Immersion refrigerant

Abstract

An immersion refrigerant of the present invention in which an apparatus is immersed may have a viscosity of 3.46 cP or greater and 6.76 cP or less, a dielectric constant of 3 or greater and less than 4, a boiling point of 300°C or greater and 320°C or less, a decomposition temperature of 150°C or greater and 200°C or less, a flash point of greater than 140°C and less than 150°C, a vapor pressure of 0.000000045 atm or greater and 0.00000032 atm or less, and a freezing point of less than -40°C, and may be free of an aromatic ring, sulfur (S), and fluorine (F).

Description

Liquid immersion refrigerant

[0001] The present invention relates to an immersion refrigerant, and more specifically, to an immersion refrigerant used to cool devices that emit a large amount of heat, such as data centers, by immersing them in a liquid refrigerant.

[0002] A data center refers to a series of structures equipped with servers, including computers, communication equipment, and storage, as a series of facilities that provide server computers and network lines.

[0003] With the development of the IT industry, public institutions and various companies are establishing large-scale IT infrastructure to provide various IT services. For instance, servers are installed to support file management, data storage, and program operation, or to share hardware resources such as fax machines, printers, and various electronic devices, providing diverse services to multiple clients connected to the servers. In this process, servers establish networks by connecting computers and manage large amounts of storage space.

[0004] Large corporations began to own large-scale facilities known as Internet Data Centers (IDCs), while smaller companies outsourced the storage and management of their equipment to specialized firms to reduce costs. Because Internet Data Centers manage companies' internet equipment (servers) on their behalf, they are also referred to as server hotels or rental server apartments.

[0005] Recently, as part of the Fourth Industrial Revolution, the use of data related to AI, 5G, big data, cloud computing, and autonomous driving has increased rapidly, leading to a growing demand for the construction of data centers. Since the functionality of these data centers is paralyzed if the power supply is interrupted even for a moment, reliability and sustainability are important design factors, such as being equipped with backup power supply devices and backup data communication equipment.

[0006] In other words, stable power supply, internet connectivity, and security are crucial for data centers to provide uninterrupted services. In particular, since the server computers installed in data centers generate a significant amount of heat, they must be cooled and maintained within a specific temperature range to ensure stable service delivery.

[0007] Regarding conventional air conditioning systems for internet data centers, Korean Published Patent No. 10-2011-0129514, titled "Internet Data Center Air Conditioning System Realizing a Green Computing Environment," has been disclosed. This system comprises an air conditioner for cooling and ventilation to maintain the indoor temperature of the internet data center, an air conditioner control device for controlling the operation of the air conditioner, a temperature sensor that detects indoor and outdoor (ground floor and basement floor) temperatures and provides such information to the air conditioner control device, a cooling duct for cooling within the internet data center from the air conditioner, and a partition equipped with a ventilation duct and a connected ventilation opening for efficiently discharging heat generated from racks equipped with servers and network devices to the outside.

[0008] These conventional air-cooled cooling systems in general data centers have a problem in that they contribute to global warming by absorbing heat emitted from server computers and releasing it into the atmosphere. Furthermore, in line with global trends, securing sustainable growth through IT has become an important mission for companies. Recently, with the expansion of the cloud computing market, computing environments have become highly integrated and a large number of servers are in operation, making the rapidly increasing energy consumption of data centers a social issue.

[0009] Research on immersion cooling systems is being conducted to address the shortcomings of conventional air-cooling methods in data centers. An immersion cooling system refers to a method in which devices, such as servers that generate large amounts of high-temperature heat, are submerged in a refrigerant to release the heat from the servers. Previously, phase-change materials capable of phase transformation, such as water and paraffin wax, were used as refrigerants; however, to improve cooling efficiency, immersion refrigerants containing fluorine were subsequently adopted. Nevertheless, fluorine-containing immersion refrigerants (HCFC, HFC, HFO, etc.) are known to have limitations in use when considering environmental pollution and the exacerbation of global warming, as they possess toxicity, flammability, and low stability. (Korean Registered Patent No. 10-2781453 (March 17, 2025))

[0010] The present invention aims to provide a new immersion refrigerant that meets the standards of the Base Specification for Immersion Fluids for Immersion Cooling of the Open Computer Project (OPC), a global collaborative project that shares open source hardware and software designs to improve the energy and operational efficiency of devices requiring cooling, such as data centers.

[0011] The refrigerant into which the device according to an embodiment of the present invention is immersed has a viscosity of 3.46 or more and 6.76 cP or less, a dielectric constant of 3 or more and less than 4, a boiling point of 300 or more and 320°C or less, a decomposition temperature of 150 or more and 200°C or less, a flash point of 140 or more and less than 150°C, a vapor pressure of 0.000000045 or more and 0.00000032 atm or less, and a freezing point of less than -40°C, and may not contain an aromatic ring; sulfur and fluorine.

[0012] Here, the refrigerant may include a substance of the following chemical formula 1.

[0013] <Chemical Formula 1>

[0014] .

[0015] In addition, the above refrigerant may include a substance of the following chemical formula 2.

[0016] <Chemical Formula 2>

[0017] .

[0018] The refrigerant into which the device according to an embodiment of the present invention is immersed has a viscosity of 6.84 cP or less, a dielectric constant of 2.13 or more and 2.15 or less, a boiling point of 321 or more and 325°C or less, a decomposition temperature of 219 or more and 225°C or less, a flash point of 150 or more and 155°C or less, a vapor pressure of 0.0000000269 or more and 0.0000000428 atm or less, and a freezing point of -48 or more and -36°C or less, and may not contain an aromatic ring; sulfur and fluorine.

[0019] Here, the refrigerant may include a substance of the following chemical formula 3.

[0020] <Chemical Formula 3>

[0021] .

[0022] In addition, the above refrigerant may include a substance of the following chemical formula 9.

[0023] <Chemical Formula 9>

[0024]

[0025] In addition, the above refrigerant may include a substance of the following chemical formula 15.

[0026] <Chemical Formula 15>

[0027] .

[0028] The refrigerant into which the device according to an embodiment of the present invention is immersed has a viscosity of 9.18 or higher and 9.97 cP or lower, a dielectric constant of 2.13 or higher and 2.14 or lower, a boiling point of 324 or higher and 328°C or lower, a decomposition temperature of 206 or higher and 214°C or lower, a flash point of 158 or higher and 163°C or lower, a vapor pressure of 0.00000000858 or higher and 0.000000014 atm or lower, and a freezing point of -46°C or lower, and may not contain an aromatic ring; sulfur and fluorine.

[0029] Here, the refrigerant may include a substance of the following chemical formula 4.

[0030] <Chemical Formula 4>

[0031] .

[0032] In addition, the above refrigerant may include a substance of the following chemical formula 7.

[0033] <Chemical Formula 7>

[0034] .

[0035] In addition, the above refrigerant may include a substance of the following chemical formula 10.

[0036] <Chemical Formula 10>

[0037] .

[0038] In addition, the above refrigerant may include a substance of the following chemical formula 13.

[0039] <Chemical Formula 13>

[0040] .

[0041] The refrigerant into which the device according to an embodiment of the present invention is immersed has a viscosity of 10.20 or more and 10.98 cP or less, a dielectric constant of 2.0 or more and 2.23 or less, a boiling point of 324 or more and 404°C or less, a decomposition temperature of 206 or more and 226°C or less, a flash point of 157 or more and 235°C or less, a vapor pressure of 0.0000000000249 or more and 0.00000000784 atm or less, and a freezing point of -48 or more and -35°C or less, and may not contain an aromatic ring; sulfur and fluorine.

[0042] Here, the refrigerant may include a substance of the following chemical formula 5.

[0043] <Chemical Formula 5>

[0044] .

[0045] In addition, the above refrigerant may include a substance of the following chemical formula 6.

[0046] <Chemical Formula 6>

[0047] .

[0048] In addition, the above refrigerant may include a substance of the following chemical formula 8.

[0049] <Chemical Formula 8>

[0050] .

[0051] In addition, the above refrigerant may include a substance of the following chemical formula 11.

[0052] <Chemical Formula 11>

[0053] .

[0054] In addition, the above refrigerant may include a substance of the following chemical formula 12.

[0055] <Chemical Formula 12>

[0056] .

[0057] In addition, the above refrigerant may include a substance of the following chemical formula 14.

[0058] <Chemical Formula 14>

[0059] .

[0060] A liquid immersion cooling system that cools a device using a liquid immersion refrigerant according to an embodiment of the present invention may include at least one liquid immersion refrigerant according to any one of claims 1 to 18.

[0061] The liquid immersion refrigerant according to the present invention has high heat dissipation efficiency, low-noise cooling, and high energy saving effects, so it can be usefully employed in liquid immersion refrigerant technology for devices that require cooling, such as data centers.

[0062] In addition, the present invention has lower toxicity or harmfulness compared to existing fluorine-based refrigerants, so concerns regarding environmental pollution or global warming can be reduced.

[0063] FIG. 1 is a drawing showing a sequential multi-agent according to one embodiment of the present disclosure.

[0064] FIG. 2 is a drawing showing a supervisory agent according to one embodiment of the present disclosure.

[0065] FIG. 3 is a diagram showing a hierarchical agent system according to one embodiment of the present disclosure.

[0066] FIG. 4 is a drawing showing a multi-agent discussion type system according to one embodiment of the present disclosure.

[0067] FIG. 5 is a diagram showing a Mixture-of-AI Agents system according to one embodiment of the present disclosure.

[0068] FIG. 6 is a drawing showing a ReAct agent system according to one embodiment of the present disclosure.

[0069] FIG. 7 is a drawing showing a CodeAct agent system according to one embodiment of the present disclosure.

[0070] FIG. 8 is a drawing showing a modern tool usage agent system according to one embodiment of the present disclosure.

[0071] FIG. 9 is a drawing showing a self-reflective agent system according to one embodiment of the present disclosure.

[0072] FIG. 10 is a drawing showing a multi-agent workflow system according to one embodiment of the present disclosure.

