Method, device, electronic equipment and storage medium for predicting inhibitor loading performance in nanofiller systems

Abstract

The present application relates to a kind of metal corrosion protection-oriented nanofiller system in the prediction method of inhibitor loading performance, device, electronic equipment and storage medium, forms the functionalized nano carrier structure of reasonable structure and reliable performance, establishes a set of standardization simulation and performance prediction method, and proposes the quantitative prediction model of inhibitor specificity loading performance, also establishes the multi-dimensional parameter comprehensive evaluation model of inhibitor loading performance, realizes the accurate and stable prediction of inhibitor loading performance in nanofiller system;And the device, electronic equipment and storage medium provided by the present application are used to realize the prediction method, the screening of nanofiller system is from complex experiment cycle front to calculation simulation stage, and the development cycle is shortened by several times to several tens of times, and the development cost is effectively reduced;The present application is closely combined with the demand of metal anticorrosion coating field, has definite engineering application value and good popularization prospect.

Description

Technical Field

[0001] This invention relates to the field of metal corrosion protection technology, and in particular to a method, apparatus, electronic device, and storage medium for predicting the loading performance of corrosion inhibitors in a nanofiller system. Background Technology

[0002] Metal corrosion poses a significant challenge to industrial facilities, energy transportation, and marine engineering, resulting in high maintenance costs and risks to equipment reliability and environmental protection. Among numerous protective technologies, the addition of corrosion inhibitors is widely used due to its advantages such as controllable cost and wide applicability. However, directly adding corrosion inhibitors has drawbacks, including easy decomposition of inhibitor molecules in the medium, poor dispersion uniformity, and short effective duration. To overcome these limitations, researchers have gradually shifted towards developing intelligent corrosion inhibition systems using nanoporous materials as carriers. These systems encapsulate corrosion inhibitors within nanopores, aiming to achieve targeted delivery and on-demand release, thereby significantly improving utilization efficiency, extending protection lifespan, and reducing environmental burden.

[0003] Covalent organic frameworks (COFs) have become an ideal platform for constructing high-performance corrosion inhibitor nanocontainers due to their highly ordered and tunable pore structure, large specific surface area, excellent chemical stability, and ease of functionalization. Dispersing COF nanoparticles loaded with corrosion inhibitors (such as benzotriazole BTA, 2-mercaptobenzothiazole MBT, or 8-hydroxyquinoline 8-HQ) as functional fillers in anti-corrosion coatings can effectively improve problems such as corrosion inhibitor dispersion, compatibility, and release kinetics in traditional methods, forming a composite system with long-lasting protection.

[0004] Despite this, the development of nanofiller systems currently relies heavily on experimental trial and error. Researchers typically synthesize a series of nanocarrier materials, load corrosion inhibitors using impregnation methods, and then evaluate their anti-corrosion effects through electrochemical testing or salt spray tests. This approach is not only inefficient and costly, but also makes it difficult to establish a structure-property relationship between the nanocarrier structure and the corrosion inhibitor loading efficiency at the molecular level. Due to a lack of in-depth understanding of the underlying mechanisms, the development of high-performance nanofiller systems remains largely in a stage of blind exploration, severely hindering their targeted design and engineering application.

[0005] Although computational simulations such as molecular dynamics (MD) and density functional theory (DFT) have been used to assist in the analysis of coating protection mechanisms, current applications are mostly limited to simple mechanistic interpretations of existing experimental results. These simulations are often small in scale and limited in duration, failing to systematically establish the correlation between microscopic molecular simulation parameters and macroscopic coating protection performance, and even less effective for pre-experiment performance prediction, carrier structure optimization, or corrosion inhibitor matching design. Although MD technology itself is relatively mature, systematic simulation applications and methodological development are still lacking in the study of nanofiller systems.

[0006] Therefore, establishing a method for designing and predicting the performance of nanofiller systems based on MD simulation, constructing a quantitative prediction model from structural characteristics to loading performance, and providing a prediction device, electronic device, and computer storage medium for implementing the above prediction method are of great theoretical significance and engineering application value for accelerating the research and development of high-performance anti-corrosion coatings. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a method, apparatus, electronic device, and storage medium for predicting the loading performance of corrosion inhibitors in nanofiller systems for metal corrosion protection. This enables the rational design of nanofiller systems, prediction and scientific evaluation of corrosion inhibitor loading performance, thereby solving the problem of poor protective effects caused by defects in existing protection technologies, such as easy decomposition of corrosion inhibitor molecules, poor dispersion uniformity, and short effective action period.

[0008] To achieve this objective, the present invention adopts the following technical solution:

[0009] In a first aspect, the present invention provides a method for predicting the loading performance of corrosion inhibitors in a nanofiller system, the prediction method comprising the following steps:

[0010] Obtaining nanofiller systems;

[0011] A hierarchical molecular dynamics simulation model was constructed, and dynamic simulations were performed on all nanofiller systems until equilibrium was reached, and simulation results were obtained.

[0012] A corrosion inhibitor loading prediction model is established, and the simulation results are input into the corrosion inhibitor loading prediction model for prediction.

[0013] This invention establishes a standardized simulation and performance prediction method, proposes a hierarchical simulation strategy, and establishes a loading prediction model applicable to three typical corrosion inhibitors, MBT, BTA and 8-HQ, based on simulation data through large-scale, long-term dynamic simulation, revealing the quantitative structure-property relationship.

[0014] Preferably, the method for obtaining the nanofiller system includes:

[0015] (1) Using TpTd-COF as the basic structure of nanocarriers, different functional groups are introduced to construct a variety of functionalized nanocarrier structures, resulting in a nanocarrier structure material library;

[0016] (2) Different corrosion inhibitor molecules are loaded into all the nanocarrier structures in the nanocarrier structure material library described in step (1) to form the nanofiller system.

[0017] Preferably, the process of obtaining the nanofiller system further includes: before step (2), performing structuralization and stability verification on all nanocarrier structures in the nanocarrier structure material library described in step (1) based on density functional theory calculations to obtain optimized nanocarrier structures.

