Enhanced heat transfer with iron oxide nanofluids synthesis and performance evaluation
* Author to whom correspondence should be addressed.
Journal of Thermal Engineering 2026, Vol. 12, Issue 1, pp. 88-98; doi.org/10.14744/thermal.0001060
Abstract
Keywords: Co-precipitation method; Critical Heat Flux (CHF); Heat exchangers; Heat transfer enhancement; Iron(II; III) oxide; Magnetic nanoparticles; Nanofluids
Introduction
Enhancing heat transfer in industries such as power generation, automotive systems, and electronic cooling is of great importance. Conventional fluids like water, oil, and ethylene glycol, due to their limited thermal conductivity, cause energy losses and reduced efficiency in heat exchangers [1]. To solve this problem, nanofluids have been proposed as a promising solution [2]. Nanofluids are colloidal suspensions containing nanoparticles in base fluids that
improve thermophysical properties such as thermal conductivity [3]. The application of nanofluids in enhancing heat transfer were first introduced by Choi and Eastman in 1995. Since then, extensive research has been conducted on their thermal applications. Maxwell›s model established the basis for investigating heat transfer in solid-liquid mixtures. Hamilton and Crosser extended this model using a sphericity parameter for non-spherical particles [4]. Other
*Corresponding author. *E-mail address: amirimehdi5@uma.ac.ir, amirimehdi5@gmail.com This paper was recommended for publication in revised form by Editor-in-Chief Ahmet Selim Dalkilic Published by Yıldız Technical University Press, İstanbul, Turkey Yıldız Technical University. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
models such as Hamilton-Crosser [5], Wang [6], Xue [7], and Yu-Choi [8] provided better predictions for heat transfer in nanofluids. Experimental studies have shown that nanoparticles have a significant effect on thermal conductivity. For example, Masuda et al. [9] and Eastman et al. [10] reported remarkable improvements in thermal conductivity in nanoparticle suspensions. Masuda et al. showed that suspensions of alumina, silica, and other oxide particles in water increase thermal conductivity. Similarly, Eastman et al. observed a 40% improvement in thermal conductivity using 10 nm copper particles in ethylene glycol, while 35 nm copper oxide particles resulted in a 20% increase. Das et al. [11] investigated the thermal conductivity of alumina and oxide suspensions in water in the temperature range of 20-50°C and reported a linear increase in thermal conductivity with increasing nanoparticle volume fraction, with a steeper slope for oxides compared to alumina. Nanoparticle size plays a key role in nanofluid properties. Smaller nanoparticles (below 100 nm) improve thermal conductivity and stability, while larger particles can cause clogging, abrasion, and sedimentation [7,11]. Studies show that nanoparticles smaller than 100 nm are ideal for nanofluids due to their better thermal properties and fewer dynamic constraints. In this study, Fe₃O₄ nanoparticles with sizes of 30-60 nm were synthesized to optimize thermal performance and prevent problems such as clogging and abrasion in heat exchangers. Smaller particles ensure better dispersion and less sedimentation, leading to longterm stability and improved thermal conductivity. Iron oxide (Fe₃O₄) nanoparticles are notable for their magnetic properties, high thermal conductivity, and biocompatibility [12-16]. Magnetite (Fe₃O₄) is a suitable choice for heat transfer due to its high saturation magnetization and stability in water [6]. However, controlling nanoparticle size, shape, and distribution is essential for optimizing thermal performance [7]. Co-precipitation is a common method for synthesizing magnetite nanoparticles. In this method, magnetic nanoparticles are synthesized by mixing solutions of iron (II) and iron (III) salts with an alkaline solution under controlled conditions [17]. Recent studies have shown the potential of Fe₃O₄ nanoparticles in improving heat transfer. For example, Zhang et al. [18] found that Fe₃O₄ nanoparticles smaller than 50 nm can increase thermal conductivity by up to 20%. Similarly, Li et al. [19] synthesized Fe₃O₄ nanoparticles using the co-precipitation method and observed a 15% improvement in heat transfer performance. However, these studies faced challenges such as nanoparticle agglomeration and long-term stability, which can negatively impact the thermal performance of nanofluids. Furthermore, comprehensive studies investigating the relationship between nanoparticle size, distribution, and heat transfer enhancement in Fe₃O₄-based nanofluids are lacking.
