A quantitative model of pollutant reaction rate and structure, a construction method and application thereof

Abstract

The present application relates to the technical field of water pollution control, and particularly relates to a quantitative model of pollutant reaction rate and structure, a construction method and application thereof. The present application provides a construction method of a quantitative model of pollutant reaction rate and structure, and uses energy change of a pollutant molecule after losing 2 electrons and 1 proton at the same time as a descriptor. The molecular descriptor is generated through quantum chemistry calculation. The QSAR model established based on the descriptor has high correlation and wide application range. The QSAR model avoids the problems of previous models, such as inability to fully reflect the real reaction process, low linear fitting correlation and narrow application range.

Description

Technical Field

[0001] This invention relates to the field of water pollution control technology, and in particular to a quantitative model of pollutant reaction rate and structure, its construction method and application. Background Technology

[0002] Heterogeneous persulfate oxidation technology has attracted widespread attention in the field of water treatment due to its advantages such as high efficiency, ease of operation, and wide pH applicability, and has been applied to the advanced treatment and pretreatment of industrial and domestic wastewater. Studies have shown that non-radical oxidation pathways, such as direct electron transfer, are prevalent in heterogeneous persulfate systems, enabling the selective oxidation of pollutants in water and reducing interference from coexisting matrices (such as chloride ions, carbonate ions, and natural organic matter). However, the mechanism of non-radical oxidation is complex, and the reaction rate is significantly affected by the structure of pollutants. Therefore, predicting the pollutant removal rate is of great significance for optimizing the treatment process.

[0003] Establishing quantitative structure-activity relationship (QSAR) relationships between pollutant structure and reaction rate is a crucial method for predicting the reaction rate of novel pollutants. Constructing suitable molecular structure descriptors is fundamental to accurate QSAR analysis. However, obtaining molecular structure descriptors experimentally (e.g., electrochemical determination of oxidation half-wave potential) is often time-consuming, laborious, and has low reproducibility. In contrast, quantum chemical calculation methods offer advantages such as convenience, speed, and high reproducibility. Current work has used quantum chemical calculation methods to obtain descriptors reflecting different molecular properties, such as molecular orbital energies and electrophilicity. However, QSAR models of heterogeneous persulfate oxidation systems built based on these descriptors cannot fully reflect the actual reaction process, exhibiting problems such as low linear correlation and narrow pollutant coverage. Therefore, it is still necessary to develop new molecular descriptors and their corresponding QSAR models to improve model fitting accuracy and expand their applicability to various 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 of previous models that cannot fully reflect the real reaction process, have low linear fitting correlation and narrow applicability.

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

[0006] S1) Obtain the descriptor:

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

[0008] Construction of the S2)QSAR model:

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

[0010] An aqueous solution containing the pollutant is stirred and mixed with a catalyst for pre-adsorption.

[0011] The pre-adsorbed mixture was mixed with persulfate, and samples were taken from the solution according to the reaction time t. The concentration of pollutants c in the solution after different reaction times was determined by high performance liquid chromatography.

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

[0013] Plot the curve of the pollutant concentration c versus the reaction time t. Then, using the adsorbed pollutant concentration as the initial concentration c0, plot the curve of c0 / c versus t and perform linear fitting. The slope of the fitted curve is the second-order kinetic reaction rate constant k.

[0014] S2-3) Model Construction:

[0015] Using the molecular descriptor ΔE of pollutants 2e1p Plotting the pollutant reaction rate constant k as the x-axis and the logarithm lgk of the pollutant reaction rate constant k as the y-axis, and performing linear fitting, the resulting linear relationship is the quantitative model of pollutant reaction rate-structure.

[0016] Preferably, step S1) includes:

[0017] S1-1) Pollutant molecule structure optimization and energy calculation: The initial structure of the pollutant molecule is constructed in GaussView software. The structure is optimized using low-to-medium precision functionals and basis sets. The single-point energy of the optimized structure is calculated using high-precision functionals and basis sets. The single-point energy is denoted as E1.

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

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

[0020] Preferably, in step S2-1), the pollutant is an organic pollutant, including at least one of monosubstituted phenols, polysubstituted phenols, and aniline substances.

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

[0022] Preferably, in step S2-1), the persulfide includes potassium persulfate (PDS) or potassium permonosulfate (PMS).

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

[0024] This invention also provides a quantitative model of the pollutant reaction rate and structure described above for the application of predicting pollutant reaction rates.

[0025] This invention also provides a quantitative model of the pollutant reaction rate and structure described above for the application of predicting the pollutant reaction rate in heterogeneous persulfate oxidation systems.

