis of particle size, PDI and Zeta potential. The analyzes were made from 10 runs with 3 reading cycles and a scattering angle of 173º and a temperature of 25 °C. The average particle diameter and the PDI were calculated from the particle size distribution and the Zeta Potential values ​​were determined by measuring the electrophoretic mobility, expressed in mV.

2.5 Statistical and experimental design

Different percentages of carbon nanotubes (0, 0.05, 0.1 and 0.2%) were added to the soil samples, as proposed by Alsharef et al. (2016), and subsequently evaluated and compared the physical properties of the soil with the addition of carbon nanotubes compared to the original soil.

For these tests, a factorial experiment installed in a completely randomized design, in triplicate, was used, and the data obtained in the mechanical tests were statistically analyzed according to the normality of the errors and the homogeneity of the variances of the data, and if assumptions were met, it was carried out an analysis of variance (ANOVA) and Tukey's test with 5% probability for comparison between means, as shown in Table 1. The homogeneity of variances was performed using the Prophet® software, while the normality of errors and ANOVA were performed using the Sisvar® program.

Table 1
Statistical and experimental design of the research.

Treatments	Repetitions
	I	II	III
Group 01 (0% of MWCNT)	G1.1	G1.2	G1.3
Group 02 (0.05% of MWCNT)	G2.1	G2.2	G2.3
Group 03 (0.1% of MWCNT)	G3.1	G3.2	G3.3
Group 04 (0.2% of MWCNT)	G4.1	G4.2	G4.3

2.6 Physical and mechanical soil tests

To carry out the liquidity limit test, the recommendations of the DNER ME 122/94 (DNER, 1994b) standard were followed. As for the plasticity limit test, it was performed according to the DNER ME 082/94 standard (DNER, 1994c). To determine the plasticity index, the averages of the liquidity limits were subtracted from those of the plasticity limits. The Atterberg limits tests were performed with the samples from the control group and the samples with the addition of carbon nanotubes. For the determination of the Atterberg limits, approximately 200 g of material (air-dried) passing through the #40 sieve (0.42 mm) was separated. Atterberg limits define the consistency and behavior of cohesive soil at different water contents. These limits are important as they are related to the parameters of contraction, expansion and settlement of the soil (Rehman et al., 2020).

To determine the maximum dry density and optimal moisture, the recommendations of the DNIT 164/2013 (DNIT, 2013) standard were followed, which provides for compaction using unworked samples, using normal compression energy. To determine the real specific mass of the lateritic soil before and after the addition of different percentages of carbon nanotubes, the recommendations of the DNER ME 093/94 (DNER, 1994a) standard were followed.

3. Results and discussions

3.1 Soil characterization

3.1.1 Granulometry

Through the granulometry test, it was possible to obtain the granulometric curve of the soil, as shown in Figure 5.

Figure 5
Graph of soil granulometry.

Through Figure 5, it is possible to see that this is a soil with more than 50% of its composition consisting of fine particles (silt and clay), being composed mainly of clay. From the percentages of each soil component, it is possible to classify the soil as a medium texture soil (Santos et al., 2018). Texture is one of the main indicators of soil quality, since it influences the dynamics of adhesion and cohesion between soil particles, as well as the dynamics of water in the soil (Centeno et al., 2017).

3.1.2 MCT test

Through the MCT test, it was possible to determine whether the soil in question has a lateritic behavior or not. Thus, according to Figure 6 and after the tests performed, the soil was classified as sandy lateritic.

Figure 6
MCT classification abacus with soil results used in research.

According to the Unified Soil Classification System (USCS), the soil used in the research was determined to be between clay or silt of low compressibility, which corroborates the statement that the soil is lateritic, with non-plastic behavior and non-expansive, according to Figure 7.

Figure 7
Plasticity chart with soil classification.