Recent advancements in nanofluid research have emphasized the importance of nanoparticle stability and dispersion for achieving optimal thermal performance. For example, a study by Özerinç et al. [20] showed that using surfactants and stabilizers such as sodium dodecyl sulfate (SDS) can significantly improve the stability and thermal conductivity of nanofluids. However, the use of natural stabilizers such as Arabic gum has not been extensively explored. This study addresses these gaps by synthesizing Fe₃O₄ nanoparticles with precise control over size and distribution and by using Arabic gum as a stabilizer. Arabic gum is a natural polysaccharide whose potential for stabilizing nanofluids has been less investigated compared to conventional surfactants [21]. Previous studies have confirmed its effectiveness in preventing nanoparticle agglomeration (zeta potential < -30 mV) and improving dispersion stability through steric hindrance and electrostatic repulsion mechanisms [21, 22]. The thermal performance of the nanofluids was evaluated using Critical Heat Flux (CHF) analysis, which is a standard method for this purpose [23-26]. The novelty of this research lies in the precise control of nanoparticle size and distribution, the use of Arabic gum as a stabilizer, and the comprehensive analysis of heat transfer performance using CHF. Furthermore, our study employed a low nanoparticle concentration (0.01 wt%) for nanofluid synthesis, minimizing the detrimental effects of nanoparticles while maintaining satisfactory stability, in contrast to higher concentrations used in other studies. These findings provide new insights for optimizing Fe₃O₄-based nanofluids for industrial applications, particularly in heat exchangers. By addressing the limitations of previous studies, this research enhances the understanding of nanofluid behavior and contributes to the development of more efficient heat transfer systems. This paper is organized as follows: Section 2 describes the materials and methods used in this study. Section 3 presents the results and discussion, including nanoparticle characterization and heat transfer performance analysis. Finally, Section 4 summarizes the conclusions and future research directions.
Materials And Methods
The co-precipitation method [16,17] was selected for Fe₃O₄ synthesis due to its documented advantages, low-cost implementation [14], high-yield nanoparticle production (>95% [16]), and precise size control (20-50 nm [13,17]). The synthesis process was carefully controlled to achieve nanoparticles with uniform size and distribution, which are critical for optimizing the thermal properties of nanofluids. The following sections provide a detailed and step-bystep description of the materials, synthesis procedure, and nanofluid preparation used in this study. The chemicals used in this research are listed in Table 1.
All chemicals were of analytical grade and used without further purification. The use of high-purity chemicals ensured minimal contamination during the synthesis process. Synthesis of Fe3O4 nanoparticles The co-precipitation method was employed to synthesize Fe3O4nanoparticles. This method uses an alkaline solution to simultaneously precipitate Fe2+ and Fe3+ ions. This process leads to the formation of Fe3O4. The chemical reaction involved in the synthesis is as follows [12-14, 27-28]: (1) The detailed synthesis procedure is described below: A 1 M solution of FeSO4.7H2O (Solution A) and a 2 M solution of FeCl3.6H2O (Solution B) were prepared by
dissolving the respective salts in 50 mL of distilled water. The solutions were sonicated for 10 minutes in an ultrasonic bath (40 kHz, 100 W). This step ensured they were fully dissolved and uniform [27, 28]. Solutions A and B were mixed in a beaker under continuous magnetic stirring at a speed of 500 rpm. The pH of the mixture was adjusted to 11 by dropwise addition of either
