Method for representing distribution uniformity of water in porous material water and application

Abstract

The invention discloses a method for characterizing water distribution uniformity of a porous material in water and application, and belongs to the technical field of porous material performance characterization. The method comprises the following steps: 1) constructing a porous material simulation system, inserting water molecules, and starting an NVT ensemble to carry out pre-balancing and finished product simulation; 2) calculating a coordination number function N1 (r) of oxygen atoms of water molecules based on the finished product simulation trajectory, and obtaining a first water molecule density rho1 (r) within a radius r range by combining the sphere volume; (3) subtracting N1 (r) and the sphere volume from the total water molecule number and the total volume of the system respectively, and calculating the second water molecule density rho2 (r) outside the range of r; and 4) calculating the density ratio R (r) = rho1 (r) / rho2 (r), and judging the uniformity according to the mean value of the R (r) within the range from 0.3 nm to half of the minimum side length of the box. The method realizes quantitative evaluation of water distribution uniformity, has the advantages of convenient operation, high quantification precision, wide applicability and reliable data, can be applied to performance evaluation of porous materials such as UiO-67 and graphene, and provides theoretical support for material design optimization in the fields of drying, atmospheric water collection and the like.

Description

Technical Field

[0001] This invention relates to the field of porous material performance characterization technology, specifically to a method for characterizing the uniformity of water distribution in porous materials based on molecular dynamics simulation. It is applicable to the evaluation of water distribution performance in various porous materials such as MOFs, COFs, carbon materials, and zeolites, and can be applied to performance optimization in scenarios such as drying and dehydration, atmospheric water collection, adsorption separation, catalytic reactions, and energy storage materials. It is particularly significant for the simulation characterization, high-throughput screening, structural design, and performance regulation of porous water-absorbing materials. Background Technology

[0002] Porous materials, due to their high specific surface area and controllable pore structure, are widely used in adsorption, catalysis, and energy storage. The uniformity of water distribution within porous materials directly affects their core performance. For example, in dry environments, uneven water distribution can lead to localized water saturation and a decrease in overall water absorption efficiency. During atmospheric water collection, uneven aggregation of water molecules within the pores can affect condensation efficiency and water collection rate. In catalytic reactions, excessively high local water concentrations may inhibit the activity of active sites. Therefore, the uniformity of water distribution within porous materials directly impacts their core performance. Accurate characterization of water distribution uniformity in porous materials is crucial for material design and performance optimization.

[0003] Existing characterization methods mainly fall into two categories: experimental characterization and simulation characterization. Experimental methods, such as nuclear magnetic resonance (NMR) and X-ray diffraction (XRD), can provide macroscopic water distribution information, but they suffer from low resolution, inability to capture microscopic molecular-level distribution details, high experimental costs, and complex operation. Molecular dynamics simulation, as the core means of simulation characterization, has significant advantages such as the ability to track particle trajectories at the atomic / molecular scale, numerous controllable variables, and the ability to simulate material properties under different working conditions, making it an important tool for studying water behavior in porous materials. However, existing molecular dynamics simulation analysis methods still have significant limitations. They mostly rely on two-dimensional density distribution maps for analysis. Although this method can show the density distribution differences of specific cross sections, it can only provide qualitative visualization results for local areas and cannot achieve quantitative evaluation across the entire system. The commonly used radial distribution function (RDF) can only reflect the average distance and coordination relationship between atoms, and cannot distinguish the density differences between local areas and the global system. Furthermore, it lacks a direct quantitative indicator of "homogeneity," resulting in highly subjective evaluation results and poor comparability between different studies. Summary of the Invention

[0004] The purpose of this invention is to provide a method for characterizing the uniformity of water distribution in porous materials with high quantitative accuracy, convenient operation, and low cost, so as to achieve accurate evaluation of the uniformity of water distribution at the microscale and provide theoretical support for the study of water absorption, mass transfer and other related mechanisms of porous materials and the optimization of material design.

