Graphene Nanoplatelets Content Research Articles

Remarkable tribo-mechanical, anticorrosion and antibacterial properties of ZnCu/GNPs composite coatings prepared by electro-co-deposition technique

Herein, we report the fabrication of graphene nanoplatelets (GNPs) reinforced zinc-copper (ZnCu) matrix composite coatings on a stainless-steel substrate using electro-co-deposition technique. The influence of varying concentrations of GNPs in the acidic electrolyte bath on the microstructure, chemical composition, phase structure, hardness, wear resistance, corrosion resistance, and antibacterial activity of ZnCu/GNPs composite coating was investigated. The microhardness of the ZnCu/GNPs composite coating with a GNPs concentration of 100 mg/L is compared with pure ZnCu coating, which has a 90 % significant enhancement, while (50 mg/L) has 86 %, and (25 mg/L) has 50 %. Also, ZnCu/GNPs composite coating showed a wear loss of 10 mg for 100 mg/L GNPs sample with an increase in microhardness. The bacterial resistance assays were conducted against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). The results reveal a notable improvement in the anti-bacterial activity of the ZnCu/GNPs composite coating. The corrosion rate of the ZnCu/GNPs composite coating in 3.5 wt % NaCl solution steadily decreased when the concentration of GNPs in the electrolyte bath was increased to 100 mg/L. These findings hold great potential for various applications, including healthcare settings where preventing healthcare-associated infections is critical, public infrastructure to prolong the lifespan of structures, and marine coatings to protect against corrosion in harsh marine environments.

Binder-free all-carbon composite supercapacitors

Carbon-based electrode materials have widely been used in supercapacitors. Unfortunately, the fabrication of the supercapacitors includes a polymeric binding material that leads to an undesirable addition of weight along with an increased charge transfer resistance. Herein, binder-free and lightweight electrodes were fabricated using powder processing of carbon nanofibers (CNFs) and graphene nanoplatelets (GNPs) resulting in a hybrid all-carbon composite material. The structural, morphological, and electrochemical properties of the composite electrodes were studied at different concentrations of GNPs. The specific capacitance (Cs) of the CNFs/GNPs composite was improved by increasing the concentration of GNPs. A maximum Cs of around 120 F g−1 was achieved at 90 wt% GNPs which is around 5-fold higher in value than the pristine CNFs in 1 M potassium hydroxides (KOH), which then further increased to 189 F g−1 in 6 M KOH electrolyte. The energy density of around 20 Wh kg−1 with the corresponding power density of 340 W kg−1 was achieved in the supercapacitor containing 90 wt% GNPs. The enhanced electrochemical performance of the composite is related to the presence of a synergistic effect and the CNFs establishing conductive/percolating networks. Such binder-free all-carbon electrodes can be a potential candidate for next-generation energy applications.

A unified hybrid micromechanical model for predicting the thermal and electrical conductivity of graphene reinforced porous and saturated cement composites

Graphene fillers have gained widespread attention as functional reinforcements for cement composites due to their excellent physical properties. The prediction of the thermal and electrical properties of graphene reinforced cement composites (GRCCs) is of great importance for developing intelligent and multifunctional civil engineering materials and structures. In this current work, a unified hybrid micromechanical model combining effective medium theory (EMT) and Mori-Tanaka-Benveniste (MTB) is developed to simultaneously predict the thermal and electrical conductivity of the GRCCs with considering pores and saturation, in which mechanisms including phonon transport, electron tunnelling and Maxwell-Wagner-Sillars (MWS) polarization are incorporated. Graphene nanoplatelets (GNPs) reinforced cement composites (GNPRCCs) samples are prepared and tested to validate the developed unified model. The results show that the electrical conductivity of the GNPRCCs is more sensitive to saturation than the thermal conductivity. When the porosity n = 0.2 and the GNP concentration is 1 wt%, the relative thermal and electrical conductivity increases by 5 % and 193 %, respectively, when the saturation index increases from 0.6 to 1. Elongated pores are more favorable for the formation of ionic networks for electrical conductivity in the saturated GNPRCCs, while spherical pores are more beneficial for heat transport. Elongated graphene fillers are preferred for enhancing both the thermal and electrical conductivity of dry and saturated GNPRCCs. The work is envisaged to provide guidelines for developing multifunctional cement composites.

