CN112381270B - MOFs material defect prediction method based on methane adsorption isotherm - Google Patents

MOFs material defect prediction method based on methane adsorption isotherm Download PDF

Info

Publication number
CN112381270B
CN112381270B CN202011187586.9A CN202011187586A CN112381270B CN 112381270 B CN112381270 B CN 112381270B CN 202011187586 A CN202011187586 A CN 202011187586A CN 112381270 B CN112381270 B CN 112381270B
Authority
CN
China
Prior art keywords
adsorption
isotherm
defect
methane
methane adsorption
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202011187586.9A
Other languages
Chinese (zh)
Other versions
CN112381270A (en
Inventor
吴颖
段海鹏
奚红霞
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
South China University of Technology SCUT
Original Assignee
South China University of Technology SCUT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by South China University of Technology SCUT filed Critical South China University of Technology SCUT
Priority to CN202011187586.9A priority Critical patent/CN112381270B/en
Publication of CN112381270A publication Critical patent/CN112381270A/en
Application granted granted Critical
Publication of CN112381270B publication Critical patent/CN112381270B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces

Abstract

The invention discloses a MOFs material defect prediction method based on a methane adsorption isotherm, which comprises the steps of firstly, simulating the content and the distribution of ligand deficiency defects of ergodic MOFs perfect crystals by a computer to generate a defect structure data set; calculating the Henry constant and high-pressure saturated adsorption capacity of all defect structures on methane adsorption in a data set based on force field simulation, and deducing a Langmuir adsorption model of the defect structures based on the data; extracting a pressure point from isothermal line experimental data of methane adsorption of a real sample and substituting the pressure point into an adsorption model to obtain a predicted methane adsorption isothermal line, calculating errors of the methane adsorption isothermal line and the experimental data within the pressure range of the isothermal line, and selecting a defect structure corresponding to the minimum error; and calculating a methane adsorption isotherm by adopting force field simulation on the defect structure, and calculating the error between the methane adsorption isotherm and experimental data to determine the defect degree of the sample. The invention can accurately determine the defect degree of the sample.