In the HRB classification system, the soil was classified as a clayey soil of the A-7-5 group, presenting general behavior as a weak to poor subgrade. However, it is important to point out that the use of techniques considered traditional for soil classification is not suitable for tropical soils, given the peculiarities inherent to this type of soil (Nogami & Villibor, 1991).

3.1.3 Chemical analysis (EDX)

Figure 8 shows the chemical composition of the soil, obtained through the EDX technique. The main constituents of the soil are the minerals silicon, iron and titanium, which are present in the form of oxides. The elements Si and Fe are considered the most important chemical elements of lateritic soils (Borba, 1981). This collaborates, together with the results obtained in the DRX, that the analyzed material has the characteristics of a lateritic soil.

Figure 8
Soil Chemical Composition.

3.1.4. Mineralogical analysis (DRX)

The X-ray diffractogram of the soil used in the research is shown in Figure 9. Although there are well-defined peaks at 2θ = 26.66° and 2θ = 20.86°, these peaks correspond to the quartz minerals present in the sample (Hazen et al., 1989). The peaks of 2θ = 50.12°, 22.15° and others correspond to the kaolinite clay mineral (Gruner, 1932). Kaolinite has a lamellar structure formed by the regular stacking of layers 1:1, in which each layer consists of a sheet of silicon and oxygen tetrahedrons and a sheet of alumina octahedrons with basal spacing of approximately 0.7 nm and of chemical composition Al₂O₃2SiO₂2H₂O (Scorzelli et al., 2008). This mineral clay, due to its low expansion potential, is indicated for use in impermeable barriers, in addition to having less water dispersion (Borba et al., 2019).

Figure 9
Lateritic soil X-ray diffractogram.

3.2 Carbon nanotubes

3.2.1 Characterization of nanotubes

The properties of the nanotubes are shown in Table 2.

Table 2
Properties of carbon nanotubes (CTube 120).

Property	CTube 120
Appearance	Black Powder
Carbon Purity (%)	≥ 97
Average diameter (nm)	20
Length (µm)	1-25
Metal oxide (%)	≤ 3
Apparent density (g/cm3)	0.030-0.050
Specific surface (m2/g)	150-250

Images of carbon nanotubes through scanning electron microscopy are shown in Figure 10.

Figure 10
Scanning Electron Microscope (SEM) image of carbon nanotubes. (A) 20x magnification; (B) 200x magnification; (C) 1000x magnification; (D) 10.000x magnification.

3.2.2 Dispersion of carbon nanotubes (average size, PDI and Zeta Potential)

Through the DLS, it was possible to analyze the average hydrodynamic diameter of the nanoparticles dispersed in the solutions. The result is shown in Figure 11, where particles with an average hydrodynamic diameter of 68.9 nm were obtained, satisfactorily meeting the requirement that the particles remain in the nanometer scale after dispersion. The results show better results than research done with other types of surfactants, as shown in Table 3.

Figure 11
DLS with the dispersion solution with 0.15% Tween 80 and 0.01% MWCNT.

Table 3
Mean hydrodynamic diameter obtained from MWCNT in solutions with different surfactants, with 0.01% MWCNT.

Author	Surfactants	Average diameter (nm)
Figueiredo et al. (2015)	Glycerox 0.5%	197.2
	Glycerox 1%	167.6
	Glycerox 3%	175.2
	Amber 4001 0.5%	521.45
	Amber 4001 1%	322.85
	Amber 4001 3%	316.8
Rafael (2017)	Pluronic F-127 0.03%	198.4
	Policarboxilato 0.05%	221.1
	SDBS 0.03%	206.9
	SDOCO 0.03%	207.1
	Triton X-100 0.1%	174.0
	SDBS 0.015% + Pluronic F-127 0.015%	191.3
	SDBS 0.015% + Policarboxilato 0.25%	200.2
	SDBS 0.015% + SDOCO 0.015%	205.1
	SDBS 0.015% +Triton X-100 0.05%	188.0
	SDOCO 0.015% + Pluronic F-127 0.015%	191.0
Moura (2015)	Amber 2006 0.1%	354.9
	Amber 2006 0.5%	431.1
	Amber 2006 1%	514.9
	Amber 2006 2%	816.0
	Amber 2002 0.1%	424.0
	Amber 2002 0.5%	304.1
	Amber 2002 1%	399.9
	Amber 2002 2%	623.9
	Amber 2001 0.1%	550.0
	Amber 2001 0.5%	548.0
	Amber 2001 1%	871.6
	Amber 2001 2%	985.0