The Effect of Surfactant on the Stability of Inorganic Salt Hydrated Phase Change Material Containing Graphene Nanoplatelet: An Experimental Investigation

The use of nano phase change material (PCM) as a heat transfer agent has spread and expanded as its thermal conductivity is higher than the base PCM, so its use has been increased in PVT systems. In this study, inorganic salt hydrated PCM was used as a base material and Graphene nanoplatelet (GNP) as an additive to improve the thermal conductivity of the resulted nano-PCM and its stability was studied using Cetyl Trimethyl Ammonium Bromide (CTAB) surfactants. The main objective of the present study was to investigate the effect of surfactant on the stability of different GNP concentration in CaCl2.6H2O PCM. The nano-PCM has been prepared using two-step method. The stability of nano-PCM depends on the surfactant used to slow the sedimentation of nanoparticles in the base PCM. The sedimentation process significantly reduces the thermal conductivity of nano-PCM and thus reduces heat transfer. The study results indicated that the employment of surfactant significantly affected the stability of nanoparticles added in the base PCM. The stability test ascertained the effectiveness of the surfactant applied in phase change material. With successive addition of surfactant, the stability of GNP in PCM was further enhanced to +49.3 mV of Zeta potential value.

Role of aloe vera based nanofluids for cool thermal energy storage system: A comparative study

The present experimental investigation incorporates the preparation of aloe vera gel-based phase change nanofluid (NFPCM) for a cool thermal energy storage (CTES) system. Two sets of NFPCMs were produced by adding graphene nanoplatelets (GNP) in deionized water and aloe vera gel (0.20 wt%, 0.40 wt%, and 0.60 wt%). The zeta potential analysis shows that the NFPCM with aloe vera gel is more stable even after 60 days from the preparation (−44 mV). The thermal conductivity of NFPCMs was analyzed through the transient hot wire method and the enhancement of 42 %, and 37.5 % were measured in the ice phase for NFPCMs with aloe vera gel and DI water (0.60 wt%) respectively at the heat transfer fluid temperature of −15 °C. The NFPCMs with aloe vera gel show that the addition of GNP prepended the peak melting temperature from −16 °C to −10.5 °C, which is due to the quick formation of nuclei as compared to NFPCMs with DI water. Additionally, a slight reduction in the latent heat of only 7 % was observed. The charging characteristics of NFPCMs were studied in a single spherical capsule at −8 °C. The maximum reduction in total charging time of 25 % was recorded for the aloe vera gel-based NFPCM with a maximum concentration of GNP (0.60 wt%). The green synthesis of aloe vera gel-based NFPCM exhibited noteworthy enhancements in thermophysical properties. Additionally, it was observed that the long-term stability of the NFPCM was achieved by excluding surfactants during the preparation process. The incorporation of graphene nanoplatelets (GNP) into aloe vera gel as the foundational phase change material (PCM) facilitates the partial charging of the CTES system with a reduced energy input.

Preparation and tribological properties of porous polyimide modified by graphene

PurposeThe purpose of this article is to prepare graphene/polyimide composite materials for use as bearing cage materials, improving the friction and wear performance of bearing cages.Design/methodology/approachThe oil absorption and discharge tests were conducted to evaluate the oil content properties of the materials, while the mechanical properties were analyzed through cross-sectional morphology examination. Investigation into the tribological behavior and wear mechanisms encompassed characterization and analysis of wear trace morphology in PPI-based materials. Consequently, the influence of varied graphene nanoplatelets (GN) concentrations on the oil content, mechanical and tribological properties of PPI-based materials was elucidated.FindingsThe composites exhibit excellent oil-containing properties due to the increased porosity of PPI-GN composites. The robust formation of covalent bonds between GN and PPI amplifies the adhesive potency of the PPI-GN composites, thereby inducing a substantial enhancement in impact strength. Notably, the PPI-GN composites showed enhanced lubrication properties compared to PPI, which was particularly evident at a GN content of 0.5 Wt.%, as evidenced by the minimization of the average coefficient of friction and the width of the abrasion marks.Practical implicationsThis paper includes implications for elucidating the wear mechanism of the polyimide composites under frictional wear conditions and then to guide the optimization of oil content and tribological properties of polyimide bearing cage materials.Originality/valueIn this paper, homogeneously dispersed PPI-GN composites were effectively synthesized by introducing GN into a polyimide matrix through in situ polymerization, and the lubrication mechanism of the PPI composites was compared with that of the PPI-GN composites to illustrate the composites’ superiority.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0415

