Quantitative model of pollutant reaction rate and structure as well as construction method and application of quantitative model

Abstract

The invention relates to the technical field of water pollution control, in particular to a pollutant reaction rate and structure quantitative model and a construction method and application thereof. The invention provides a method for constructing a quantitative model of a pollutant reaction rate and a structure. The energy change of a pollutant molecule after two electrons and one proton are lost at the same time is used as a descriptor. The molecular descriptors are generated by quantum chemistry calculations. The QSAR model established based on the descriptor has high correlation and a wide application range. According to the QSAR model, the problems that a real reaction process cannot be completely reflected, the linear fitting correlation is low and the application range is narrow in a previous model are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of water pollution control, and in particular to a quantitative model of pollutant reaction rate and structure, a construction method and application thereof. Background Art

[0002] Heterogeneous persulfate oxidation technology has received extensive attention in the field of water treatment due to its advantages such as high efficiency, easy operation, and wide pH range, and has been applied to the deep treatment and pretreatment of industrial and domestic sewage. Studies have shown that non-radical oxidation pathways such as direct electron transfer are common in heterogeneous persulfate systems, which can achieve selective oxidation of pollutants in water and reduce the interference of coexisting matrices (such as chloride ions, carbonate ions, and natural organic matter). However, the mechanism of non-radical oxidation processes is complex, and the reaction rate is significantly affected by the structure of the pollutants. Therefore, predicting the removal rate of pollutants is of great significance for optimizing the treatment process.

[0003] Establishing a quantitative structure-activity relationship (QSAR) between pollutant structure and reaction rate is an important method for predicting the reaction rate of new pollutants. Among them, constructing appropriate molecular structure descriptors is the basis for accurate QSAR analysis. Obtaining molecular structure descriptors through experiments (such as electrochemical determination of oxidation half-wave potential) is often time-consuming and laborious, and has low repeatability. In comparison, quantum chemical calculation methods have the advantages of convenience, speed, and high repeatability. At present, there are works that use quantum chemical calculation methods to obtain descriptors such as molecular orbital energy and electrophilicity that reflect different properties of molecules. However, the QSAR model of the heterogeneous persulfate oxidation system established based on these descriptors cannot fully reflect the real reaction process, and there are problems such as low linear fitting correlation and narrow coverage of pollutants. Therefore, it is still necessary to develop new molecular descriptors and their corresponding QSAR models to improve the fitting accuracy of the model and expand its scope of application of pollutants. Summary of the invention

[0004] In view of this, the technical problem to be solved by the present invention is to provide a quantitative model of pollutant reaction rate and structure, its construction method and application. The quantitative model avoids the problems in previous models that the real reaction process cannot be fully reflected, the linear fitting correlation is low, and the scope of application is narrow.

[0005] The present invention provides a method for constructing a quantitative model of pollutant reaction rate and structure, comprising the following steps:

[0006] S1) Get the descriptor:

[0007] The energy difference between the pollutant molecule after losing 2 electrons and 1 proton and the initial state is used as the descriptor, that is, ΔE 2e1p =E2-E1;

[0008] S2) Construction of QSAR model:

[0009] S2-1) Conduct pollutant removal experiments:

[0010] The aqueous solution containing the pollutant is stirred and mixed with the catalyst for pre-adsorption;

[0011] The pre-adsorbed mixed liquid is mixed with persulfate, samples are taken from the solution according to the reaction time t, and the pollutant concentration c in the solution after different reaction times is determined by high performance liquid chromatography;

[0012] S2-2) Calculation of pollutant reaction rate:

[0013] Plotting a curve of the pollutant concentration c versus the reaction time t, then taking the pollutant concentration after adsorption as the initial concentration c0, plotting a curve of c0 / c versus t, and performing a linear fit, the slope of the fitting curve is the second-order kinetic reaction rate constant k;

[0014] S2-3) Model building:

[0015] The molecular descriptor of the pollutant is ΔE 2e1p The horizontal axis is used, and the logarithm lgk of the pollutant reaction rate constant k is used as the vertical axis to draw a graph, and a linear fit is performed. The resulting linear relationship is the quantitative model of the pollutant reaction rate-structure.