It is possible to notice that the solution becomes more homogeneous with low levels of surfactants, followed by a relative trend of stability as the concentration increases. This variation can be divided into two states: one where the surfactant is limited and another where there is an excess. At low concentrations, there is not enough surfactant to protect all particles present in the solution. Consequently, small oil droplets collide with each other, which increases the particle size. The dimensions of the particles, in this state, are defined by the type and concentration of surfactant present in the solution (Yang et al., 2013; Zhu et al., 2019). The surfactant used in the research, Tween 80, is highly recommended to obtain homogeneous solutions and highly ordered nanoparticles, due to its hydrophilic character (Hamza et al., 2020).

Figure 12 shows the graph of the Zeta Potential of the solution. The average Zeta potential obtained in all experimental runs was around -24.87 mV and the PDI obtained was around 0.231. In general, PDI < 0.1, 0.1 – 0.4 and > 0.4 represent highly dispersed, moderately dispersed and colloidal solutions, respectively (Zaib et al., 2020). The closer to 0 mV the Zeta Potential value, the greater the tendency for the solution to agglomerate, while values ​​close to -40 mV represent solutions with good stability (Salopek et al., 1992; Clogston & Patri, 2011). These results indicate that the solution appears as moderately dispersed, and the Zeta Potential far from 0 mV indicates that the particles present in the solution present enough repulsion to keep it stable.

Figure 12
Graph of the Zeta Potential of the solution with 0.15% Tween 80 and 0.01% MWCNT.

3.3 Physical tests

3.3.1 Atterberg limits

The results of the Atterberg limits tests are shown in Figure 13. First, the homogeneity of the data variances was calculated using the Bartlett test and the normality of the errors using the Shapiro-Wilk test, where the hypothesis of difference in variances and abnormality in errors was rejected. With that, we proceeded with the ANOVA.

Figure 13
Averages of the Atterberg limits for the samples (control groups and with different additions of CNTs). Obs: Groups with the same letter do not differ significantly in relation to their means, according to the Tukey Test (α = 5%).

From the results obtained, the addition of CNTs to the soil showed a significant difference in the parameters of liquidity limit and plasticity limit, while for the plasticity index there was no significant difference. The percentage of addition that proved to be the most efficient was 0.2%, presenting a reduction according to the percentage variation in relation to the initial value of about 10% in the LL and 13% in the PL, followed by the percentages of 0.1%, which showed a reduction of 15% in the PL and 0.05% with similar values ​​for LL and PL.

Atterberg limits play an important role in soil identification and classification. These parameters may indicate some possible geotechnical problems that may arise in the soil, such as potential for expansion and workability (Alsharef et al., 2016). Reductions in the Atterberg limits are indicators of soil improvement (Majeed & Taha, 2012). A possible explanation for the reduction of these indices is that the carbon nanotubes form a coating layer around the clay lamellae, which causes a suppression of its reactivity, reducing the possible absorption of water from the soil (Krishnan & Shukla, 2019).

The result of soil classification according to the Unified Soil Classification System is shown in Figure 14. It is possible to see that, even after changes to the Atterberg limits, the soil maintained its non-plastic and non-expansive behavior characteristics. This behavior is advantageous, as an increase in Atterberg limits is related to a greater potential for soil expansibility (Mearek et al., 2022).