[0018] Preferably, the density functional theory calculation includes first-principles calculations of density functional theory.

[0019] Preferably, the structural optimization includes optimizing the nanocarrier structure to a stable configuration with the lowest energy.

[0020] Preferably, the stability verification includes calculating the cohesive energy of each of the nanocarrier structures, and a positive cohesive energy indicates that the stability verification is passed.

[0021] The design method described in this invention forms a functionalized nanocarrier structure that has been theoretically designed and verified. Using TpTd-COF as the basic nanocarrier structure, functional groups with different polarities, electronegativity, and coordination abilities are introduced into the basic framework, thereby constructing a nanocarrier structure material library containing the basic nanocarrier structure and various functionalized nanocarrier structures. All nanocarrier structures are structurally optimized and their stability verified, confirming their thermodynamic stability and theoretically ensuring their feasibility as practical carriers. Furthermore, the nanocarrier structures are designed to load corrosion inhibitor molecules, aiming to form a predictable and controllable "host-guest" nanofiller system for metal corrosion protection through non-covalent interactions such as hydrogen bonds or van der Waals forces. The construction of this series of structural models provides a fundamental model for screening and designing nanofiller systems suitable for corrosive environments at the molecular level.

[0022] The long-term structural stability of the carrier material in corrosive media is a prerequisite for its practical application. To evaluate the reliability of the designed nanocarrier structures, this invention calculates and analyzes the cohesive energy (Ecohesive) of all nanocarrier structures. coh E cohIt is an important physical quantity that characterizes the strength of interatomic interactions in crystal or molecular systems. Its value directly reflects the total energy required to decompose the structure into isolated constituent atoms, and thus becomes a key indicator for evaluating the intrinsic stability of materials.

[0023] Preferably, the functional groups in step (1) include -F, -OH, -SH, -CN and -CCH.

[0024] Preferably, the nanocarrier structures in the nanocarrier structure material library in step (1) include TpTd-H, TpTd-F, TpTd-OH, TpTd-SH, TpTd-CN and TpTd-CCH.

[0025] Preferably, the functional groups in step (1) include -F, -OH, -SH, -CN and -CCH.

[0026] Preferably, the nanocarrier structures in the nanocarrier structure material library in step (1) include TpTd-H, TpTd-F, TpTd-OH, TpTd-SH, TpTd-CN and TpTd-CCH.

[0027] This invention introduces five functional groups with different properties onto a TpTd-COF initial nanostructure model framework, and systematically constructs six nanocarrier structures with the same topological framework but different pore chemical environments.

[0028] Preferably, the corrosion inhibitor molecules in step (2) include MBT, 8-HQ and BTA.

[0029] Preferably, the process of obtaining the nanofiller system further includes molecular modeling and configuration optimization of all corrosion inhibitor molecules before loading in step (2).

[0030] Preferably, the configuration optimization includes optimizing the molecular structure of all corrosion inhibitors to a stable state with the lowest energy.

[0031] The present invention preferably includes obtaining the nanofiller system further by performing molecular modeling and configuration optimization on all corrosion inhibitor molecules before loading in step (2), in order to ensure that the corrosion inhibitor molecules are in the lowest energy state so as to accurately reflect their true conformation during loading.

[0032] It is worth noting that in step (2), all nanocarrier structures in the nanocarrier structure material library described in step (1) are loaded with different corrosion inhibitor molecules to form the nanofiller system; that is, corrosion inhibitor molecules (MBT, 8-HQ and BTA) are loaded into the six nanocarrier structures in the above nanocarrier structure material library respectively. The combination of a nanocarrier structure and a corrosion inhibitor molecule forms a nanofiller system for metal corrosion protection.

[0033] Preferably, the layered molecular dynamics simulation model includes a first inert plate, a nanocarrier layer, a corrosion inhibitor molecule layer, and a second inert plate arranged sequentially.

[0034] Preferably, the nanocarrier layer comprises a nanocarrier bilayer structure.

[0035] Preferably, the interlayer spacing of the nanocarrier bilayer structure is 8~10 Å, for example, it can be 8 Å, 9 Å or 10 Å.

[0036] Preferably, the distance between the side of the nanocarrier bilayer structure closest to the first inert plate and the first inert plate is 30~50 Å, for example, it can be 30 Å, 35 Å, 40 Å, 45 Å or 50 Å.

[0037] Preferably, the distance between the side of the nanocarrier bilayer structure closest to the second inert plate and the second inert plate is 30~50 Å, for example, it can be 30 Å, 35 Å, 40 Å, 45 Å or 50 Å.

[0038] The loading and release kinetics of corrosion inhibitors on the carrier directly affect the early protection and long-term maintenance capabilities of anti-corrosion coatings. To explore this crucial process, this invention constructs a layered molecular dynamics simulation system consisting of an "inert plate-nanocarrier bilayer-corrosion inhibitor layer" to simulate the initial loading behavior of corrosion inhibitors in anti-corrosion applications.

[0039] Preferably, the establishment of the corrosion inhibitor loading prediction model includes:

[0040] Based on the simulation results, the number of corrosion inhibitor molecules between the corresponding nanocarrier layers in each nanofiller system under equilibrium conditions was counted, and the mass loading of the corresponding corrosion inhibitor molecules in each nanofiller system was calculated. Then, the correlation analysis between the mass loading and the variable parameters of the corresponding nanofiller system was performed. Based on the analysis results, a prediction model for the corrosion inhibitor loading was established.

[0041] Preferably, the variable parameters of the corresponding nanofiller system include the pore characteristic parameters of the corresponding nanocarrier structure and the interaction energy between the corresponding corrosion inhibitor molecular layer and the corresponding nanocarrier layer under equilibrium conditions.

[0042] Preferably, the pore characteristic parameters include porosity, pore volume, pore size, and specific surface area.

[0043] Preferably, the corrosion inhibitor loading prediction model includes:

[0044]

[0045] Where a0, a1, a2, and a3 are fitting coefficients; E is the average value of the interaction energy between the corrosion inhibitor molecules as a whole and the nanocarrier layer in the equilibrium stage; F is the comprehensive index of pore structure; and G is the index of the influence of pore structure equilibrium.