[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] This 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 its initial state as the descriptor, i.e., ΔE. 2e1p =E2-E1; S2) Construction of QSAR model: S2-1) Conduct pollutant removal experiment: Mix the aqueous solution containing the pollutant with the catalyst for pre-adsorption; mix the pre-adsorbed mixture 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) Calculate pollutant reaction rate: Plot the curve of the pollutant concentration c with the reaction time t, then use the adsorbed pollutant concentration as the initial concentration c0, plot the curve of c0 / c with t, and perform linear fitting. The slope of the fitted curve is the second-order kinetic reaction rate constant k; S2-3) Construct model: Using the molecular descriptor ΔE of the pollutant... 2e1p Plotting the pollutant reaction rate constant k as the x-axis and the logarithm lgk of the pollutant reaction rate constant k as the y-axis, and performing linear fitting, the resulting linear relationship is the quantitative model of pollutant reaction rate-structure.

[0028] This invention provides a method for constructing a quantitative model of the reaction rate and structure of pollutants, using the energy change of a pollutant molecule after simultaneously losing two electrons and one proton as a descriptor. This molecular descriptor is generated through quantum chemical calculations. The QSAR model built based on this descriptor exhibits high correlation and a wide range of applicability. This QSAR model avoids the problems of previous models, such as inability to fully reflect the actual reaction process, low linear correlation, and narrow applicability. Attached Figure Description

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

[0030] Figure 2 The removal curves and kinetic fitting curves of p-nitrophenol in the CNT / PDS system are shown.

[0031] Figure 3 The fitting results of the QSAR model under the CNT / PDS system are shown.

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

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

[0034] Figure 6 This represents the fitting results of the QSAR model constructed based on other descriptors under the CNT / PDS system. Detailed Implementation

[0035] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

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

[0037] S1) Obtain the descriptor:

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

[0039] Construction of the S2)QSAR model:

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

[0041] An aqueous solution containing the pollutant is stirred and mixed with a catalyst for pre-adsorption.

[0042] The pre-adsorbed mixture was mixed with persulfate, and samples were taken from the solution according to the reaction time t. The concentration of pollutants c in the solution after different reaction times was determined by high performance liquid chromatography.

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

[0044] Plot the curve of the pollutant concentration c versus the reaction time t. Then, using the adsorbed pollutant concentration as the initial concentration c0, plot the curve of c0 / c versus t and perform linear fitting. The slope of the fitted curve is the second-order kinetic reaction rate constant k.

[0045] S2-3) Model Construction:

[0046] The molecular descriptor ΔE of the pollutant 2e1p Plotting the pollutant reaction rate constant k as the x-axis and the logarithm lgk of the pollutant reaction rate constant k as the y-axis, and performing linear fitting, the resulting 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 this invention, a descriptor is first obtained.

[0049] Molecular descriptors are a form of quantification of the molecular structure of pollutants. They convert the physicochemical properties, spatial structure, and other characteristics of pollutant molecules into a series of numerical values ​​to facilitate further analysis. Molecular descriptors are diverse in type and origin, including not only the simplest atomic number and relative molecular mass, but also information on the three-dimensional structure and functional groups of molecules. Different descriptors emphasize different aspects of molecular properties. Descriptors can be generated through experiments, quantum chemical calculations, and other methods.

[0050] This invention, based on the understanding of the reaction mechanism of heterogeneous persulfate systems, innovatively uses the energy change of a pollutant molecule after simultaneously losing two electrons and one proton as a descriptor. This molecular descriptor is generated through quantum chemical calculations. The QSAR model built based on this descriptor exhibits high correlation and a wide range of applicability.

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

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

[0053] S1-1) Pollutant molecule structure optimization and energy calculation: The initial structure of the pollutant molecule is constructed in GaussView software. The structure is optimized using low-to-medium precision functionals and basis sets. The single-point energy of the optimized structure is calculated using high-precision functionals and basis sets. The single-point energy is denoted as E1.

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

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

[0056] Figure 1 This is a schematic diagram of a descriptor calculation method provided in an embodiment of the present invention.

[0057] In step S1-1):

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

[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 are M06-2X / def2-TZVP. The single-point energy calculation code is as follows:

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

[0062] In step S1-2):

[0063] Ion structure optimization and energy calculation: The initial structure of the positive ion generated by the loss of 2 electrons and 1 proton by the molecule was constructed in GaussView software. The lost proton comes from hydroxyl or amino groups, and the 2 electrons come from hydroxyl oxygen or amino nitrogen. Therefore, the ion carries 1 positive charge and has a spin multiplicity of 1. The structure was optimized using low-to-medium precision functionals and basis sets. The single-point energy of the optimized structure was calculated using high-precision functionals and basis sets. The single-point energy is denoted as E2.

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

[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 are M06-2X / def2-TZVP. The single-point energy calculation code is as follows:

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

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

[0069] In step S2-1):

[0070] Conduct pollutant removal experiments:

[0071] An aqueous solution containing the pollutant is stirred and mixed with a catalyst for pre-adsorption.