[0005] The technical solution of the present invention: In a first aspect, the present invention provides a method for characterizing the uniformity of water distribution in porous materials, the method comprising the following steps: Step 1: Construct a porous material model and insert water molecules into the pores of the porous material model to simulate the movement behavior of water molecules in the porous material, such as self-diffusion; Step 2: Obtain the density ρ1(r) of the first water molecule within a radius r centered on the water molecule; Step 3: Obtain the density ρ2(r) of the second water molecule outside the radius r centered on the water molecule; Step 4: Obtain the ratio R(r) of the density of the first water molecule ρ1(r) to the density of the second water molecule ρ2(r). Compare the mean of R(r) within the range from the initial value of R(r) to half the minimum side length of the simulation box. Compare the mean of R(r) with the threshold value. If the mean of R(r) is less than or equal to the threshold value, the water molecules are determined to be uniformly distributed. If the mean of R(r) is greater than the threshold value, the water molecules are determined to be unevenly distributed.

[0006] Preferably, the construction of the porous material model in step 1 includes the following steps: Construct a simulated box with volume V0; The porous material model is placed inside the simulation box; Set the periodic boundary conditions for the simulated box; A certain number of water molecules are inserted into the pores of the porous material.

[0007] Preferably, the porous material model is any one of MOFs, COFs, carbon materials, or zeolites.

[0008] Preferably, in step 1, the simulated self-diffusion of water molecules in the porous material includes the following steps: The simulation system was subjected to NVT ensemble simulation. First, a pre-equilibrium simulation was performed at the first time step to eliminate the initial stress of the system and bring the water molecules to a dynamic equilibrium state. Then, a final simulation was performed at the second time step. The simulation step size was set to 1-4 fs.

[0009] Preferably, step 2 includes the following steps: The oxygen atoms of all water molecules in the system are defined as separate atomic groups; the first time interval to the second time interval of the simulated trajectory of the finished product is selected for analysis, and the oxygen atom groups of water molecules are selected as both the reference group and the target group, and the coordination number function N1(r) of the water molecules is output; the density ρ1(r) of the first water molecule is obtained according to the coordination number function N1(r).

[0010] Preferably, the total number N of water molecules in the simulation system is obtained. 总 , using N 总Subtract the number of water molecules N1(r) within the radius r range to obtain the number of water molecules N2(r) outside the radius r range; Obtain the volume V0 of the simulated box, and subtract the volume V(r) of the sphere with radius r from V0 to obtain the volume V2(r) outside the radius r, where r ≤ half the minimum side length of the simulated box.

[0011] Preferably, in step 4, R(r) = ρ1(r) / ρ2(r); the range of the abscissa of R(r) is limited to greater than 0.3nm.

[0012] Preferably, the threshold value in step 4 is 0.9-1.1.

[0013] Preferably, water molecules can also be replaced with any one of helium, argon, or nitrogen.

[0014] Preferably, a simulation system is constructed: a simulation box with a volume of V0 is constructed in GROMACS software. Based on the crystal structure or micromorphology of the porous material, the porous material model (such as a 2×2×2 supercell of UiO-67 or a three-layer graphene sheet) is placed inside the simulation box, and periodic boundary conditions are enabled. According to the actual research needs, a certain number of water molecules are inserted into the pores of the porous material in the box.

[0015] Preferably, the self-diffusion of water molecules in porous materials is simulated by performing an NVT ensemble (canonical ensemble, constant particle number, volume and temperature) simulation on the simulation system, using a V-rescale thermostat to maintain the system temperature stability (e.g., 298K); a 10 ns pre-equilibrium simulation is first performed to eliminate the initial stress of the system and allow the water molecules to reach a dynamic equilibrium state, followed by a 60 ns final simulation, with the simulation step size set to 2 fs.

[0016] Preferably, the coordination number function of water molecules is calculated as follows: an index file is created using the make_ndx command of the GROMACS software, defining the oxygen atoms of all water molecules in the system as separate atomic groups; the last 50 ns of the simulated trajectory of the finished product (the interval in which the system has reached a stable diffusion state) is selected for analysis, the gmx rdf command is executed and the -cn option is enabled, the oxygen atom groups of water molecules are selected as both the reference group and the target group, and the coordination number function N1(r) of water molecules (oxygen atoms) is output. This function represents the total number of oxygen atoms of water molecules within a radius r centered on the oxygen atom of the water molecule.

[0017] Preferably, the local density ρ1(r) is calculated using the formula for the volume of a sphere: V(r) = 4 / 3πr. 3To calculate the volume of a sphere with radius r, divide the coordination number function value N1(r) by V(r) to obtain the water molecule density ρ1(r) within a radius r centered on the water molecule. The calculation formula is: ρ1(r) = N1(r) / [4 / 3πr] 3 ].