[0073] FIG. 11 is a drawing showing an Agentic RAG (Retrieval-Augmented Generation) system according to one embodiment of the present disclosure.

[0074] FIG. 12 is a drawing showing a Multi-Agent Debate (MAD) system according to one embodiment of the present disclosure.

[0075] FIG. 13 is a diagram showing an A2A (Agent2Agent) protocol system according to one embodiment of the present disclosure.

[0076] FIG. 14 is a drawing showing an Agentic RAG system according to one embodiment of the present disclosure.

[0077] FIG. 15 is a schematic diagram of an AI agent system according to one embodiment of the present disclosure.

[0078] FIG. 16 is a schematic diagram of an LLM chatbot according to one embodiment of the present disclosure.

[0079] FIG. 17 is a schematic diagram of a Robotic Process Automation (RPA) system according to one embodiment of the present disclosure.

[0080] FIG. 18 is a schematic diagram of a RAG (Retrieval-Augmented Generation) system according to one embodiment of the present disclosure.

[0081] FIG. 19 is a schematic diagram of a Learning-Augmented Mechanism (LAM) according to one embodiment of the present disclosure.

[0082] FIG. 20 is a diagram showing an AI agent memory structure according to one embodiment of the present disclosure.

[0083] FIG. 21 is a drawing showing a GPT (General Pretrained Transformer) model according to one embodiment of the present disclosure.

[0084] FIG. 22 is a drawing showing a Mixture of Experts (MoE) model according to one embodiment of the present disclosure.

[0085] FIG. 23 is a drawing showing a Large Reasoning Model (LRM) according to one embodiment of the present disclosure.

[0086] FIG. 24 is a drawing showing a Vision Language Model (VLM) according to one embodiment of the present disclosure.

[0087] FIG. 25 is a drawing showing a Small Language Model (SLM) according to one embodiment of the present disclosure.

[0088] FIG. 26 is a drawing showing a Large Action Model (LAM) according to one embodiment of the present disclosure.

[0089] FIG. 27 is a drawing showing a Hierarchical Reasoning Model (HRM) according to one embodiment of the present disclosure.

[0090] FIG. 28 is a drawing showing a ToolFormer (Tools-trained Model) according to one embodiment of the present disclosure.

[0091] FIGS. 29 to 34 are drawings illustrating vulnerabilities of an MCP according to one embodiment of the present disclosure.

[0092] FIG. 35 is a diagram illustrating a context engineering structure in an AI agent system according to one embodiment of the present disclosure.

[0093] FIG. 36 is a drawing showing an electronic device according to one embodiment of the present disclosure.

[0094] FIG. 37 is a diagram showing an exemplary on-premises full-stack structure.

[0095] FIGS. 38a to 38c are drawings illustrating the workflow of an agent according to one embodiment of the present disclosure.

[0096] FIG. 39 is a diagram showing the structure of four major types of an artificial intelligence system according to one embodiment of the present disclosure.

[0097] The terms used in this invention are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as “to constitute,” “to provide,” “to include,” or “to have” are intended to specify the existence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0098] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which this invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this invention.

[0099] The present invention will be described in more detail below through specific embodiments. These embodiments are intended solely to illustrate the present invention, and it will be obvious to those skilled in the art that the scope of the present invention is not to be interpreted as being limited by these embodiments.

[0100] The Base Specification for Immersion Fluids, released by the Open Computer Project (OPC) in December 2022, discloses specific measurements and key parameters for immersion refrigerants. Embodiments of the present invention have predicted, derived, or generated novel immersion refrigerant materials that correspond at least partially to these OCP requirements using artificial intelligence.

[0101] The immersion refrigerant baseline disclosed in the OCP white paper includes the viscosity (cP), dielectric constant, boiling point (°C), decomposition temperature (°C), flash point (°C), vapor pressure (atm), melting point (°C), heat capacity (kJ / kg / K), density (kg / L), etc. of the refrigerant.

[0102] In the requirements for immersion refrigerants in the OCP White Paper, criteria are disclosed as follows: viscosity less than 15 cP (<15 cP), dielectric constant less than 2.3 (<2.3), boiling point, decomposition temperature, and flash point greater than 150°C (>150°C), vapor pressure less than 7.8 E-3 atm (<0.0078 atm), freezing point less than -32.5°C (<-32.5°C), heat capacity in the high range (higher values ​​indicate better performance), and density in the low range (lower values ​​indicate better performance) (Table 1). Additionally, the OCP White Paper specifies criteria for critical point (>155°C), dielectric breakdown strength (>6 kV / mm), dielectric loss tangent (≥0.05), and volume resistivity (>1.0 × 10⁻⁶). 11Additional standards such as ohm-cm), ozone depletion index (0) have been disclosed.

[0103] Viscosity (cP) Dielectric Constant Boiling Point (°C) Decomposition Temperature (°C) Flash Point (°C) Vapor Pressure (atm) Freezing Point (°C) Heat Capacity (kJ / kg / K) Density (kg / L) Single-phase OCP Standard < 15 < 2.3 > 150 > 150 > 150 < 7.8 E-3 < -3 2.5 Large Large Small

[0104] The criteria for immersion refrigerants in the OCP White Paper serve only as guidelines for manufacturing and producing immersion refrigerants and are not limited thereto. That is, the immersion refrigerant materials according to the embodiments of the present invention may include materials that satisfy at least some of the criteria presented in the guidelines of the White Paper, and these may be screened from a database of materials (mCule 42M) that are synthesized, produced, or available for purchase. The characteristics or physical properties of the immersion refrigerants according to the embodiments of the present invention are based on the criteria in [Table 1], but under certain conditions, they may have criteria different from those of the OCP White Paper.

[0105] Specifically, although the OCP White Paper criteria suggest that the dielectric constant of the refrigerant material be less than 2.3, the insulation characteristics of the immersion refrigerant can be sufficiently ensured even if the dielectric constant is less than 4; therefore, the refrigerant material according to the present invention may have a dielectric constant of less than 4. Although the dielectric constant is one of several factors determining the insulation characteristics of an immersion refrigerant, excellent insulation performance can be achieved even if the dielectric constant value is around 4, provided that a high dielectric breakdown voltage (dielectric strength; the limit of voltage at which the refrigerant loses its properties as an insulator and begins to conduct electricity) is maintained. During the production of the immersion refrigerant, the dielectric breakdown voltage can be improved or controlled by enhancing the purity of the refrigerant itself. For example, purity can be improved by removing impurities such as water or dissolved gases, which are the factors that have the greatest impact on the dielectric breakdown voltage of the liquid insulator. In addition, although the OCP White Paper's criteria specify that the flash point of a refrigerant substance should be greater than 150°C, the fire risk of the immersion refrigerant is not significant and sufficient stability can be ensured even if the flash point is greater than 140°C; therefore, the refrigerant substance according to the present invention may have a flash point greater than 140°C. In this specification, the refrigerant substance satisfaction criteria are referred to as 'relaxed OCP criteria'.

[0106] Meanwhile, immersion refrigerants for data centers can be classified into single-phase refrigerants (1-Phase) that absorb heat while maintaining a liquid state and two-phase refrigerants (2-Phase) that undergo a phase change from liquid to gas when the refrigerant absorbs heat. Accordingly, the refrigerant materials of the embodiments of the present invention may have additional criteria regarding non-toxicity, corrosion resistance, and the presence of fluorine, in addition to the physical property criteria of the OCP White Paper. Specifically, it may be desirable for single-phase refrigerants to satisfy the conditions of not containing aromatic rings, not containing sulfur, and not containing fluorine, whereas for two-phase refrigerants, the presence of aromatic rings, sulfur, and fluorine may not be considered. In addition, the boiling point criteria for two-phase immersion refrigerants may differ as the phase change between liquid and gas occurs through heat absorption. The boiling point criteria for two-phase immersion refrigerants in the OCP White Paper is disclosed as greater than 45°C and less than 55°C.

[0107] Single-phase liquid immersion refrigerant

[0108] Examples 1 to 15 below represent single-phase liquid immersion refrigerant materials derived or generated through an artificial intelligence model.

[0109] Viscosity (cP) Dielectric Constant Boiling Point (°C) Decomposition Temperature (°C) Flash Point (°C) Vapor Pressure (atm) Freezing Point (°C) Heat Capacity (kJ / kg / K) Density (kg / L) OCP Standard < 15 < 2.3 > 150 > 150 > 150 < 7.8 E-3 <-3 2.5 Large Large Small Example 1 3.46 3.58 307 200 142 3.2 E-7 <-40 1.78 0.9 Example 2 6.76 3.25 315 151 147 4.5 E-8 <-40 2. 090.87 Example 36.84 2.133212251504.28 E-8-362.200.80 Example 49.182.133242141581.40 E-8-462.260.80 Example 510.412.133332171655.90 E-9-402.240.81 Example 610.202.234042262352.49 E-11-481.620.83 Example 79.972.1 43282141638.58E-9-462.200.80Example 810.982.003242061577.84E-9-352.420.81Example 96.842.133212251504.28E-8-362.200.80Example 109.182.133242141581.40E-8-462.260.80Example 1110.412.133332171655.90E -9-402.240.81 Example 1210.202.234042262352.49E-11-481.620.83 Example 139.972.143282141638.58E-9-462.200.80 Example 1410.982.003242061577.84E-9-352.420.81 Example 156.842.153252191552.69E-8-482.100.80

[0110] Referring to [Table 2], Examples 1 to 15 are novel immersion refrigerant materials derived based on an artificial intelligence model.

[0111] The single-phase liquid immersion refrigerant according to the embodiments of the present invention has a viscosity of 3.46 or more and 6.76 cP or less, a dielectric constant of 3 or more and less than 4, a boiling point of 300 or more and 320°C or less, a decomposition temperature of 150 or more and 200°C or less, a flash point of 140 or more and less than 150°C, a vapor pressure of 0.000000045 or more and 0.00000032 atm or less, and a freezing point of less than -40°C, and may not contain aromatic rings; sulfur and fluorine (Examples 1 and 2).

