Prediction Models of Saturated Vapor Pressure, Saturated Density, Surface Tension, Viscosity and Thermal Conductivity of Electronic Fluoride Liquids in Two-Phase Liquid Immersion Cooling Systems: A Comprehensive Review
Round 1
Reviewer 1 Report
Comments and suggestions to the authors:
Comment 1. Font size. Please make the corrections to different font size
Comment 2. Equations #. Please review the numbering of equations
Comment 3. English redaction. Please ask a native English expertise to check the redaction of the document.
Comment 3. References. Use of the DOI is highly encouraged
Comments and suggestions to the authors:
Comment 1. Font size. Please make the corrections to different font size
Comment 2. Equations #. Please review the numbering of equations
Comment 3. English redaction. Please ask a native English expertise to check the redaction of the document.
Comment 3. References. Use of the DOI is highly encouraged
Comments for author File: 
Comments.pdf
Author Response
Comment 1. Font size. Please make the corrections to different font size.
Comment 2. Equations #. Please review the numbering of equations.
Comment 3. English redaction. Please ask a native English expertise to check the redaction of the document.
Comment 4. References. Use of the DOI is highly encouraged.
√Thank you very much for your comments.
All the comments have been accepted and revised in the revised manuscript.
Please see the attachment.
Author Response File: 
Author Response.docx
Reviewer 2 Report
Reviewer’s Comments:
The manuscript “Prediction Models of Thermophysical Properties of Electronic Fluoride Liquids in Two-phase Liquid Immersion Cooling Systems: A Comprehensive Review” is a very interesting work. In this work, As the carrier of massive data, data centers are needed constantly to process and calculate all kinds of information from various fields, and they have become an important infrastructure for the convenience of human life. In recent years, the data center market has been thriving in China. However, the problems of high power consumption and poor heat dissipation are also being faced. One of the most effective measures to solve these problems is to adapt a two-phase liquid immersion cooling technology, which is a more energy-saving and more efficient method than the traditional cooling methods, the reason is mainly that in the two-phase liquid immersion cooling technology, the heat transfer caused by the phase change of liquid coolants (electronic fluoride liquids) helps to cool and improve the temperature uniformity of electronic components. But the requirements for the electronic fluoride liquids used in two-phase liquid immersion cooling systems are strict. The thermophysical properties (saturated vapor pressure, density, surface tension, viscosity, thermal conductivity and latent heat of vaporization, etc.) of the liquid coolants play a very key role in the heat dissipation capacity in the two-phase liquid immersion cooling systems. While I believe this topic is of great interest to our readers, I think it needs major revision before it is ready for publication. So, I recommend this manuscript for publication with major revisions.
1. In this manuscript, the authors did not explain the importance of the Thermophysical Properties in the introduction part. The authors should explain the importance of Thermophysical Properties.
2) Title: The title of the manuscript is not impressive. It should be modified or rewritten it.
3) Correct the following statement “Thus, the main aim of this work is to review and analyze the accuracy and practicality of the prediction models of the main thermophysical properties based on three common used electronic fluoride liquids in the two-phase liquid immersion cooling systems (FC-72, Novec 649 and HFE-7100) and to recommend suitable prediction models for different thermophysical properties. This work will provide a valuable reference for the actual engineering application”.
4) Keywords: The Thermophysical Properties is missing in the keywords. So, modify the keywords.
5) Introduction part is not impressive. The references cited are very old. So, Improve it with some latest literature like 10.3390/pr10081455, 10.1016/j.arabjc.2022.103937
6) The authors should explain the following statement with recent references, “However, the critical compressibility factor of the cubic equation of state is a fixed value, which deviates from the real value of a fluid and leads to a large deviation of the calculated results of the saturated liquid density. It is a great limitation for the PR equation inevitably”.
7) Add space between magnitude and unit. For example, in synthesis “21.96g” should be 21.96 g. Make the corrections throughout the manuscript regarding values and units.
8) The author should provide reason about this statement “In the
existing literatures, the correlations of the cognate organics are only conducted by the data
of methane series, ethane series and propane series halogenated materials, which are inapplicable for the electronic fluoride liquids studied in this paper”.
9. Comparison of the present results with other similar findings in the literature should be discussed in more detail. This is necessary in order to place this work together with other work in the field and to give more credibility to the present results.
10) Conclusion part is very long. Make it brief and improve by adding the results of your studies.
11) There are many grammatic mistakes. Improve the English grammar of the manuscript.