Description

MOFs material defect prediction method based on methane adsorption isotherm
Technical Field
The invention relates to the technical field of MOFs material defect degree measurement, in particular to a MOFs material defect prediction method based on a methane adsorption isotherm.
Background
Metal-organic frameworks (MOFs) materials are a new type of porous crystalline materials formed by self-assembly of Metal clusters and organic ligands. Due to the variety of composition units (including metal precursors and organic ligands) and topological structures, the size, shape, specific surface area, chemical environment and the like of the pore channels of the MOFs material can be highly customized and modularly designed so as to meet the requirements of different application fields (gas adsorption separation, sensors, catalysis, drug transportation and the like). In particular in the context of adsorptive separation, MOFs materials have been proposed for the first time since 1995 as high-performance adsorbatesThe research on the separation of materials from hydrogen storage, natural gas storage and CO is always a hotspot2Trapping and the like have shown great application potential in various fields.
To date, a great deal of literature on MOFs materials is reported every year, and most of these research works are mainly based on structure-activity relationship, mechanism research, and mainly on the ideal perfect crystal structure of the MOFs materials. However, in the real self-assembly synthesis process of the MOFs, crystal defects (including point defects and bulk defects) inevitably occur due to dislocation arrangement of atoms and crystals, so that the adsorption and separation rules and properties of the MOFs may deviate from perfect crystals. Such crystal defects are ubiquitous, but the effect of structural defects on the performance of MOFs materials is difficult to predict due to the randomness of defect formation.
The mechanism and the rule of the influence of the defects on the material performance are fully mastered, on one hand, the defects in the material (namely defect engineering technology) can be artificially manufactured and regulated, on the basis of maintaining the stability of the material, the pore channels can be locally enlarged (mass transfer enhancement) and the active sites can be increased in a proper amount, so that the adsorption separation and the catalysis performance of the material can be improved; on the other hand, the method can inhibit the formation of defects and synthesize perfect crystals with long-range order. However, the structural defects destroy the crystal symmetry of the MOFs, so that the photon diffraction techniques (such as XRD and the like) in the traditional experiments cannot accurately characterize the defect degree. At present, the method for measuring the defects of the MOFs materials in experiments mainly comprises the following steps: thermogravimetric Technology (TGA), acid-base titration, nitrogen adsorption, Nuclear Magnetic Resonance (NMR), high resolution electron microscopy (HRTEM), etc., but these characterization methods still cannot directly and quantitatively measure the degree of MOFs defects, and it is difficult to distinguish "metal cluster deletion" or "organic ligand deletion" defects.
Therefore, a method for quantitatively characterizing the crystal defects of the MOFs material is needed in the art, the content and the distribution of the defects can be accurately described, and a theoretical basis and a technical support are provided for the subsequent research of the structure-activity relationship of the defective MOFs material.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a MOFs material defect prediction method based on a methane adsorption isotherm, which can accurately determine the defect degree of MOFs material samples.
A second object of the present invention is to provide a computer-readable storage medium.
It is a third object of the invention to provide a computing device.
The first purpose of the invention is realized by the following technical scheme: a MOFs material defect prediction method based on methane adsorption isotherm comprises the following steps:
s1, taking a perfect crystal structure of the MOFs as a parent, traversing the content and distribution of the missing defects of the MOFs ligand based on computer simulation, and deriving a defect structure data set of the MOFs;
s2, calculating the adsorption quantity of each defect structure to methane based on force field simulation aiming at all defect structures in the defect structure data set of the MOFs, thereby obtaining a Henry constant and a high-pressure saturated adsorption quantity of methane adsorption;
s3, deducing a Langmuir adsorption model corresponding to each defect structure in the defect structure data set of the MOFs based on the Henry constant and the saturated adsorption quantity in the step S2;
s4, acquiring isotherm experimental data of methane adsorption of real samples of MOFs materials, extracting pressure points from the isotherm experimental data, substituting the pressure points into Langmuir adsorption models of all defect structures to obtain a predicted isotherm for methane adsorption, calculating the error between the predicted isotherm for methane adsorption and the experimental isotherm for methane adsorption within the pressure range of the isotherm, and selecting a defect structure corresponding to the minimum error;