[0018] Preferably, the number of water molecules in the global remaining region N2(r) is calculated: the total number of water molecules N in the statistical simulation system. 总 , using N 总 Subtracting the number of water molecules N1(r) within the radius r, we obtain the number of water molecules N2(r) outside the radius r. The formula is: N2(r) = N 总 -N2(r).

[0019] Preferably, the global residual region volume V2(r) is calculated: the simulated box volume V0 is a known fixed value (e.g., V0 = 153.7 nm for the UiO-67 supercell system). 3 The graphene system has a V0 of 59.6 nm. 3 Subtracting the volume V(r) of the sphere with radius r from V0 gives the volume V2(r) outside the radius r. The formula is: V2(r) = V0 - 4 / 3πr 3 The radius r must satisfy the condition that r ≤ half the minimum side length of the simulated box to avoid the sphere exceeding the box boundary and causing volume calculation errors.

[0020] Preferably, the density ρ2(r) of the remaining global region is calculated as follows: N2(r) is divided by V2(r) to obtain the water molecule density ρ2(r) outside the radius r. The calculation formula is: ρ2(r) = [N 总 -N1(r)] / [V0-4 / 3πr 3 ].

[0021] Preferably, the density ratio R(r) is calculated by comparing ρ1(r) obtained in step 2 with ρ2(r) obtained in step 3 to obtain the density ratio R(r). The calculation formula is: R(r) = ρ1(r) / ρ2(r). In order to eliminate the interference of short-range hydrogen bonding on the density calculation, the range of the abscissa of R(r) is limited to greater than 0.3 nm.

[0022] Preferably, the mean value of the density ratio R(r) is calculated over the entire range from 0.3 nm to half the minimum side length of the simulated box. If the mean value is approximately equal to 1, the molecules are determined to be uniformly distributed in the porous material. If the mean value deviates significantly from 1, the molecules are determined to be unevenly distributed, and the greater the deviation of the mean value from 1, the more significant the unevenness of distribution.

[0023] A second aspect of this invention is the application of a method for characterizing the uniformity of water distribution in porous materials, characterized by its use in the structural design and performance regulation of porous materials in the fields of drying and dehydration, atmospheric water collection, adsorption separation, catalytic reactions, or energy storage materials. It is also used in the field of simulation characterization of porous materials, specifically including performance simulation characterization related to water distribution in porous materials, high-throughput screening of porous materials that meet the requirements for uniform water distribution, guiding the construction and optimization of porous material simulation systems, and providing quantitative basis for the simulation prediction of porous material performance.

[0024] The present invention has the following advantages: (1) High quantification accuracy, superior to traditional simulation analysis methods: Compared with the qualitative visualization of two-dimensional density distribution maps, this method achieves quantitative evaluation of the water distribution of the entire system through density ratio R(r); Compared with the limitation that the radial distribution function can only reflect the average coordination distance and distribution probability between atoms, this method can accurately capture the difference between the local density and the global density around water molecules, and achieve direct quantification of "uniformity", making the evaluation results more objective and comparable.

[0025] (2) Easy to operate and low cost: Based on the mature GROMACS software, simulation and analysis are realized without the need for complex experimental equipment. The simulation parameters are simple to set and multiple samples can be characterized quickly. (3) Wide applicability: It is applicable to different types of porous materials (such as MOFs, COFs, carbon materials, zeolites and other porous materials), and can be adapted to different pore structures and water content scenarios by adjusting the simulation system parameters. Attached Figure Description

[0026] Figure 1 These are simulated snapshots of water molecules in UiO-67 obtained from Example 1, corresponding to systems with 40, 80, 800, and 3200 water molecules, respectively.

[0027] Figure 2 The image shown is the R(r) image representing the uniformity of water molecule distribution in UiO-67 calculated in Example 1. The four curves, light blue, dark blue, yellow, and orange, correspond to systems with 40, 80, 800, and 3200 water molecules, respectively.

[0028] Figure 3 These are simulated snapshots of water molecules in graphene obtained from Example 2, corresponding to systems with 20, 200, 800, and 1260 water molecules, respectively.