[0016] Preferably, step S1) comprises:

[0017] S1-1) Pollutant molecular structure optimization and energy calculation: construct the initial structure of the pollutant molecule in GaussView software, use low- and medium-precision functionals and basis sets to optimize the structure, and use high-precision functionals and basis sets to calculate the single-point energy of the optimized structure. The obtained single-point energy is recorded as E1;

[0018] S1-2) Pollutant ion structure optimization and energy calculation: In GaussView software, the initial structure of the positive ion generated by the pollutant molecule losing 2 electrons and 1 proton is constructed, and the structure is optimized using medium and low precision functionals and basis sets. The single point energy of the optimized structure is calculated using high precision functionals and basis sets, and the obtained single point energy is recorded as E2;

[0019] S1-3) Calculation of descriptors: The descriptor is the energy difference between the pollutant molecule after losing 2 electrons and 1 proton and the initial state, that is, ΔE 2e1p =E2-E1.

[0020] Preferably, in step S2-1), the pollutants are organic pollutants, including at least one of monosubstituted phenols, polysubstituted phenols and anilines.

[0021] Preferably, in step S2-1), the catalyst includes carbon nanotubes (CNT), Co3O4, MnFe2O4 or CuO.

[0022] Preferably, in step S2-1), the persulfide comprises potassium peroxodisulfate (PDS) or potassium peroxomonosulfate (PMS).

[0023] The present invention also provides a quantitative model of pollutant reaction rate and structure obtained by the construction method described above.

[0024] The present invention also provides an application of the above-mentioned quantitative model of pollutant reaction rate and structure to predict pollutant reaction rate.

[0025] The present invention also provides an application of the above-mentioned quantitative model of pollutant reaction rate and structure to predict the reaction rate of pollutants in a heterogeneous persulfate oxidation system.

[0026] Preferably, the heterogeneous persulfate oxidation system includes a CNT / PDS system, a Co3O4 / PMS system, a MnFe2O4 / PMS system or a CuO / PDS system.

[0027] The present invention provides a method for constructing a quantitative model of pollutant reaction rate and structure, comprising the following steps: S1) obtaining a descriptor: using the energy difference between the pollutant molecule after losing 2 electrons and 1 proton and the initial state as the descriptor, that is, ΔE 2e1p =E2-E1; S2) Construction of QSAR model: S2-1) Conduct pollutant removal experiment: Stir and mix the aqueous solution containing the pollutant with the catalyst for pre-adsorption; Mix the pre-adsorbed mixed liquid with persulfate, take samples from the solution according to the reaction time t, and determine the pollutant concentration c in the solution after different reaction times by high performance liquid chromatography; S2-2) Calculation of pollutant reaction rate: Plot the change curve of the pollutant concentration c with the reaction time t, and then use the pollutant concentration after adsorption as the initial concentration c0, plot the change curve of c0 / c with t, and perform linear fitting. The slope of the fitting curve is the secondary kinetic reaction rate constant k; S2-3) Construct model: Take the molecular descriptor ΔE of the pollutant as the 2e1p The horizontal axis is used, and the logarithm lgk of the pollutant reaction rate constant k is used as the vertical axis to draw a graph, and a linear fit is performed. The resulting linear relationship is the quantitative model of the pollutant reaction rate-structure.

[0028] The present invention provides a method for constructing a quantitative model of pollutant reaction rate and structure, using the energy change after the pollutant molecule loses two electrons and one proton at the same time as a descriptor. The molecular descriptor is generated by quantum chemical calculation. The QSAR model established based on this descriptor has high correlation and a wide range of application. The QSAR model avoids the problems of the previous model that the real reaction process cannot be fully reflected, the linear fitting correlation is low, and the scope of application is narrow. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A schematic diagram of a descriptor calculation method provided by an embodiment of the present invention;

[0030] Figure 2 The removal curve and removal kinetic fitting curve of p-nitrophenol in CNT / PDS system;

[0031] Figure 3 is the fitting result of QSAR model in CNT / PDS system;

[0032] Figure 4 The QSAR model fitting results in different heterogeneous persulfate oxidation systems;

[0033] Figure 5 Removal curves and QSAR model fitting results of pollutants in simulated wastewater prepared for secondary effluent and mixed pollutants from sewage treatment plants;

[0034] Figure 6 It is the fitting result of the QSAR model constructed based on other descriptors in the CNT / PDS system. DETAILED DESCRIPTION

[0035] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] The present invention provides a method for constructing a quantitative model of pollutant reaction rate and structure, comprising the following steps:

[0037] S1) Get the descriptor:

[0038] The energy difference between the pollutant molecule after losing 2 electrons and 1 proton and the initial state is used as the descriptor, that is, ΔE 2e1p =E2-E1;

[0039] S2) Construction of QSAR model:

[0040] S2-1) Conduct pollutant removal experiments:

[0041] The aqueous solution containing the pollutant is stirred and mixed with the catalyst for pre-adsorption;

[0042] The pre-adsorbed mixed liquid is mixed with persulfate, samples are taken from the solution according to the reaction time t, and the pollutant concentration c in the solution after different reaction times is determined by high performance liquid chromatography;

[0043] S2-2) Calculation of pollutant reaction rate:

[0044] Plotting a curve of the pollutant concentration c versus the reaction time t, then taking the pollutant concentration after adsorption as the initial concentration c0, plotting a curve of c0 / c versus t, and performing a linear fit, the slope of the fitting curve is the second-order kinetic reaction rate constant k;

[0045] S2-3) Model building:

[0046] The molecular descriptor of the pollutant is ΔE 2e1p The horizontal axis is used, and the logarithm lgk of the pollutant reaction rate constant k is used as the vertical axis to draw a graph, and a linear fitting is performed. The obtained linear relationship is the quantitative model of pollutant reaction rate-structure.

[0047] In step S1):

[0048] In the method for constructing a quantitative model provided by the present invention, a descriptor is first obtained.

[0049] Molecular descriptors are a form of quantification of the molecular structure of pollutants. They convert the physical and chemical properties, spatial structure and other characteristics of pollutant molecules into a series of numerical values ​​for further analysis. Molecular descriptors are of various types and sources, including not only the simplest atomic number and relative molecular mass, but also the three-dimensional structure of molecules, functional group information, etc. Different descriptors focus on different aspects of molecular properties. Descriptors can be generated through experiments, quantum chemical calculations, etc.

[0050] The present invention is based on the understanding of the reaction mechanism of heterogeneous persulfate system, and innovatively adopts the energy change after the pollutant molecule loses two electrons and one proton at the same time as a descriptor. The molecular descriptor is generated by quantum chemical calculation. The QSAR model established based on this descriptor has high correlation and a wide range of application.

[0051] The pollutants in this step include all pollutants involved in the construction method of the quantitative model.

[0052] In some embodiments of the present invention, step S1) comprises:

[0053] S1-1) Pollutant molecular structure optimization and energy calculation: construct the initial structure of the pollutant molecule in GaussView software, use low- and medium-precision functionals and basis sets to optimize the structure, and use high-precision functionals and basis sets to calculate the single-point energy of the optimized structure. The obtained single-point energy is recorded as E1;

[0054] S1-2) Pollutant ion structure optimization and energy calculation: In GaussView software, the initial structure of the positive ion generated by the pollutant molecule losing 2 electrons and 1 proton is constructed, and the structure is optimized using medium and low precision functionals and basis sets. The single point energy of the optimized structure is calculated using high precision functionals and basis sets, and the obtained single point energy is recorded as E2;

[0055] S1-3) Calculation of descriptors: The descriptor is the energy difference between the pollutant molecule after losing 2 electrons and 1 proton and the initial state, that is, ΔE 2e1p =E2-E1.

[0056] Figure 1 A schematic diagram of a descriptor calculation method provided by an embodiment of the present invention.

[0057] In step S1-1):

[0058] In certain embodiments of the present invention, the medium and low precision functional and basis set is B3LYP / 6-31G(d,p). The structural optimization code is:

[0059] #opt freqb3lyp / 6-31g(d,p)Scrf=(SMD, Solvent=water)

[0060] In some embodiments of the present invention, the high-precision functional and basis set is M06-2X / def2-TZVP. The single-point energy calculation code is:

[0061] #m062x / def2tzvp Scrf=(SMD, Solvent=water)

[0062] In step S1-2):

[0063] Ion structure optimization and energy calculation: In GaussView software, construct the initial structure of the positive ion generated by the molecule losing 2 electrons and 1 proton, where the lost proton comes from the hydroxyl or amino group, and the 2 electrons come from the hydroxyl oxygen or amino nitrogen. Therefore, the ion has 1 positive charge and a spin multiplicity of 1. Use medium and low precision functionals and basis sets to optimize the structure, and use high precision functionals and basis sets to calculate the single point energy of the optimized structure. The obtained single point energy is recorded as E2.

[0064] In certain embodiments of the present invention, the medium and low precision functional and basis set is B3LYP / 6-31G(d,p). The structural optimization code is:

[0065] #opt freqb3lyp / 6-31g(d,p)Scrf=(SMD, Solvent=water)

[0066] In some embodiments of the present invention, the high-precision functional and basis set is M06-2X / def2-TZVP. The single-point energy calculation code is:

[0067] #m062x / def2tzvp Scrf=(SMD, Solvent=water)

[0068] After obtaining the descriptors, the QSAR model is constructed.

[0069] In step S2-1):

[0070] Conduct pollutant removal experiments:

[0071] The aqueous solution containing the pollutant is stirred and mixed with the catalyst for pre-adsorption;

[0072] The pre-adsorbed mixed liquid is mixed with persulfate, samples are taken from the solution according to the reaction time t, and the pollutant concentration c in the solution after different reaction times is determined by high performance liquid chromatography.

[0073] In certain embodiments of the present invention, the pollutant is an organic pollutant, including at least one of monosubstituted phenols, polysubstituted phenols and anilines; for example, at least one of phenol and anilines.

[0074] In certain embodiments of the present invention, the stirring and mixing is performed in a magnetic stirrer.

[0075] In certain embodiments of the present invention, the catalyst is carbon nanotube (CNT), Co3O4, MnFe2O4 or CuO.

[0076] In certain embodiments of the present invention, the ratio of the pollutant to the catalyst is 0.1-0.2 mmol: 200-500 mg, such as 0.13 mmol: 200 mg, 0.13 mmol: 500 mg. When the catalyst is Co3O4, MnFe2O4 or CuO, the pre-adsorption pH is 8.0; the pH value can be adjusted by using a borate buffer. Specifically, a borate buffer containing 200 mmol / L borate is used to adjust the pH value.

[0077] In certain embodiments of the present invention, the pre-adsorption time is 25 to 35 minutes, such as 30 minutes.

[0078] In certain embodiments of the present invention, the persulfate is an oxidant for removing organic pollutants, selected from potassium peroxydisulfate (PDS) or potassium peroxymonosulfate (PMS). The molar ratio of the persulfate to the pollutant is 1.5-2:1, such as 1.8:1.

[0079] The pollutant removal experiment was repeated three times, and the standard deviation is given as error bars.

[0080] In step S2-2):

[0081] Pollutant reaction rate calculation:

[0082] The curve of the pollutant concentration c changing with the reaction time t is plotted, and then the pollutant concentration after adsorption is taken as the initial concentration c0, the curve of c0 / c changing with t is plotted, and a linear fit is performed. The slope of the fitting curve is the second-order kinetic reaction rate constant k.

[0083] In step S2-3):

[0084] Build the model:

[0085] The molecular descriptor of the pollutant is ΔE 2e1p The horizontal axis is used, and the logarithm lgk of the pollutant reaction rate constant k is used as the vertical axis to draw a graph, and a linear fitting is performed. The obtained linear relationship is the quantitative model of pollutant reaction rate-structure.

[0086] The present invention also provides a quantitative model of pollutant reaction rate and structure obtained by the construction method described above.

[0087] The present invention also provides an application of the quantitative model described above for predicting the reaction rate of pollutants. Specifically, the quantitative model is used to predict the reaction rate of pollutants in a heterogeneous persulfate oxidation system; the pollutants include at least one of monosubstituted phenols, polysubstituted phenols and anilines.

[0088] The heterogeneous persulfate oxidation system includes a CNT / PDS system, a Co3O4 / PMS system, a MnFe2O4 / PMS system or a CuO / PDS system.

[0089] The present invention proposes a new molecular descriptor and establishes a QSAR model for predicting the reaction rate of phenol and aniline pollutants in a heterogeneous persulfate oxidation system.

[0090] The descriptor is generated by calculating the single point energy difference between the initial state of the molecule and the state after losing 2 electrons and 1 proton through the quantum chemical calculation software Gaussian 16. In the calculation process, the low- and medium-precision functionals and basis sets (such as B3LYP / 6-31G (d, p)) are first used for geometry optimization, and the single point energy calculation is performed on the results after geometry optimization using high-precision functionals and basis sets (such as M06-2X / def2-TZVP).

[0091] Compared with the existing QSAR model, the quantitative model provided by the present invention has the following advantages:

[0092] 1. Linear fitting has high correlation, R 2 Can achieve 0.9 or above;

[0093] 2. Applicable to pollutants with different structures, including at least one of monosubstituted phenols, polysubstituted phenols and anilines;

[0094] 3. Applicable to different heterogeneous persulfate oxidation systems, such as CNT / PDS system, Co3O4 / PMS system, MnFe2O4 / PMS system or CuO / PDS system;

[0095] 4. Applicable to complex mixed pollutants and water matrix;

[0096] 5. The descriptor used is easy to obtain and has high repeatability.

[0097] The present invention has no particular limitation on the sources of the raw materials used above, and they can be generally commercially available.

[0098] In order to further illustrate the present invention, a quantitative model of pollutant reaction rate and structure, its construction method and application provided by the present invention are described in detail below in combination with embodiments, but it should not be understood as limiting the scope of protection of the present invention.

[0099] Example 1

[0100] Descriptor acquisition:

[0101] 1-1) Pollutant molecular structure optimization and energy calculation: Construct the initial structure of the pollutant molecule in GaussView software, use medium and low precision functionals and basis sets such as B3LYP / 6-31G(d,p) for structure optimization, and use high precision functionals and basis sets such as M062X / def2-TZVP for single point energy calculation of the optimized structure. The obtained single point energy is recorded as E1.

[0102] The structure optimization code is: #opt freqb3lyp / 6-31g(d,p)Scrf=(SMD,Solvent=water)

[0103] The single point energy calculation code is: #m062x / def2tzvp Scrf = (SMD, Solvent = water)

[0104] 1-2) Pollutant ion structure optimization and energy calculation: In GaussView software, construct the initial structure of the positive ion generated by the pollutant molecule losing 2 electrons and 1 proton, where the lost proton comes from the hydroxyl group (or amino group) and the 2 electrons come from the hydroxyl oxygen (or amino nitrogen), so the ion has 1 positive charge and a spin multiplicity of 1. Use medium- and low-precision functionals and basis sets such as B3LYP / 6-31G (d, p) for structural optimization, and use high-precision functionals and basis sets such as M06-2X / def2-TZVP for single-point energy calculation of the optimized structure, and the obtained single-point energy is recorded as E2.

[0105] The structure optimization code is: #opt freqb3lyp / 6-31g(d,p)Scrf=(SMD,Solvent=water)

[0106] The single point energy calculation code is: #m062x / def2tzvp Scrf = (SMD, Solvent = water)

[0107] 1-3) Calculation of descriptors: The descriptor is the energy difference between the pollutant molecule after losing 2 electrons and 1 proton and the initial state, that is, ΔE 2e1p =E2-E1.

[0108] Example 2

[0109] Select 15 pollutants as shown in the following formula to obtain descriptors according to the method of Example 1;

[0110]

[0111] Construction of QSAR model in CNT / PDS system:

[0112] 2-1) Pollutant removal experiment:

[0113] The 15 pollutants were selected for oxidation removal experiments;

[0114] The experiment was carried out in a 50mL beaker with a reaction volume of 40mL. 40mL of an aqueous solution containing 5.2μmol of pollutants was added to the beaker, which was placed on a magnetic stirrer for continuous stirring, and 8mg of CNT was added for pre-adsorption. After 30min of pre-adsorption, 9.6μmol of PDS was added, samples were taken from the solution according to the reaction time t, and the pollutant concentration c in the solution after different reaction times was determined by high performance liquid chromatography. The above experiment was repeated 3 times, and the standard deviation is given in the form of error bars.

[0115] 2-2) Calculation of pollutant reaction rate:

[0116] Taking the calculation of nitrophenol (NP) as an example, first draw a curve of the change of pollutant concentration c versus reaction time t (such as Figure 2 As shown in Figure (a), Figure 2 is the removal curve and removal kinetic fitting curve of p-nitrophenol in CNT / PDS system; Figure 2 Figure (a) shows the removal curve of p-nitrophenol in the CNT / PDS system. Figure 2 Figure (b) is the removal kinetic fitting curve of p-nitrophenol in the CNT / PDS system), and then the concentration of pollutants after adsorption is taken as the initial concentration c0, the c0 / c curve with t is plotted, and a linear fitting is performed (such as Figure 2 The slope of the fitting curve is the second-order kinetic reaction rate constant k. The results show that the fitting curve has a high correlation coefficient (R 2 =0.951).

[0117] 2-3) Construction of QSAR model:

[0118] Figure 3 is the fitting result of the QSAR model in the CNT / PDS system. Figure 3 As shown, the molecular descriptor of the pollutant is ΔE 2e1p The QSAR model has good linear correlation (R 2 =0.935), the model better reflects the relationship between the reaction rate of pollutants and their molecular structure properties.

[0119] Example 3

[0120] Six pollutants including phenol (PhOH), 4-chlorophenol (CP), guaiacol (MOP), 4-ethylguaiacol (4,E-MOP), 2,6-dimethylphenol (2,6-MPhOH) and bisphenol A (BPA) were selected to obtain descriptors according to the method of Example 1;

[0121] Construction of QSAR model in Co3O4 / PMS system:

[0122] 3-1) Pollutant removal experiment:

[0123] Six pollutants including phenol (PhOH), 4-chlorophenol (CP), guaiacol (MOP), 4-ethylguaiacol (4,E-MOP), 2,6-dimethylphenol (2,6-MPhOH) and bisphenol A (BPA) were selected for oxidation removal experiments; the experiment was carried out in a 50mL beaker with a reaction volume of 40mL. 36mL of an aqueous solution containing 5.2μmol of pollutants was added to the beaker, and 4mL of a borate buffer containing 200mmol / L borate was added to adjust the pH value of the solution to 8.0. The solution was placed on a magnetic stirrer and stirred continuously, and 20mg Co3O4 was added for pre-adsorption. After 30min of pre-adsorption, 9.6μmol potassium permonosulfate (PMS) was added, and samples were taken from the solution at the predetermined time t, and the pollutant concentration c in the solution after different reaction times was determined by high performance liquid chromatography. The above experiment was repeated 3 times, and the standard deviation is given in the form of error bars.

[0124] 3-2) Calculation of pollutant reaction rate:

[0125] Plot the curve of pollutant concentration c versus reaction time t, then use the pollutant concentration after adsorption as the initial concentration c0, plot the curve of c0 / c versus t, and perform linear fitting. The slope of the fitting curve is the second-order kinetic reaction rate constant k.

[0126] 3-3) Construction of QSAR model:

[0127] Figure 4 are the QSAR model fitting results in different heterogeneous persulfate oxidation systems; Figure 4 Figure (a) shows the QSAR model fitting results in the Co3O4 / PMS system; Figure 4 Figure (b) shows the QSAR model fitting results in the MnFe2O4 / PMS system; Figure 4 Figure (c) shows the QSAR model fitting results in the CuO / PDS system.

[0128] like Figure 4 As shown in Figure (a), the molecular descriptor of the pollutant is ΔE 2e1p The QSAR model has good linear correlation (R 2 =0.902), the model better reflects the relationship between the reaction rate of pollutants and their molecular structure properties.

[0129] Example 4

[0130] Construction of QSAR model in MnFe2O4 / PMS system:

[0131] 4-1) Pollutant removal experiment:

[0132] The difference from Example 3 is that Co3O4 is replaced by MnFe2O4;

[0133] 4-2) Calculation of pollutant reaction rate:

[0134] Pollutant reaction rate calculation:

[0135] Plot the curve of pollutant concentration c versus reaction time t, then use the pollutant concentration after adsorption as the initial concentration c0, plot the curve of c0 / c versus t, and perform linear fitting. The slope of the fitting curve is the second-order kinetic reaction rate constant k.

[0136] 4-3) Construction of QSAR model:

[0137] like Figure 4 As shown in Figure (b), the molecular descriptor of the pollutant ΔE 2e1p The QSAR model has good linear correlation (R 2 =0.949), the model better reflects the relationship between the reaction rate of pollutants and their molecular structure properties.

[0138] Example 5

[0139] Construction of QSAR model in CuO / PDS system:

[0140] 5-1) Pollutant removal experiment:

[0141] The difference from Example 3 is that Co3O4 is replaced by CuO, and PMS is replaced by PDS;

[0142] 5-2) Calculation of pollutant reaction rate:

[0143] Pollutant reaction rate calculation:

[0144] Plot the curve of pollutant concentration c versus reaction time t, then use the pollutant concentration after adsorption as the initial concentration c0, plot the curve of c0 / c versus t, and perform linear fitting. The slope of the fitting curve is the second-order kinetic reaction rate constant k.

[0145] 5-3) Construction of QSAR model:

[0146] like Figure 4 As shown in Figure (c), the molecular descriptor of the pollutant ΔE2e1p The QSAR model has good linear correlation (R 2 =0.940), the model better reflects the relationship between the reaction rate of pollutants and their molecular structure properties.

[0147] Example 6

[0148] Construction of QSAR models in real water and mixed pollutant systems:

[0149] Acquisition and pretreatment of actual water: 200 mL of water was taken from the outlet of the secondary sedimentation tank of the Wangtang Wastewater Treatment Plant in Hefei City, filtered through a 0.45 μm filter membrane, and stored in a 4°C refrigerator for later use.

[0150] Six pollutants including phenol (PhOH), nitrophenol (NP), guaiacol (MOP), 2,6-dimethylphenol (2,6-MPhOH), p-hydroxybenzaldehyde (HBAl) and sulfonamide (SA) were selected to obtain descriptors according to the method of Example 1;

[0151] 6-1) Pollutant removal experiment:

[0152] Six pollutants including phenol (PhOH), nitrophenol (NP), guaiacol (MOP), 2,6-dimethylphenol (2,6-MPhOH), p-hydroxybenzaldehyde (HBAl) and sulfonamide (SA) were selected for oxidation removal experiments; the experiment was carried out in a 100mL beaker with a reaction volume of 80mL. 80mL of pretreated sewage was added to the beaker, and 4μmol of each pollutant was added. The mixture was placed on a magnetic stirrer for continuous stirring, and 24mg of CNT was added for pre-adsorption. After 30min of pre-adsorption, 72μmol of PDS was added, and samples were taken from the solution at the predetermined time t, and the pollutant concentration c in the solution after different reaction times was determined by high performance liquid chromatography. The above experiment was repeated 3 times, and the standard deviation is given in the form of error bars.

[0153] 6-2) Calculation of pollutant reaction rate:

[0154] Plot the pollutant concentration c versus reaction time t curve, such as Figure 5 As shown in Figure (a). Figure 5 The removal curves and QSAR model fitting results of pollutants in simulated wastewater prepared for secondary effluent and mixed pollutants of sewage treatment plants; Figure 5 Figure (a) shows the removal curve of pollutants in simulated wastewater prepared from secondary effluent of a sewage treatment plant and mixed pollutants. Figure 5Figure (b) is the QSAR model fitting result. It can be seen that the removal rate of mixed pollutants in actual water is significantly different. Then, the concentration of pollutants after adsorption is taken as the initial concentration c0, and the c0 / c curve with t is plotted, and a linear fit is performed. The slope of the fitting curve is the second-order kinetic reaction rate constant k.

[0155] 6-3) Construction of QSAR model:

[0156] like Figure 5 As shown in Figure (b), the molecular descriptor of the pollutant ΔE 2e1p The QSAR model has good linear correlation (R 2 =0.986), indicating that the model can still maintain its applicability under the conditions of actual sewage and the coexistence of multiple pollutants, and has good practical application potential.

[0157] Comparative Example

[0158] The molecular descriptor highest occupied orbital energy (E HOMO ) and proton ionization energy (ΔE 1p ), because it only reflects the energy change of a molecule losing one electron or one proton, it does not match the actual reaction process, so its model relevance is very low. HOMO The linear correlation of the established model is only 0.653, based on ΔE 1p The linear correlation (linear fitting correlation) of the established model is only 0.102, such as Figure 6 shown.

[0159] Figure 6 is the fitting result of the QSAR model constructed based on other descriptors in the CNT / PDS system. Figure 6 (a) is the highest occupied orbital energy (E HOMO ) is the QSAR model fitting result constructed by the descriptor, Figure 6 (b) is the proton ionization energy (ΔE 1p ) is the fitting result of the QSAR model constructed by the descriptor.

[0160] The description of the above embodiments is only used to help understand the method of the present invention and its core idea. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing a quantitative model of pollutant reaction rate and structure, comprising the following steps: S1) Get the descriptor: The energy difference between the pollutant organic molecule after losing 2 electrons and 1 proton and the initial state is used as the descriptor, that is, ΔE 2e1p =E2-E1; S2) Construction of QSAR model: S2-1) Conduct pollutant removal experiments: The aqueous solution containing the pollutant is stirred and mixed with the catalyst for pre-adsorption; The pre-adsorbed mixed liquid is mixed with persulfate, samples are taken from the solution according to the reaction time t, and the pollutant concentration c in the solution after different reaction times is determined by high performance liquid chromatography; S2-2) Calculation of pollutant reaction rate: Plotting a curve of the pollutant concentration c versus the reaction time t, then taking the pollutant concentration after adsorption as the initial concentration c0, plotting a curve of c0 / c versus t, and performing a linear fit, the slope of the fitting curve is the second-order kinetic reaction rate constant k; S2-3) Model building: The molecular descriptor of the pollutant is ΔE 2e1p The horizontal axis is used, and the logarithm lgk of the pollutant reaction rate constant k is used as the vertical axis to draw a graph, and a linear fitting is performed. The obtained linear relationship is the quantitative model of pollutant reaction rate-structure.

2. The construction method according to claim 1, characterized in that: Step S1) comprises: S1-1) Pollutant molecular structure optimization and energy calculation: construct the initial structure of the pollutant molecule in GaussView software, use low- and medium-precision functionals and basis sets to optimize the structure, and use high-precision functionals and basis sets to calculate the single-point energy of the optimized structure. The obtained single-point energy is recorded as E1; S1-2) Pollutant ion structure optimization and energy calculation: In GaussView software, the initial structure of the positive ion generated by the pollutant molecule losing 2 electrons and 1 proton is constructed, and the structure is optimized using medium and low precision functionals and basis sets. The single point energy of the optimized structure is calculated using high precision functionals and basis sets, and the obtained single point energy is recorded as E2; S1-3) Calculation of descriptors: The descriptor is the energy difference between the pollutant molecule after losing 2 electrons and 1 proton and the initial state, that is, ΔE 2e1p =E2-E1.

3. The construction method according to claim 1, characterized in that: The pollutants are organic pollutants, including at least one of monosubstituted phenols, polysubstituted phenols and anilines.

4. The construction method according to claim 1, characterized in that: In step S2-1), the catalyst includes carbon nanotubes, Co3O4, MnFe2O4 or CuO.

5. The construction method according to claim 1, characterized in that: In step S2-1), the persulfate includes potassium peroxydisulfate or potassium peroxymonosulfate.

6. A quantitative model of pollutant reaction rate and structure obtained by the construction method according to any one of claims 1 to 5.

7. Application of the quantitative model of pollutant reaction rate and structure as claimed in claim 6 to predict pollutant reaction rate.

8. Application of the quantitative model of pollutant reaction rate and structure as claimed in claim 6 to predict the reaction rate of pollutants in a heterogeneous persulfate oxidation system.

9. The use according to claim 8, characterized in that: The heterogeneous persulfate oxidation system includes a CNT / PDS system, a Co3O4 / PMS system, a MnFe2O4 / PMS system or a CuO / PDS system.

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