Figure 14
Sample results with CNTs according to USCS.

3.3.2 Real specific mass

The results of the real specific mass tests are shown in Figure 15. First, the homogeneity of the data variances was calculated using the Bartlett test and the normality of the errors using the Shapiro-Wilk test, where the hypothesis of difference in variances and abnormality in errors was rejected. With that, we proceeded with the ANOVA.

Figure 15
Averages of the real specific mass of the samples of the control groups and with different additions of CNTs. Obs: Groups with the same letter do not differ significantly in relation to their means, according to the Tukey Test (α = 5%).

Although there was a reduction in the mean real specific mass as the percentage of CNTs in the samples increased, this reduction was not significant. This reduction is expected, due to the low density of CNTs, in addition to meeting the results obtained by Taha & Alsharef (2018). However, due to the non-significant result, it is possible that higher percentages of addition of carbon nanotubes in the soil could lead to relevant results, due to the tendency of reduction as the percentage of CNTs increases, observed in Figure 15.

3.3.3 Maximum dry density and optimum moisture

The homogeneity of data variances was calculated using the Bartlett test and the normality of errors using the Shapiro-Wilk test, where the hypothesis of difference in variances and abnormality in errors was rejected. With that, we proceeded with the ANOVA. The results of the mean maximum dry density and optimal moisture of the samples from the control group and with different additions of CNTs are shown in Figure 16.

Figure 16
Means of maximum dry densities and optimal moisture of soil samples (control groups and with different additions of CNTs). Obs: Groups with the same letter do not differ significantly in relation to their means, according to the Tukey Test (α = 5%).

The values ​​of maximum dry density showed an increase in the percentages of addition of 0.05% and 0.1% of CNTs, while there was a reduction in the values ​​of optimal moisture in the observed treatments. However, there was no significant variation in both maximum dry density values ​​and optimal moisture values. The increase and decrease in the values ​​of maximum dry density and optimal moisture, respectively, are in line with those obtained by Alsharef et al. (2016), which also showed a small variation in these values ​​of 2% and 6%.

The increase in density can be caused by the rearrangement and consolidation of soil particles promoted by filling the nanoparticles in the voids present in the specimen (Krishnan & Shukla, 2019). The reduction in the optimal moisture content can be explained by the formation of nanocomposites that fill the voids in the soil, occupying the water space, where the ion exchange mechanism is the main phenomenon that maintains the connection between clay minerals and carbon nanotubes (Azzam, 2014). However, despite these mechanisms, the results obtained here do not demonstrate that there was a significant change in these parameters, so performing treatments with different percentages can lead to values ​​with a significant difference.

4. Conclusions

The granulometry test revealed that the soil has more than 50% of its weight passing through the #200 sieve, being predominantly composed of silt with 24.76% and clay with 33.05%, being able to classify the soil as being a clayey soil of texture average. The MCT methodology allowed classifying the soil as a lateritic sandy type. Through the granulometry results and the Atterberg Limits, the soil was classified as low compressibility clay/silt in the USCS soil classification system and A-7-5 in the HRB system, classifying it as a clayey soil with weak behavior as subgrade. The X-Ray Diffraction analysis allowed us to identify the clay minerals present in the soil, where kaolinite was the main component of the soil, and together with the chemical test through EDX it allowed us to identify the main chemical compounds present in the soil, with predominance for silicon and iron. The results obtained from these tests corroborate the statement that the soil used in the research was a soil with lateritic behavior.

The Dynamic Light Scattering measurements showed that solutions with the dispersion of carbon nanotubes resulted in an average hydrodynamic diameter of the particles of 68.9 nm, a result considered to be of better quality compared to other studies, and the Zeta Potential was -24.87 mV and PDI of 0.231. These results confirm that the dispersion was carried out properly and that the solution is considered moderately dispersed, with a good tendency towards kinetic stability, where it was possible to reach the nanometric scale of carbon nanotubes in the solution. These factors collaborate so that the results of the mechanical and physical tests are reliable.