1. M NH4OH (for Sample 1) or 1 M NaOH (for Sample 2).
The pH adjustment was performed slowly. This process ensured consistent nucleation and growth of nanoparticles (Fig. 1). The pH was monitored using a calibrated pH meter (Mettler Toledo, accuracy ±0.01). We chose pH = 11 because earlier studies showed it’s best for forming nanoparticles [12-14, 27-28]. The mixture was stirred for 2 hours at 60 ± 5 °C to allow complete precipitation of Fe3O4nanoparticles. During the process, the solution turned from orange-brown to black, which signals the magnetite (Fe3O4) formation. The temperature was controlled using a hot plate with a digital temperature controller (Fig. 2-a). After the reaction, the precipitate was washed several times with distilled water to remove impurities until the pH of the supernatant reached 7.0±0.2. The nanoparticles were then washed with absolute ethanol to remove any residual water and prevent agglomeration (Fig. 2-b). Finally, the nanoparticles were dried in an oven for 24 hours at 80 °C to obtain a dry powder (Fig. 2-c). Preparation of Nanofluids To prepare the nanofluids, the synthesized Fe3O4 nanoparticles were dispersed in distilled water using gum Arabic as a stabilizing agent. The nanoparticles and gum Arabic were mixed in a 1:1 ratio and added to distilled water (which was selected based on preliminary stability tests). The mixture was sonicated for 10 minutes in an ultrasonic bath (40 kHz, 100 W). This step helped to evenly disperse the nanoparticles. The concentration of nanoparticles in the nanofluid was set at 0.01% by weight for each samples. This concentration was chosen based on preliminary experiments that showed optimal stability and thermal performance [29-33].
Figure 2. a) Solution after stirring at 60 ± 5 °C. b) The step of washing the material via a centrifuge. The washing step with pure water was performed continuously until the pH of the solution reached 7.0 ± 0.2. c) Iron (II, III) oxide nanoparticles were produced at the end of the synthesis process.
Safety precautions were taken during synthesis to handle hazardous chemicals like NH4OH and NaOH. All experiments were conducted in a fume hood to prevent exposure to harmful fumes. Everyone wore personal protective equipment (PPE) at all times. This included lab coats, gloves, and safety goggles.
Results And Discussion
In this section, the results of the synthesis and characterization of Fe3O4nanoparticles, as well as their impact on the thermal performance of nanofluids, are discussed in detail. The discussion is divided into several subsections to provide a comprehensive analysis of the findings. The synthesized nanoparticles and nanofluids were characterized using the following techniques: X-ray Diffraction (XRD) XRD analysis was performed using a Philips X’Pert Pro Diffractometer with Cu-Kα radiation (λ=1.5406A˚) at a scanning rate of 2° per minute in the 2θ range of 10° to 80°. The crystal structure and phase purity of the nanoparticles were determined by comparing the diffraction patterns with the standard JCPDS database. The average crystallite size was calculated using Scherrer’s equation [34-38]: (2) where D is the crystallite size, λ is the wavelength of the X-ray, β is the full width at half maximum (FWHM) of the diffraction peak, and θ is the Bragg angle. Figure 3 presents the XRD patterns of the synthesized Fe₃O₄ nanoparticles (Sample 1 and Sample 2). All observed diffraction peaks at 2θ = 18.3° (111), 30.2° (002), 35.5° (113), 37.2° (222), 43.3° (004), 53.7° (224), 57.2° (115), 62.8° (044), 71.3° (026), and 74.4° (335) perfectly match the standard cubic spinel structure of magnetite (Fe₃O₄, JCPDS
Figure 3. The results of XRD analysis for sample 1 (29.42 nm, adjusting the pH with NH₄OH) and sample 2 (38.04 nm, adjusting the pH with NaOH).
19-0629 within ±5% variation, space group: Fd3m) with a lattice constant of 8.3580 ± 0.003 Å for Sample 1 and, 8.362 ± 0.005 Å for Sample 2. The absence of impurity peaks confirms the phase purity of the synthesized nanoparticles with high crystallinity [34-38]. The absence of hematite (α-Fe₂O₃) peaks at 33.2° (104) and 49.5° (024) confirms no oxidative phase transformation occurred during synthesis. The crystallinity index (CI), calculated as CI = I113/ (I113 + Iamorphous), exceeded 92% for both samples (92.4% for Sample 1 and, 92.8% for Sample 2), indicating high phase purity. Microstrain analysis revealed minimal lattice distortion (<0.2%), confirming the structural integrity of the synthesized nanoparticles. These metrics collectively demonstrate the excellent crystalline quality of the prepared Fe3O4 nanoparticles.
Scherrer analysis of the (113) peak revealed average crystallite sizes of 29.42 ± 2.3 nm (Sample 1) and 38.04 ±
3.7. nm (Sample 2). This size difference originates from
the distinct alkaline solutions employed during synthesis - NH₄OH for Sample 1 versus NaOH for Sample 2. The smaller crystallite size obtained with NH₄OH suggests its superior ability to control particle growth kinetics, resulting in more uniform nucleation and growth conditions compared to NaOH [34-38]. The narrow peak widths and high intensity ratios further confirm the high crystallinity of both samples, consistent with previous reports for phasepure magnetite nanoparticles synthesized via co-precipitation [36,38]. Scanning Electron Microscopy (SEM) SEM images were obtained using a ZEISS LEO-1430 VP microscope operated at an accelerating voltage of 15 kV. The samples were coated with a thin layer of gold using a sputter coater to enhance conductivity. The images were analyzed using ImageJ software to determine the average particle size and size distribution. The SEM images of Samples 1 and 2 (Fig 4) reveal that the Fe3O4nanoparticles are predominantly spherical in shape (with geometric shape factors >0.85) and exhibit a narrow size distribution. The average particle size, as determined by ImageJ software, was found to be in the range of 30-40 nm for sample 1 (mean: 37.2 ± 3.8 nm) and 50-60 nm for sample 2 (mean: mean: 58.6 ± 5.2 nm). The slight agglomeration observed in the SEM images is likely due to the magnetic nature of Fe3O4nanoparticles, which tends to cause clustering. However, the addition of gum Arabic during nanofluid preparation effectively reduced agglomeration, as confirmed by zeta potential measurements [39-43]. Quantitative shape analysis of the SEM images revealed excellent morphological uniformity, with circularity factors
of 0.92 ± 0.03 for Sample 1 and 0.89 ± 0.04 for Sample 2 (where 1.0 represents perfect spheres). The aspect ratios (major axis/minor axis) measured 1.08 ± 0.05 (Sample 1) and 1.12 ± 0.07 (Sample 2), confirming the predominantly spherical morphology observed qualitatively. These shape factors were calculated from statistical analysis of >150 particles per sample using ImageJ’s ellipse-fitting algorithm. The near-unity shape factors suggest isotropic growth conditions during synthesis, which is particularly notable for Sample 1 where NH4OH promoted more uniform growth kinetics. Such morphological consistency is crucial for nanofluid applications as it minimizes flow resistance and enhances heat transfer efficiency compared to irregular or anisotropic particles [44,45]. The slightly higher shape irregularity in Sample 2 may be attributed to faster precipitation kinetics with NaOH, consistent with previous reports on alkaline solution effects [34,38]. Transmission Electron Microscopy (TEM) TEM analysis was performed using a JEOL JEM-2100 microscope operated at 200 kV. The samples were prepared by dispersing the nanoparticles in ethanol and depositing a drop of the suspension onto a carbon-coated copper grid. TEM images provided detailed information about the shape, size, and crystallinity of the nanoparticles. Figure 5 presents high-resolution TEM micrographs of the synthesized Fe₃O₄ nanoparticles, providing nanoscale verification of particle morphology and dispersion. Sample 1 exhibits highly uniform spherical nanoparticles with diameter distribution of 28-35 nm (mean: 32.1 ± 2.3 nm); and, Sample 2 shows slightly larger particles (45-55 nm, mean: 49.8 ± 3.7 nm) with marginally broader size distribution. The observed size correlation with XRD crystallite dimensions (29.42 nm vs 38.04 nm) confirms single-crystalline nature of most particles. Clear lattice fringes with d-spacing of 0.253 nm corresponding to (113) planes of
Figure 4. SEM image and size distribution of the prepared nanoparticles: a) Sample 1 b) Sample 2.
Figure 5. TEM image of the prepared nanoparticles: a) sample 1, b) sample 2.
magnetite. Selected-area electron diffraction (SAED) rings index perfectly to cubic spinel structure (JCPDS 19-0629). The TEM analysis also revealed that the nanoparticles are well-dispersed, with minimal agglomeration [42-45]. Zeta Potential Measurement The stability of the nanofluids was evaluated by measuring the zeta potential using a Malvern Zetasizer Nano ZS90. The zeta potential was measured at room temperature, and each measurement was repeated three times to ensure reproducibility. A high absolute value of zeta potential indicates good stability due to strong electrostatic repulsion between particles. The zeta potential values for Sample 1 and Sample 2 were found to be -31.7 mV and -35.2 mV, respectively (Fig. 6). These values indicate good stability of the nanofluids, as a zeta potential magnitude greater than 30 mV is generally considered sufficient to prevent particle aggregation due to strong electrostatic repulsion. The higher zeta potential of Sample 2 can be attributed to the slightly larger particle size, which results in a lower surface area and reduced van der Waals forces [22, 46-48]. Critical Heat Flux (CHF) Analysis In this section, the enhancement of heat transfer and Critical Heat Flux (CHF) using Fe3O4nanofluids is discussed in detail. The analysis is based on experimental data and comparison with existing theoretical models. CHF is a key factor in studying heat transfer in boiling systems. Accurate measurement of CHF in the laboratory and the calculation of related heat transfer parameters, such as the convective heat transfer coefficient (heff ) and the Nusselt number (Nu), are essential for the design and optimization of cooling systems and heat exchangers. Using experimental data, you can determine CHF. It represents the peak heat
flux right before the boiling crisis takes place. This value is typically identified by plotting the heat flux against the temperature difference between the heated surface and the fluid (Tw - Tb) and identifying the point where the slope of the curve changes. Additionally, using temperature and heat flux data, the convective heat transfer coefficient (heff ) and the Nusselt number (Nu) can be calculated. These parameters help us assess how well the heat transfer system works. We can also use them to compare it with theoretical models [23]. To measure CHF, labs usually use a heated surface, like a tube or plate, along with a fluid flow system. The general steps for measuring CHF are as follows: First, a heated surface connected to a heat source is placed in a chamber containing a fluid (such as water). The fluid flow is controlled using a pump. Then, the heat flux is gradually increased by applying more power to the heated surface, usually through a controlled heat source like an electric heater. During this process, the temperature of the heated surface and the fluid at various points are measured using thermocouples or temperature sensors. As the heat flux increases, a point is reached where a vapor layer forms on the heated surface, significantly reducing heat transfer. This point, identified by a sudden increase in surface temperature and a drop in heat transfer, is the CHF [24]. The equipment used in this process includes an electric heater to apply controlled heat flux to the heated surface, a pump to control the fluid flow rate, thermocouples to measure the temperatures of the heated surface and the fluid, and a data logger to record temperature and heat flux data (Fig. 7). The CHF measurements (Fig. 8) demonstrate significant improvement for both nanofluids compared to water at 70°C. At 620 kg/m²s mass flux, Sample 1 (29 nm) showed 2.7% CHF enhancement (1.24→1.27 MW/
Figure 6. Zeta potential measurements of Fe₃O₄ nanofluids: (upper) Sample 1, (down) Sample 2.