Effects of temperature and nanoparticle mixing ratio on the thermophysical properties of GNP–Fe2O3 hybrid nanofluids: an experimental study with RSM and ANN modeling

This study investigated the impact of temperature and nanoparticle mixing ratio on the thermophysical properties of hybrid nanofluids (HNFs) made with graphene nanoplatelets (GNP) and iron oxide nanoparticles (Fe2O3). The results showed that increased temperature led to higher thermal conductivity (TC) and electrical conductivity (EC), and lower viscosity in HNFs. Higher GNP content relative to Fe2O3 also resulted in higher TC but lower EC and viscosity. Artificial neural network (ANN) and response surface methodology (RSM) were used to model and correlate the thermophysical properties of HNFs. The ANN models showed a high degree of correlation between predicted and actual values for all three properties (TC, EC, and viscosity). The optimal number of neurons varied for each property. For TC, the model with six neurons performed the best, while for viscosity, the model with ten neurons was optimal. The best ANN model for EC contained 18 neurons. The RSM results indicated that the 2-factor interaction term was the most significant factor for optimizing TC and EC; while, the linear term was most important for optimizing viscosity. The ANN models performed better than the RSM models for all properties. The findings provide insights into factors affecting the thermophysical properties of HNFs and can inform the development of more effective heat transfer fluids for industrial applications.

Self-sensing cementitious geocomposites in rail track substructures

A self-sensing cementitious geocomposite was developed based on laboratory data, and it demonstrated good physical and mechanical properties, durability, and piezoresistivity performance. It consisted of stabilized cemented sand containing multiwalled carbon nanotubes (MWCNTs) with graphene nanoplatelets (GNPs) as conductive fillers. This geocomposite could be used to detect damage based on the relationship between electrical impedance and mechanical performance, making it suitable for use as structural layers in railway lines. In this study, the effects of MWCNTs and GNPs, as well as of degree of saturation, were evaluated on the compaction, secant modulus, resilient modulus, electrical resistance, and piezoresistivity of the geocomposite. Scanning electron microscopy (SEM) and microscopic imaging were used to analyse microstructural alterations induced by varying concentrations of MWCNTs and GNPs. This innovative geocomposite, intended for installation in Portuguese railway lines as part of the EU project IN2TRACK3, is aimed at capturing performance data, identifying structural damage levels, and estimating load intensity, axle numbers, and train speed. The feasibility of its use is discussed based on literature studies and research conducted under the IN2TRACK2 and IN2TRACK3 projects, and its potential advantages over traditional methods for monitoring rail track health are highlighted.

The influence of GNP on the mechanical and thermomechanical properties of epoxy adhesive: Pearson correlation matrix and heatmap application in data interpretation

AbstractThis article explores the impact of Graphene Nanoplatelet (GNP) reinforcement on the mechanical and thermomechanical properties of epoxy adhesive. The impact of GNPs on the structural adhesive largely depends on factors like dispersion stability and GNP concentration. Given the complexity of these interactions, it is imperative to obtain indicators for the degree of each interaction. In this study, Pearson correlation matrix and heatmaps analysis were used to analyze the relationship between various variables and the strength of the connection. The addition of GNP at various concentrations to the epoxy adhesive led to a reduction in storage modulus, loss modulus, and glass transition temperature, attributed to the agglomeration of GNP. In bulk specimens, the incorporation of GNP, regardless of its content, has decreased tensile stress up to 70.7% while increasing its fracture elongation up to 396.3%. Within joining specimens with GNP, specimens with 1.5 wt% GNP content showed the highest cohesive fracture mode and largest shear strength. Pearson correlation matrix and heatmaps have demonstrated very good accuracy in reflecting the experimental data. They offer a comprehensive assessment of the relationship between various variables while indicating the degree of influence each variable has on the properties of the adhesive and joining specimens.Highlights GNP effects in adhesive can be accurately analyzed with Pearson correlation matrix & heatmaps. Connections strength for various variables can be shown by Pearson correlation matrix & heatmaps. Pearson correlation matrix & heatmaps assess variable relationships comprehensively.