[0046] Preferably, the establishment of the corrosion inhibitor loading prediction model further includes performing collinearity analysis on the pore characteristic parameters of the nanocarrier structure to determine pore variable indices.

[0047] Preferably, the porosity variable index includes a comprehensive pore structure index F and a pore structure balance influence index G.

[0048] Preferably, the comprehensive index F of the hole structure is calculated using the following formula;

[0049]

[0050] Porosity is dimensionless; pore volume is measured in cm³. 3 / g; pore size is measured in nm.

[0051] It is worth noting that the calculation of the comprehensive pore structure index F and the pore structure balance influence index G only involves the calculation between numerical values.

[0052] Preferably, the pore structure balance influence index G is calculated using the following formula;

[0053]

[0054] The unit for specific surface area is m. 2 / g; porosity is dimensionless.

[0055] Preferably, the prediction method further includes: establishing a comprehensive evaluation model for the loading performance of the nanofiller system based on the prediction results obtained from the corrosion inhibitor loading prediction model.

[0056] Preferably, the comprehensive evaluation model includes the Loading Comprehensive Efficiency Index (CLEI), calculated using the following formula:

[0057]

[0058]

[0059]

[0060]

[0061]

[0062] in, The theoretical mass loading of corrosion inhibitor, calculated using the corrosion inhibitor loading prediction model, is expressed in percentage (%). This is a baseline reference value, corresponding to the maximum corrosion inhibitor mass loading in all experimental data, expressed in % (%). The value represents the average mass loading of the target corrosion inhibitor across all nanocarrier structures, expressed as a percentage (%). ω1 represents the average mass loading of MBT corrosion inhibitor in all nanocarrier structures, expressed as %; α represents the corrosion inhibitor compatibility coefficient, used to correct for the inherent differences between different corrosion inhibitors; β represents the functionalization correction coefficient, used to correct for the inherent effects of different functional groups; ω1 represents the corrosion inhibitor compatibility integration coefficient; and ω2 represents the functionalization correction integration coefficient.

[0063] Secondly, the present invention provides a device for predicting the loading performance of corrosion inhibitors in a nanofiller system, the device comprising:

[0064] The acquisition module is used to acquire the nanofiller system;

[0065] The simulation module is used to input all the nanofiller systems acquired by the acquisition module into the hierarchical molecular dynamics simulation model and perform dynamic simulations.

[0066] The prediction module is used to input the simulation results of the simulation module into the corrosion inhibitor loading prediction model and make predictions.

[0067] Preferably, the prediction device further includes:

[0068] The evaluation module is used to input the prediction results of the prediction module into the comprehensive evaluation model for comprehensive evaluation.

[0069] Thirdly, the present invention provides an electronic device, the electronic device comprising:

[0070] At least one processor; and a memory communicatively connected to said at least one processor;

[0071] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for predicting the corrosion inhibitor loading performance in the nanofiller system described in the first aspect.

[0072] Fourthly, the present invention provides a computer storage medium storing computer-executable instructions, which, when executed by a processor, implement the method for predicting the loading performance of corrosion inhibitors in the nanofiller system described in the first aspect.

[0073] Compared with the prior art, the present invention has at least the following beneficial effects:

[0074] (1) The present invention forms a functionalized nanocarrier structure that has been theoretically designed and verified. Based on TpTd-COF as the basic framework, a nanocarrier structure material library containing six derived structures has been constructed by precisely introducing five functional groups: —F, —OH, —SH, —CN and —CCH. Its stability has been verified by first-principles calculations.

[0075] (2) This invention establishes a set of standardized simulation and performance prediction methods, and proposes a layered simulation strategy of “inert plate-nanocarrier double layer-corrosion inhibitor layer”. Through large-scale, long-term MD simulation, the key parameters such as the dynamic loading amount and interfacial interaction energy of the nanofiller system are systematically extracted.

[0076] (3) This invention proposes a quantitative prediction model for the specific loading performance of corrosion inhibitors. Based on simulation data, multiple linear prediction equations applicable to three typical corrosion inhibitors, MBT, BTA and 8-HQ, are established, revealing the quantitative structure-performance relationship.

[0077] (4) This invention creates a multi-dimensional comprehensive evaluation index and innovatively constructs a loading comprehensive performance index. This index integrates four key performance dimensions: loading amount, bonding strength, pore size adaptability and pore volume potential, and provides an objective standard that can quantify and compare the comprehensive performance of different nanofiller systems.

[0078] (5) The present invention provides a set of prediction devices, electronic devices and computer storage media for predicting the loading performance of corrosion inhibitors in nanofiller systems, which are used to realize the above prediction method. It has the advantages of high prediction accuracy, fast calculation efficiency and convenient operation, and solves the defects of traditional experimental trial and error method that rely on a large number of experiments, have a long cycle, high cost and accuracy is greatly affected by human factors. Attached Figure Description

[0079] Figure 1 This is a flowchart illustrating the method for predicting the loading performance of corrosion inhibitors in a nanofiller system provided by the present invention.

[0080] Figure 2 This is a schematic diagram of the structure of the device for predicting the loading performance of corrosion inhibitors in the nanofiller system provided by the present invention.

[0081] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0082] Figure 4 This is a schematic diagram of the nanocarrier structure and corrosion inhibitor molecular configuration in Example 1 of the present invention.

[0083] Figure 5 This is a schematic diagram of the layered molecular dynamics simulation model of the nanofiller system in Example 1 of the present invention and its dynamics simulation process.

[0084] Figures 6 to 11 These are snapshots of the dynamic distribution of the corresponding corrosion inhibitor molecules at the interface in the corresponding nanofiller systems of Embodiment 1 of the present invention.

[0085] Figure 12 This is a graph showing the variation in the mass loading of the three corrosion inhibitor molecules in different nanocarrier structures in Example 1 of the present invention.

[0086] Figure 13 This is a correlation analysis graph showing the relationship between the mass loading of the three corrosion inhibitors and the variable parameters of the corresponding nanofiller system in Example 1 of this invention.