[0029] Figure 4 The image shown is the R(r) image representing the uniformity of water molecule distribution in graphene, calculated in Example 2. The four curves, light blue, dark blue, yellow, and orange, correspond to systems with 20, 200, 800, and 1260 water molecules, respectively.

[0030] Figure 5 These are simulated snapshots of helium atoms in UiO-67 obtained from Example 3, corresponding to systems with 40, 80, 800, and 3200 helium atoms, respectively.

[0031] Figure 6 The image shown is the R(r) image representing the uniformity of helium atom distribution in UiO-67 obtained from Example 3. The four curves, light blue, dark blue, yellow, and orange, correspond to systems with 40, 80, 800, and 3200 helium atoms, respectively. Detailed Implementation

[0032] To better understand the present invention, the following embodiments are further illustrations of the present invention, but the content of the present invention is not limited to the following embodiments.

[0033] The present invention will be further described in detail below with reference to specific embodiments. The software used in this embodiment is GROMACS version 2024.4. Example 1

[0034] Characterizing the uniformity of water molecule distribution in UiO-67 Model Construction and Self-Diffusion Simulation: A cubic simulation box with a side length of 5.35660 nm (V0 = 5.35660³ = 153.7 nm) was constructed in GROMACS. 3 The 2×2×2 supercell of UiO-67 (whose original crystal structure file was obtained from the Cambridge Crystal Data Center CCDC database) was placed in the center of the box; 40, 80, 800, and 3200 water molecules were randomly inserted into the channels (using the OPC3 water model); the temperature was set to 298 K, a v-rescale thermostat was used, and a 10 ns NVT pre-equilibrium simulation was performed with a step size of 1 fs, followed by a 60 ns final simulation.

[0035] Calculate the coordination number function N1(r): Use the make_ndx command to define the oxygen atoms of all water molecules as the group O_water; select the finished product to simulate the 10~60 ns trajectory, execute gmx rdf -f traj.xtc -s topol.tpr -nindex.ndx -cn coord.xvg -ref O_water -sel O_water, and output the N1(r) data.

[0036] Calculate the local density ρ1(r): According to the formula ρ1(r) = N1(r) / (4 / 3πr) 3 For example, with 3200 water molecules inserted, when r = 1.0 nm, N1(r) = 88, then ρ1(r) = 88 / (4 / 3π × 1.0 nm). 3 )=20.9 nm-3 .

[0037] Calculate the global residual density ρ²(r): Taking the insertion of 3200 water molecules as an example (N) 总 =3200), when r=1.0 nm, N2(r)=3200-88=3112; V2(r)=153.7-4 / 3π×1.0 3 =149.5 nm 3 Therefore, ρ²(r) = 3112 / 149.5 ≈ 20.8 nm -3 .

[0038] Uniformity assessment: Based on the formula R(r) = ρ1(r) / ρ2(r), taking the insertion of 3200 water molecules as an example, when r = 1.0 nm, R(r) = 20.9 / 20.8 = 1. Within the range of 0.3 to 2.678 nm (half the side length of the box), the average value of R(r) is 1.02, indicating that the water molecules are uniformly distributed in UiO-67. (Based on Table 1 and appendix...) Figure 1 , 2 It can be determined that the uniformity of water distribution in UiO-67 gradually increases with the increase of the number of water molecules, and reaches a uniform distribution at 3200 water molecules / supercell.

[0039] Table 1. R(r) values ​​of water molecule distribution in UiO-67 under different numbers of water molecules. <![CDATA[Number of water molecules (H2O)]]> average value of R(r) Distribution uniformity judgment 40 41.84 Uneven 80 31.06 Uneven 800 2.76 Uneven 3200 1.02 uniform Example 2

[0040] Characterizing the uniformity of water molecule distribution in graphene Model Construction and Self-Diffusion Simulation: A cuboid box with dimensions of 3.87979 nm × 3.94000 nm × 3.90000 nm (V0 = 3.87979 × 3.94000 × 3.90000 ≈ 59.6 nm³) was constructed. Three layers of graphene sheets with a length of 3.87979 nm and a width of 2.94000 nm were shifted 2 nm along the y-axis and placed at the center of the box to form a 1 nm slit channel. 20, 200, 800, and 1260 water molecules were inserted, respectively. The temperature was set to 298 K, and a 10 ns pre-equilibration and 60 ns final product simulation were performed with a step size of 2 fs.