Effect of water immersion on shear strength of epoxy adhesive filled with graphene nanoplatelets

Abstract Adhesive bonds are frequently used in industries such as aerospace, automotive, and civil engineering due to their ability to reduce damage to the adherend and their lightweight. However, their application is restricted by their inadequate durability and reliability in hostile environments. Graphene nanoplatelets (GNPs) are employed to enhance epoxy adhesives in this article. The thick adherend shear test is utilized to examine how the shear properties change with different water ageing times. Before exposure to water ageing conditions, GNP-reinforced adhesives exhibit a 3.51% increase compared with neat epoxy at a GNP content of 0.25 wt%. However, after 56 days of water ageing, the increase in shear strength is found to be 13.79%. This suggests that the well-dispersed GNP can reduce the degradation rate in shear properties by half, from 16.71 to 8.44%, at a GNP content of 0.25 wt%. Additionally, as water ageing time increases, the positive influence of GNP on shear properties becomes more evident. The addition of GNP delays the degradation of shear properties caused by water ageing conditions. The effect of GNP does not improve with higher GNP content. When the GNP contents increase to 1 wt%, the shear strength of the GNP-reinforced adhesive decreases compared to neat epoxy.

Fatigue response of multiscale extrusion-based additively manufactured acrylonitrile butadiene styrene-graphene nanoplatelets composites

This study investigates the effect of varying concentrations of graphene nanoplatelets (GNPs) on the mechanical and fatigue characteristics of acrylonitrile butadiene styrene (ABS)/GNPs nanocomposites, both in filament form and as 3D-printed parts with different raster orientations. Quasi-static and cyclic loading tests were performed on specimens containing 0.1 wt%, 0.5 wt%, 1.0 wt%, and 2.0 wt% GNPs. The results indicated that the addition of 0.1 wt% GNPs to the ABS polymer matrix increased the ultimate tensile strength (UTS) of the filament by 18 %. Addition of 1.0 wt% and 2.0 wt% GNPs to the matrix improved the Young's modulus of the filament by up to about 35 % but did not enhance the UTS. The filament containing 0.5 wt% GNPs demonstrated the highest fatigue life, even higher than that of the standard samples fabricated through injection molding technique. This was due to its high static strength, Young's modulus, and ductility. However, when the ABS/GNPs nanocomposites were 3D-printed, the addition of GNPs had no major impact on their fatigue life, especially in the higher load levels. Inadequate adhesion and reduced interlayer bonding strength resulted in a detrimental impact on fatigue life of 3D-printed objects. The extrudate-swell phenomenon seems to significantly affect this reduction in fatigue life. To address this, larger nozzle diameter (0.6 mm) was used in fabricating 3D-printed samples. Results indicated that samples with less distortion and smaller extrudate-swell had higher fatigue life. It highlights the importance of considering GNP concentration and printing parameters for optimal fatigue properties in ABS/GNPs composites.

Enhanced magneto-dielectric characteristics of Ni–Cd ferrites/GNPs composites synthesized via sol-gel auto-combustion method