Author Response
Q1: In this manuscript, the authors did not explain the importance of the Thermophysical Properties in the introduction part. The authors should explain the importance of Thermophysical Properties.
√Thank you very much for your comments.
The importance of the Thermophysical Properties has been explained in the second paragraph for bottom of section 1 in the revised manuscript. The specific contents are as follows:
For the marketization of the two-phase liquid immersion cooling systems, the screening of liquid coolants is one of the most important technologies. The coolants used in the liquid immersion cooling systems are in direct contact with the electronic components, so they must have good compatibility with the materials of all components under long-term operation, excellent electrical insulation to ensure the safety of the electronics, a low dielectric constant and dielectric loss factor to avoid signal interference. At the same time, the coolants must be environmentally friendly, non-toxic, non-flammable. Because the generation and evolution of bubbles is essential for the two-phase immersion cooling technology, the related thermodynamic properties have great impact on the heat dissipation capacity of the cooling technology, including boiling point, thermal conductivity, viscosity, surface tension and latent heat of vaporization, etc. For example, the greater the thermal conductivity of the coolant is, the smaller the heat loss (thermal resistance) along the heat transfer direction is, which is the more conducive to the boiling heat transfer. The surface tension greatly affects the size of the boiling bubble and the wettability of the coolant to the surface of the heat source. According to the bubble dynamics [43, 44], a smaller surface tension always means a smaller diameter of the separation bubbles, and the shorter the time from generating to detachment from the heat surface of the bubbles is, the faster the surrounding liquid can wet the heat surface, and the more intense the boiling heat transfer is. The viscosity of the coolant is also an important property that greatly affects the flow the coolant and the movement of the bubbles. Generally, a lower viscosity is more conducive to reducing the resistance of the flow of coolants and the movement of bubbles, which is more conducive to the boiling heat transfer. The higher the latent heat of vaporization is, the greater the ability for a given mass of coolant to taking away the heat generated by the electronic devices is. Therefore, to determine whether a coolant is suitable for a two-phase immersion cooling system, it is essential to study the key thermodynamic properties first.
Q2: The title of the manuscript is not impressive. It should be modified or rewritten it.
√Thank you very much for your comments.
The title of the manuscript has been changed to ‘Prediction Models of saturated vapor pressure, saturated density, surface tension, viscosity and thermal conductivity of Electronic Fluoride Liquids in Two-phase Liquid Immersion Cooling Systems: A Comprehensive Review’.
Q3: Correct the following statement “Thus, the main aim of this work is to review and analyze the accuracy and practicality of the prediction models of the main thermophysical properties based on three common used electronic fluoride liquids in the two-phase liquid immersion cooling systems (FC-72, Novec 649 and HFE-7100) and to recommend suitable prediction models for different thermophysical properties. This work will provide a valuable reference for the actual engineering application”.
√Thank you very much for your comments.
This statement has been revised in the abstract of the revised manuscript. The specific contents are as follows:
Thus, the prediction models of key thermophysical properties (saturated vapor pressure, saturation density, surface tension, viscosity, and thermal conductivity) are reviewed, and the accuracy and practicality of these prediction models in predicting the thermophysical properties of electronic fluoride liquids (FC-72, HFE-7100 and Novec 649) are evaluated. This work will provide a valuable reference for the actual engineering application.
Q4: Keywords: The Thermophysical Properties is missing in the keywords. So, modify the keywords.
√Thank you very much for your comments.
‘Thermophysical Properties’ has been listed in Keywords. The specific contents of the Keywords are as follows: ‘two-phase liquid immersion cooling; electronic fluoride liquid; thermophysical properties; prediction model; thermal management’.
Q5: Introduction part is not impressive. The references cited are very old. So, Improve it with some latest literature like 10.3390/pr10081455, 10.1016/j.arabjc.2022.103937
√Thank you very much for your comments.
The Introduction part has been revised in the revised manuscript. The specific contents are as follows:
- Introduction
As the world develops under the COVID-19 pandemic in recent years, all walks of life are accelerating their digital transformations, accompanied by a rapid development of information technologies such as the Internet of things, cloud computing and big data. Due to work-from-home economy, the cloud services are constantly soaring in the IT industry, especially in the hyper-scale enterprises. Statistically, the number of global Internet users is expected to increase from 3.7 billion in 2018 to 5 billion in 2025 [1], which means that the modern society is entering the era of digital economy. And the global digitalization market size is expected to grow from USD 594.5 billion in 2022 to USD 1548.9 billion by 2027, at a CAGR (compound annual growth rate) of 21.1% according to MarketsandMakets [2]. And an explosively developing technology——ChatGPT may also greatly promote the digitalization in the future. However, the urgency and inevitability of digital transformation require massive data to be processed, which creates many difficulties in the development of powerful chips, system design, thermal management, etc. Since it was launched a few months ago, ChatGPT has received more than billions of computing requests so that OpenAI has been forced to introduce queuing systems and other traffic shaping measures [3], which means that its ability to process data is still limited, even though a high-performance network cluster with more than 10,000 GPUs has been adopted [4, 5]. Thus, to improve the capacity and efficiency of processing data is a key project for the development of data centers, which are the main supporter of the massive data. However, a larger amount of data always means bigger data centers, and the power consumption is also greater. For most data centers, the cooling system accounts for the largest proportion of the power consumption. Thus, a good thermal management is an important means to maintain the safe and reliable operation of data centers.
Under the background, the thermal management of data centers faces the dual challenges of "quality" and "quantity". From the challenge of "quality", with the massive growth in the scale of data generated through cloud computing, data centers need to use higher-performance processors to cope with the greater computing power demands. Driven by innovative achievements such as 3D integration and heterogeneous integration [6], the miniaturization and structure complexity of chip components are the inevitable trends, which will lead to a continuous increase in the heat flux of the chips. How to effectively and stably control the chip temperature under the extremely high heat flux is a huge challenge for the thermal management of the chips in data centers. From the challenge of "quantity", according to Forbes' report, the total power consumption of data centers reached 416 tWh in 2017, accounting for 3% of the world's total power consumption [7]. Some studies predict [8] that the total power demand of ICT (information and communication technology) will account for 20.9% of global power consumption in 2030, and the power consumption of data centers will account for 1/3 of ICT, that is, 7% ~ 8% of global power consumption. And nearly 40% of the total energy consumption of data centers is used by cooling systems [9]. How to reduce the energy consumption of the cooling system and improve the power usage effectiveness (PUE) is another major challenge for data centers. Hence, it can be implied that most of the IT enterprises are expected to have energy-efficient data center solutions, and liquid immersion cooling technology has been regarded as a promising solution for the thermal management of data centers, due to its relatively simple mechanisms, low risks of hotspots, high heat dissipation efficiency, noise-less well temperature uniformity, free noise and dust pollution, great integration and high reliability, etc. [10-19].
The development of liquid immersion cooling technology for electronic devices has roughly experienced the exploration period (before 1992), the research period (1992-2016), and the small-scale application period (2016-present) [20-42], shown in Fig. 1. According to MarketsandMarkets, the market is expected to grow from $244 million in 2021 to $1710 million by 2030, at a GACR of 24.2% from 2022 to 2030 [25]. And many companies such as Huawei, Lenovo, Inspur, Facebook, Microsoft, etc. have developed immersion liquid-cooled high-density servers. Before the liquid immersion cooling technology steps forward to overall large-scale deployments, there are lots of key technologies to be solved, for example, product development, formulation of unified standards, screening of liquid coolants, etc. [38].
According to whether the liquid coolants undergo a phase change, the liquid immersion cooling can be divided into single-phase liquid immersion cooling and two-phase liquid immersion cooling. Although single-phase systems are more attractive for some enterprises than two-phase systems by considering the difficulty of system design and the balance between the cost and benefits [39], the two-phase liquid immersion cooling systems receive more and more attention with the rapid increase of heat fluxes of power devices, and it is urgent to overcome the key technologies and develop an efficient two-phase immersion cooling system due to the growing heat dissipation demand. For the marketization of the two-phase liquid immersion cooling systems, the screening of liquid coolants is one of the most important technologies. The coolants used in the liquid immersion cooling systems are in direct contact with the electronic components, so they must have good compatibility with the materials of all components under long-term operation, excellent electrical insulation to ensure the safety of the electronics, a low dielectric constant and dielectric loss factor to avoid signal interference. At the same time, the coolants must be environmentally friendly, non-toxic, non-flammable. Because the generation and evolution of bubbles is essential for the two-phase immersion cooling technology, the related thermodynamic properties have great impact on the heat dissipation capacity of the cooling technology, including boiling point, thermal conductivity, viscosity, surface tension and latent heat of vaporization, etc. For example, the greater the thermal conductivity of the coolant is, the smaller the heat loss (thermal resistance) along the heat transfer direction is, which is the more conducive to the boiling heat transfer. The surface tension greatly affects the size of the boiling bubble and the wettability of the coolant to the surface of the heat source. According to the bubble dynamics [43, 44], a smaller surface tension always means a smaller diameter of the separation bubbles, and the shorter the time from generating to detachment from the heat surface of the bubbles is, the faster the surrounding liquid can wet the heat surface, and the more intense the boiling heat transfer is. The viscosity of the coolant is also an important property that greatly affects the flow the coolant and the movement of the bubbles. Generally, a lower viscosity is more conducive to reducing the resistance of the flow of coolants and the movement of bubbles, which is more conducive to the boiling heat transfer. The higher the latent heat of vaporization is, the greater the ability for a given mass of coolant to taking away the heat generated by the electronic devices is. Therefore, to determine whether a coolant is suitable for a two-phase immersion cooling system, it is essential to study the key thermodynamic properties first.
However, for a new coolant (or a new electronic fluoride liquid), it is always difficult and quite costly to obtain the above-mentioned properties comprehensively by experiment. The reasonable prediction models are expected for the preliminary screening of the coolants. Based on this, the aim of this work is that the key thermophysical properties closely related to the two-phase immersion cooling technology are summarized according to the basic boiling and condensation processes, several prediction models of the above-mentioned thermophysical properties (saturated vapor pressure, saturation density, surface tension, viscosity, and thermal conductivity) are discussed, and the availability of these prediction models for predicting the thermophysical properties of electronic fluoride liquids are evaluated by means of FC-72, HFE-7100 and Novec 649.
Q6: The authors should explain the following statement with recent references, “However, the critical compressibility factor of the cubic equation of state is a fixed value, which deviates from the real value of a fluid and leads to a large deviation of the calculated results of the saturated liquid density. It is a great limitation for the PR equation inevitably”.
√Thank you very much for your comments.
The explanation of this statement has been added into the third paragraph of section 3.2.1.1 in the revised manuscript. The specific contents are as follows:
However, according to the constants of the two-parameter cubic equation of state determined by the critical condition, only a fixed critical compressibility factor can be obtained. For PR equation, the critical compressibility factor is 0.3074. While the actual critical compressibility factor of fluids mostly varies between 0.21 and 0.31 [88], which may lead to a large deviation of the calculated results of the saturated liquid density. It is a great limitation for the PR equation inevitably.
Q7: Add space between magnitude and unit. For example, in synthesis “21.96g” should be 21.96 g. Make the corrections throughout the manuscript regarding values and units.
√Thank you very much for your comments.
This comment has been accepted and revised in the revised manuscript.
Q8: The author should provide reason about this statement “In the existing literatures, the correlations of the cognate organics are only conducted by the data of methane series, ethane series and propane series halogenated materials, which are inapplicable for the electronic fluoride liquids studied in this paper”.
√Thank you very much for your comments.
The explanation of this statement has been added into the third paragraph of section 3.2.1.1 in the revised manuscript. The specific contents are as follows:
The prediction models of the thermal conductivity of organics, are classified into five categories by Govender et al. [113]: ① the group contribution method; ② empirical formulas based on the basic thermophysical properties; ③ corresponding states principle; ④ correlations of the data of the cognate organics; ⑤ Molecular dynamics model. The models based on the corresponding states principle have been described above. The correlations of the cognate organics also can be called family methods, whose formulas contain parameters that vary moving from family to family [114], and in the existing literatures, the correlations of the cognate organics are only conducted by the data of methane series, ethane series and propane series halogenated materials, but the electronic fluoride liquids studied in this paper are belonging to fully or almost fully-halogenated hydrocarbon with more than five carbons, which means the existing correlations of the cognate organics are inapplicable for the electronic fluoride liquids.
Q9: Comparison of the present results with other similar findings in the literature should be discussed in more detail. This is necessary in order to place this work together with other work in the field and to give more credibility to the present results.
√Thank you very much for your comments.
The comparisons of the present results with other similar findings in literatures have been improved to give more credibility to the present results in the revised manuscript. The specific contents are as follows:
3.2.3. Comparison and discussion for different models of saturated vapor pressures:
It can be found from Figs. 2-4 and Table 5 that for the three electronic fluoride liquids, the MRDs between the predicted and reference values appear at low temperature for the PR, MAPR1, MAPR2 and MPPR models. The reason is mainly that the saturated vapor pressures at lower temperatures are small, which leads to large relative deviations even if the absolute deviations are less than 0.5 kPa. For all the three electronic fluoride liquids, the ARDs between the predicted and reference values for the PR model and its modified forms are all within 1.22%, which are lower than those obtained by PT model and NEOS model. But when the saturated vapor pressures of the three electronic fluoride liquids are predicted by the original PR equation below 300 K, the relative deviations between the predicted and reference values are more than 1%, even more than 5%, and the predictive accuracy is improved at low temperatures by using its modified forms (MAPR1, MAPR2, and MPPR). The MAPR2 model has the highest accuracy, while the MAPR1 model has the lowest accuracy. The MRDs between the reference [60, 66, 73] and predicted values when using the PT equation are smaller than the MRDs when using the PR equation, but its ARDs are higher than the ARDs when using the PR equation. Compared with other models, the NEOS model fails to show any advantages in most cases. Combined with the definition of NEOS model by Nasrifar et al. [97], some parameters (CE4，CE5，CE6) are determined by certain substances, where the fully or almost fully-halogenated hydrocarbon with more than 5 carbons are not involved, and this may be a cause of its low accuracy in predicting the saturation vapor pressure of the electronic fluoride liquids compared with other models.
3.2.4. Comparison and discussion for different models of saturated liquid density:
From Figs. 5-7, it can be known that compared with the PR model, the modification of α function (MAPR1 model and MAPR2 model) cannot significantly improve the predictive accuracy of the saturated liquid densities, exactly as some scholars proposed that the modification of the α function was more beneficial for predicting the saturated vapor pressure, but for the liquid density, this modification didn't seem to work [89-92]. The specific volume translation modification method of the PR equation has the direct impact on the prediction of the liquid densities. Except for the VTPR3 model, the VTPR1 and VTPR2 models have higher predictive accuracy than the PR model, and the VTPR1 model has the highest accuracy. In most cases, the PT model has a poor accuracy when the saturated liquid densities are predicted. And for some models, good predictability of the saturated vapor pressures does not mean good predictability of the saturated liquid densities. For instance, the MPPR model has a high accuracy ranking only second to the MAPR2 model for the saturated vapor pressures, while its accuracy is really low for the saturated liquid densities. And these results also can be summarized by Haghtalab et al. which shows a low accuracy of the PT model [98] and the MPPR model [94] when the liquid densities are predicted, especially for some fluorinated compounds, such as [-(CF2)4-]. The predictive accuracy of the NEOS model is better than that of the PR equation for FC-72 and Novec 649 for the saturated liquid densities, but its relative deviations between the predicted and reference values are the largest in all the models in most cases when the saturated vapor pressures are predicted. Therefore, in consideration of versatility, the MPPR and NEOS models are not recommended.
3.3.2. Comparison and discussion for different models of surface tension:
As Gharagheizi et al. [101] found that for most studied compounds, the GST model had the lowest average relative deviations compared with the other two investigated models (Brock et al. [99] and Pitzer et al. [100], named the PST model in this paper) when predicting the surface tension, the results showed in Table 8 also indicate that the GST model has the best universality in all the models, and it has a good accuracy for the electronic fluoride liquids.
3.4.2.1. Discussion for the CSP models:
From the Figs. 11-12, it can be found that when the viscosities and the thermal conductivities of the electronic fluoride liquids are predicted, the relative deviations between the predicted values and the reference values are high in most conditions. Given these results, the empirical correction factor ψ for the viscosity and χ for the thermal conductivity are introduced to further modify the conformal density [107], as shown in equations (88) ~ (89), which will greatly improve the predictive accuracies of the viscosity and thermal conductivity. However, these two new parameters are fitted by a great number of experimental data, and it may make the work more difficulty and may be contrary to the goal of this work to obtain the properties by the prediction models directly. Therefore, for the viscosity and thermal conductivity, the CSP model is not an ideal prediction model, but its predictive values still have some reference significance.
3.4.2.2. Comparison and discussion for different models of viscosity:
In general, all the prediction models described above are facing the problems of the inaccurate contribution value of the fluorine-containing functional groups. Except for the above models, a prediction model based on another group contribution method proposed by Velzen et al. [120] also shows the same result (not listed in this paper because of its high deviation). And according to the studies in this work, there is no prediction model of the viscosity with high precision and strong universal.
Q10: Conclusion part is very long. Make it brief and improve by adding the results of your studies.
√Thank you very much for your comments.
The Conclusion part has been improved in the revised manuscript. The specific contents are as follows:
Finding a new electronic fluoride liquid that is suitable for the two-phase liquid immersion cooling technology is challenging. Preliminary analysis of the key thermodynamic properties through the prediction models can provide a good guide. According to the above discussion in this paper, it can be obtained that:
(1) For the saturated vapor pressure, the accuracy of the MAPR2 model is best in all models, while for the saturated liquid density, the VTPR2 model has the highest accuracy. Therefore, it is recommended to combine the MAPR2 model and the VTPR2 model to predict the saturated vapor pressure and the saturated density for a coolant.
(2) For the surface tension, the GST model has a good accuracy for most electronic fluoride liquids, and different models are suitable for the fluids with different polarity ranges, that’s why the best prediction models for the three coolants are not the same.
(3) For the viscosity and thermal conductivity, there are no prediction models with strong versatility and high accuracy in this work. The effect of the fluorine-containing functional groups on the predictive accuracy of viscosity and thermal conductivity for electronic fluoride liquids need to be further developed.
Q11: There are many grammatic mistakes. Improve the English grammar of the manuscript.
√Thank you very much for your comments.
The English grammar of the manuscript has been improved in the revised manuscript.
Author Response File: Author Response.docx
Reviewer 3 Report
This review paper is fully researched and detailed, and is recommended for publication. Suggested questions are as follows：
1、Are the test data used representative when comparing different fitting formulas? After all, different test data have different uncertainties due to different test methods. It is suggested to supplement the uncertainty of the selected test data.
2、Some fitting formulas are not accurate enough. Can the authors come up with a better fitting formula?
3、The label of the formula is wrong. There are two formulas label (1). Labels in Section 3.2.1.1 are incorrect.
Author Response
Q1: Are the test data used representative when comparing different fitting formulas? After all, different test data have different uncertainties due to different test methods. It is suggested to supplement the uncertainty of the selected test data.
√Thank you very much for your comments.
The uncertainty of the selected test data has been added in the revised manuscript. The specific contents are as follows:
The experimental data of the saturated vapor pressures for FC-72, Novec 649 and HFE-7100 are taken from literatures [60, 66, 73], and the combined uncertainties in measurements (level of confidence = 0.95, coverage factor = 2) of Novec 649 and HFE-7100 are 1.21% [60], 0.82% [66], respectively, the uncertainty in measurements of FC-72 is not mentioned in the literatures [73].
The experimental data of the saturated liquid densities are taken from the literatures [60, 66, 71], and the combined uncertainties in measurements (level of confidence = 0.95, coverage factor = 2) of FC-72, Novec 649 and HFE-7100 are 0.029% [60], 0.0075% [66], 0.02% [71], respectively.
The surface tensions of FC-72, Novec 649 and HFE-7100 are predicted, respectively, by using the above six prediction models (shown in Table 7), and the relative deviations between the predicted results and the reference values from literatures [62, 70, 71] are shown in Figs. 8-10. The combined expanded uncertainties in measurements (level of confidence = 0.95, coverage factor = 2) of FC-72, Novec 649 and HFE-7100 are 0.45% [62], 1.5% [70], 1.5% [71], respectively.
The combined expanded uncertainties in viscosity measurements (level of confidence = 0.95, coverage factor = 2) of FC-72, Novec 649 and HFE-7100 are 0.8% [64], 3.0% [70], 2.0% [74], respectively. And the combined expanded uncertainties in thermal conductivity measurements (level of confidence = 0.95, coverage factor = 2) of Novec 649 and HFE-7100 are 3.0% [78], 2.0% [72], respectively.
Q2: Some fitting formulas are not accurate enough. Can the authors come up with a better fitting formula?
√Thank you very much for your comments.
The main aim of this work is to give a guide to screen a new coolant which can be used in the two-phase immersion cooling systems through reviewing and evaluating the existing prediction models. The existing prediction models are obtained based on the thermodynamic properties of dozens or even hundreds of working substances, but for the two-phase immersion cooling technology, which is a relatively new thermal management solution, the available coolants are few, not to mention that some thermodynamic properties of some coolants are not publicly available. Thus, a better fitting form which is appropriate for the coolants (electronic fluoride liquids) is lacking in data support. That comes to the significance of this article which is to give a reference in preliminary judging whether a new coolant can be used in the two-phase immersion cooling technology through predicting the thermodynamic properties of the coolant. And after the electronic fluoride liquids used in the two-phase immersion cooling technology is vigorously developed in the future, it can be further considered to come up with a better fitting form based on a sufficient data support.
Q3: The label of the formula is wrong. There are two formulas label (1). Labels in Section 3.2.1.1 are incorrect.
√Thank you very much for your comments.
The English grammar of the manuscript has been improved in the revised manuscript.
Author Response File: Author Response.docx