[0112] In addition, the single-phase liquid immersion refrigerant according to the embodiments of the present invention has a viscosity of 6.84 cP or less, a dielectric constant of 2.13 or more and 2.15 or less, a boiling point of 321 or more and 325°C or less, a decomposition temperature of 219 or more and 225°C or less, a flash point of 150 or more and 155°C or less, a vapor pressure of 0.0000000269 or more and 0.0000000428 atm or less, and a freezing point of -48 or more and -36°C or less, and may not contain aromatic rings; sulfur and fluorine (Examples 3, 9, and 15).

[0113] In addition, the single-phase liquid immersion refrigerant according to the embodiments of the present invention has a viscosity of 9.18 or higher and 9.97 cP or lower, a dielectric constant of 2.13 or higher and 2.14 or lower, a boiling point of 324 or higher and 328°C or lower, a decomposition temperature of 206 or higher and 214°C or lower, a flash point of 158 or higher and 163°C or lower, a vapor pressure of 0.00000000858 or higher and 0.000000014 atm or lower, and a freezing point of -46°C or lower, and may not contain aromatic rings; sulfur and fluorine (Examples 4, 7, 10, 13).

[0114] In addition, the single-phase liquid immersion refrigerant according to the embodiments of the present invention has a viscosity of 10.20 or more and 10.98 cP or less, a dielectric constant of 2.0 or more and 2.23 or less, a boiling point of 324 or more and 404°C or less, a decomposition temperature of 206 or more and 226°C or less, a flash point of 157 or more and 235°C or less, a vapor pressure of 0.0000000000249 or more and 0.00000000784 atm or less, and a freezing point of -48 or more and -35°C or less, and may not contain aromatic rings; sulfur and fluorine (Examples 5, 6, 8, 11, 12, 14).

[0115] Example 1 is a substance of Chemical Formula 1 represented by the skeletal structure (Skeletal Formula) below.

[0116]

[0117] Example 1 may have physical property information such as viscosity of 3.46, dielectric constant of 3.58, boiling point of 307, decomposition temperature of 200, flash point of 142, vapor pressure of 3.2E-7, freezing point of less than -40 (<-40℃), heat capacity of 1.78, and density of 0.9.

[0118] Example 2 is a substance of Chemical Formula 2 represented by the skeletal structure below.

[0119]

[0120] Example 2 may have physical property information such as viscosity of 6.76, dielectric constant of 3.25, boiling point of 315, decomposition temperature of 151, flash point of 147, vapor pressure of 4.5E-8, freezing point of less than -40 (<-40℃), heat capacity of 2.09, and density of 0.87.

[0121] Examples 3 to 15 are similar molecules generated based on an artificial intelligence model and can be candidates for immersion refrigerant materials.

[0122] Example 3 is a substance of Chemical Formula 3 represented by the skeletal structure below.

[0123]

[0124] The substance is 2,7,12-Trimethylheptadecane, which is represented by the skeletal structure of Example 3. As physical property information, it may have a viscosity of 6.84, a dielectric constant of 2.13, a boiling point of 321, a decomposition temperature of 225, a flash point of 150, a vapor pressure of 4.28E-8, a freezing point of -36, a heat capacity of 2.20, and a density of 0.8.

[0125] Example 4 is a substance of Chemical Formula 4 represented by the skeletal structure below.

[0126]

[0127] Example 4 may have physical property information such as viscosity of 9.18, dielectric constant of 2.13, boiling point of 324, decomposition temperature of 214, flash point of 158, vapor pressure of 1.40E-8, freezing point of -46, heat capacity of 2.26, and density of 0.8.

[0128] Example 5 is a substance of Chemical Formula 5 represented by the skeletal structure below.

[0129]

[0130] Example 5 may have physical property information such as viscosity of 10.41, dielectric constant of 2.13, boiling point of 333, decomposition temperature of 217, flash point of 165, vapor pressure of 5.90E-9, freezing point of -40, heat capacity of 2.24, and density of 0.81.

[0131] Example 6 is a substance of chemical formula 6 represented by the skeletal structure below.

[0132]

[0133] Example 6 may have physical property information such as viscosity of 10.20, dielectric constant of 2.23, boiling point of 404, decomposition temperature of 226, flash point of 235, vapor pressure of 2.49E-11, freezing point of -48, heat capacity of 1.62, and density of 0.83.

[0134] Example 7 is a substance of Chemical Formula 7 represented by the skeletal structure below.

[0135]

[0136] Example 7 may have physical property information such as viscosity 9.97, dielectric constant 2.14, boiling point 328, decomposition temperature 214, flash point 163, vapor pressure 8.58E-9, freezing point -46, heat capacity 2.20, and density 0.80.

[0137] Example 8 is a substance of chemical formula 8 represented by the skeletal structure below.

[0138]

[0139] Example 8 may have physical property information such as viscosity of 10.98, dielectric constant of 2.00, boiling point of 324, decomposition temperature of 206, flash point of 157, vapor pressure of 7.84E-9, freezing point of -35, heat capacity of 2.42, and density of 0.81.

[0140] Example 9 is a substance of chemical formula 9 represented by the skeletal structure below.

[0141]

[0142] Example 9 may have physical property information such as viscosity of 6.84, dielectric constant of 2.13, boiling point of 321, decomposition temperature of 225, flash point of 150, vapor pressure of 4.28E-8, freezing point of -36, heat capacity of 2.20, and density of 0.80.

[0143] Example 10 is a substance of chemical formula 10 represented by the skeletal structure below.

[0144]

[0145] Example 10 may have physical property information such as viscosity of 9.18, dielectric constant of 2.13, boiling point of 324, decomposition temperature of 214, flash point of 158, vapor pressure of 1.40E-8, freezing point of -46, heat capacity of 2.26, and density of 0.80.

[0146] Example 11 is a substance of Chemical Formula 11 represented by the skeletal structure below.

[0147]

[0148] Example 11 may have physical property information such as viscosity of 10.41, dielectric constant of 2.13, boiling point of 333, decomposition temperature of 217, flash point of 165, vapor pressure of 5.90E-9, freezing point of -40, heat capacity of 2.24, and density of 0.81.

[0149] Example 12 is a substance of Chemical Formula 12 represented by the skeletal structure below.

[0150]

[0151] Example 12 may have physical property information such as viscosity of 10.20, dielectric constant of 2.23, boiling point of 404, decomposition temperature of 226, flash point of 235, vapor pressure of 2.49E-11, freezing point of -48, heat capacity of 1.62, and density of 0.83.

[0152] Example 13 is a substance of Chemical Formula 13 represented by the skeletal structure below.

[0153]

[0154] Example 13 may have physical property information such as viscosity 9.97, dielectric constant 2.14, boiling point 328, decomposition temperature 214, flash point 163, vapor pressure 8.58E-9, freezing point -46, heat capacity 2.20, and density 0.80.

[0155] Example 14 is a substance of Chemical Formula 14 represented by the skeletal structure below.

[0156]

[0157] Example 14 may have physical property information such as viscosity of 10.98, dielectric constant of 2.00, boiling point of 324, decomposition temperature of 206, flash point of 157, vapor pressure of 7.84E-9, freezing point of -35, heat capacity of 2.42, and density of 0.81.

[0158] Example 15 is a substance of chemical formula 15 represented by the skeletal structure below.

[0159]

[0160] Example 15 may have physical property information such as viscosity of 6.84, dielectric constant of 2.15, boiling point of 325, decomposition temperature of 219, flash point of 155, vapor pressure of 2.69E-8, freezing point of -48, heat capacity of 2.10, and density of 0.80.

[0161] 2-phase immersion refrigerant

[0162] Examples 16 to 24 below represent candidate two-phase immersion refrigerant materials screened from a purchasable material database (mCule 42M) through an artificial intelligence model. Among the criteria for two-phase immersion refrigerants listed in the OCP white paper, criteria such as boiling point, freezing point, dielectric constant, flash point, and heat of combustion may be used to derive the candidate materials of the following examples.

[0163] In the requirements for two-phase immersion refrigerants in the OCP White Paper, the boiling point (BP) is set above 45°C and below 55°C, the freezing point (MP) is set below -32.5°C (<-32.5°C), the dielectric constant is set below 2.3 (<2.3), and the flash point is set above 150°C (>150°C) (Table 3). However, since two-phase immersion refrigerants cool by absorbing the heat of vaporization during the phase change process in which a liquid boils and turns into a gas, the flash point may not be considered for two-phase refrigerants. In addition, the screening criteria for two-phase immersion refrigerants may include the inclusion or exclusion of some of the elements fluorine (F), sulfur (S), iodine (I), chlorine (Cl), or nitrogen (N). Furthermore, the screening criteria for two-phase immersion refrigerants may be modified and applied to the OCP standards with an allowance for error.

[0164] Boiling Point (°C) Dielectric Constant Freezing Point (°C) 2-Phase Immersion Refrigerant OCP Standard 45 < Bp < 55 < 2.3 < -32.5

[0165] Hereinafter, Examples 16 to 24 are materials that satisfy the dielectric constant and boiling point criteria of the OCP standard among materials that do not contain all of the elements fluorine (F), sulfur (S), iodine (I), chlorine (Cl), and nitrogen (N) as screening conditions, and these can be selected as candidate materials for two-phase immersion refrigerants.

[0166] Boiling Point (°C) Dielectric Constant Freezing Point (°C) 2-Phase OCP Standard 45 < Bp < 55 < 2.3 < -32.5 Example 16 46.5364732 1.38412954 -132.11953 Example 17 53.3271556 2.11428252 -136.4054 Example 18 56.6476929 1.57210639 -110.0807 Example 19 59.7988936 2.0787861 -103.74012 Example 20 51 .93977532.08202667-101.59475 Example 2150.73941662.21197521-146.50238 Example 2259.55299112.024208-123.78057 Example 2359.47572821.99202655-134.21193 Example 2454.4823611.32076796-98.376491

[0167] The two-phase liquid immersion refrigerant according to the embodiments of the present invention has a boiling point greater than 45°C and less than 60°C, a dielectric constant greater than 1.3 and less than 2°C, and a freezing point greater than -135°C and less than -97°C, and may be a non-flammable material that does not contain at least fluorine (F) and sulfur (S) among the elements fluorine (F), sulfur (S), iodine (I), chlorine (Cl), and nitrogen (N) (Examples 16, 18, 23, 24).