and S5, calculating the methane adsorption isotherm corresponding to the selected defect structure by adopting force field simulation, comparing the methane adsorption isotherm obtained by the force field simulation calculation with the experimental methane adsorption isotherm, calculating the error of the methane adsorption isotherm and the experimental methane adsorption isotherm, and finally determining the defect degree of the real MOFs material sample according to the error.
Preferably, in step S1, the content range of the traversal defects is 0.1-0.9; the distribution of the defects is quantitatively described by using Warren-Cowley parameters, and the parameter range is-1.0;
the Warren-Cowley parameters are defined as:
Figure BDA0002751845670000031
wherein α represents the Warren-Cowley parameter; A. b represents two groups divided by organic ligands, the ligands in the group A are defined as A ligands, the ligands in the group B are defined as B ligands, the B ligands represent ligands lost when defects are formed, and the A ligands represent the rest ligands; pA(B)Representing the probability that a B ligand will appear in the vicinity of an a ligand, i.e., the A, B ligand is connected to the same node; x is the number ofBThe ratio of B ligand to all ligands is indicated.
Preferably, the Langmuir adsorption model is:
Figure BDA0002751845670000032
wherein P is pressure; q is the adsorption capacity under pressure P; k is Henry constant, qsatThe amount of the adsorbed substance was saturated.
Preferably, in step S2, the force field calculated by simulation is UFF force field, and the henry constant of methane adsorption is directly calculated by monte carlo method, or calculated by calculating the adsorption amount at several low pressure points and then fitting these adsorption amounts linearly.
Preferably, in step S2, the high-pressure saturated adsorption amount of methane is calculated by a giant canonical monte carlo method, and the corresponding pressure is 100.0bar or more.
Preferably, in step S5, the force field simulation calculation method uses a giant canonical monte carlo method, and the force field uses a UFF force field.
Preferably, in steps S4 and S5, the error is an error between the calculated methane adsorption amount and the experimental methane adsorption amount, and includes a mean absolute error rate, a mean absolute error, a mean square error, and a root mean square error.
Preferably, the isothermal line experimental data of methane adsorption of real samples of MOFs materials are obtained from a NIST adsorption isothermal line database.
The second purpose of the invention is realized by the following technical scheme: a computer-readable storage medium storing a program which, when executed by a processor, implements the method for predicting defects in MOFs materials based on methane adsorption isotherms according to the first object of the present invention.
The third purpose of the invention is realized by the following technical scheme: a computing device comprises a processor and a memory for storing a program executable by the processor, wherein when the processor executes the program stored in the memory, the method for predicting the defects of the MOFs based on the methane adsorption isotherm, which is the first object of the invention, is realized.
Compared with the prior art, the invention has the following advantages and effects:
(1) the invention provides a MOFs material defect prediction method based on a methane adsorption isotherm, which is based on all possible defect structures of the MOFs material and predicts quantitative comparison between adsorption quantity and experimental adsorption quantity through an adsorption isotherm model, so that the defect content and distribution of an experimental synthetic sample can be quantitatively, accurately, quickly and efficiently determined, and high-quality material science data are provided for the subsequent research of quantitative structure-activity relationship of defect MOFs.
(2) The method is easy to apply to quantitative measurement of the defect degrees of various MOFs materials, has wide application range and is beneficial to promoting the development of MOFs material defect engineering technology.
Drawings
FIG. 1 is a flow chart of the MOFs material defect prediction method based on methane adsorption isotherm.
FIG. 2 is a graph of the Henry constant and the saturation adsorption of UiO-66 to methane as a function of crystal defect content and distribution.
FIG. 3 is a diagram of the pore structure of two UiO-66 defect bodies and a UiO-66 perfect crystal.
FIG. 4 is a comparative schematic of the experimental adsorption isotherm of UiO-66 sample A on methane, the simulated calculated methane adsorption isotherm of UiO-66 perfect crystals and 0.6-0.38 defect bodies.
FIG. 5 is a schematic comparison of the experimental adsorption isotherm of UiO-66 sample B on methane and the simulated calculated methane adsorption isotherm of the UiO-66 perfect crystal.