[0087] Figure 14 This is a thermal matrix diagram of the pore characteristic parameters in Embodiment 1 of the present invention.

[0088] Figure 15 This is an accuracy analysis diagram of the corrosion inhibitor loading prediction model described in Embodiment 1 of the present invention.

[0089] In the diagram: 1. Acquisition module; 2. Simulation module; 3. Prediction module; 4. Evaluation module; 5. Electronic device; 501. Processor; 502. Memory; 503. Computer program. Detailed Implementation

[0090] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments. However, the following examples are merely simplified examples of the present invention and do not represent or limit the scope of protection of the present invention. The scope of protection of the present invention is determined by the claims.

[0091] Typical, but not limiting, embodiments of the present invention are as follows:

[0092] I. This embodiment provides a method for predicting the loading performance of corrosion inhibitors in a nanofiller system, the process of which is as follows: Figure 1 As shown, the prediction method includes the following steps:

[0093] Obtaining nanofiller systems;

[0094] A hierarchical molecular dynamics simulation model was constructed, and dynamic simulations were performed on all nanofiller systems until equilibrium was reached, and simulation results were obtained.

[0095] Establish a corrosion inhibitor loading prediction model and input the simulation results into the corrosion inhibitor loading prediction model for prediction;

[0096] Based on the prediction results obtained from the corrosion inhibitor loading prediction model, a comprehensive evaluation model for the loading performance of the nanofiller system is established.

[0097] The method for obtaining the nanofiller system includes:

[0098] (1) Using TpTd-COF as the basic structure of nanocarriers, different functional groups are introduced to construct a variety of functionalized nanocarrier structures, resulting in a nanocarrier structure material library;

[0099] (2) Based on density functional theory calculations, all nanocarrier structures in the nanocarrier structure material library described in step (1) are structured and their stability verified to obtain optimized nanocarrier structures; molecular modeling and configuration optimization are performed on all corrosion inhibitor molecules; then, different corrosion inhibitor molecules are loaded onto all nanocarrier structures in the optimized nanocarrier structure material library to form the nanofiller system.

[0100] The functional groups mentioned in step (1) include -F, -OH, -SH, -CN and -CCH.

[0101] Among them, the nanocarrier structures in the nanocarrier structure material library in step (1) include TpTd-H, TpTd-F, TpTd-OH, TpTd-SH, TpTd-CN and TpTd-CCH.

[0102] The corrosion inhibitor molecules in step (2) include MBT, 8-HQ and BTA.

[0103] The density functional theory calculations include first-principles calculations of density functional theory.

[0104] The structural optimization includes optimizing the nanocarrier structure to a stable configuration with the lowest energy.

[0105] The stability verification includes calculating the cohesive energy of each nanocarrier structure, and a positive cohesive energy indicates that the stability verification is passed.

[0106] The configuration optimization includes optimizing the molecular structure of all corrosion inhibitors to the stable state with the lowest energy.

[0107] The layered molecular dynamics simulation model includes a first inert plate, a nanocarrier layer, a corrosion inhibitor molecule layer, and a second inert plate arranged sequentially; the nanocarrier layer includes a nanocarrier bilayer structure.

[0108] The establishment of the corrosion inhibitor loading prediction model includes:

[0109] Based on the simulation results, the number of corrosion inhibitor molecules between the corresponding nanocarrier layers in each nanofiller system under equilibrium conditions was counted, and the mass loading of the corresponding corrosion inhibitor molecules in each nanofiller system was calculated. Then, correlation analysis was performed on the mass loading and the variable parameters of the corresponding nanofiller system, and collinearity analysis was performed on the pore characteristic parameters of the nanocarrier structure to determine the pore variable index. Based on the analysis results, a prediction model for the corrosion inhibitor loading was established.

[0110] The variable parameters of the corresponding nanofiller system include the pore characteristic parameters of the corresponding nanocarrier structure and the interaction energy between the corresponding corrosion inhibitor molecular layer and the corresponding nanocarrier layer under equilibrium conditions.

[0111] The pore characteristic parameters include porosity, pore volume, pore size, and specific surface area.

[0112] The corrosion inhibitor loading prediction model includes:

[0113]

[0114] Where a0, a1, a2, and a3 are fitting coefficients; E is the average value of the interaction energy between the corrosion inhibitor molecules as a whole and the nanocarrier layer in the equilibrium stage; F is the comprehensive index of pore structure; and G is the index of the influence of pore structure equilibrium.

[0115] The porosity variable indices include the comprehensive pore structure index F and the pore structure balance influence index G.

[0116] The comprehensive index F of the hole structure is calculated using the following formula;

[0117]

[0118] Porosity is dimensionless, and the unit of pore volume is cm³. 3 / g, where the pore size is measured in nm.

[0119] The pore structure balance influence index G is calculated using the following formula;

[0120]

[0121] The unit for specific surface area is m². 2 / g, porosity is dimensionless.

[0122] The comprehensive evaluation model includes the Loading Comprehensive Efficiency Index (CLEI), calculated using the following formula:

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] in, This refers to the theoretical mass loading of the corrosion inhibitor calculated using the corrosion inhibitor loading prediction model. This serves as a baseline reference value, corresponding to the maximum corrosion inhibitor mass loading in all experimental data. The average mass loading of the target corrosion inhibitor in all nanocarrier structures; ω1 represents the average mass loading of MBT corrosion inhibitor in all nanocarrier structures; α is the corrosion inhibitor fit coefficient, used to correct for the inherent differences between different corrosion inhibitors; β is the functionalization correction coefficient, used to correct for the inherent effects of different functional groups; ω1 is the corrosion inhibitor fit integration coefficient; and ω2 is the functionalization correction integration coefficient.

[0129] II. This embodiment also provides a device for predicting the loading performance of corrosion inhibitors in a nanofiller system, such as... Figure 2 As shown, the prediction device includes:

[0130] Acquisition module 1 is used to acquire the nanofiller system;

[0131] Simulation module 2 is used to input all the nanofiller systems acquired by acquisition module 1 into the hierarchical molecular dynamics simulation model and perform dynamic simulation;

[0132] The prediction module 3 is used to input the simulation results of the simulation module 2 into the corrosion inhibitor loading prediction model and make a prediction.