[0041] Calculate the coordination number function N1(r): Use the make_ndx command to define the oxygen atoms of all water molecules as the group O_water; select the finished product to simulate the 10~60 ns trajectory, execute gmx rdf -f traj.xtc -s topol.tpr -nindex.ndx -cn coord.xvg -ref O_water -sel O_water, and output the N1(r) data.

[0042] Calculate ρ1(r) and ρ2(r): Taking the insertion of 200 water molecules as an example, when r = 1.0 nm, N1(r) = 51, ρ1(r) = 51 / (4 / 3π×1.0) 3 )=12.2 nm -3 ;N2(r)=200-51=149, V2(r)=59.6-4.19=55.4 nm 3 ρ²(r) = 12.2 / 55.4 ≈ 2.69 nm -3 .

[0043] Uniformity assessment: Based on the formula R(r) = ρ1(r) / ρ2(r), taking the insertion of 200 water molecules as an example, when r = 1.0 nm, R(r) = 12.2 / 2.69 ≈ 4.5. Within the range of 0.3 to 1.940 nm (half the minimum side length of the box), the average value of R(r) is 4.80, indicating that the water molecules are not uniformly distributed in the graphene slit.

[0044] Table 2. R(r) values ​​of water molecule distribution in graphene for different numbers of water molecules. <![CDATA[Number of water molecules (H2O)]]> average value of R(r) Distribution uniformity judgment 20 10.74 Uneven 200 4.80 Uneven 800 2.36 Uneven 1260 1.02 uniform Example 3

[0045] Characterizing the uniformity of helium atom distribution in UiO-67 The purpose of this comparative example is to further highlight the distinguishability and applicability of the method of this invention by comparing the distribution behavior of different particles (He atoms and water molecules) in the same porous material (UiO-67): He atoms are small in size and have weak interaction with the pores of UiO-67, and theoretically they are easy to achieve uniform distribution in the pores. Characterizing them with this method and obtaining the evaluation result of uniform distribution can form a sharp contrast with the distribution pattern of water molecules in UiO-67. This not only verifies the method's accurate identification ability in the "uniform distribution" scenario, but also provides data support for analyzing the influence of molecular characteristics (size, interaction) on the uniformity of distribution in porous materials.

[0046] Model Construction and Self-Diffusion Simulation: A cubic simulation box with a side length of 5.35660 nm (V0=5.35660 nm) was constructed in GROMACS. 3=153.7 nm 3 The 2×2×2 supercell of UiO-67 (whose original crystal structure file was obtained from the Cambridge Crystal Data Centre CCDC database) was placed in the center of the box; 40, 80, 800, and 3200 helium atoms were randomly inserted into the channels respectively; the temperature was set to 298 K, and a v-rescale thermostat was used to perform a 10 ns NVT pre-equilibrium simulation with a step size of 1 fs, followed by a 60 ns final simulation.

[0047] Calculate the coordination number function N1(r): Use the make_ndx command to define all helium atoms as the group He; select the finished product to simulate the 10~60 ns trajectory, execute gmx rdf -f traj.xtc -s topol.tpr -n index.ndx -cncoord.xvg -ref He -sel He, and output the N1(r) data.

[0048] Calculate the local density ρ1(r): According to the formula ρ1(r) = N1(r) / (4 / 3πr) 3 For example, with 800 helium atoms inserted, when r = 1.0 nm, N1(r) = 22, then ρ1(r) = 22 / (4 / 3π × 1.0 nm). 3 )=5.2 nm -3 .

[0049] Calculate the global residual density ρ²(r): Taking the insertion of 800 helium atoms as an example (N 总 =800), when r=1.0 nm, N2(r)= N 总 - N1(r)=778; V2(r)=153.7-4 / 3π×1.0 3 =149.5 nm 3 Therefore, ρ²(r) = 778 / 149.5 ≈ 5.2 nm -3 .