FIG. 6 is a graphical comparison of the experimental adsorption isotherm of UiO-66 sample C for methane and the simulated calculated methane adsorption isotherm for the 0.5-0.02 defect body.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Example 1
The embodiment discloses a method for predicting defects of MOFs materials based on methane adsorption isotherms, which comprises the following steps as shown in FIG. 1:
s1, taking the perfect crystal structure of the MOFs as a parent, traversing the content and distribution of the 'ligand deficiency' defects of the MOFs based on computer simulation, and deriving a defect structure data set of the MOFs, wherein the data set contains more than 400 defect structures.
The content range of the traversal defects is 0.1-0.9, the content of the defects refers to the proportion of the lost ligands in all the ligands, and the larger the proportion is, the larger the content of the defects is.
The distribution of defects can be quantitatively described by using Warren-Cowley parameters, the parameter range is-1.0, and the uniform-random-agglomeration state of MOFs is covered, wherein, -1.0 represents that the lost ligands are uniformly distributed in the material, 1.0 represents the agglomeration distribution, and 0 represents the random distribution. The Warren-Cowley parameters are defined as:
Figure BDA0002751845670000051
wherein α represents the Warren-Cowley parameter; A. b represents two groups divided by organic ligands, the ligands in the group A are defined as A ligands, the ligands in the group B are defined as B ligands, the B ligands represent ligands lost when defects are formed, and the A ligands represent the rest ligands; pA(B)Representing the probability that a B ligand will appear in the vicinity of an a ligand (i.e., A, B ligands are connected to the same node); x is the number ofBRepresenting B ligands as all ligandsAnd (4) proportion.
S2, calculating the adsorption quantity of each defect structure to methane based on force field simulation aiming at all defect structures in the defect structure data set of the MOFs, and obtaining the Henry constant and the high-pressure saturated adsorption quantity of methane adsorption.
Here, the force field calculated by simulation is the UFF force field. The henry constant of methane adsorption can be directly calculated by a simple monte carlo method, and can also be obtained by calculating the adsorption amount at a plurality of low pressure points and then linearly fitting the adsorption amounts. The high-pressure saturated adsorption capacity of the methane can be calculated by adopting a giant regular Monte Carlo method (GCMC), and the corresponding pressure is more than 100.0 bar.
And S3, deducing a Langmuir adsorption model corresponding to each defect structure in the defect structure data set of the MOFs based on the Henry constant and the saturated adsorption quantity in the step S2, wherein the Langmuir adsorption model is used as an adsorption isotherm model for predicting a methane adsorption isotherm.
The Langmuir adsorption model is:
Figure BDA0002751845670000061
wherein P is pressure; q is the adsorption capacity under pressure P; k is Henry constant, qsatThe amount of the adsorbed substance was saturated.
S4, acquiring isotherm experimental data of methane adsorption of real samples of MOFs materials, extracting pressure points from the isotherm experimental data, and substituting the pressure points into Langmuir adsorption models of all defect structures to obtain a predicted isotherm of methane adsorption;
and calculating the error between the predicted methane adsorption isotherm and the experimental methane adsorption isotherm within the pressure range of the isotherm, and selecting the defect structure corresponding to the minimum error.
And S5, calculating the methane adsorption isotherm corresponding to the selected defect structure by adopting force field simulation, comparing the methane adsorption isotherm obtained by the force field simulation calculation with the experimental methane adsorption isotherm, calculating the error of the methane adsorption isotherm and the experimental methane adsorption isotherm, and finally determining the defect degree of the real MOFs material sample according to the error.
Here, the force field simulation calculation method adopts a giant canonical monte carlo method, and the force field adopts a UFF force field.
The errors of steps S4 and S5 are errors between the calculated methane adsorption amount and the experimental methane adsorption amount, and include mean absolute error rate (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), root-mean-square error (RMSE).
The error formula is as follows:
Figure BDA0002751845670000062
Figure BDA0002751845670000063
Figure BDA0002751845670000064
Figure BDA0002751845670000065
wherein i is the serial number of a pressure point in the isotherm; n issamplesThe number of pressure points in the isotherm; x is the number ofiThe methane adsorption capacity at the ith pressure point represented by the experiment; y isiThe amount of methane adsorbed at the ith pressure point predicted for the Langmuir adsorption model or calculated for GCMC simulation.
To further illustrate the above method, this embodiment also takes uo-66 as an example for verification:
(1) with the perfect crystal structure of UiO-66 as a matrix, various defect structures are simulated based on a computer: firstly, the simulated size is expanded to 3 multiplied by 3 unit cell size, then the ligand deficiency defect content of UiO-66 is in the range of [0.1, 0.9] and is taken as a value at 0.05 interval, the defect distribution is described by Warren-Cowley parameters, and is taken as a value at 0.02 interval in the range of [ -0.1, 0.38], and 425 defect structures are derived, and the 425 defect structures form a data set.
(2) And (3) simulating and calculating the adsorption quantity of all defect structures to methane under 0.1 bar, 0.5 bar, 1.0bar and 100.0bar in the step (1) in the UFF force field based on a giant regular Monte Carlo algorithm.
(3) Performing linear fitting on the adsorption quantity of all defect structures to methane at 0.1 bar, 0.5 bar and 1.0bar, wherein the slope of a fitting straight line is a Henry constant; the Langmuir adsorption model was derived for each defect structure with an adsorption capacity of 100.0bar as the saturation adsorption capacity. The effect of defect content and defect distribution on the henry constant and saturation adsorption of methane by the material can be seen in figure 2.
(4) Three groups of real UiO-66 samples (sample A, sample B, sample C) were selected for methane adsorption isotherm experimental data in the NIST adsorption isotherm database (the database website: https:// adsorbents. NIST. gov /).
Here, the data of sample A is the experimental data of "Unnusulal and Highly Tunable Missing-Linker Defects in Zirconium Metal-Organic Framework UiO-66and the theory of aromatic Effects on Gas addition" from Journal of the American Chemical Society (2013), volume 135(28), pages 10525 to 10532.
The data for sample B are experimental data taken from "instruments on the physical adsorption of moisture and methane in UO series of MOFs using molecular relationships" at volume 1061, pages 36-45 of computer and therapeutic Chemistry (2015).
The data for sample C were obtained from experiments in "Experimental Study of CO2, CH4, and Water Vapor Adsorption on a digital-Functionalized UiO-66 Framework" at volume 117, page 7062 and page 7068 of The Journal of Physical Chemistry C (2013).
Then, for each sample, the pressure points in the experimental data are substituted into the Langmuir adsorption model of all defect structures to obtain corresponding predicted isotherms, the mean absolute error rates (MAPE) of the predicted isotherms and the experimental isotherms of all the defect structures are calculated within the pressure range of the isotherms, and then the defect structure corresponding to the minimum error is selected.
The isotherm MAPE error for sample A, B, C, calculated by the Langmuir adsorption model, is shown in table 1. As can be seen from table 1, for sample a, the MAPE minimum value among all defect structures was 0.0498, the defect content of the corresponding structure was 0.6, and the defect distribution was 0.38. For sample B, the MAPE minimum in all defect structures was 0.0275, corresponding to a perfect crystal. For sample C, the MAPE minimum for all defect structures was 0.0830, the defect content for the corresponding structure was 0.5, and the defect distribution was-0.02. Sample A, B, C can be seen in FIG. 3.
TABLE 1
Figure BDA0002751845670000081
(5) And (3) based on the defect structure selected in the step (4), simulating and calculating a corresponding methane isotherm in the UFF force field by adopting a giant regular Monte Carlo (GCMC) method, and comparing the simulated and calculated methane isotherm with experimental data, which can be seen in fig. 4-6. Calculating MAPE errors of the MOFs and the standard matrix materials, and finally determining the defect degree of the real samples of the MOFs according to the errors.
As shown in table 1, the isotherm MAPE error of the sample a and the selected corresponding defect structure calculated by the GCMC is 0.0496, the isotherm MAPE error of the sample B and the selected corresponding defect structure is 0.0696, the isotherm MAPE error of the sample C and the selected corresponding defect structure is 0.0913, and the isotherm MAPE errors are all small, as can be seen from fig. 4 to 6, the isotherm difference between the samples a to C and the corresponding simulated-defect body is small, it can be seen that the selected defect body can accurately describe the methane adsorption performance of the corresponding sample, the defect structure of the defect body can be basically regarded as the defect structure of the sample, i.e. the defect content of the sample a can be regarded as 0.6, and the defect distribution is 0.38; sample B is perfect crystal; the defect content of sample C was 0.5 and the defect distribution was-0.02.
By analyzing the data of 3 samples in combination, it can be found that the defect content and defect distribution have a significant influence on the Henry constant and saturation adsorption amount of UiO-66 adsorbing methane, and especially the influence on the saturation adsorption amount is more complicated (see FIG. 2).