[0133] Evaluation module 4 is used to input the prediction results of prediction module 3 into the comprehensive evaluation model for comprehensive evaluation.

[0134] III. This embodiment provides an electronic device 5, which is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 5 can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0135] like Figure 3As shown, the electronic device 5 includes at least one processor 501; and a memory 502 communicatively connected to the at least one processor 501; wherein the memory 502 stores a computer program 503 executable by the at least one processor 501, the computer program 503 being executed by the at least one processor 501 to enable the at least one processor 501 to execute the above-described method for predicting the corrosion inhibitor loading performance in the nanofiller system.

[0136] The processor 501 may be a read-only memory (ROM) or random access memory (RAM); the memory 502 may be a disk, optical disk, etc.; the communication connection may be established through a network card, modem, or wireless transceiver, allowing the exchange of information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0137] The processor 501 can be any general-purpose and / or special-purpose processing component with processing and computing capabilities. Examples of the processor 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 501 performs the various methods and processes described above, such as the method for predicting the corrosion inhibitor loading performance in the aforementioned nanofiller system.

[0138] In some embodiments, the method for predicting the corrosion inhibitor loading performance in the nanofiller system can be implemented as a computer program 503, which is tangibly contained in a computer-readable storage medium. When the computer program 503 is loaded into the memory 502 and executed by the processor 501, one or more steps of the method for predicting the corrosion inhibitor loading performance in the nanofiller system described above can be performed.

[0139] Alternatively, in other embodiments, the processor 501 may be configured by any other suitable means (e.g., by means of firmware) to perform a method for predicting the corrosion inhibitor loading performance in the nanofiller system.

[0140] The computer program 503 for implementing the method of the present invention can be written in any combination of one or more programming languages. The computer program 503 can be provided to a processor 501 of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor 501, the computer program 503 causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer program 503 can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0141] In the context of this invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program 503 for use by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof.

[0142] Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0143] Those skilled in the art should also understand that the various illustrative logic blocks, modules, etc., described in conjunction with the embodiments of the present invention can all be implemented as electronic hardware, computer software, or a combination thereof. To clearly illustrate the interchangeability between hardware and software, the various illustrative modules and steps described above are generally described in terms of their functions. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. Skilled personnel can implement the described functions in a modified manner for each specific application; however, such implementation decisions should not be construed as departing from the scope of protection of the present invention.

[0144] The prediction methods or calculation steps described in conjunction with the embodiments herein can be directly embodied in hardware, software modules executed by processor 501, or a combination thereof. The software modules can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium well known in the art. An exemplary storage medium is connected to processor 501, enabling processor 501 to read information from and write information to the storage medium. Of course, the storage medium can also be a component of processor 501. Processor 501 and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. Alternatively, processor 501 and the storage medium can exist as discrete components in the user terminal.

[0145] For software implementation, the techniques described in this invention can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. This software code can be stored in memory 502 and executed by processor 501. Memory 502 can be implemented within processor 501 or externally; in the latter case, it is communicatively coupled to processor 501 via various means, as is well known in the art.

[0146] IV. To illustrate the effectiveness of the method, apparatus, electronic device, and storage medium for predicting corrosion inhibitor loading performance in nanofiller systems provided by this invention, Example 1 is described below (wherein all periodic cell models are constructed in simulation software). The details are as follows:

[0147] Example 1

[0148] This embodiment provides a method for predicting the corrosion inhibitor loading performance in a nanofiller system for metal corrosion protection. The prediction method includes the following steps:

[0149] Obtaining nanofiller systems;

[0150] A hierarchical molecular dynamics simulation model was constructed, and dynamic simulations were performed on all nanofiller systems until equilibrium was reached, and simulation results were obtained.

[0151] Establish a corrosion inhibitor loading prediction model and input the simulation results into the corrosion inhibitor loading prediction model for prediction;

[0152] Based on the prediction results obtained from the corrosion inhibitor loading prediction model, a comprehensive evaluation model for the loading performance of the nanofiller system is established.

[0153] The method for obtaining the nanofiller system includes:

[0154] (1) such as Figure 4 As shown in Figure a, using TpTd-COF as the basic nanocarrier structure, five functional groups with different properties—F, —OH, —SH, —CN and —CCH—were introduced into the TpTd-COF framework according to theoretical design. Six nanocarrier structures with the same topological framework but different pore chemical environments were systematically constructed and named TpTd-H, TpTd-F, TpTd-OH, TpTd-SH, TpTd-CN and TpTd-CCH, forming a nanocarrier structure material library.

[0155] (2) Based on the first-principles calculations of density functional theory (using PBE functionals, DZP basis set, 400 eV cutoff energy), the structure of all nanocarrier structures in the nanocarrier structure material library described in step (1) is optimized to obtain the stable configuration with the lowest energy and the stability is verified to obtain the optimized nanocarrier structure; at the same time, molecular modeling is performed on the three corrosion inhibitor molecules, namely MBT, 8-HQ and BTA (e.g., Figure 4 As shown in Figure b), its structure is optimized to a stable state with the lowest energy to accurately reflect its true conformation during loading; then MBT, 8-HQ and BTA are loaded using the optimized nanocarrier structure to form the nanofiller system.

[0156] The stability verification includes calculating the cohesive energy (E) of the six nanocarrier structures. coh The calculation results are shown in Table 1. It can be seen that the E values ​​of all nanocarrier structures are... coh All values ​​were positive, and compared to unmodified TpTd-H, all functionally modified nanocarrier structures exhibited higher cohesive energy values. These results fully demonstrate that the nanocarrier structures designed in this embodiment possess excellent thermodynamic stability, and that specific functional group modifications effectively strengthen their framework structure. This ensures that this series of materials maintains structural integrity when used as corrosion inhibitor carriers in complex corrosive environments, thereby providing a stable and durable storage space for the corrosion inhibitor, which is the foundation for achieving long-term corrosion protection.

[0157] The energies of the relevant atoms in all nanocarrier structures are as follows: F: -0.052 eV; O: -0.084 eV; N: -0.089 eV; S: -0.093 eV; C: -0.084 eV; H: -0.028 eV.

[0158] Table 1

[0159]

[0160] like Figure 5As shown, this embodiment constructs a layered molecular dynamics simulation model consisting of a "first inert helium plate - nanocarrier layer - corrosion inhibitor layer - second inert helium plate" to simulate the initial loading behavior of the corrosion inhibitor in corrosion protection applications. Figure 5 In diagram I, the nanofiller system is loaded. In I, m1 represents a corrosion inhibitor molecule, and m2 represents a nanocarrier structure. Figure 5 II is a simulation box. Figure 5 In the model III, which represents a layered molecular dynamics simulation, n1 is the first inert helium plate, n2 is the nanocarrier layer, n3 is the corrosion inhibitor layer, and n4 is the second inert helium plate. The interlayer spacing of the nanocarrier bilayer structure is 8 Å; the distance between the side of the nanocarrier bilayer structure closest to the first inert helium plate and the first inert helium plate is 3 nm (30 Å); the distance between the side of the nanocarrier bilayer structure closest to the second inert helium plate and the corrosion inhibitor layer is 1 nm (10 Å); and the distance between the corrosion inhibitor layer and the second inert helium plate is 3 nm (30 Å).

[0161] This embodiment systematically captures the dynamic interface evolution process of the nanofiller system at different simulation times by conducting large-scale, long-term molecular dynamics simulations (e.g., ...). Figures 6-11 As shown in the figure, the simulation results show that within 0 to 1500 ps, ​​all three corrosion inhibitor molecules can rapidly and effectively migrate from their initial dispersed state and adsorb onto the nanocarrier layer, confirming the carrier's excellent capture ability for the corrosion inhibitor. After 1500 ps, ​​the interfacial distribution of all systems no longer changes significantly, and the density distribution of corrosion inhibitor molecules on the surface and near the pores of the nanocarrier layer tends to stabilize. This indicates that the simulated system has reached a dynamic equilibrium state, meaning that the loading capacity of the nanocarrier for corrosion inhibitor molecules has reached saturation under these conditions. This state simulates the stable storage stage after the corrosion inhibitor is successfully encapsulated in the coating. The dynamic simulation in this embodiment intuitively reveals the differences in the efficiency and ability of different nanocarrier structures to load corrosion inhibitors, providing key mechanistic references and screening criteria for predicting and optimizing the loading rate and potential release behavior of corrosion inhibitors in actual anti-corrosion coatings.

[0162] The establishment of the corrosion inhibitor loading prediction model includes:

[0163] Based on the simulation results, the number of corrosion inhibitor molecules between the corresponding nanocarrier layers in each nanofiller system under equilibrium conditions was counted, and the mass loading of the corresponding corrosion inhibitor molecules in each nanofiller system was calculated (results are shown in the figure). Figure 12 (as shown in the figure). Then, correlation analysis is performed on the mass loading amount and the variable parameters of the corresponding nanofiller system, and collinearity analysis is performed on the pore characteristic parameters of the nanocarrier structure to determine the pore variable index; based on the analysis results, a prediction model for the corrosion inhibitor loading amount is established.

[0164] The corrosion inhibitor loading prediction model described in this embodiment includes:

[0165]

[0166] Where a0, a1, a2, and a3 are fitting coefficients; E is the average value of the interaction energy between the corrosion inhibitor molecules as a whole and the nanocarrier layer in the equilibrium stage, in Kcal / mol; F is the comprehensive index of pore structure, and G is the index of the influence of pore structure equilibrium.

[0167] The variable parameters of the corresponding nanofiller system include the pore characteristic parameters of the corresponding nanocarrier structure and the interaction energy between the corresponding corrosion inhibitor molecular layer and the corresponding nanocarrier layer under equilibrium conditions; the pore characteristic parameters include porosity, pore volume, pore size and specific surface area.

[0168] like Figure 12 As shown in the figure, a, b, and c represent the changes in mass loading of BTA, 8-HQ, and MBT in six different nanocarrier structures, respectively. It can be seen that the introduction of functional groups generally reduces the loading due to steric hindrance, revealing the need to balance "strong adsorption" and "pore unobstructedness" in the design. From the overall loading performance perspective, the nanocarrier structure exhibits a significantly higher mass loading advantage for MBT, indicating that its pore chemical environment is better suited to the size and polarity of the MBT molecule. For BTA and 8-HQ, BTA has a higher mass loading on most carriers, but a lower mass loading on TpTd-SH and TpTd-CCH, with comparable loading capacities for both. This suggests that specific functional groups (such as —SH and —C≡CH) can effectively regulate pore properties, thereby differentially affecting the adsorption and filling behavior of different corrosion inhibitors. In summary, the quantitative evaluation in this embodiment not only reveals the complex mechanism by which functionalization modification affects loading performance, but more importantly, it screens out the most promising nanocarrier materials for different corrosion environments (corresponding to different preferred corrosion inhibitors) from the perspective of loading capacity.