Graphene nanoplatelets (GNPs) and spinel ferrites (SFs) composites enhance their properties and enable multifunctional applications, ranging from electronics to environmental and biomedical fields. This study investigates the Ni0.4Cd0.6Fe2O4/x-GNPs (x = 0 %, 2.5 %, and 5 %) [NCF/0%GNPs, NCF/2.5%GNPs, and NCF/5%GNPs] composites prepared via sol-gel auto combustion (SGAC) synthesis and their enhanced magneto-dielectric characteristics. XRD analysis confirmed the formation of a single-phase spinel matrix of NCF/GNPs composites and observed that the crystallite size (D) was enhanced from 22.8 nm to 33.6 nm with the increasing concentration of GNPs. Moreover, the bandgap energy (Eg) was reduced with the incorporation of GNPs in the NCF sample. The dielectric constant and tangent loss were enhanced with the addition of GNPs in the NCF spinel lattice. The saturation magnetization and microwave operating frequency were reduced from 97.42 emu/g to 55.13 emu/g and 21.49 GHz–11.97 GHz, respectively, while the coercivity was observed to increase from 92.66 Oe to 263 Oe with the incorporation of GNPs in the NCF lattice. The study highlights the potential of NCF/GNPs composites for applications in electronics, energy storage, sensors, and biomedical devices.

Composites with aligned and plasma-surface-modified graphene nanoplatelets and high dielectric constants

We have developed a method for designing polymer and graphene nanoplatelet (GNP) composites that show high dielectric constants over a wide range of GNP contents. GNPs are dispersed in the composites through plasma-surface modification and aligned by applying an electric field (EF). This creates a large number of microcapacitor structures of GNPs separated by the polymer. The maximum dielectric constant of the sample to which the EF is applied is approximately twice that of the sample to which the EF is not applied. Furthermore, the maximum dielectric constants of the samples with plasma-surface modified GNPs are higher than those of the samples with unmodified GNPs. The composites show high dielectric constants (∼500 at 100 Hz) over a wide range of GNP contents (6 ∼ 10 wt%) while maintaining mechanical flexibility (Young’s modulus:12 ± 4 MPa).

A Taguchi Optimization of Stir Casting Process Parameters for graphene Nanoplatelets/ A356 Alloy Composite

Aluminum metal matrix composite (AMC) is a potential material for diverse applications in the field of automotive and aerospace owing to their superior mechanical properties, lightweight behavior, and low thermal expansion. Graphene nanoplatelets (GNP) have emerged as the preferred reinforcement in AMC. They are incorporated into the matrix by stir-casting methods to generate semi-solid feedstock suitable for thixoforming. The effects of the stirrer parameter and GNP content were examined using Taguchi design of experiments with an L8 (23) orthogonal array. The parameters tested were stirring speed (300-500rpm), GNP content (0.3- 0.7 wt%) and stirring time (5-10 minutes). The signal-to-noise ratio (S/N) and hardness of the GNP /A356 composite were used as response variables. The contribution of three factors in enhancing hardness has been identified. The optimum parameter obtained with stirring speed, GNP content, and stirring time is 500rpm, 0.7wt.%GNP and 5 min respectively.

Formulation of Graphene Nanoplatelets Water-Based Nanofluids using Polyvinylpyrrolidone (PVP) as Surfactant

Graphene nanoplatelets (GNP) have garnered significant interest owing to their remarkable mechanical properties, making them ideal materials for enhancing the heat transfer properties of nanofluids. However, the agglomeration of GNP in water-based nanofluids remains a significant challenge, limiting their practical applications. In this study, a comprehensive investigation into the formulation of water-based nanofluids using GNP is presented, with a focus on the incorporation of polyvinylpyrrolidone (PVP) as a surfactant to enhance their stability and dispersion. The nanofluid formulations were systematically prepared with varying concentrations of GNP and PVP to optimize their thermal properties. Additionally, the thermal conductivity and viscosity of the nanofluids were examined over a range of temperatures and concentrations. The results show that the formulation of GNP with 0.6 wt.% shows great potential as a nanofluid with 0.6611 W/mK and 1.22 mPa.s for thermal conductivity and viscosity, respectively. The findings also demonstrate that the incorporation of PVP significantly improves the stability and dispersion of GNP in water, leading to enhanced thermal conductivity and manageable viscosity. These findings contribute to the application of this nanofluid in various industries, specifically solar energy.