If the perfect crystal based on the UiO-66 is directly used for GCMC to calculate the methane adsorption isotherm, the difference of the isotherm data with the experimental sample is obvious (see figure 4), which indicates that a larger error exists when the perfect crystal of the UiO-66 is directly used for describing the methane adsorption performance of the experimental sample. If the defect structure selected based on the method of this embodiment adopts GCMC to calculate the isotherm, and the MAPE error with the experimental data is only 0.04-0.09 (see table 1), it is demonstrated that the UiO-66 structure containing defects selected by the method of this embodiment can more accurately describe the methane adsorption performance of the real sample, and in turn can more accurately and quantitatively determine the defect content and distribution of the experimental synthesized sample.
Example 2
The embodiment discloses a computer-readable storage medium, which stores a program, and when the program is executed by a processor, the method for predicting the defects of the MOFs materials based on the methane adsorption isotherm described in embodiment 1 is implemented, specifically as follows:
s1, taking a perfect crystal structure of the MOFs as a parent, traversing the content and distribution of the missing defects of the MOFs ligand based on computer simulation, and deriving a defect structure data set of the MOFs;
s2, calculating the adsorption quantity of each defect structure to methane based on force field simulation aiming at all defect structures in the defect structure data set of the MOFs, thereby obtaining a Henry constant and a high-pressure saturated adsorption quantity of methane adsorption;
s3, deducing a Langmuir adsorption model corresponding to each defect structure in the defect structure data set of the MOFs based on the Henry constant and the saturated adsorption quantity in the step S2;
s4, acquiring isotherm experimental data of methane adsorption of real samples of MOFs materials, extracting pressure points from the isotherm experimental data, substituting the pressure points into Langmuir adsorption models of all defect structures to obtain a predicted isotherm for methane adsorption, calculating the error between the predicted isotherm for methane adsorption and the experimental isotherm for methane adsorption within the pressure range of the isotherm, and selecting a defect structure corresponding to the minimum error;
and S5, calculating the methane adsorption isotherm corresponding to the selected defect structure by adopting force field simulation, comparing the methane adsorption isotherm obtained by the force field simulation calculation with the experimental methane adsorption isotherm, calculating the error of the methane adsorption isotherm and the experimental methane adsorption isotherm, and finally determining the defect degree of the real MOFs material sample according to the error.
The computer-readable storage medium in this embodiment may be a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a Random Access Memory (RAM), a usb disk, a removable hard disk, or other media.
Example 3
The embodiment discloses a computing device, which includes a processor and a memory for storing an executable program of the processor, and when the processor executes the program stored in the memory, the method for predicting the defects of the MOFs materials based on the methane adsorption isotherm described in embodiment 1 is implemented, specifically as follows:
s1, taking a perfect crystal structure of the MOFs as a parent, traversing the content and distribution of the missing defects of the MOFs ligand based on computer simulation, and deriving a defect structure data set of the MOFs;
s2, calculating the adsorption quantity of each defect structure to methane based on force field simulation aiming at all defect structures in the defect structure data set of the MOFs, thereby obtaining a Henry constant and a high-pressure saturated adsorption quantity of methane adsorption;
s3, deducing a Langmuir adsorption model corresponding to each defect structure in the defect structure data set of the MOFs based on the Henry constant and the saturated adsorption quantity in the step S2;
s4, acquiring isotherm experimental data of methane adsorption of real samples of MOFs materials, extracting pressure points from the isotherm experimental data, substituting the pressure points into Langmuir adsorption models of all defect structures to obtain a predicted isotherm for methane adsorption, calculating the error between the predicted isotherm for methane adsorption and the experimental isotherm for methane adsorption within the pressure range of the isotherm, and selecting a defect structure corresponding to the minimum error;
and S5, calculating the methane adsorption isotherm corresponding to the selected defect structure by adopting force field simulation, comparing the methane adsorption isotherm obtained by the force field simulation calculation with the experimental methane adsorption isotherm, calculating the error of the methane adsorption isotherm and the experimental methane adsorption isotherm, and finally determining the defect degree of the real MOFs material sample according to the error.
The computing device described in this embodiment may be a desktop computer, a notebook computer, a smart phone, a PDA handheld terminal, a tablet computer, or other terminal device with a processor function.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (10)

