Factors affecting the pH and electrical conductivity of MgO–ethylene glycol nanofluids

Adio, Saheed Adewale; Sharifpur, Mohsen; Meyer, Josua P.

doi:10.1007/s12034-015-1020-y

Cited by 40 publications

(23 citation statements)

References 61 publications

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“…As can be observed from this table, the electrical conductivity values strongly deviated from reference values. The reason for these discrepancies are unclear but the differences in purity levels or ionic species could be a probable cause [13]. Figure 2.…”

Section: Resultsmentioning

confidence: 99%

Evaluation on Thermo-Electrical Characteristics of Sio2 Nanofluids for Fuel Cell Cooling Application in Automobiles

Akilu¹,

Baheta²,

Sharma³

2018

IJET

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Effective thermal management is critical requirement in fuel cell technologies to avoid the performance degradation during operation. Nanofluids offer the potential to address this cooling challenge in fuel cells better than pure fluids. However, due to the electrochemical changes associated with the proton exchange membrane system, a strict limit on thermal and electrical properties of coolant needs to comply. In this study, the thermal and electrical conductivities of silicon dioxide (SiO2) dispersion of ethylene glycol (EG), glycerol (G), and 40:60 by mass ethylene glycol-glycerol (EG/G) was investigated experimentally. Measurements were carried for particle volume concentrations of 0.25–2.0% at a temperature of 30 °C. The thermal and electrical conductivity of the nanofluids significantly increases with SiO2 loading. Maximum enhancements of ∼4.0% and ∼198% at a volume concentration of 2.0% were obtained with SiO2-EG/G, respectively. Further, analysis of the results reveals that SiO2/G exhibited the greatest thermo-electrical performance, followed by SiO2-EG/G and EG. Therefore, SiO2-EG/G nanofluid is best-suited coolant for PEM fuel cell thermal applications.

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Section: Resultsmentioning

confidence: 99%

Evaluation on Thermo-Electrical Characteristics of Sio2 Nanofluids for Fuel Cell Cooling Application in Automobiles

Akilu¹,

Baheta²,

Sharma³

2018

IJET

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“…Various investigations have been presented for the sake of identifying the dynamics of nanofluids heat transfer [2][3][4][5][6][7][8][9][10] and thermal properties like specific heat capacity [11], thermal diffusivity [12], density [13]. Adio et al [14] studied factors affecting the electrical conductivity of magnesium oxide-ethylene glycol (MgO-EG) nanofluid. The effects of temperature and volume fraction were investigated.…”

Section: Introductionmentioning

confidence: 99%

Measurement and Artificial Neural Network Modeling of Electrical Conductivity of CuO/Glycerol Nanofluids at Various Thermal and Concentration Conditions

et al. 2018

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Abstract:In this work, the electrical conductivity of CuO/glycerol nanofluid was measured at a temperature range of 20-60 • C, volume fraction of 0.1-1.5% and nanoparticle size of 20-60 nm. The experimental data were predicted by the perceptron neural network. The results showed that the electrical conductivity increases with temperature, especially in higher volume fractions. These results are attributed to the accumulation of nanoparticles in the presence of the field and their Brownian motion at different temperatures and the reduction of electrical conductivity at higher nanoparticle sizes is attributed to the decreased mobility of nanoparticles as load carriers as well as to their decrease in volume unit per constant volume fraction. The results revealed that sonication time up to 70 min increases the nanofluid stability, while further increase in the sonication time decreases the nanofluid stability. In the modeling, input data to perceptron artificial neural network are nanofluid temperature, nanoparticle size, sonication time and volume fraction and electrical conductivity is considered as output. The results obtained from self-organizing map (SOM) showed that the winner neuron which has the most data is neuron 31. The values of the correlation coefficient (R 2 ), the mean of squared errors (MSE) and maximum error(e max ) used to evaluate the perceptron artificial neural network with 2 hidden layers and 31 neurons are 1, 2.3542 × 10 −17 and 0 respectively, indicating the high accuracy of the network.

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“…Other properties of this material, which are extensively studied, are the pH and electrical conductivity. Adio et al [40] present results of experimental investigation into the pH and electrical conductivity of MgOethylene glycol nanofluids containing nanoparticles with various diameters, and in other paper, they presented factors affecting this properties [41]. Thermal transport properties of MgO-EG nanofluids was the object of study to Yu et al [42].…”

Section: Introductionmentioning

confidence: 99%

Viscosity and thermal conductivity of MgO–EG nanofluids

Żyła

2017

J Therm Anal Calorim

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Nanofluids are a group of novel engineering materials that are increasingly being used, particularly in the processes of heat exchange. One of the most promising materials in this group is magnesium oxide-ethylene glycol (MgO-EG) nanofluid. The literature informs that this material is characterized by an significant increase in thermal conductivity with low dynamic viscosity increase. The aim of this paper is to provide experimental data on the dynamic viscosity and thermal conductivity of nanofluids containing MgO nanoparticles with 20 nm average size and ethylene glycol as base fluid. To determine dynamic viscosity and thermal conductivity of samples, a HAAKE MARS 2 rheometer (Thermo Electron Corporation, Karlsruhe, Germany) and KD2 Pro Thermal Properties Analyzer (Decagon Devices Inc., Pullman, Washington, USA) were used. Additionally, a comparison of the experimental results and the predictions of theoretical models was presented. It was presented that the vast majority of theoretical models does not describe in a correct way both viscosity and thermal conductivity. It was also shown that the enhancement of this basic physical properties might be described with good result with second degree polynomials. Finally, evaluation of the heat transfer performance was presented.

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Factors affecting the pH and electrical conductivity of MgO–ethylene glycol nanofluids

Cited by 40 publications

References 61 publications

Evaluation on Thermo-Electrical Characteristics of Sio2 Nanofluids for Fuel Cell Cooling Application in Automobiles

Evaluation on Thermo-Electrical Characteristics of Sio2 Nanofluids for Fuel Cell Cooling Application in Automobiles

Measurement and Artificial Neural Network Modeling of Electrical Conductivity of CuO/Glycerol Nanofluids at Various Thermal and Concentration Conditions

Viscosity and thermal conductivity of MgO–EG nanofluids

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