[0168] The two-phase liquid immersion refrigerant according to the embodiments of the present invention has a boiling point greater than 50 and less than 60°C, a dielectric constant greater than 2 and less than 2.22, and a freezing point greater than -147 and less than -101°C, and may be a non-flammable material that does not contain at least fluorine (F) and sulfur (S) among the elements fluorine (F), sulfur (S), iodine (I), chlorine (Cl), and nitrogen (N) (Examples 17, 19, 20, 21, 22).

[0169] Example 16 has the molecular formula C5H8 with C=C=CCC in SMILES (Simplified Molecular Input Line Entry System) notation, and Example 17 has the molecular formula C6H with C=CCCC=C in SMILES notation. 10 And, Example 18 uses Smiles notation C=C(C)C(=C)C, molecular formula C6H 10 Examples 16 to 18 satisfy the boiling point, dielectric constant, and freezing point criteria relative to the OPC standards for two-phase immersion refrigerants, and thus can be candidate materials for the two-phase immersion refrigerant of the present invention.

[0170] Example 19 uses Smiles notation C=C / C(C)=C / C, molecular formula C6H 10 Example 19 satisfies the dielectric constant and freezing point standards relative to the OPC standards for two-phase immersion refrigerants, but exceeds the boiling point standard, and the error is not large, so it can be a candidate material for the two-phase immersion refrigerant of the present invention.

[0171] Example 20 uses the Smiles notation CCC(C)(C)C and the molecular formula C5H 12 And, Example 21 uses Smiles notation C=CCC(C)C, molecular formula C5H 10 Examples 20 and 21 satisfy the boiling point, dielectric constant, and freezing point criteria relative to the OPC standards for two-phase immersion refrigerants, and thus can be candidate materials for the two-phase immersion refrigerant of the present invention.

[0172] Example 22 uses Smiles notation CC=C(C)CC, molecular formula C6H 12 And, Example 23 has the Smiles notation CCCC(C)C and the molecular formula C6H 14 Examples 22 and 23 and Example 19 satisfy the dielectric constant and freezing point criteria compared to the OPC criteria for two-phase immersion refrigerants, but exceed the boiling point criteria, so the error is not large, so they can be candidate materials for the two-phase immersion refrigerant of the present invention.

[0173] Example 24 uses Smiles notation C=CC(C)(C)C, molecular formula C5H 10 As such, it satisfies the boiling point, dielectric constant, and freezing point standards compared to the OPC standards related to two-phase immersion refrigerants, and can be a candidate material for the two-phase immersion refrigerant of the present invention.

[0174] Examples 25 to 31 below are non-flammable materials that satisfy criteria more lenient than the OCP criteria among materials that do not contain both fluorine (F) and sulfur (S) elements under screening conditions, and these can be selected as candidate materials for two-phase immersion refrigerants. However, since two-phase immersion refrigerants cool by absorbing the heat of vaporization during the phase change process in which a liquid boils and turns into a gas, there is no need to consider the flash point in two-phase refrigerants.

[0175] Boiling point (°C) Dielectric constant Freezing point (°C) 2-phase OPC Relaxation criteria 0 < Bp < 60 < 10 < -32.5

[0176] As shown in [Table 5], the screening criteria for two-phase immersion refrigerants were relaxed, with the boiling point set to be greater than 0°C and less than 60°C, and the dielectric constant set to less than 10. Additionally, among the screened materials, candidate materials were selected by adding non-flammability conditions.

[0177] Boiling point (°C) Dielectric constant Freezing point (°C) 2-phase OPC Relaxation criteria 0 < Bp < 60 < 10 < -32.5 Example 25 21.7883814.36269969 -97.843943 Example 26 4.325945668.32724126 -116.38498 Example 27 1.645689856.13049793 -84.205899 Example 28 50. 98624694.4478282-100.84517 Example 2950.92627155.90637216-32.751963 Example 3054.80933715.58933306-85.540167 Example 3151.57605533.86126894-81.955167

[0178] The two-phase immersion refrigerant according to the embodiments of the present invention has conditions of a boiling point greater than 21 and less than 52°C, a dielectric constant of 3 and less than 4.5, and a freezing point of -101 and less than -81°C, and may be a non-flammable substance that does not contain at least fluorine (F) and sulfur (S) among the elements fluorine (F), sulfur (S), iodine (I), chlorine (Cl), and nitrogen (N) (Examples 25, 28, 31). The two-phase immersion refrigerant according to the embodiments of the present invention has conditions of a boiling point greater than 1.6 and less than 55°C, a dielectric constant of 5.5 and less than 6.2, and a freezing point of -86 and less than -32°C, and may be a non-flammable substance that does not contain at least fluorine (F) and sulfur (S) among the elements fluorine (F), sulfur (S), iodine (I), chlorine (Cl), and nitrogen (N) (Example 27, 29, 30).

[0179] The two-phase liquid immersion refrigerant according to an embodiment of the present invention has a boiling point greater than 4 and less than 5°C, a dielectric constant greater than 8 and less than 8.5, and a freezing point greater than -117 and less than -33°C, and does not contain at least fluorine (F) and sulfur (S) among the elements fluorine (F), sulfur (S), iodine (I), chlorine (Cl), and nitrogen (N), and may be a non-flammable material (Example 26).

[0180] Example 25, with the Smiles notation ClB(Cl)Cl and molecular formula BCl3, meets the criteria for boiling point, dielectric constant, and freezing point relative to the OPC standards for relaxed two-phase immersion refrigerants and is a non-flammable material, and can be a candidate material for the two-phase immersion refrigerant of the present invention.

[0181] Example 26, with the Smiles notation CBr and molecular formula CH3Br, meets the criteria for boiling point, dielectric constant, and freezing point relative to the OPC standards for relaxed two-phase immersion refrigerants and is a non-flammable material, and can be a candidate material for the two-phase immersion refrigerant of the present invention.

[0182] Example 27 is Smiles notation [C-]#N, molecular formula CN -As such, it meets the boiling point, dielectric constant, and freezing point standards relative to the relaxed OPC standards for two-phase immersion refrigerants and is a non-flammable material, and can be a candidate material for the two-phase immersion refrigerant of the present invention.

[0183] Example 28 has a Smiles notation of Cl / C=C / Cl and a molecular formula of C2H2Cl2, and meets the criteria for boiling point, dielectric constant, and freezing point relative to the OPC standards for relaxed two-phase immersion refrigerants and is a non-flammable material, and can be a candidate material for the two-phase immersion refrigerant of the present invention.

[0184] Example 29, with the Smiles notation Cl=COCOCl and molecular formula C2O2Cl2, meets the criteria for boiling point, dielectric constant, and freezing point relative to the OPC standards for relaxed two-phase immersion refrigerants and is a non-flammable material, and can be a candidate material for the two-phase immersion refrigerant of the present invention.

[0185] Example 30, with the Smiles notation CON(C)C and molecular formula C3H7NO, meets the criteria for boiling point, dielectric constant, and freezing point relative to the OPC standards for relaxed two-phase immersion refrigerants and is a non-flammable material, and can be a candidate material for the two-phase immersion refrigerant of the present invention.

[0186] Example 31, with the Smiles notation BN(C)C and molecular formula C2H6BN, meets the criteria for boiling point, dielectric constant, and freezing point relative to the OPC standards for relaxed two-phase immersion refrigerants and is a non-flammable material, and can be a candidate material for the two-phase immersion refrigerant of the present invention.

[0187] Meanwhile, Example 25 is toxic and corrosive, and Examples 27 to 29 are toxic substances, so toxicity control may be required for them. Example 31 is a non-toxic and non-flammable substance. That is, Example 31, which is non-toxic and non-flammable while meeting relaxed standards, may be the most desirable as a two-phase liquid immersion refrigerant material.

[0188] The liquid immersion refrigerant according to the embodiments of the present invention meets relaxed OCP standards and provides high heat dissipation efficiency, low-noise cooling, and high energy saving effects, so it can be usefully used in liquid immersion refrigerant technology.

[0189] According to another embodiment, the present invention discloses an immersion cooling system. The system refers to a set of components that are interrelated or connected for a specific purpose or specific implementation, and the immersion cooling system according to the present invention includes at least one component that functions to achieve cooling of said device, etc. by immersing said device, etc. in an immersion refrigerant. The immersion cooling system according to the present invention may include all known or known cooling systems in the relevant industry, and a detailed description of the cooling system below is omitted.

[0190] The liquid immersion cooling system according to the present invention may include a liquid immersion refrigerant, and the liquid immersion refrigerant may include at least one of the refrigerant materials according to embodiments of the present invention. The liquid immersion cooling system may include a device for cooling, and said device may be immersed in the refrigerant material according to embodiments of the present invention.

[0191] For example, the cooling system may additionally include a device for removing heat from a refrigerant, and said device may include a cooler, etc. The cooler may be a refrigerated container in which the refrigerant forms a cooling tank in which a device requiring cooling is immersed. The cooling system may additionally include a circulation component that causes the refrigerant to flow around the device immersed in the refrigerant. Additionally, the circulation component may be a circulation pump or other circulation component. However, the present invention is not limited thereto.

[0192] An immersion refrigerant according to embodiments of the present invention can be derived by an artificial intelligence model comprising: a feature extraction model that generates a manifold vector compressing key information from a molecular structure; and a lightweight surrogate model that receives the extracted manifold vector as input and predicts a molecule.