1. A MOFs material defect prediction method based on methane adsorption isotherm is characterized by comprising the following steps:
s1, taking a perfect crystal structure of the MOFs as a parent, traversing the content and distribution of the missing defects of the MOFs ligand based on computer simulation, and deriving a defect structure data set of the MOFs;
s2, calculating the adsorption quantity of each defect structure to methane based on force field simulation aiming at all defect structures in the defect structure data set of the MOFs, thereby obtaining a Henry constant and a high-pressure saturated adsorption quantity of methane adsorption;
s3, deducing a Langmuir adsorption model corresponding to each defect structure in the defect structure data set of the MOFs based on the Henry constant and the saturated adsorption quantity in the step S2;
s4, acquiring isotherm experimental data of methane adsorption of real samples of MOFs materials, extracting pressure points from the isotherm experimental data, substituting the pressure points into Langmuir adsorption models of all defect structures to obtain a predicted isotherm for methane adsorption, calculating the error between the predicted isotherm for methane adsorption and the experimental isotherm for methane adsorption within the pressure range of the isotherm, and selecting a defect structure corresponding to the minimum error;
and S5, calculating the methane adsorption isotherm corresponding to the selected defect structure by adopting force field simulation, comparing the methane adsorption isotherm obtained by the force field simulation calculation with the experimental methane adsorption isotherm, calculating the error of the methane adsorption isotherm and the experimental methane adsorption isotherm, and finally determining the defect degree of the real MOFs material sample according to the error.
2. The MOFs material defect prediction method based on methane adsorption isotherm of claim 1, wherein in step S1, the content range of the ergodic defects is 0.1-0.9; the distribution of the defects is quantitatively described by using Warren-Cowley parameters, and the parameter range is-1.0;
the Warren-Cowley parameters are defined as:
Figure FDA0002751845660000011
wherein α represents the Warren-Cowley parameter; A. b represents two groups divided by organic ligands, the ligands in the group A are defined as A ligands, the ligands in the group B are defined as B ligands, the B ligands represent ligands lost when defects are formed, and the A ligands represent the rest ligands; pA(B)Representing the probability that a B ligand will appear in the vicinity of an a ligand, i.e., the A, B ligand is connected to the same node; x is the number ofBThe ratio of B ligand to all ligands is indicated.
3. The MOFs material defect prediction method based on methane adsorption isotherm of claim 1, wherein the Langmuir adsorption model is:
Figure FDA0002751845660000021
wherein P is pressure; q is the adsorption capacity under pressure P; k is Henry constant, qsatThe amount of the adsorbed substance was saturated.
4. The method of claim 1, wherein in step S2, the UFF force field is used as the force field for simulation calculation, and the henry constant of methane adsorption is directly calculated by the monte carlo method, or is obtained by calculating the adsorption amount at several low pressure points and then fitting these adsorption amounts linearly.
5. The MOFs material defect prediction method based on methane adsorption isotherm of claim 1, wherein in step S2, the high pressure saturation adsorption quantity of methane is calculated by a giant canonical Monte Carlo method, and the corresponding pressure is above 100.0 bar.
6. The MOFs material defect prediction method based on methane adsorption isotherm of claim 1, wherein in step S5, the force field simulation calculation method adopts a giant canonical Monte Carlo method, and the force field adopts a UFF force field.
7. The MOFs material defect prediction method based on methane adsorption isotherms of claim 1, wherein the errors in steps S4 and S5 are errors between the calculated methane adsorption amount and the experimental methane adsorption amount, including mean absolute error rate, mean absolute error, mean square error, and root mean square error.
8. The method for predicting the defects of the MOFs materials based on the methane adsorption isotherms according to claim 1, wherein the isotherm experimental data of the methane adsorption of the real samples of the MOFs materials is obtained from a NIST adsorption isotherm database.
9. A computer-readable storage medium storing a program, wherein the program, when executed by a processor, implements the method for methane adsorption isotherm-based MOFs material defect prediction according to any one of claims 1 to 8.
10. A computing device comprising a processor and a memory for storing a processor-executable program, wherein the processor, when executing the program stored in the memory, implements the method for methane adsorption isotherm based MOFs material defect prediction according to any one of claims 1 to 8.
CN202011187586.9A 2020-10-30 2020-10-30 MOFs material defect prediction method based on methane adsorption isotherm Active CN112381270B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011187586.9A CN112381270B (en) 2020-10-30 2020-10-30 MOFs material defect prediction method based on methane adsorption isotherm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011187586.9A CN112381270B (en) 2020-10-30 2020-10-30 MOFs material defect prediction method based on methane adsorption isotherm