[0050] Uniformity assessment: Using the formula R(r) = ρ1(r) / ρ2(r), taking the insertion of 800 helium atoms as an example, when r = 1.0 nm, R(r) = 5.2 / 5.2 = 1. Within the range of 0.35–2.678 nm (half the side length of the box), the average value of R(r) is 1.00, indicating that the helium atoms are uniformly distributed in UiO-67. Table 3 shows that the helium atom content is uniformly distributed across all helium atom concentrations, consistent with theoretical expectations, verifying the reliability of the method.

[0051] Table 3. R(r) values ​​of helium distribution in UiO-67 with different numbers of helium atoms. number of helium atoms average value of R(r) Distribution uniformity judgment 40 0.98 uniform 80 0.99 uniform 800 1.00 uniform 3200 1.01 uniform The embodiments described above are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The embodiments and features described in this application can be arbitrarily combined with each other without conflict. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A method of characterizing the uniformity of water distribution in a porous material, characterized by, The method comprises the following steps: Step 1: constructing a porous material model and inserting water molecules into the pores of the porous material model to simulate the motion behavior of the water molecules in the porous material; Step 2: obtaining a first water molecule density ρ1(r) within a radius r range centered on the water molecule; Step 3: obtaining a second water molecule density ρ2(r) outside the radius r range centered on the water molecule; Step 4: obtaining a ratio R(r) of the first water molecule density ρ1(r) to the second water molecule ρ2(r), comparing the average value of R(r) within the range from the initial value of R(r) to half the minimum side length of the simulation box, and comparing the average value of R(r) with a threshold value, if the average value of R(r) ≤ the threshold value, it is determined that the water molecule distribution is uniform, and if the average value of R(r) > the threshold value, it is determined that the water molecule distribution is non-uniform.

2. The method of characterizing the uniformity of water distribution in a porous material of claim 1, wherein, The construction of the porous material model in step 1 comprises the following steps: constructing a simulation box with a volume V0; placing the porous material model into the simulation box; setting the periodic boundary condition of the simulation box; inserting a certain number of water molecules into the pores of the porous material.

3. A method of characterizing the uniformity of water distribution in a porous material according to either of claims 1 or 2, characterized in that, The porous material model is any one of MOFs, COFs, carbon materials, and zeolites.

4. The method of characterizing the uniformity of water distribution in a porous material of claim 1, wherein, In step 1, the self-diffusion of the simulated water molecules in the porous material comprises the following steps: performing NVT ensemble simulation on the simulation system; first, performing pre-equilibrium simulation for a first time to eliminate the initial stress of the system and make the water molecules reach a dynamic equilibrium state, and the first time is preferably 10-20 ns; then, performing product simulation for a second time, and the second time is preferably 50-65 ns, and the simulation step is set to 1-2 fs.

5. The method of characterizing the uniformity of water distribution in a porous material of claim 1, wherein, Step 2 comprises the following steps: setting the oxygen atoms of all water molecules in the system as a separate atom group; selecting a trajectory interval after the product simulation reaches a stable diffusion state for analysis, selecting the oxygen atom group of the water molecules as the reference group and the target group at the same time, outputting the coordination number function N1(r) of the water molecules; and obtaining the first water molecule density ρ1(r) according to the coordination number function N1(r).

6. The method of characterizing the uniformity of water distribution in a porous material of claim 1, wherein, Obtain the total number N of water molecules in the simulation system. 总 , using N 总 Subtract the number of water molecules N1(r) within the radius r range to obtain the number of water molecules N2(r) outside the radius r range; Obtaining the volume V0 of the simulation box, subtracting the spherical volume V(r) of the radius r from V0 to obtain the volume V2(r) outside the radius r range, and the r ≤ half the minimum side length of the simulation box.

7. The method of characterizing the uniformity of water distribution in a porous material of claim 1, wherein, In step 4, R(r)= ρ1(r) / ρ2(r); the value range of the abscissa of R(r) is limited to greater than 0.3 nm.

8. The method of characterizing the uniformity of water distribution in a porous material of claim 1, wherein, In step 4, the threshold value is 0.9-1.

1.

9. The method of characterizing the uniformity of water distribution in a porous material of claim 1, wherein, The water molecules can also be replaced by any one of helium, argon, and nitrogen.

10. Use of a method of characterizing the uniformity of water distribution in a porous material according to any one of claims 1 to 9, characterized in that, It is used for drying dehydration, atmospheric water collection, adsorption separation, catalytic reaction, or design and performance regulation of porous material structure.

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