[0193] The feature extraction model converts complex information, such as the atomic composition and bonding patterns of a molecule, into manifold vectors—high-dimensional numerical vectors suitable for prediction—and can be a weighted model that is accurate but slow in processing speed. The feature extraction model can input the structure of the reactant molecules (e.g., SMILES) into a graph neural network. The graph neural network recognizes the molecule as a graph of atoms (nodes) and chemical bonds (edges) and performs molecular embedding to generate initial vectors that numerically represent information about each atom and its surrounding local chemical environment. The generated initial vectors pass through an encoder and a transfer network composed of multiple MLP (Multilayer Perceptron) layers. Through this process, the latent spaces between different properties (Source / Target) are geometrically aligned, allowing for the construction of a Locally Flat Frame (LF) that is meaningful for all property predictions. Ultimately, this process generates a 50-dimensional manifold vector containing the core characteristics of the molecule. These manifold vectors can be high-quality information that takes into account molecular compression information and the relationships between various physical properties.

[0194] A surrogate model can predict reactants using multiple manifold vectors generated from a feature extraction model. The surrogate model may be input with an input vector generated by concatenating the manifold vectors of the reactants used to generate the manifold vector for predicting molecules. For example, the input vector may be generated as a 100-dimensional input vector by combining the 50-dimensional manifold vector of reactant 1 and the 50-dimensional manifold vector of reactant 2. The surrogate model may be composed of multiple shared layers and multiple task-specific layers.

[0195] Input vectors are fed into multiple shared layers. The 100-dimensional input vector passes through multiple fully connected layers, and the dimensionality of the input vector passing through these layers can be reduced. In this process, the dimensionality of the vector can be progressively reduced from 100 dimensions to 70 dimensions to 50 dimensions. These shared layers can learn common information that affects all physical properties. For example, boiling point, viscosity, and surface tension are all influenced by a common physical principle (common information) known as intermolecular attraction, and the shared layers can learn these fundamental and general rules (common information) from the data. In other words, as tasks for all physical properties share the parameters (weights) of the shared layers, learning one task contributes to improving the performance of all other tasks, thereby maximizing data efficiency.

[0196] In the task-specific hierarchy, a 50-dimensional vector that has passed through the shared layer can be divided into multiple branches corresponding to multiple reactants. Each branch can be composed of an independent head designed to predict only the corresponding physical property. In other words, based on the common information learned in the shared layer, each task-specific hierarchy can derive a final predicted value by interpreting the information according to the characteristics of the individual physical property. For example, since the influence of intermolecular forces on boiling point and viscosity differs, each task-specific head can perform the role of an expert in learning these unique relationships.

[0197] The aforementioned surrogate model is a multi-task learning model that uses a hard parameter sharing method, and by learning multiple related tasks simultaneously, it can improve the overall performance and generalization ability of the model while significantly enhancing processing speed. In an embodiment of the present invention, by utilizing such a surrogate model, an immersion refrigerant applied to a data center can be derived at high speed.

[0198] An artificial intelligence model for deriving an immersion refrigerant according to embodiments of the present invention as described above can be performed by an artificial intelligence agent. The agent's workflow may include the types of FIGS. 38a to 38c as follows.

[0199] In one embodiment, most of the structure may consist of a hierarchical structure of "Input → Plan / Evaluate / Branch → Output". Additionally, due to the agentic nature, complex problem solving may be possible through patterns such as iteration, parallelism, and collaboration rather than a single LLM call. Furthermore, since each structure is designed to suit a specific business purpose, selecting the optimal structure according to the purpose is important. For example, a workflow such as [Table 7] may be recommended depending on the business purpose.

[0200] Business Purpose Recommendation Workflow Conversational AI (e.g., Chatbots, Consultation) Prompt Chaining, Routing Real-time Analysis and Evaluation Evaluator-Optimizer, Reflection Complex Business Planning and Automation Plan and Execute, Rewoo Autonomous Behavior-based Systems Autonomous Workflow Large-scale Parallel Processing or Synthesis Parallelization, Orchestrator-Worker

[0201] FIG. 1 is a drawing showing a sequential multi-agent according to one embodiment of the present disclosure.

[0202] Referring to FIG. 1, a sequential multi-agent may include a user agent, a write agent, a style agent, etc. In one embodiment, the sequential agents communicate sequentially and can perform a single task in order. Additionally, each agent can receive the result of the previous step and perform the next task. For example, the user agent may obtain user input from the user to generate a first processing result, the write agent may generate a second processing result including the written text by generating the first processing result from the user agent, and the style agent may generate a third processing result by receiving the second processing result from the write agent and applying a style. Accordingly, the third processing result may finally be output. Such a sequential multi-agent has a linear flow and is suitable for processing a single task. For example, the sequential multi-agent can be used in the field of creative writing.

[0203] FIG. 2 is a drawing showing a supervisory agent according to one embodiment of the present disclosure.

[0204] Referring to FIG. 2, a supervised agent may refer to an agent in which a centrally located Supervisory Language Model (LLM) coordinates the entire process. In one embodiment, the supervised agent may direct necessary tasks to appropriate agents and synthesize results according to a user's request. That is, the supervised agent can manage communication between agents. Accordingly, the supervised agent can enable flexible task distribution. For example, the supervised agent may request research from a research agent and request calculations from a mathematics agent. Such a supervised agent can be used in fields such as deep research.

[0205] FIG. 3 is a diagram showing a hierarchical agent system according to one embodiment of the present disclosure.

[0206] Referring to FIG. 3, a hierarchical agent system can refer to a system in which a meta-agent controls and coordinates lower-level agents. For example, a meta-agent can obtain user input from a user, request tasks from research agents, data analysis agents, etc., receive task results from each agent, and generate output. This hierarchical agent system has a hierarchical control structure, allowing tasks to be divided and managed in a more complex manner. In other words, a meta-agent acting as an intermediate manager can be utilized. This hierarchical agent system is suitable for complex systems or coding, and can be used as a coding agent, etc.

[0207] FIG. 4 is a drawing showing a multi-agent discussion type system according to one embodiment of the present disclosure.

[0208] Referring to Fig. 4, a multi-agent discussion system is a system in which multiple agents present different opinions and select the most appropriate result by voting or evaluating it, thereby deriving the optimal solution based on discussion. That is, multiple agents perform discussion and evaluation based on user input and can output the optimal answer among them. Such a multi-agent discussion system can make the best choice by comparing various perspectives in a competitive structure and can be used in fields such as world simulation.

[0209] FIG. 5 is a diagram showing a Mixture-of-AI Agents system according to one embodiment of the present disclosure.

[0210] Referring to FIG. 5, in a hybrid AI agent system, multiple agents perform parallel processing layer by layer, and an aggregator can integrate the results at the end. For example, a first agent and a second agent may perform a process in parallel at the first layer, and then the first agent and the second agent may perform a process in parallel at the second layer, and an aggregator may synthesize this to generate an output. In one embodiment, the hybrid AI agent system may use a multi-stage approach for complex problems. The hybrid AI agent system has a hierarchical and parallel structure and is characterized by the distribution and combination of expertise, so it can be used for medical research, etc.

[0211] FIG. 6 is a drawing showing a ReAct agent system according to one embodiment of the present disclosure.

[0212] Referring to FIG. 6, ReAct is a compound word of "Reason + Act," and a ReAct agent can refer to an agent that solves problems by repeating reasoning (Reason) and action (Act). For example, if a user asks, "What is the weather like in New York these days?", the LLM can interpret the meaning of the question, search for the current weather in New York through a search engine's search tool, summarize the results, and deliver them back to the user. Such a ReAct agent can be used in AI chatbots, etc. According to one embodiment, the ReAct agent has excellent tool usage capabilities and can generate more accurate responses through the repetition of reasoning and action.

[0213] FIG. 7 is a drawing showing a CodeAct agent system according to one embodiment of the present disclosure.

[0214] Referring to Fig. 7, the CodeAct agent can handle more flexible and complex logic by executing Python code instead of JSON. For example, when it receives input from a user such as "Analyze sales data for the last 3 months," the LLM analyzes the request, and Pandas can be used for Python code. Subsequently, tasks such as loading CSV files, calculating statistics, generating graphs, and creating summary reports can be performed. Because the CodeAct agent is code-based, it is strong in handling complex calculations and logic, and the LLM can directly program and execute it.

[0215] FIG. 8 is a drawing showing a modern tool usage agent system according to one embodiment of the present disclosure.

[0216] Referring to Fig. 8, the agent using modern tools can easily utilize various SaaS tools or APIs (e.g., AWS, Brave search, etc.) through a Multi-Channel Processing (MCP) server. For example, when the agent receives a text request from a user saying "Stop my AWS EC2 instance," it can call the AWS API through the MCP server and return a message indicating successful stop. The agent using modern tools can be used in developer IDE-integrated AI, etc., and has the advantage of enabling tool control with almost no code and facilitating easy integration with various cloud or web functions.

[0217] FIG. 9 is a drawing showing a self-reflective agent system according to one embodiment of the present disclosure.

[0218] Referring to Fig. 9, the self-reflective agent system can refer to a metacognitive mechanism in which the LLM evaluates and modifies its own responses. For example, it could be a system in which, upon receiving input from a user such as "Write a cover letter that fits my resume," the LLM generates a draft, the Critique LLM checks for logical and contextual errors, and after iterative modification and improvement, generates a final output. The self-reflective agent system can automatically improve quality and incrementally enhance performance through a feedback loop.

[0219] FIG. 10 is a drawing showing a multi-agent workflow system according to one embodiment of the present disclosure.

[0220] Referring to Fig. 10, a multi-agent workflow system can refer to a system in which multiple specialized agents cooperate to perform a single task. For example, upon receiving input from a user such as "Please write a startup market research report," the first agent can collect the latest market trends, the second agent can analyze competitors, and the third agent can summarize investment trends. An aggregator can synthesize the information generated by the first, second, and third agents to generate a report. The multi-agent workflow system can improve accuracy through a cooperative structure and distribute and process complex tasks.

[0221] FIG. 11 is a drawing showing an Agentic RAG (Retrieval-Augmented Generation) system according to one embodiment of the present disclosure.

[0222] Referring to Fig. 11, an Agentic RAG (Retrieval-Augmented Generation) system can be described as a system in which AI retrieves information from external databases or search engines in real time and generates a response based on it. For example, when a query such as "What are the major issues of the 2024 US presidential election?" is obtained from a user, the AI ​​performs a vector DB / web search (search engines, news, etc.), extracts relevant articles and summaries, and can write an explanation using that information. Such an Agentic RAG (Retrieval-Augmented Generation) system can provide the latest information and generate an accurate response that fits the context.

[0223] FIG. 12 is a drawing showing a Multi-Agent Debate (MAD) system according to one embodiment of the present disclosure.

[0224] Referring to FIG. 12, a Multi-Agent Debate (MAD) system may refer to a system in which multiple small language models derive an answer through discussion. An aggregator, such as an aggregating LLM, can receive a query from a user and combine the opinions of multiple small language models to generate a final answer. In one embodiment, when a user inputs a question, the aggregator generates an initial answer, and multiple Small Language Models (SLMs) can present different answers and refute each other. For example, when the first SLM presents an answer such as "This is the answer," the second SLM presents "No, this is the answer. I verified it this way," and the third SLM presents "I think it is almost correct, but there are these points," the aggregator can make an intermediate judgment based on the discussion content. Based on this first discussion and previous judgment, a more refined second discussion can proceed. Some models can verify facts by utilizing tools (search, vector DB, etc.). After repeating these discussions, the aggregator can determine the most appropriate response as the final verdict and deliver it to the user. In one embodiment, various SLMs can participate in discussion and verification with each other.

[0225] FIG. 13 is a diagram showing an A2A (Agent2Agent) protocol system according to one embodiment of the present disclosure.

[0226] Referring to Fig. 13, the A2A (Agent2Agent) protocol enables communication without sharing data with each other, allows for task distribution and negotiation among multiple agents, and enables each agent to maintain shared context and state information. In the example of Fig. 13, the first AI agent (AI Agent 1) can primarily perform local-based file or search tasks and can connect to various MCP servers via the MCP Protocol. In the example of Fig. 13, the second AI agent (AI Agent 2) can primarily handle cloud and communication tasks and can connect to various MCP servers via the MCP Protocol. MCP allows for communication by separating each function (file access, search, cloud, etc.) into separate servers. Furthermore, A2A has the advantage of high security because it enables direct communication between agents without sharing data. Each agent can operate independently using its own language model, framework, and database.

[0227] FIG. 14 is a drawing showing an Agentic RAG system according to one embodiment of the present disclosure.

[0228] Referring to FIG. 14, the Agentic RAG system may refer to a Retrieval-Augmented Generation (RAG) system that extracts data from a website, stores it in a vector database, searches for similar information in response to a user query, and generates a response through a Large Language Model (LLM). In particular, this structure can support advanced question-answering by including agent functions (Memory, Tools, Planning, etc.). In one embodiment, the Agentic RAG system may include a data extraction step, a search step, and a generation step. The data extraction step may include a step of extracting data from a designated website (e.g., GitHub, Hacker News, etc.) or web content (website content in various formats such as text, images, audio, video, etc.). Additionally, the data extraction step may include a preprocessing and storage step. The preprocessing and storage step may include a step of extracting text and metadata from the content, a step of chunking the text into small units, a step of vectorizing each piece through an embedding model, and a step of storing the vectorized data in a vector database (Vector DB).

[0229] In one embodiment, the search step of the Agentic RAG pipeline may include the steps of: inputting a user query; performing embedding after query rewriting; searching for similarity in a vector database; configuring the search results into context; and ranking based on relevance for the context.

[0230] In one embodiment, the generation step of the Agentic RAG pipeline may include: a step of generating an input by combining a user query and a retrieved context; a step in which a large language model (LLM) generates a response—including agentic elements such as memory functions, tool calls, and planning functions; and a step of providing the generated response to the user.

[0231] In one embodiment, website content is collected and textified, text segmentation and embedding are performed, stored in a vector database, user query embeddings and similar content are searched, relevant context is constructed and LLM input is expanded, and an LLM-based response can be generated and delivered to the user.

[0232] In one embodiment, the Agentic RAG system is a search-based generation (RAG) system capable of generating precise and contextually relevant answers based on its architecture, and capable of handling complex queries through memory storage, calling external tools, and planning. It also supports various forms of content such as images, audio, and video in addition to text, enabling multimodal input processing, and can improve search accuracy by including query rewriting and ranking functions.

[0233] In one embodiment, the Agentic RAG system can collect real-time information from a website and utilize it for question and answer, and can be applied to various services such as technical support, search engine enhancement, and personal assistant services, and can be utilized for complex task automation through Agentic components.

[0234] FIG. 15 is a schematic diagram of an AI agent system according to one embodiment of the present disclosure.

[0235] Referring to FIG. 15, based on system prompts and user prompts, the AI ​​system can generate a final response by performing actions such as formulating a plan, calling a tool, storing it in memory, and collecting feedback. This AI agent system features an autonomous planning and execution structure and can dynamically select and execute tools. In addition, the AI ​​agent system enables continuous learning and improvement through a feedback loop, so it can be used for complex multi-step tasks, business automation, research, etc.

[0236] An agent system according to one embodiment of the present disclosure may be an advanced agent system that goes beyond a simple input-output structure by incorporating complex functions such as memory, reasoning, tool integration, and planning.

[0237] FIG. 16 is a schematic diagram of an LLM chatbot according to one embodiment of the present disclosure.

[0238] Referring to Fig. 16, a Large Language Model (LM) can take user input as input and output a response. This system consists of a single LLM call and does not involve external tool calls or complex workflows, making it suitable for simple question and answer, FAQ, and customer service. However, this system may lack the ability to maintain context or perform repetitive tasks.

[0239] FIG. 17 is a schematic diagram of a Robotic Process Automation (RPA) system according to one embodiment of the present disclosure.

[0240] Referring to Fig. 17, the tool can be executed according to predefined fixed rules using user input. In this case, the LLM may be limited to a secondary role. Such an RPA system is suitable for repetitive automation and simple back-office tasks, but it may have limitations in flexible flow control or advanced decision-making.

[0241] FIG. 18 is a schematic diagram of a RAG (Retrieval-Augmented Generation) system according to one embodiment of the present disclosure.

[0242] Referring to Fig. 18, user input is embedded and converted into a vector DB, and relevant information is searched to create an augmented prompt that is processed via LLM, and a final response can be output. This RAG system is suitable for fields such as generating correct answers using external knowledge (documents, etc.), improving search-based LLM accuracy, QA systems, document search, and summarization, but there are limitations in processing information outside the scope of the searched information.

[0243] FIG. 19 is a schematic diagram of a Learning-Augmented Mechanism (LAM) according to one embodiment of the present disclosure.

[0244] Referring to Fig. 19, the LAM system can perform a process in which user input is processed by an LLM based on training data, and a task is executed by running a tool to output a response. The LAM system uses a model trained using tool usage data, enables learning and execution based on actual behavior, and is suitable for the automation of repeatable GUI tasks. However, since the LAM system requires a training process, there are limitations to generalization.

[0245] These AI systems are summarized as shown in [Table 8] below.

[0246] Item Structure Key Features Limitations LLM Chatbot Single LLM call, simple response generation, lack of context retention RPA Rule-based + fixed tool, repetitive automation, lack of flexibility RAG Search-based + prompt augmentation, utilizes external knowledge, lack of information other than search LAM Behavior learning-based GUI automation, requires learning, limited versatility AI Agents Planning + memory + tool + repetition, performs autonomous tasks, complex structure, consumes resources

[0247] FIG. 20 is a diagram showing an AI agent memory structure according to one embodiment of the present disclosure.

[0248] FIG. 20 is a diagram showing an AI agent memory structure according to one embodiment of the present disclosure.

[0249] Referring to FIG. 20, the process of an AI agent generating a response to a query by utilizing memory is illustrated. Memory is divided into short-term and long-term, and each can support complex decision-making and task execution through various types of memory. In one embodiment, memory may consist of short-term memory and long-term memory. Short-term memory is a temporary memory space activated during work, allowing focus on the currently ongoing workflow (task execution). Short-term memory may include Working Memory, which manages reasoning and task flow per workflow, and Cache Memory, which provides rapid access to frequently used data and result values. Long-term memory is knowledge and experience-based memory that is continuously preserved, and may include Episodic Memory, which stores events or incidents manually saved in a specific workflow; Semantic Memory, which stores conceptual and factual knowledge (e.g., "Paris is the capital of France"); and Procedural Memory, which stores methods of task execution or procedural knowledge (e.g., "How to reset a server").

[0250] In one embodiment, all memory can operate in conjunction with a Language Model Framework through a central Memory controller. An input query can go through a process via the framework of 1) query analysis and memory referencing, 2) retrieving relevant information from memory if necessary, 3) the language model generating a response through a decision procedure, and 4) delivering the response result to the user.

[0251] In one embodiment, the MCP server is responsible for interfacing with external knowledge and tools and may include a vector database which is a search-based embedding vector repository, a Semantic Database which is a conceptual knowledge base database, a third-party API integration unit, etc.

[0252] That is, the process can be handled as shown in the following [Table 9].

[0253] [User Query]↓[Language Model Framework]↔ Memory Controller (Short-Term / Long-Term Memory)↔ MCP Server (Vector / Semantic DB, External APIs)↓[Response]

[0254] One embodiment of the present disclosure can systematically integrate design elements essential for implementing an agent system that can continuously learn and make decisions according to the situation, as shown in [Table 10] below.

[0255] Item Description Integration of Memory Structures Combines short-term (Working / Cache) and long-term (Episodic / Semantic / Procedural) memory to enhance context awareness and learning. Includes decision logic for prompt adjustment and response generation based on a framework linked to memory. External Information Extensibility Supports information enrichment and real-time integration via databases and APIs. User-Customized Responses Capable of generating responses that reflect past experiences and current context.

[0256] Next, I would like to explain the types of language models utilized by AI agent systems. Each model has a specific processing method and role, and a suitable model can be selected and utilized depending on the nature of the task.

[0257] FIG. 21 is a drawing showing a GPT (General Pretrained Transformer) model according to one embodiment of the present disclosure.

[0258] Referring to Fig. 21, the GPT model is a general-purpose language model pre-trained on a large text corpus, capable of tokenizing and embedding input prompts, generating hidden states through a transformer layer, calculating logits and probabilities for the next token, and sequentially sampling or selecting tokens to generate text. This GPT model has the advantage of high generality.

[0259] FIG. 22 is a drawing showing a Mixture of Experts (MoE) model according to one embodiment of the present disclosure.

[0260] Referring to Fig. 22, the Mixture of Experts (MoE) model is a decentralized model structure in which only some experts (sub-networks) selected based on the input are activated. The input is tokenized and embedded, a gating network selects a top expert sub-model per token, the outputs of the selected experts are merged (weighted average or aggregated), and decoding is performed based on the merged result. This MoE model has the advantage of high computational efficiency relative to the number of parameters.

[0261] FIG. 23 is a drawing showing a Large Reasoning Model (LRM) according to one embodiment of the present disclosure.

[0262] Referring to FIG. 23, the LRM is a model capable of processing a chain of thought for complex reasoning, performing input and context tokenization, internally generating a reasoning path, evaluating or regenerating possible logical paths, and determining and outputting a final logical answer. Such an LRM may be suitable for solving high-difficulty problems, logic-based question answering, etc.

[0263] FIG. 24 is a drawing showing a Vision Language Model (VLM) according to one embodiment of the present disclosure.

[0264] Referring to FIG. 24, VLM is a multimodal model that integrates and processes images and text. It can perform image encoding, perform text tokenization, combine both modalities into an integrated embedding, and generate a response by inferring based on the integrated representation. VLM can be advantageous for generating explanations and answering questions that include visual information.

[0265] FIG. 25 is a drawing showing a Small Language Model (SLM) according to one embodiment of the present disclosure.

[0266] Referring to FIG. 25, the SLM is a language model with a lightweight structure that can be used in environments with limited computational resources. It performs input tokenization, projects embedded tokens into a low-dimensional space, passes them through a simplified transformer layer, calculates token probabilities, and generates outputs. Such an SLM can be suitable for edge devices, on-device AI environments, etc.

[0267] FIG. 26 is a drawing showing a Large Action Model (LAM) according to one embodiment of the present disclosure.

[0268] Referring to FIG. 26, LAM is a model trained to perform actions in a real environment, capable of tokenizing and embedding task descriptions and environmental states as inputs, planning a sequence of actions (based on Chain-of-Thought), executing actions and making API calls within the environment, and monitoring results. LAM can perform modification and iteration procedures as needed. In one embodiment, LAM can be used in robot control, game agents, automation systems, etc.

[0269] FIG. 27 is a drawing showing a Hierarchical Reasoning Model (HRM) according to one embodiment of the present disclosure.

[0270] Referring to Fig. 27, HRM is a hierarchical model that processes inference by dividing it into high-level planning (H-layer) and low-level computation (L-layer). It can derive a final result by performing high-level planning after input encoding, performing detailed operations and iterations at each step, and constructing an iterative feedback loop until convergence. HRM can be effective for complex multi-step planning and inference tasks.

[0271] FIG. 28 is a drawing showing a ToolFormer (Tools-trained Model) according to one embodiment of the present disclosure.

[0272] Referring to Fig. 28, ToolFormer is a language model trained to use various external tools. It starts based on a pre-trained LLM, samples examples of tool calls, determines valid tool usage through test and evaluation, and performs fine-tuning with filtered data. ToolFormer has advantages in integration with calculators, searches, API calls, etc.

[0273] The four major types of artificial intelligence systems—basic LLM workflow, RAG (Retrieval-Augmented Generation), single AI agent, and multi-agent-based Agentic AI—are compared and summarized in terms of structure, function, characteristics, and use cases as shown in Figure 39 and the following [Table 11].

[0274] Item LLM Workflow RAGAI Agent Agentic AI Functionality Input-based next token prediction Answer search and augmentation through external knowledge Autonomous execution + component combination Autonomous tasks based on multi-agent collaboration Representative Use Cases Text generation, summarization Accurate question answering tools and planning Workflow Large-scale tasks, collaboration-based problem solving Strengths Fast, simple, easy to deploy Improved accuracy through external knowledge Planning + reasoning-based automation Flexible division of labor, solving complex problems Weaknesses Limited contextual understanding Sensitivity to data quality Requires access to clear goals and tools Increased design and control complexity Examples Chatbots, email generators Graph RAG, Modular RAG ReAct Agent, Rewoo Agent CUA (Computer Using Agent), Embodied Agents

[0275] FIGS. 29 to 34 are drawings illustrating vulnerabilities of an MCP according to one embodiment of the present disclosure.

[0276] Referring to Fig. 29, a command injection problem may occur in the MCP. That is, hidden commands (intended meanings) can be inserted into the prompt entered by the user to induce an agent to operate on the MCP server without authorization. For example, the agent may access external resources without authorization.

[0277] Referring to Fig. 30, a tool addiction problem may occur in the MCP. By including a tool with malicious code inserted into the MCP, it may be induced to produce incorrect results for specific tasks or perform intended actions. For example, an attacker may gain access to the instant messenger platform service and steal API keys or personal information.

[0278] Referring to Figure 31, server-sent event issues may occur in MCP. Since the Server-Sent Events (SSE) method transmits data in segments, the connection must remain open for a long time, which can cause delays and security issues. For example, when transmitting to various clients and integrated systems (instant messengers, vector DB, etc.), the connection may be maintained for a long time, which may result in security risks.

[0279] Referring to Fig. 32, privilege escalation issues can occur in MCP. A malicious tool can intercept or overwrite calls going to other trusted tools, thereby stealing the privileges of the tools trusted by the user. For example, an attacker can override tools such as instant messengers within the MCP server to connect to a malicious server.

[0280] Referring to Fig. 33, persistent context issues can occur in MCPs. MCPs record and maintain context throughout a user's session. This can lead to context tampering. For example, there is a risk that session contexts linked with tools such as cloud platforms, search engines, and knowledge bases may be tampered with.

[0281] Referring to Fig. 34, a problem of server data theft may occur in MCP. If a tool server connected to the MCP server is hacked, data and passwords from other servers can be stolen or controlled. For example, a client may be able to access user data from another server through a malicious MCP tool.

[0282] FIG. 35 is a diagram illustrating a context engineering structure in an AI agent system according to one embodiment of the present disclosure.

[0283] Referring to Fig. 35, a flow and memory structure can be used to effectively configure and utilize context in an AI Agent system. User input may include input queries or requests provided by the user to the system. The agent may be a central component that plans tasks and coordinates execution based on the given input and context. Additionally, the RAG (Retrieval-Augmented Generation) may include a knowledge retrieval component that retrieves relevant documents through vector search based on long-term memory. Action Tools may perform functions such as calling various external tools for code execution, document lookup, time checking, and calendar processing. Long-Term memory includes memory based on persistent knowledge stored through an MCP server and database, and Short-Term memory may store prompt components containing the context of the current conversation session (input, reasoning, tool usage history, etc.).

[0284] In one embodiment, a user may input a question or request into the system. Based on user input, the agent may formulate a plan and coordinate tasks according to the current context and purpose. The RAG system may perform a search on the vector DB if necessary and collect relevant information or documents. Action Tools may call external tools according to the requested task to retrieve execution results. The prompt generation and update unit may generate or update prompts based on the collected information (search, tools, results, inference, etc.). A final response may be generated and delivered to the user following these procedures. All contextual elements may be stored in the 'Chat History' within short-term memory. For example, information related to input, tools, usage, inference, etc., may be stored in short-term memory. Specific contexts, results, etc., may be added to long-term memory (e.g., MCP server / DB) to ensure reusability.

[0285] In one embodiment, user input includes initial input such as user questions or instructions, and tools may include external systems, APIs, calculators, document tools, etc. Agents may perform internal judgment, planning, state management, etc. as inference. RAG context includes document-based knowledge through vector search, and user children may include user settings, profiles, IDs, etc. Conversation history may include records of previous queries and responses.

[0286] According to one embodiment, context-based accuracy can be improved by utilizing both short-term memory (prompt) and long-term memory (DB). Additionally, a dynamic prompt can be configured by integrating not only user input but also tool usage results, search documents, and reasoning content. Furthermore, memory layers can be separated. For example, short-term memory is maintained during the session, while long-term memory can be used for long-term strategic iterations. An agent is responsible for the decision-making and execution of each step and can play a key role in coordinating the overall flow.

[0287] In one embodiment, an agentic AI system centered on user input, including search (RAG), tool calling, internal reasoning, and memory integration, may be utilized. The system aims to provide sophisticated responses and perform tasks tailored to the situational context by sophisticatedly structuring and utilizing various contextual information generated during interaction with the user. In one embodiment, user input includes various forms of user requests such as text, voice, and images; the agent module performs planning, determines whether to search or execute a tool, and generates responses based on user requests; and the RAG module performs vector-based similar document searches and can be used for external knowledge and context reinforcement. Action tools can call various functional tools such as external APIs, calculators, calendars, search engines, and databases. In one embodiment, the prompt engine can construct a final prompt by integrating various contextual elements. Short-Term Memory (STM) is a temporary storage for maintaining context and constructing prompts within a session, and Long-Term Memory (LTM) is a persistent memory structure based on an MCP server and database, which can be used for the agent's long-term learning and the utilization of accumulated experience.

[0288] The context processing pipeline can be as shown in the following [Table 12].

[0289] [1] User Input ↓ [2] Agent Module ↓ [3] If necessary → RAG Module → Search and return related documents ↓ [4] If necessary → Call Action Tools and obtain results ↓ [5] Prompt Engine: Integrate contextual information (input + search + tool results + inference, etc.) ↓ [6] Generate response via LLM ↓ [7] Store entire current context in Short-Term Memory ↓ [8] Transfer and accumulate important information in Long-Term Memory ↓ [9] Deliver response to user (Answer)

[0290] In one embodiment, the prompt may be composed of user input corresponding to a query or command, tool usage results including API call results, calculation results, etc., search-based context including documents retrieved from the RAG, agent reasoning including internal reasoning and planning, user information including preferences, ID, status, etc., conversation history including the previous conversation context, etc.

[0291] In one embodiment, the prompt may be deleted based on priority when the maximum prompt length is exceeded. For example, the priority may be configured in the order of agent inference > search context > tool results > user information > past conversation history. In one embodiment, result values ​​after a tool call may be inserted into the prompt in the form of a "contextual tag." Additionally, the search context may be inserted along with a summary and confidence score, rather than the original text.

[0292] In one embodiment, short-term memory is intended to maintain the entire context within a session and may store user input, prompt components, reasoning processes, tool usage results, etc. Short-term memory is deleted upon session termination, but important information may be transferred to long-term memory.

[0293] In one embodiment, the long-term memory may be composed of an MCP server (Agent Metadata) and a domain knowledge DB (Structured Knowledge), etc. The long-term memory may be updated when the Add to memory command is executed or when automatic saving conditions are satisfied. For example, information such as "User A prefers tools related to 'data visualization'" and "On August 7, 2025, the 'RAG + Tools' path was used in the 'Context Engineering' flow" may be stored in the long-term memory.

[0294] In one embodiment, the agent can determine whether to call a tool, the necessity of searching, and the possibility of repeated calls. Additionally, the agent can perform priority-based reasoning. For example, priorities may proceed in the order of user goal → environment state → available resources → execution strategy. In one embodiment, the agent can support parallel calls to multiple tools and support feedback-based iterative execution after execution.

[0295] For example, in the case of multimodal question and answer, when a user image is uploaded, a corresponding description is generated, and a date corresponding to the user image can be calculated via a tool call.

[0296] As another example, in the case of report generation, the search and summarization process proceeds based on user instructions, and templates can be inserted and edited.

[0297] As another example, in the case of automated schedule coordination, the calendar API is invoked based on natural language requests, and schedule recommendations can be provided.

[0298] According to one embodiment, a multi-agent-based Agentic AI extension structure may be supported. Additionally, according to one embodiment, a prompt dynamic optimization (auto-slimming) algorithm may be implemented. Furthermore, a memory vectorization-based summary storage module may be constructed, and user-specific customized context weighting profiling may be performed.

[0299] As illustrated in FIG. 36, an electronic device (100) (hereinafter referred to as the electronic device) operating an artificial intelligence model for deriving a liquid immersion refrigerant according to an embodiment of the present invention may include at least one processor (110), a memory (120), and a communication unit (130). The electronic device (100) is a basic configuration for performing a computing environment, and the electronic device (100) may be implemented by including some other components additionally or substantially in other embodiments, implemented as a single or multiple entity, or implemented as only some of the disclosed configurations. Internal or external components of the electronic device (100), or at least some of the components, may transmit and receive data or signals by being connected to each other through a BUS, GPIO (General Purpose Input / Output), SPI (Serial Peripheral Interface), or MIPI (Mobile Industry Processor Interface), etc.

[0300] The processor (110) may mean a set of one or more processors unless the context clearly indicates otherwise, and can control components of the processor (110) and the electronic device (100) by running software (e.g., instructions, programs, etc.) stored in memory (120). Additionally, the processor (110) can perform various operations such as computation, processing, data generation or processing, and can read data from memory (120) or store it in memory (120). The processor (110) may be composed of at least one core and may include a processor for data analysis, machine learning (ML), or deep learning (DL), such as a Central Processing Unit (CPU), a General Purpose Graphics Processing Unit (GPGPU), or a Tensor Processing Unit (TPU). The processor (110) can read software stored in memory (120) and perform data processing for machine learning (or deep learning) according to the present invention. According to one embodiment of the present disclosure, the processor (110) can perform operations for training a neural network. The processor (110) can perform operations for training a neural network, such as processing input data for training in deep learning, extracting features from input data, calculating errors, and updating the weights of the neural network using backpropagation. At least one of the CPU, GPGPU, and TPU of the processor (110) can process the training of the neural network model. For example, the CPU and GPGPU can together process the training of the neural network model and data classification using the neural network model. In addition, in one embodiment of the present disclosure, at least one processor (110) of the electronic device (100) can be used together to process the training of the neural network model and data classification using the neural network model.

[0301] Memory (120) is for storing various data, and the data is data acquired, processed, or used by at least one component of the electronic device (100), and may include software (e.g., instructions, programs, etc.). Memory (130) may mean a set of one or more memories unless clearly expressed otherwise in the context, and may include at least one type of storage medium among flash memory type, hard disk type, multimedia card micro type, card type memory (e.g., SD or XD memory, etc.), RAM, SRAM (Static Random Access Memory), ROM, EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, optical disk, and web storage that performs storage functions on the internet. The instruction, program, or software stored in the memory (120) may be used to refer to an operating system, an application, or middleware that provides various functions to an application to enable the application to utilize the components of the electronic device (100) for controlling the components of the electronic device (100). In one embodiment, when the processor (110) performs a specific operation, the memory (120) may store instructions that are performed by the processor (110) and correspond to the specific operation.

[0302] The communication unit (130) performs wireless or wired communication between the electronic device (100) and another device (e.g., a user terminal or another server), and the communication unit (130) may use wireless communication systems according to methods such as eMBB, URLLC, MMTC, LTE, LTE-A, NR, UMTS, GSM, CDMA, WCDMA, TDMA, FDMA, OFDMA, SCFDMA, WiBro, WiFi, Bluetooth, NFC, GPS, or GNSS. Additionally, the communication unit (130) may use various wired communication systems such as USB, HDMI, RS-232 (Recommended Standard-232), POTS (Plain Old Telephone Service), Public Switched Telephone Network (PSTN), xDSL (x Digital Subscriber Line), RADSL (Rate Adaptive DSL), MDSL (Multi Rate DSL), VDSL (Very High Speed ​​DSL), UADSL (Universal Asymmetric DSL), HDSL (High Bit Rate DSL), and Local Area Network (LAN). In the present disclosure, the communication unit (130) may be configured regardless of the mode of communication, such as wired or wireless, and may be configured with various communication networks such as a Personal Area Network (PAN) and a Wide Area Network (WAN). Additionally, the communication unit may be the known World Wide Web (WWW) and may also utilize wireless transmission technologies used for short-range communication, such as Infrared Data Association (IrDA) or Bluetooth. The technologies described in this disclosure may also be used in other networks mentioned above.

[0303] Optimal embodiments have been disclosed in the specification as described above. Specific terms have been used herein, but they are used only for the purpose of describing the invention and are not intended to limit the meaning or the scope of the invention as described in the claims. Therefore, those skilled in the art will understand that various modifications and equivalent alternative embodiments are possible therefrom. Accordingly, the true technical scope of protection of the invention should be determined by the technical spirit of the appended claims.

Claims

1. As a refrigerant in which the device is immersed, Having a viscosity of 3.46 or more and 6.76 cP or less, a dielectric constant of 3 or more and less than 4, a boiling point of 300 or more and 320°C or less, a decomposition temperature of 150 or more and 200°C or less, a flash point of 140 or more and less than 150°C, a vapor pressure of 0.000000045 or more and 0.00000032 atm or less, and a freezing point of less than -40°C, and having an aromatic ring; not containing sulfur and fluorine, Liquid refrigerant.

2. In claim 1, the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 1: <Chemical Formula 1> .

3. The refrigerant of Claim 1, wherein the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 2: <Chemical Formula 2> .

4. As a refrigerant in which the device is immersed, Having a viscosity of 6.84 cP or less, a dielectric constant of 2.13 or more and 2.15 or less, a boiling point of 321 or more and 325°C or less, a decomposition temperature of 219 or more and 225°C or less, a flash point of 150 or more and 155°C or less, a vapor pressure of 0.0000000269 or more and 0.0000000428 atm or less, and a freezing point of -48 or more and -36°C or less, and having an aromatic ring; not containing sulfur and fluorine, Liquid refrigerant.

5. In claim 4, the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 3: <Chemical Formula 3> .

6. In claim 4, the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 9: <Chemical Formula 9> 7. In claim 4, the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 15: <Chemical Formula 15> .

8. As a refrigerant in which the device is immersed, Having a viscosity of 9.18 to 9.97 cP, a dielectric constant of 2.13 to 2.14, a boiling point of 324 to 328°C, a decomposition temperature of 206 to 214°C, a flash point of 158 to 163°C, a vapor pressure of 0.00000000858 to 0.000000014 atm, and a freezing point of -46°C or lower, and comprising an aromatic ring; not containing sulfur and fluorine, Liquid refrigerant.

9. In claim 8, the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 4: <Chemical Formula 4> .

10. In claim 8, the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 7: <Chemical Formula 7> .

11. In claim 8, the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 10: <Chemical Formula 10> .

12. In claim 8, the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 13: <Chemical Formula 13> .

13. As a refrigerant in which the device is immersed, Having a viscosity of 10.20 or more and 10.98 cP or less, a dielectric constant of 2.0 or more and 2.23 or less, a boiling point of 324 or more and 404°C or less, a decomposition temperature of 206 or more and 226°C or less, a flash point of 157 or more and 235°C or less, a vapor pressure of 0.0000000000249 or more and 0.00000000784 atm or less, and a freezing point of -48 or more and -35°C or less, an aromatic ring; not containing sulfur and fluorine, Liquid refrigerant.

14. In claim 13, the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 5: <Chemical Formula 5> .

15. In claim 13, the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 6: <Chemical Formula 6> .

16. In claim 13, the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 8: <Chemical Formula 8> .

17. In claim 13, the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 11: <Chemical Formula 11> .

18. In claim 13, the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 12: <Chemical Formula 12> .

19. In claim 13, the refrigerant is an immersion refrigerant comprising a substance of the following chemical formula 14: <Chemical Formula 14> .

20. An immersion cooling system for cooling a device using an immersion refrigerant, comprising at least one immersion refrigerant according to any one of claims 1 to 18.

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