Publications (2)

Publication Number Publication Date
CN112381270A CN112381270A (en) 2021-02-19
CN112381270B true CN112381270B (en) 2022-03-29

Family

ID=74576415

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011187586.9A Active CN112381270B (en) 2020-10-30 2020-10-30 MOFs material defect prediction method based on methane adsorption isotherm

Country Status (1)

Country Link
CN (1) CN112381270B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115831286B (en) * 2022-11-24 2023-09-15 南京工业大学 Prediction method of MOF film lattice defects

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101806762A (en) * 2010-05-31 2010-08-18 哈尔滨工业大学 Method for identifying thermal defects by means of auxiliary heat source under condition of small temperature difference
CN108181200A (en) * 2017-12-27 2018-06-19 中国石油化工股份有限公司江汉油田分公司勘探开发研究院 Mud shale adsorption isotherm experiment curve correcting method and device
CN108460219A (en) * 2018-03-16 2018-08-28 中石化华东石油工程有限公司测井分公司 Shale adsorbed gas air content method is calculated based on overcritical mono layer adsorption model
CN110364227A (en) * 2019-07-18 2019-10-22 中国石油大学(华东) A kind of mud shale supercritical methane isotherm adsorption model based on variable density

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101806762A (en) * 2010-05-31 2010-08-18 哈尔滨工业大学 Method for identifying thermal defects by means of auxiliary heat source under condition of small temperature difference
CN108181200A (en) * 2017-12-27 2018-06-19 中国石油化工股份有限公司江汉油田分公司勘探开发研究院 Mud shale adsorption isotherm experiment curve correcting method and device
CN108460219A (en) * 2018-03-16 2018-08-28 中石化华东石油工程有限公司测井分公司 Shale adsorbed gas air content method is calculated based on overcritical mono layer adsorption model
CN110364227A (en) * 2019-07-18 2019-10-22 中国石油大学(华东) A kind of mud shale supercritical methane isotherm adsorption model based on variable density

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
基于红外热成像的金属材料气孔缺陷识别研究;陈曦等;《热加工工艺》;20200625;第49卷(第12期);第66-70页 *
烷烃在金属有机骨架MOF-5和Cu-BTC中吸附的蒙特卡罗模拟;王斌等;《南京工业大学学报(自然科学版)》;20080515;第30卷(第03期);第38-42页 *
金属-有机骨架材料的吸附分离机理及功能化的计算化学研究;吴颖;《中国优秀博硕士学位论文全文数据库(博士)工程科技Ⅰ辑》;20180615(第6期);第B014-122页 *

Also Published As

Publication number Publication date
CN112381270A (en) 2021-02-19

Similar Documents

Publication Publication Date Title
Cho et al. Isotherms of individual pores by gas adsorption crystallography
Rahman et al. A statistical approach to determine optimal models for IUPAC-classified adsorption isotherms
Qiao et al. High-throughput computational screening of metal-organic framework membranes for upgrading of natural gas
Parkes et al. Molecular dynamics simulation of framework flexibility effects on noble gas diffusion in HKUST-1 and ZIF-8
Fischer et al. Computational approaches to study adsorption in MOFs with unsaturated metal sites
Fu et al. Classical density functional theory for methane adsorption in metal‐organic framework materials
Cirera et al. The effects of electronic polarization on water adsorption in metal-organic frameworks: H2O in MIL-53 (Cr)
Li et al. Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures
Turunen et al. Quantifying the pore structure of different biochars and their impacts on the water retention properties of Sphagnum moss growing media
CN112381270B (en) MOFs material defect prediction method based on methane adsorption isotherm
Yang et al. Analyzing acetylene adsorption of metal–organic frameworks based on machine learning
Zhang et al. Machine-learning-based prediction of hydrogen adsorption capacity at varied temperatures and pressures for MOFs adsorbents
Ren et al. Thermodynamic exploration of xenon/krypton separation based on a high-throughput screening
Jeong et al. Modeling adsorption properties of structurally deformed metal–organic frameworks using structure–property map
Rodriguez-Cantano et al. Examination of the Feynman–Hibbs approach in the study of Ne N-coronene clusters at low temperatures
Guan et al. The origin of the reproduction of different nitrogen uptakes in covalent organic frameworks (COFs)
Pétuya et al. Unraveling the metastability of the SI and SII carbon monoxide hydrate with a combined DFT-neutron diffraction investigation
Men’shchikov et al. Peculiarities of thermodynamic behaviors of xenon adsorption on the activated carbon prepared from silicon carbide
Villajos Experimental volumetric hydrogen uptake determination at 77 K of commercially available metal-organic framework materials
CN112927769B (en) MOFs material defect structure prediction method based on pore size distribution curve
Torré et al. Insights into the crystal structure and clathration selectivity of organic clathrates formed with hydroquinone and (CO2+ CH4) gas mixtures
Tan et al. Competitive and synergistic adsorption of mixtures of polar and nonpolar gases in carbonaceous nanopores
Zhou et al. Massively parallel GPU-accelerated string method for fast and accurate prediction of molecular diffusivity in nanoporous materials
Niu et al. Controls of distinct mineral compositions on pore structure in over-mature shales: A case study of Lower Cambrian Niutitang shales in South China
Wang et al. Large-scale computational screening of metal–organic frameworks for D2/H2 separation

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant