A method for predicting the structure of a layered mineral aggregate based on density functional theory

Abstract

The application provides a method for predicting the aggregation structure of layered minerals based on density functional theory, adopts the density functional theory in the first principle, constructs a layered mineral cell model and an inhibitor molecule model by using Material Studio quantum simulation software, and cuts the cell model; the structure of the inhibitor molecule and the layered mineral cell is optimized by a CASTEP module, and the adsorption energy of the inhibitor molecule on the edge and the surface of the layered mineral is calculated, the aggregation structure of the inhibitor molecule to the layered mineral is predicted according to the adsorption energy data; finally, the prediction result is verified by a cryo-EM, and a basis is provided for further optimization. The adsorption energy of the layered mineral is calculated based on the density functional theory, the aggregation structure between the layered minerals can be quickly predicted; the application is beneficial to understanding the aggregation structure of the layered minerals from the atomic level, so that more efficient flotation reagents and optimized flotation processes can be designed, and the experimental cost in the research process is reduced.

Description

Technical Field

[0001] This invention relates to the field of layered mineral flotation, and in particular to a method for predicting the agglomeration structure of layered minerals based on density functional theory. Background Technology

[0002] Low-grade mineral resources in my country are generally characterized by high content and complex composition of fine-grained gangue minerals, with layered silicate minerals being the main gangue minerals. Besides layered silicate gangue minerals, some useful minerals such as molybdenite are also layered minerals. Based on chemical composition and crystal structure, layered minerals can be classified into four categories: silicate layered minerals, sulfide layered minerals, oxide layered minerals, and hydroxide layered minerals. Silicate and sulfide layered minerals are prone to co-occurrence; for example, molybdenite is often associated with argillaceous layered silicate minerals such as talc, forming talc-type difficult-to-process molybdenum ore. Due to their similar layered structures and anisotropic particle surfaces, layered silicate and sulfide minerals easily form different agglomeration structures during flotation. Changes in agglomeration structure lead to changes in pulp viscosity. If pulp viscosity increases, the probability of gangue minerals adhering to the target mineral surface increases, ultimately resulting in a decrease in concentrate recovery and grade.

[0003] The agglomeration structure of layered mineral particles in slurry mainly falls into three categories: face-to-face (FF) structure, edge-to-face (EF) structure, and edge-to-edge (EE) structure. For example, agglomeration occurs in the flotation of talc and molybdenite: 1) the network structure formed by talc itself covers the surface of molybdenite; 2) due to their similar layered structures and particle surface anisotropy, talc and molybdenite form a network structure. This network structure formed by talc and molybdenite increases slurry viscosity and alters slurry rheology, leading to a decrease in molybdenum concentrate recovery rate and grade.

[0004] To regulate the network-like aggregate structure of layered minerals, inhibitors or dispersants are typically added. The aggregate structure of layered minerals is mainly influenced by the charge and interaction forces on their bottom and end faces. Inhibitors can adsorb onto the bottom and end faces of layered silicate minerals, thereby altering their aggregate structure. If the aggregate structure of layered minerals can be predicted, it can provide theoretical guidance for the selection of inhibitors, thus enabling the development of efficient formulations. This invention uses density functional theory to calculate the adsorption energy of inhibitors on the surface of layered minerals, aiming to predict the aggregate structure of layered minerals.

[0005] Density Functional Theory (DFT) is a quantum mechanical method used to study the electronic structure of atoms and molecules. Based on electron density rather than wave functions, this theory describes the ground and excited state properties of atoms and molecules by varying electron density. It has wide applications in computational chemistry, condensed matter physics, and materials science, and is particularly significant in calculating material properties and molecular structures. Daniel Tunedga et al. used DFT to study the structural and property differences of monomolecular water layers on the octahedral and tetrahedral surfaces of kaolinite, finding that the octahedral and tetrahedral surfaces of kaolinite are hydrophilic and hydrophobic, respectively. While there has been considerable research on the surface chemistry of mineral crystals based on DFT in recent years, no studies have proposed using DFT to predict the structure of layered mineral aggregates. This invention proposes for the first time a method for predicting the structure of layered mineral aggregates based on DFT, aiming to solve the technical problem of calculating the adsorption energy between layered minerals, thereby enabling rapid prediction of aggregate structures between layered minerals. This invention is beneficial for understanding the agglomeration structure of layered minerals at the atomic level, thereby enabling the design of more efficient flotation reagents and optimization of flotation processes, and reducing unnecessary experimental costs in the research process. Summary of the Invention

[0006] The purpose of this invention is to provide a method for predicting the structure of layered mineral aggregates based on density functional theory, in order to solve the problem of predicting the structure of layered mineral aggregates.

[0007] This invention employs density functional theory from first-principles calculations and utilizes Material Studio quantum simulation software to construct a model of layered minerals. The CASTEP module is used to characterize the ability of the layered minerals to adsorb inhibitors on their edges (E) and faces (F). Based on the characterization data, it is predicted whether the layered minerals can form new aggregate structures after adsorbing inhibitors.

[0008] This invention discloses a method for predicting the structure of layered mineral aggregates based on density functional theory, the method comprising at least the following steps:

[0009] (1) Establishing the model:

[0010] Import the cell parameters of the layered minerals into Materials Studio software, construct a cell model, and then import the constructed cell model into the CASTEP module in Materials Studio.

[0011] Constructing inhibitor molecular models;

[0012] (3) Cutting the model:

[0013] The cell model is cut using the CleaveSurface function of the Build module in Materials Studio software, along the cell edge (E) and face (F).

[0014] (6) Optimize the model:

[0015] Based on the principles of lowest energy and most stable structure, the CASTEP module of Materials Studio software was used to optimize the structure of the inhibitor molecule and the structure of the cut unit cell model to find the lowest energy point and the most stable structure.

[0016] (7) Calculate the adsorption energy:

[0017] Based on the optimized cut structure, the adsorption energy of inhibitor molecules on the edges (E) and faces (F) of the layered mineral was calculated using the CASTEP module;

[0018] (8) Predicting cluster structure:

[0019] By comparing the adsorption energies of inhibitor molecules on the edges (E) and faces (F) of layered minerals, the spatial configuration of possible clusters formed by layered minerals can be predicted according to the principle of low adsorption energy.

[0020] This invention provides a method for predicting the aggregate structure of layered minerals based on density functional theory. It can predict the aggregate structure of different layered minerals and has universality.

[0021] This invention discloses a method for predicting the structure of layered mineral aggregates based on density functional theory, which obtains the unit cell parameters described in step (1) from crystal structure data of the Mineralogical Society of America; or,

[0022] The cell parameters are obtained by measurement.

[0023] The present invention provides a method for predicting the structure of layered mineral aggregates based on density functional theory, wherein the unit cell model in step (1) includes the unit cell model.

[0024] This invention provides a method for predicting the structure of layered mineral aggregates based on density functional theory. In step (4), the method for calculating the adsorption energy is as follows:

[0025] E ads =E total -(E surface +E adsorbate )

[0026] Among them, E ads E is the adsorption energy. total E represents the total energy of the adsorption system. surface For layered minerals, E adsorbateThe energy of a single inhibitor molecule.

[0027] In this invention, the corresponding model energy can be obtained by calculating the results using the CASTEP module. Specifically, the overall model energy of the inhibitor adsorbing layered minerals is first calculated to obtain E. total Then, the model energy of the layered minerals alone after removing the inhibitor molecule was calculated, and E was obtained. surface Finally, the model energy of the individual inhibitor molecule after removing the layered minerals was calculated, yielding E. adsorbate .

[0028] In this invention, the unit of adsorption energy can be eV.

[0029] The present invention provides a method for predicting the aggregate structure of layered minerals based on density functional theory. In step (5), the predicted aggregate structure is as follows: by comparing the adsorption energy, if the adsorption energy of the layered mineral surface (F) is less than that of the edge (E), it indicates that the inhibitor molecules are more likely to adsorb on the layered mineral surface (F), and the layered mineral is predicted to easily form a surface-to-surface (FF) aggregate structure.

[0030] This invention discloses a method for predicting the aggregate structure of layered minerals based on density functional theory. The method is verified by cryo-electron microscopy. A mineral sample is directly transferred from the slurry to the sample cell using a dropper, then frozen in liquid nitrogen. Finally, the surface of the cut sample is observed. By observing the spatial configuration of the aggregates of layered minerals, the results of computational chemistry are verified.

[0031] This invention discloses a method for predicting the structure of layered mineral aggregates based on density functional theory. Sodium carboxymethyl cellulose is used as an inhibitor, and talc and / or molybdenite are used as the prediction targets. The prediction results are as follows: after adding sodium carboxymethyl cellulose, the aggregate structure between talc, between talc and molybdenite, and between molybdenite changes from a network structure to a face-to-face layered structure.

[0032] If the results of cryo-electron microscopy verification are inconsistent with the previously predicted results, proceed as follows:

[0033] Step 1: Re-section and repositioning of the mineral model and the adsorption sites of the inhibitor molecules

[0034] The mineral model was re-sectioned using the Build module, and the adsorption sites of inhibitor molecules were repositioned using the Adsorption Locator module.

[0035] Step 2: Re-optimize parameters

[0036] The crystal structure parameters of the layered minerals were re-optimized using the CASTEP module, mainly including the exchange-correlation functional, cutoff energy (Ecut), and K-point. The optimal optimization parameters were selected based on the calculated model energy. Different exchange-correlation functionals, cutoff energies (Ecut), and K-points were used to optimize the original unit cell of the layered minerals, obtaining the corresponding model energies. The exchange-correlation functional, cutoff energy (Ecut), and K-point were selected when the energy values ​​were balanced and the difference was less than 0.1 eV.

[0037] Then follow steps (4) and (5) and perform cryo-electron microscopy verification until the results of cryo-electron microscopy verification are consistent with the predicted results.

[0038] This invention discloses a method for predicting the aggregate structure of layered minerals based on density functional theory. The method is verified using cryo-electron microscopy. Mineral samples are directly transferred from the slurry to a sample cell using a dropper, then frozen in liquid nitrogen. Finally, the surface of the cut sample is observed. By observing the spatial configuration of the aggregates of layered minerals, the verification results show that after adding sodium carboxymethyl cellulose, the aggregate structure between talc, between talc and molybdenite, and between molybdenite changes from a network structure to a face-to-face layered structure, thus verifying the feasibility of the prediction method.

[0039] The main difficulty in the technical development of this invention lies in:

[0040] The adsorption energy was calculated to be positive, but the experimental results are inconsistent with the calculated predictions.

[0041] The solution is:

[0042] (1) The mineral model was re-sectioned using the Build module and the adsorption sites of the inhibitor molecules were repositioned using the Adsorption Locator module;

[0043] (2) Re-optimize the parameters

[0044] The crystal structure parameters of the layered minerals were re-optimized using the CASTEP module, primarily including the exchange-correlation functional, cutoff energy (Ecut), and K-point. The optimal optimization parameters were selected based on the calculated model energy. Different exchange-correlation functionals, cutoff energies (Ecut), and K-points were used to optimize the original unit cell of the layered minerals, obtaining the corresponding model energies. The exchange-correlation functional, cutoff energy (Ecut), and K-point were selected when the energy values ​​were balanced and the difference was less than 0.1 eV.

[0045] The advantages and positive effects of this invention are:

[0046] To address the question of whether inhibitors can regulate the agglomeration structure of layered minerals, a computer chemical simulation was employed. Using Materials Studio software, DFT calculations were performed to establish the adsorption configurations of inhibitor molecules and layered mineral cells. Furthermore, by matching appropriate adsorption theories and models, simulation calculations can accurately predict the agglomeration structure of layered minerals after adsorbing inhibitor molecules. Attached Figure Description

[0047] Figure 1 Predicted results for the FF aggregate structure formed by talc;

[0048] Figure 2 Predicted results for the FF agglomerate structure formed from molybdenite;

[0049] Figure 3 Predicted results for the FF aggregate structure formed by talc and molybdenite;

[0050] Figure 4 Cryo-electron microscopy image of a mixed talc and molybdenite ore flotation without the addition of inhibitors;

[0051] Figure 5 Cryo-electron microscopy image of a talc-molybdenite mixed ore flotation with the addition of inhibitors;

[0052] Figure 6 This is the cell model constructed using Materials Studio software in Example 1.

[0053] Figure 7 This is the cell model constructed using Materials Studio software in Example 2. Detailed Implementation

[0054] Example 1

[0055] The unit cell data of talc was obtained from the Mineral Institute of America's Crystal Structure Database. The space group is C1, and the unit cell parameters are as follows: α = 90.570°, β = 98.910°, γ = 90.030°, and these values ​​are imported into Materials Studio software to construct a unit cell model (e.g., Figure 6 (as shown), and import the constructed cell model into the CASTEP module in Materials Studio.

[0056] Using the Cleave Surface function of the Build module in Materials Studio, cut along the cell edge (E) and face (F); specifically: cut the talc cell unit model, cut the (0 0 1) face on the talc cell face (F), and cut the (1 0 0) face on the talc cell edge (E). Based on the cell model, translate the unit, and add elements along the c direction at the cut surface. A vacuum layer of thickness is used to form the initial crystal plane model.

[0057] The molecular structure of sodium carboxymethyl cellulose (CMC) was constructed using Materials Studio software. Density functional theory (DFT) was then used in the CASTEP module of Materials Studio to optimize the structure of talc and CMC molecules, identifying the lowest energy point and the most stable structure. Based on the optimized, segmented structure, the adsorption energies of CMC molecules on the edges (E) and faces (F) of talc were calculated using the CASTEP module. The agglomeration structure of talc was predicted based on the calculation results (Table 1). Figure 1 ).

[0058] Example 2

[0059] The unit cell data for molybdenite was obtained from the Mineral Institute of America's Crystal Structure Database. The space group is D6h4-P63 / mmc, and the unit cell parameters are as follows: α = 90°, β = 90°, γ = 120°, and these values ​​are imported into Materials Studio software to construct a unit cell model (e.g., Figure 7 (As shown). Then import the constructed cell model into the CASTEP module in Materials Studio.

[0060] Using the Cleave Surface function of the Build module in Materials Studio, cut along the cell edge (E) and face (F); specifically: cut the molybdenite cell model, cut the (0 01) face on the molybdenite cell face (F), and cut the (1 0 0) face on the molybdenite cell edge (E). Based on the cell model, translate the cells, and add more cells along the c direction at the cut surfaces. A vacuum layer of thickness is used to form the initial crystal plane model.

[0061] The molecular structure of sodium carboxymethyl cellulose (CMC) was constructed using Materials Studio software. Density functional theory (DFT) was then used in the CASTEP module of Materials Studio to optimize the structures of molybdenite and CMC molecules, identifying the lowest energy point and the most stable structure. Based on the optimized, segmented structure, the adsorption energies of CMC molecules on the edges (E) and faces (F) of molybdenite were calculated using the CASTEP module using the following method:

[0062] The method for calculating the adsorption energy is as follows:

[0063] E ads =E total -(E surface +E adsorbate )

[0064] Among them, E ads E is the adsorption energy. total E represents the total energy of the adsorption system. surface

[0065] For layered minerals, E adsorbate The energy of a single inhibitor molecule.

[0066] Based on the calculation results (Table 1), the agglomeration structure of molybdenite is predicted. Figure 2 ).

[0067] Table 1. Adsorption energies of CMC molecules on the edges (E) and faces (F) of talc and molybdenite.

[0068] <![CDATA[Adsorption energy E ads / eV]]> -4.2546 -2.7324 -5.2242 -3.2576

[0069] Result Prediction: Calculation results show that after CMC molecules are adsorbed, the adsorption energy of the talc surface (F) is less than that of the talc edge (E), and the adsorption energy of the molybdenite surface (F) is less than that of the molybdenite edge (E). This indicates that inhibitor molecules are more likely to adsorb on the talc surface (F) and the molybdenite surface (F). Therefore, it is predicted that talc and molybdenite are likely to form the following structures: 1) Talc with talc FF structure ( Figure 1 ); 2) FF structure of molybdenite and molybdenite ( Figure 2 ); 3) FF structure of talc and molybdenite ( Figure 3 ).

[0070] Prediction verification: The verification was carried out using cryo-electron microscopy (Cryo-SEM). The mineral sample was directly transferred from the slurry (25 wt% solids, 80 wt% talc in the solids) to the sample cell using a dropper, and then quickly placed in liquid nitrogen to freeze. Finally, the surface of the cut sample was observed. Figure 4The results show that without the addition of CMC, talc and molybdenite aggregate together to form a network structure similar to EF and EE. Figure 5 The results show that after the addition of CMC, the agglomeration structure between talc and molybdenite changes from a network structure to an FF layered structure, verifying the feasibility of a method for predicting layered mineral structures based on density functional theory.

[0071] Example 3

[0072] In the exploration process of Example 1, it was found that: the adsorption energy of CMC on the edge (E) and face (F) of talc was initially calculated using the CASTEP module. The adsorption energies obtained were all positive, and the edge (E) < face (F), indicating that adsorption was difficult to occur. However, in the subsequent cryo-electron microscopy verification experiment, it was found that CMC easily adsorbed on the surface of talc and caused talc to form a face-to-face (FF) structure. Therefore, the following optimizations were made: (1) the mineral model was re-sectioned using the Build module and the adsorption sites of inhibitor molecules were repositioned using the Adsorption Locator module; (2) the parameters were re-optimized. The crystal structure of the layered mineral was re-optimized using the CASTEP module, which mainly included the exchange-related functional, cutoff energy (Ecut) and K-point. The optimal optimization parameters were selected based on the calculated model energy. Different exchange-correlation functionals, cutoff energies (Ecut), and K-points were used to optimize the original unit cell of the layered minerals, obtaining the corresponding model energies. The exchange-correlation functionals, cutoff energies (Ecut), and K-points with energy balance and differences less than 0.1 eV were selected. After this series of optimizations and calibrations, negative adsorption energies were obtained, and the adsorption energy of CMC on the talc edge (E) was greater than that on the surface (F), consistent with experimental results. This demonstrates that CMC readily adsorbs on the talc surface (F), altering the talc aggregate structure.

Claims

1. A method for predicting the structure of layered mineral aggregates based on density functional theory, characterized in that, The method includes at least the following steps: (1) Establishing a model: Import the cell parameters of the layered minerals into Materials Studio software, construct a cell model, and then import the constructed cell model into the CASTEP module in Materials Studio. Constructing inhibitor molecular models; (2) Cutting model: The cell model is cut using the Cleave Surface function in the Build module of Materials Studio software, along the cell edge (E) and face (F); (3) Optimization model: Based on the principles of lowest energy and most stable structure, the CASTEP module of Materials Studio software was used to optimize the structure of the inhibitor molecule and the structure of the cut unit cell model to find the lowest energy point and the most stable structure. (4) Calculate the adsorption energy: Based on the optimized cut structure, the adsorption energy of inhibitor molecules on the edges (E) and faces (F) of the layered mineral was calculated using the CASTEP module; (5) Predicting cluster structure: By comparing the adsorption energies of inhibitor molecules on the edges (E) and faces (F) of layered minerals, the spatial configuration of possible clusters formed by layered minerals can be predicted according to the principle of low adsorption energy. In step (5), the predicted agglomeration structure is as follows: by comparing the adsorption energy, if the adsorption energy of the layered mineral surface (F) is less than that of the edge (E), it indicates that the inhibitor molecules are more likely to be adsorbed on the layered mineral surface (F), and the layered mineral is predicted to easily form a surface-to-surface (FF) agglomeration structure. The results of computational chemistry were verified by using cryo-electron microscopy. Mineral samples were directly transferred from the slurry to the sample cell using a dropper, then frozen in liquid nitrogen. Finally, the surface of the cut sample was observed. By observing the aggregate spatial configuration of layered minerals, the results of computational chemistry were verified. If the results of cryo-electron microscopy verification are inconsistent with the previously predicted results, proceed as follows: Step 1: Re-section and repositioning of the mineral model and the adsorption sites of the inhibitor molecules The mineral model was re-sectioned using the Build module, and the adsorption sites of inhibitor molecules were repositioned using the Adsorption Locator module. Step 2: Re-optimize parameters The crystal structure parameters of the layered minerals were re-optimized using the CASTEP module, mainly including the exchange-correlation functional, cutoff energy, and K-point. The optimal optimization parameters were selected based on the calculated model energy. The original unit cell of the layered minerals was optimized using different exchange-correlation functionals, cutoff energies, and K-points to obtain the corresponding model energies. The exchange-correlation functional, cutoff energy, and K-point with energy numerical balance and a difference of less than 0.1 eV were selected. Then follow steps (4) and (5) and perform cryo-electron microscopy verification until the results of cryo-electron microscopy verification are consistent with the predicted results.

2. The method for predicting the structure of layered mineral aggregates based on density functional theory as described in claim 1, characterized in that: It has universal applicability in predicting the agglomeration structure of different layered minerals.

3. The method for predicting the structure of layered mineral aggregates based on density functional theory as described in claim 1, characterized in that: The cell parameters are obtained by measurement.

4. The method for predicting the structure of layered mineral aggregates based on density functional theory as described in claim 1, characterized in that, In step (4), the adsorption energy is calculated as follows: ； in, For adsorption energy, The total energy of the adsorption system. The energy of layered minerals, The energy of a single inhibitor molecule.

5. The method for predicting the structure of layered mineral aggregates based on density functional theory as described in claim 1, characterized in that: With the addition of inhibitors, and taking layered minerals as the prediction target, the prediction result is: after the addition of inhibitors, the agglomeration structure between layered minerals changes from a network structure to a face-to-face layered structure.

6. The method for predicting the structure of layered mineral aggregates based on density functional theory as described in claim 5, characterized in that: The experiment was validated using cryo-electron microscopy. Mineral samples were directly transferred from the slurry to the sample cell using a dropper, then frozen in liquid nitrogen. Finally, the surface of the cut sample was observed. By observing the spatial configuration of the aggregates of layered minerals, the validation results showed that after the addition of inhibitors, the aggregate structure between layered minerals changed from a network structure to a face-to-face layered structure, thus verifying the feasibility of the prediction method.

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