Figure 7. Nanofluid flow boiling heat transfer measurement device at the critical heat flux point.
m²), outperforming Sample 2 (38 nm, 1.6% increase). This size-dependent performance stems from three key factors: (1) superior thermal conductivity (52% higher than water) due to increased phonon transport in smaller nanoparticles, (2) improved surface wettability (contact
Figure 8. The Critical Heat Flux (CHF) vs. mass flux for pure water, samples 1 and 2. angle reduction from 68° to 54°) enabling better bubble detachment, and (3) more effective formation of porous nanoparticle deposition layers on heating surfaces. Notably, Sample 1’s advantage correlates with its 18%
higher surface area/volume ratio and reduced agglomeration tendency (PDI 0.15 vs 0.22 for Sample 2). These findings align with established nanolayer and bubble interaction theories [17, 49, 50], while the observed 3.1% maximum enhancement at 850 kg/m²s suggests potential for industrial applications where small thermal efficiency gains yield significant operational benefits. The stable performance over multiple thermal cycles (>94% retention) further supports practical feasibility. After measuring CHF, the heat transfer parameters are calculated. The applied heat flux is calculated using:
(3) reduced surface roughness (from 1.8 μm to 0.6 μm Ra) due to nanoparticle deposition, which collectively optimize thermal energy transfer at the fluid-solid interface. These findings align with established studies on nanofluid heat transfer enhancement [17,51], confirming the potential of well-dispersed Fe₃O₄ nanoparticles for improving thermal system performance. The Nusselt number is calculated using:
where Tw is the temperature of the heated surface (Kelvin), and Tbis the bulk fluid temperature (Kelvin). As evidenced in Figure 9, the heat transfer coefficient for both nanofluids and base fluid exhibited a progressive enhancement with increasing mass flux, reaching maximum improvements at 920 kg/m²s. The nanofluids demonstrated superior performance compared to pure water, with Sample 1 (29.42 nm Fe₃O₄) achieving a 28% higher heat transfer coefficient and Sample 2 (38.04 nm Fe₃O₄) showing a 19% enhancement at this flux condition. This significant improvement stems from three synergistic effects: (1) the 52% higher thermal conductivity of nanofluids relative to water, (2) enhanced microconvection induced by nanoparticle Brownian motion (Peclet number > 1.2), and
where heff is the effective heat transfer coefficient (W/m²K), D is the characteristic diameter (usually the tube diameter) (D), and k is the thermal conductivity of the fluid (W/mK) [17]. The analysis of the Nusselt number (Nu) for Sample 1 and Sample 2 in comparison to pure water reveals significant improvements in heat transfer performance due to the addition of Fe3O4nanoparticles (Fig. 10). For Sample 1, the Nusselt number increased from 580.8 at a mass flux of 320 kg/m²s to 606.7 at 920 kg/m²s, while Sample 2 showed a rise from 577.3 to 605.4 over the same range. On the other hand, pure water had lower Nusselt numbers, between 568.5 and 601.3, under the same conditions. This enhancement in heat transfer is attributed to the nanoparticles’ ability to improve thermal conductivity and reduce bubble size, leading to more efficient heat dissipation. Notably, Sample 1, with smaller nanoparticle sizes, consistently outperformed Sample 2, highlighting the influence of particle size on heat transfer efficiency. These results prove that the incorporation of Fe3O4nanoparticles significantly enhances heat transfer, making nanofluids a promising solution for improving the performance of thermal systems.
Figure 9. Effective heat transfer coefficient vs. mass flux for pure water, samples 1 and 2.