[0169] This embodiment uses univariate linear regression analysis to examine the correlation between mass loading and interaction energy, porosity, pore volume, pore size, and specific surface area. Figure 13As shown in the figure, figures a to e represent the correlation analysis between the mass loading of the three corrosion inhibitors and the interaction energy, porosity, pore volume, pore size, and specific surface area of ​​the corresponding nanofiller systems. The results show that, regarding interactions, the correlation coefficients r for 8-HQ, BTA, and MBT are 0.151, -0.570, and -0.764, respectively, indicating that the mass loading of 8-HQ is almost unrelated to the interaction energy, while BTA and MBT show a moderate to strong negative correlation, meaning that the more negative the interaction energy, the higher the mass loading. Regarding pore characteristic parameters, the loading of all three corrosion inhibitors is strongly positively correlated with porosity (the correlation coefficients r for 8-HQ, BTA, and MBT are 0.885, 0.879, and 0.811, respectively), indicating that porosity is a key factor in regulating the loading. Furthermore, the mass loading of 8-HQ, BTA, and MBT showed a strong positive correlation with pore volume and pore size (r ranged from 0.640 to 0.869), with 8-HQ being most significantly affected by these pore characteristic parameters, consistent with the phenomena partially reflected by the aforementioned interaction energies. In contrast, the mass loading of 8-HQ, BTA, and MBT showed a negative correlation with specific surface area, with significant differences in correlation strength (correlation coefficient of MBT = -0.623 > correlation coefficient of BTA = -0.274 > correlation coefficient of 8-HQ r = -0.129), with only MBT showing a significant influence. In summary, porosity had the most significant impact on the mass loading of the three corrosion inhibitors; the loading of 8-HQ was mainly affected by pore structure, MBT was affected by both energy and porous structure parameters, while BTA was in between. These results provide a crucial basis for the targeted design of high-performance COF-based nanocarriers.

[0170] This embodiment also analyzes the correlation between various pore characteristic parameters (such as...). Figure 14 (As shown in the figure). The results show that porosity, pore volume, and pore size exhibit high collinearity (correlation coefficient r ≥ 0.90), indicating that these parameters essentially reflect the degree of development of the pore structure and should not be used simultaneously as independent variables in the statistical model. In contrast, the correlation between specific surface area and the above parameters is weak (r = -0.36~0.0051), indicating that it has relative independence in structural characterization and can be used as an auxiliary indicator to distinguish different pore configurations. In summary, this embodiment clarifies the intrinsic relationship between pore characteristic parameters and determines the pore variable indicators, namely the comprehensive pore structure index F and the pore structure equilibrium influence index G, providing an important basis for subsequent multi-factor modeling and carrier optimization.

[0171] The comprehensive index F of the hole structure is calculated using the following formula;

[0172]

[0173] Porosity is dimensionless, and the unit of pore volume is cm.3 / g, where the pore size is measured in nm.

[0174] The equilibrium influence index G of the pore structure is calculated using the following formula;

[0175]

[0176] The unit for specific surface area is m². 2 / g, porosity is dimensionless.

[0177] For the three corrosion inhibitor molecules, specific loading capacity prediction models were established in this embodiment (as shown in Table 2). The goodness of fit and error of each model are as follows: the coefficient of determination R of the 8-HQ loading capacity prediction model is... 2 =0.830, RMSE = 2.440; R² of the BTA prediction model 2 =0.756, RMSE=9.060; R-value of the BTA prediction model 2 =0.992, RMSE=1.415. These results indicate that the established model has high explanatory power and prediction accuracy, with the MBT model showing the best fit.

[0178] Table 2

[0179]

[0180] To systematically evaluate the reliability of the model, this embodiment further compares the overlap between the predicted and actual mass loading of the three corrosion inhibitor molecules for each of the described nanocarrier structures, and analyzes the relationship between the residuals and the predicted values ​​(e.g., Figure 15 (As shown in the figure). The results show that the predicted values ​​of the three corrosion inhibitors have a high degree of overlap with the actual values, verifying the effectiveness of the model; the residual distribution is uniform and there is no particularly large fluctuation trend, indicating that the model meets the basic assumptions of linear regression. In summary, the corrosion inhibitor loading prediction model can well reflect the loading behavior of different corrosion inhibitors in nanostructures, providing a reliable theoretical tool for the targeted design and screening of high-performance corrosion inhibitor fillers.

[0181] To comprehensively evaluate the overall corrosion resistance of the nanofiller system and achieve rational screening, this embodiment also establishes a comprehensive evaluation model for the loading performance of the nanofiller system; the Comprehensive Loading Efficiency Index (CLEI) is calculated, and the calculation formula is as follows:

[0182]

[0183]

[0184]

[0185]

[0186]

[0187] in, The theoretical mass loading of corrosion inhibitor is calculated using the corrosion inhibitor loading prediction model described in step S2. As a baseline reference value, the maximum corrosion inhibitor mass loading in all experimental data is 72.064. The average mass loading of the target corrosion inhibitor in all nanocarrier structures; α represents the average mass loading of MBT corrosion inhibitor in all nanocarrier structures; α is the corrosion inhibitor fit coefficient, used to correct for the inherent differences between different corrosion inhibitors. The value is 1; β is the functionalization correction coefficient, used to correct the inherent effects of different functional groups; To adapt the integration coefficient to the corrosion inhibitor; The functionalized modification integration coefficient.

[0188] The specific settlement results are shown in Table 3;

[0189] Table 3

[0190]

[0191] As shown in Table 3, the CLEI values ​​of different nanofiller systems differ significantly, intuitively revealing their order of superiority and inferiority in comprehensive performance: the nanofiller systems composed of TpTd-H, TpTd-F, and TpTd-OH with the three corrosion inhibitors generally exhibit high CLEI values, indicating that they achieve a good balance between mass loading, binding strength, and structural adaptability, making them highly promising general-purpose and efficient carriers; MBT's CLEI values ​​on most carriers are significantly higher than those of BTA and 8-HQ, confirming its superior position in this system from a comprehensive evaluation perspective. This may stem from the better matching of its molecular structure and pore environment in multiple dimensions. The loading of BTA depends on the synergy of adsorption and space, so TpTd-H with the largest pore volume is the best; the loading of 8-HQ is mainly dominated by physical filling, so large-pore-volume carriers such as TpTd-H and TpTd-OH perform outstandingly; while the loading of MBT is dominated by strong adsorption, the pore volume should not be too large, and the pore size is positively correlated, making TpTd-H the overall winner in the balance between adsorption and space. This embodiment uses the CLEI index to quantitatively rank the performance of nanofiller composite systems, providing a tool for quickly and rationally selecting the optimal carrier for specific corrosion protection needs.