Multilayer coatings of periodically co-deposited graphene and ag substrate: Improving the electrified friction interface by modifying the strength-ductility combination

Multifunctional and highly adaptable multilayer coatings are considered the future development trend in the field of protective coating design. To enhance wear resistance when subjected to an electric current and to reduce internal stresses, we developed a multilayer structure in which graphene nanoplatelets (GNPs) were deposited within pure silver using a dual electrolyte alternating deposition technique, resulting in the creation of Ag-Ag/GNP multilayer coatings. In this multilayer coating design, the continuous conductive silver layer enhances the overall electrical properties of the coating, and the soft silver layers absorb part of the internal stress in the coating. The deposited GNPs provide crucial structural reinforcement to the coating, thereby improving its mechanical and tribological properties. These coatings were characterized with respect to their structure and mechanical properties using X-ray diffraction, hardness testing, and electronic tensile testing, and their current-carrying tribological performance was studied. Experiments revealed that the crystal structure of single-layer composite coatings changed with increasing GNP content, whereas the multilayer coatings exhibited improved strength and toughness. In current-carrying tribological tests, the multilayer coatings demonstrated significant adaptability to increasing wear depth and current. The wear mechanism of multilayer coatings transitioned from adhesive to abrasive wear. The results indicate that the multilayer coating design effectively enhances friction stability, reduces electrical contact resistance, and decreases wear volume. The findings of this study provide new insights for the design of multilayer structured coatings and will help reduce the loss of rare metals in electrified friction applications.

Entropy Barrier Mitigation and Harnessing Localized Upcycled Graphene Nanoplatelets in Polypropylene/High-Density Polyethylene Nanoblends

In pursuit of a sustainable future, the focus on thermoplastic compounding emerges as a transformative avenue. Strategically blending and compounding thermoplastics unlock the potential for eco-friendly materials, addressing pressing environmental concerns. Polymer blending is a widely utilized technique that offers significant advantages in terms of cost-effectiveness and the development of materials with diverse properties. However, achieving compatibility between polymers remains a challenge due to their non-negligible entropy, particularly in the case of immiscible polymers like Polypropylene (PP) and High-Density Polyethylene (HDPE). The success of such systems heavily depends on optimizing factors such as additive selection, mixing methodology, composition, and processing conditions. Despite the extensive industrial usage of polymers like PP and HDPE, there is still limited understanding regarding the impact of blending these polymers, especially when graphene is introduced. This study addresses these challenges by overcoming the entropy barrier between PP and HDPE using a high shear rate thermo-kinetic mixer and employing upcycled graphene nanoplatelets (GNP) as a type of low-cost graphene material through interface engineering. The GNP content in the blends ranged from 0 to 1 wt%, and through meticulous selection of the polymer weight fraction and the use of minimal GNP content, GNP was strategically localized at the blend interface. This resulted in remarkable mechanical performance achieved through the optimized manufacturing technique. Incorporating 0.1 wt% GNP resulted in a significant 38% increase in tensile modulus, while flexural modulus and flexural strength saw respective increments of 39% and 22% compared to neat PP. Further enhancements were observed with higher GNP contents. This study illuminates the transformative potential of thermoplastic compounding as a key driver toward a sustainable future.Graphical

Incorporation of Graphene Nanoplatelets into Fiber-Reinforced Polymer Composites in the Presence of Highly Branched Waterborne Polyurethanes.

Enhancing interfacial interactions in fiber-reinforced polymer composites (FRPCs) is crucial for improving their mechanical properties. This can be achieved through the incorporation of nanomaterials or chemically functional agents into FRPCs. This study reports the tailoring of the fiber-matrix interface in FRPCs using non-functionalized graphene nanoplatelets (GNPs) in combination with a waterborne, highly branched, multi-functional polyurethane dispersion (HBPUD). A unique ultrasonic spray deposition technique was utilized to deposit aqueous mixtures of GNP/HBPUDs onto the surfaces of carbon fiber fabrics, which were used to prepare epoxy-prepreg sheets and corresponding FRPC laminates. The influence of the polyurethane (PU) and GNP content and their ratio at the fiber-matrix interface on the tensile properties of resulting high-performance composites was systematically investigated using stress-strain analysis of the produced FRPC plates and SEM analysis of their fractured surfaces. A synergistic stiffening and toughening effect was observed when as low as 20 to 30 mg of GNPs was deposited per square meter of each side of the carbon fiber fabrics in the presence of the multi-functional PU layer. This resulted in a significant improvement in the tensile strength from 908 to 1022 MPa, while maintaining or slightly improving the initial Young's modulus from approximately 63 to 66 MPa.