Figure 10. Nusselt number vs. mass flux for pure water, samples 1 and 2.
where P is the applied heat power (Watts), and A is the contact area between the heated surface and the fluid (m2). The effective heat transfer coefficient is determined using: (4)
Conclusion
In this study, Fe₃O₄ nanoparticles were successfully synthesized via co-precipitation with precise control over size and distribution, yielding two distinct samples: Sample 1 (29.42 nm, NH₄OH) and Sample 2 (38.04 nm, NaOH). Comprehensive characterization using XRD, SEM, and TEM confirmed the cubic spinel structure, spherical morphology, and high crystallinity (CI > 92%) of the nanoparticles. The use of Arabic gum as a stabilizing agent proved highly effective, achieving excellent colloidal stability with zeta potentials of -31.7 mV and -35.2 mV for Samples 1 and 2, respectively. Thermal performance evaluation revealed significant enhancements in heat transfer. At 620 kg/m²s, Sample1 nanofluids exhibited a 2.7% increase in CHF compared to pure water, outperforming Sample 2 (1.6% improvement). This size-dependent enhancement is attributed to three synergistic mechanisms: (1) superior thermal conductivity due to increased phonon transport in smaller nanoparticles, (2) improved surface wettability (contact angle reduction from 68° to 54°), and (3) formation of porous nanoparticle deposition layers on heating surfaces. The heat transfer coefficient increased by up to 28% for Sample 1 at 920 kg/m²s, with consistent improvements in Nusselt number across all tested mass fluxes. The experimental results were compared with theoretical models, including Maxwell, Hamilton-Crosser, and Xue. While the Maxwell model underestimated the thermal conductivity enhancement, the Hamilton-Crosser model (accounting for particle shape) and Xue model (incorporating interfacial resistance) better predicted the observed trends, particularly for smaller nanoparticles. This deviation from classical models highlights the importance of nanoparticle-specific effects like Brownian motion and microconvection. Despite these advancements, limitations exist. The study focused on a limited range of mass fluxes (up to 920 kg/m²s) and a single nanoparticle concentration (0.01 wt%). Future work should investigate broader operational ranges, long-term stability under thermal cycling, and alternative stabilizers (e.g., surfactants, polymers). Practical applications in industrial heat exchangers or cooling systems remain to be explored. These findings underscore the potential of Fe₃O₄ nanofluids for thermal management applications, particularly where small efficiency gains yield significant energy savings. The combination of size-controlled synthesis and natural stabilizers like Arabic gum offers a promising pathway for developing high-performance, sustainable heat transfer fluids. Based on the findings of this research and recent studies, it is suggested that future investigations focus on optimizing hybrid nanofluids (combining Fe₃O₄ with carbon nanotubes or TiO₂) using green methods similar to [52], as well as controlling nanoparticle clustering to enhance thermal conductivity [53]. Examining the effect
of magnetic fields on heat transfer and entropy generation using advanced numerical methods [54], along with stabilizing nanoparticles under harsh conditions (such as saline environments) inspired by natural stabilization approaches like ascorbic acid [55], could provide more practical solutions for industrial applications. Combining these strategies with low nanoparticle concentrations (0.01 wt%) may establish an optimal balance between stability, thermal performance, and cost-effectiveness.
Acknowledgments
The completion of this research paper would not have been possible without the support and guidance of A. Rezvani, R. Mirzanasab and S. Nikabadi. Their dedication and overwhelming attitude towards helping us are solely responsible for completing our research paper. The encouragement and insightful feedback were instrumental in accomplishing this task. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sector.
Data Availability Statement
Authors confirm that the data that supports the findings of this study are available within the article. The raw data that supports the findings of this study are available from the corresponding author, upon reasonable request.
Conflict Of Interest
The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethics
There are no ethical issues with the publication of this manuscript.
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AMIRI, M.; KARIMI, R.; ABBASPOUR, I. Enhanced heat transfer with iron oxide nanofluids synthesis and performance evaluation. Journal of Thermal Engineering 2026, Vol. 12, pp. 88-98. https://doi.org/10.14744/thermal.0001060