[0192] The method, apparatus, electronic device, and storage medium for predicting the loading performance of corrosion inhibitors in nanofiller systems for metal corrosion protection provided by this invention have the following advantages:

[0193] ① Significantly improved R&D efficiency: The screening of nanofiller systems is moved from the complex experimental cycle to the computational simulation stage, shortening the R&D cycle by several times to tens of times;

[0194] ②Effectively reduced development costs: Significantly reduced unnecessary material synthesis and performance testing experiments, saving a large amount of raw materials, energy consumption and labor costs;

[0195] ③ Guided performance prediction: The quantitative prediction model based on molecular dynamics simulation allows the loading performance to be estimated before synthesis, which greatly improves the scientific nature, predictability and success rate of material design;

[0196] ④ The evaluation system is comprehensive and objective: The proposed comprehensive loading index integrates key parameters from multiple dimensions such as mass loading, bonding strength, and structural adaptability, overcoming the one-sidedness of evaluation by a single indicator;

[0197] ⑤ Broad engineering application prospects: The design and prediction methods described are closely integrated with the needs of the field of metal anti-corrosion coatings, and have clear engineering application value and good prospects for promotion.

[0198] The applicant declares that the above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A method for predicting the loading performance of corrosion inhibitors in a nanofiller system, characterized in that, The prediction method includes the following steps: Obtaining nanofiller systems; A hierarchical molecular dynamics simulation model was constructed, and dynamic simulations were performed on all nanofiller systems until equilibrium was reached, and simulation results were obtained. A corrosion inhibitor loading prediction model is established, and the simulation results are input into the corrosion inhibitor loading prediction model for prediction.

2. The prediction method according to claim 1, characterized in that, The obtained nanofiller system includes: (1) Using TpTd-COF as the basic structure of nanocarriers, different functional groups are introduced to construct a variety of functionalized nanocarrier structures, resulting in a nanocarrier structure material library; (2) Different corrosion inhibitor molecules are loaded into all the nanocarrier structures in the nanocarrier structure material library described in step (1) to form the nanofiller system; Preferably, the functional groups in step (1) include -F, -OH, -SH, -CN and -CCH; Preferably, the nanocarrier structures in the nanocarrier structure material library in step (1) include TpTd-H, TpTd-F, TpTd-OH, TpTd-SH, TpTd-CN and TpTd-CCH; Preferably, the corrosion inhibitor molecules in step (2) include MBT, 8-HQ and BTA.

3. The prediction method according to claim 1 or 2, characterized in that, The layered molecular dynamics simulation model includes a first inert plate, a nanocarrier layer, a corrosion inhibitor molecule layer, and a second inert plate arranged sequentially. Preferably, the nanocarrier layer comprises a nanocarrier bilayer structure.

4. The prediction method according to any one of claims 1 to 3, characterized in that, The establishment of the corrosion inhibitor loading prediction model includes: Based on the simulation results, the number of corrosion inhibitor molecules between the corresponding nanocarrier layers in each nanofiller system under equilibrium conditions was counted, and the mass loading of the corresponding corrosion inhibitor molecules in each nanofiller system was calculated. Then, the correlation analysis between the mass loading and the variable parameters of the corresponding nanofiller system was performed. Based on the analysis results, a prediction model for the corrosion inhibitor loading was established. Preferably, the variable parameters of the corresponding nanofiller system include the pore characteristic parameters of the corresponding nanocarrier structure and the interaction energy between the corresponding corrosion inhibitor molecular layer and the corresponding nanocarrier layer under equilibrium conditions; Preferably, the pore characteristic parameters include porosity, pore volume, pore size, and specific surface area; Preferably, the corrosion inhibitor loading prediction model includes: Where a0, a1, a2, and a3 are fitting coefficients; E is the average value of the interaction energy between the corrosion inhibitor molecules as a whole and the nanocarrier layer in the equilibrium stage; F is the comprehensive index of pore structure; and G is the index of the influence of pore structure equilibrium.

5. The prediction method according to claim 4, characterized in that, The establishment of the corrosion inhibitor loading prediction model also includes performing collinearity analysis on the pore characteristic parameters of the nanocarrier structure to determine the pore variable index. Preferably, the porosity variable index includes a comprehensive pore structure index F and a pore structure equilibrium influence index G; Preferably, the comprehensive index F of the hole structure is calculated using the following formula; Preferably, the pore structure balance influence index G is calculated using the following formula; 6. The prediction method according to any one of claims 1 to 5, characterized in that, The prediction method further includes: establishing a comprehensive evaluation model for the loading performance of the nanofiller system based on the prediction results obtained from the corrosion inhibitor loading prediction model; Preferably, the comprehensive evaluation model includes the Loading Comprehensive Efficiency Index (CLEI), calculated using the following formula: in, This refers to the theoretical mass loading of the corrosion inhibitor calculated using the corrosion inhibitor loading prediction model. This serves as a baseline reference value, corresponding to the maximum corrosion inhibitor mass loading in all experimental data. The average mass loading of the target corrosion inhibitor in all nanocarrier structures; ω1 represents the average mass loading of MBT corrosion inhibitor in all nanocarrier structures; α is the corrosion inhibitor fit coefficient, used to correct for the inherent differences between different corrosion inhibitors; β is the functionalization correction coefficient, used to correct for the inherent effects of different functional groups; ω1 is the corrosion inhibitor fit integration coefficient; and ω2 is the functionalization correction integration coefficient.

7. A device for predicting the loading performance of corrosion inhibitors in a nanofiller system, characterized in that, The prediction device includes: The acquisition module is used to acquire the nanofiller system; The simulation module is used to input all the nanofiller systems acquired by the acquisition module into the hierarchical molecular dynamics simulation model and perform dynamic simulations. The prediction module is used to input the simulation results of the simulation module into the corrosion inhibitor loading prediction model and make predictions.

8. The prediction device according to claim 7, characterized in that, The prediction device further includes: The evaluation module is used to input the prediction results of the prediction module into the comprehensive evaluation model for comprehensive evaluation.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for predicting the corrosion inhibitor loading performance in the nanofiller system according to any one of claims 1 to 6.

10. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, which, when executed by a processor, implement the method for predicting the corrosion inhibitor loading performance in the nanofiller system according to any one of claims 1 to 6.

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