Effect of graphene nanoplatelets induced ethylene glycol/water mixture (50:50) fluid on lithium-battery cooling

Battery technology is the main driving force behind the shift towards emission-free transportation. However, a major obstacle to the widespread adoption of this technology is the need to maintain the battery's temperature at a standard of 27 °C. Traditionally, a mixture of ethylene glycol and water is circulated through the battery pack for cooling, but this method is not sufficient. By contrast, the use of graphene nanoplatelets (GNPs) can improve heat transfer, reducing temperature rise in the battery cell. In this study, a customized battery pack has been simulated using coolants containing varying concentrations of GNPs (ranging from 0.001 vol% to 0.01 vol%) to assess their effectiveness in lowering the operating temperature. The Lagrangian approach has been employed to track the discrete phase particles which couples with Eulerian continuous phase fluid. Results have shown that the pure EG/Water coolant decreases the peak temperature of the system by 16.67% (60 °C–50 °C) which further decreases to 26.85 °C (reduction of 55.25%) till the addition of optimum 0.03 vol% GNP. With the addition of only 0.001 vol% of GNPs, the difference in the peak temperature in the model increases from 10 °C to 31.15 °C as compared to the pure mixture. Higher thermal conductivity, greater surface area and higher specific heat capacity of the particles are attributed for this enhanced cooling.

Predicting the properties of concrete incorporating graphene nano platelets by experimental and machine learning approaches

Modern infrastructure requirements necessitate structural components with improved durability and strength properties. The incorporation of nanomaterials (NMs) into concrete emerges as a viable strategy to enhance both the durability and strength of the concrete. Nevertheless, the complexities inherent in these nanoscale cementitious composites are notably intricate. Traditional regression models face constraints in comprehensively capturing these intricate compositions. Thus, posing challenges in delivering precise and dependable estimations. Therefore, the current study utilized three machine learning (ML) methods, including artificial neural network (ANN), gene expression programming (GEP), and adaptive neuro-fuzzy inference system (ANFIS), in conjunction with experimental investigation to study the effect of the integration of graphene nanoplatelets (GNPs) on the electrical resistivity (ER) and compressive strength (CS) of concrete containing GNPs. Concrete containing GNPs demonstrated an improved fractional change in resistivity (FCR) and strength. The experimental measures depict that strength enhancement was notable at GNP concentrations of 0.05% and 0.1%, showcasing increases of 13.23% and 16.58%, respectively. Simultaneously, the highest observed FCR change reached −12.19% and −13%, respectively. The prediction efficacy of the three models proved to be outstanding in forecasting the characteristics of concrete containing GNPs. For CS, the GEP, ANN, and ANFIS models demonstrated impressive correlation coefficient (R) values of 0.974, 0.963, and 0.954, respectively. For electrical resistivity, the GEP, ANN, and ANFIS models exhibited high R-values of 0.999, 0.995, and 0.987, respectively. The comparative analysis of the models revealed that the GEP model delivered precise predictions for both ER and CS. The mean absolute error (MAE) of the GEP-CS model demonstrated a 14.51% reduction compared to the ANN-CS model and a substantial 48.15% improvement over the ANFIS-CS model. Similarly, the ANN-CS model displayed an MAE that was 38.14% lower compared to the ANFIS-CS model. Moreover, the MAE of the GEP-ER model demonstrated a 56.80% reduction compared to the ANN-CS model and a substantial 82.47% improvement over the ANFIS-CS model. The Shapley Additive explanation (SHAP) analysis provided that curing age exhibited the highest SHAP score. Thus, indicating its predominant contribution to CS prediction. In predicting ER, the graphene content exhibited the highest SHAP score, signifying its predominant contribution to ER estimation. This study highlights ML's accuracy in predicting the properties of concrete with graphene nanoplatelets, offering a fast and cost-effective alternative to time-consuming experiments.