According to the physical tests, the addition of different percentages of carbon nanotubes showed a reduction of 10 and 13%, respectively, in the parameters of liquidity limit and plasticity limit, and these results indicate a tendency for improvement in the soil, since as the liquidity limit decreases, so does soil compressibility, among other variables. However, despite the variation in the mean values ​​of real specific mass, maximum dry density and optimal soil moisture, it was not possible to observe a significant difference in the results of these parameters after the addition of different percentages of carbon nanotubes in comparison with the samples. control group soil. Running these tests with different percentages of nanotubes can lead to results with significant variation, so further studies are needed to investigate these variations.

Advances in MWCNT synthesis methods have resulted in a substantial decrease in their cost, which opens several possibilities for their use in different fields. In the present study, the percentage that proved to be most advantageous was 0.2% of carbon nanotubes, which represents a very small amount in relation to the soil, which leads to low implementation costs. However, special attention must be paid to the techniques for dispersing carbon nanotubes before mixing, as it plays a fundamental role in the correct incorporation of nanomaterials into the soil.

Through this work, it is possible to state that carbon nanotubes interact with soils with lateritic behavior, and that further studies are essential to better understand the mechanisms behind this interaction. During the research, it was not possible to find in the scientific literature works that use carbon nanotubes in the region of Acre or even in the North of Brazil, so that this work constitutes a pioneer in the investigation related to the addition of nanomaterials in soils of the region, establishing it as a scientific basis for further research with the use of nanoparticles in soils in the region to have a scientific-technical basis.

List of symbols and abbreviations

c’: Parameter that differentiates sandy and clayey materials

d’: Parameter of the compaction curve that is directly related to the maximum density

e’: Parameter that classifies the soil as being lateritic or non-lateritic

ANOVA: Analysis of Variance

CBR: California Bear Rating

CH: Highly plasticity inorganic clays

CL: Low plasticity inorganic clays

CNT: Carbon Nanotubes

DLS: Dynamic Light Scattering

DNER: National Department of Highways

DNIT: National Department of Transport Infrastructure

DRX: X-Ray Diffractometer

EDS: Energy Dispersive Spectroscopy

EDX: Energy Dispersive X-ray System

EMURB: Municipal Urbanization Company of Rio Branco

FUNTAC: Technology Foundation of the State of Acre

HRB: Highway Research Board

LA: Lateritic Sand

LA’: Lateritic Sandy

LG’: Lateritic Clayish

LL: Liquid Limit

PL: Plastic limit

MCT: Tropical Compressed Miniature

MCV: Moisture Condition Value

MH: High plasticity silt

ML: Low plasticity silt

MWCNT: Multi Wall Carbon Nanotubes

NA: Non lateritic Sand

NA’: Non lateritic Sandy

NG’: Non lateritic Clayish

NS’: Non lateritic Silty

OH: High plasticity organic clay

OL: Low plasticity organic clay

PDI: Polidispersivity Index

Pi: Parameter that quantifies the degree of soil laterization

PI: Plasticity Index

SDBS: Sodium Dodecyl Benzene Sulfonate

SDOCO: Sodium Deoxy Cholate

SDS: Sodium Dodecyl Sulfate

USCS: Unified Soil Classification System

UFAC: Federal University of Acre

Acknowledgements

The authors are appreciative of the funding support provided by Higher Education Personnel Improvement Coordination (CAPES), Federal University of Acre (UFAC).

• Discussion open until February 28, 2025.

• Data availability

  All the data and models that support the findings of this study are available from the corresponding author upon request.

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Data availability

All the data and models that support the findings of this study are available from the corresponding author upon request.

Publication Dates

• Publication in this collection
  25 Mar 2024
• Date of issue
  2024

History

• Received
  21 June 2023
• Accepted
  27 Feb 2024