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

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

[0074] In some embodiments of the invention, the stirring and mixing is carried out in a magnetic stirrer.

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

[0076] In some embodiments of the present invention, the ratio of the contaminant to the catalyst is 0.1–0.2 mmol: 200–500 mg, for example, 0.13 mmol: 200 mg or 0.13 mmol: 500 mg. When the catalyst is Co3O4, MnFe2O4, or CuO, the pre-adsorption pH is 8.0; the pH can be adjusted using a borate buffer solution. Specifically, a 200 mmol / L borate buffer solution is used to adjust the pH.

[0077] In some embodiments of the present invention, the pre-adsorption time is 25 to 35 minutes, for example, 30 minutes.

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

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

[0080] In step S2-2):

[0081] Calculation of pollutant reaction rate:

[0082] Plot the curve of the pollutant concentration c versus reaction time t. Then, using the adsorbed pollutant concentration as the initial concentration c0, plot the curve of c0 / c versus t and perform linear fitting. The slope of the fitted curve is the second-order kinetic reaction rate constant k.

[0083] In steps S2-3):

[0084] Model building:

[0085] The molecular descriptor ΔE of the pollutant 2e1p Plotting the pollutant reaction rate constant k as the x-axis and the logarithm lgk of the pollutant reaction rate constant k as the y-axis, and performing linear fitting, the resulting 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] This 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] This invention proposes a novel molecular descriptor and establishes a QSAR model for predicting the reaction rate of phenol with aniline pollutants in a heterogeneous persulfate oxidation system.

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

[0091] Compared to existing QSAR models, the quantitative model provided by this invention has the following advantages:

[0092] 1. High correlation in linear fitting, R0 2 It can achieve a performance level of 0.9 or higher;

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

[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. Suitable for complex mixed pollutants and water matrices;

[0096] 5. The descriptor acquisition method used is simple and highly repeatable.

[0097] The present invention does not impose any special restrictions on the source of the raw materials used above, and they can be commercially available.

[0098] To further illustrate the present invention, the following detailed description, in conjunction with embodiments, describes a quantitative model for the reaction rate and structure of pollutants provided by the present invention, its construction method, and its application, but this should not be construed as limiting the scope of protection of the present invention.

[0099] Example 1

[0100] Descriptor retrieval:

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

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

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

[0104] 1-2) Pollutant Ion Structure Optimization and Energy Calculation: An initial structure of the positive ion generated by the pollutant molecule losing 2 electrons and 1 proton was constructed in GaussView software. The lost proton originates from the hydroxyl group (or amino group), and the 2 electrons originate from the hydroxyl oxygen (or amino nitrogen). Therefore, the ion carries one positive charge and has a spin multiplicity of 1. The structure was optimized using low-to-medium precision functionals and basis sets such as B3LYP / 6-31G(d,p). The optimized structure was then used to calculate the single-point energy using high-precision functionals and basis sets such as M06-2X / def2-TZVP. The obtained single-point energy is denoted as E2.

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

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

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

[0108] Example 2

[0109] Descriptors were obtained for 15 pollutants as shown in the following formula, according to the method in 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 conducted in a 50 mL beaker with a reaction volume of 40 mL. 40 mL of an aqueous solution containing 5.2 μmol of contaminant was added to the beaker, and the mixture was continuously stirred on a magnetic stirrer. 8 mg of CNT was added for pre-adsorption. After 30 min of pre-adsorption, 9.6 μmol of PDS was added. Samples were taken from the solution at reaction time t, and the contaminant concentration c in the solution after different reaction times was determined by high-performance liquid chromatography (HPLC). The experiment was repeated three 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, firstly, the curve of pollutant concentration c versus reaction time t is plotted (e.g., Figure 2 As shown in Figure (a), Figure 2 The figures show the removal curves and kinetic fitting curves for p-nitrophenol in the CNT / PDS system; among them, Figure 2 Figure (a) shows the removal curve of p-nitrophenol in the CNT / PDS system. Figure 2 Figure (b) shows the kinetic fitting curve for the removal of p-nitrophenol in the CNT / PDS system. Then, using the concentration of the adsorbed pollutant as the initial concentration c0, the c0 / c curve as a function of t was plotted, and a linear fit was performed (e.g., Figure 2 As shown in Figure (b), the slope of the fitted curve is the second-order kinetic rate constant k. The results show that the fitted curve has a high correlation coefficient (R²). 2 =0.951).

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

[0118] Figure 3 This shows the fitting results of the QSAR model under the CNT / PDS system. Figure 3 As shown, the molecular descriptor ΔE of the pollutant is used. 2e1p Plotting lgk (the logarithm of the pollutant reaction rate constant) on the x-axis and lgk on the y-axis, and performing linear fitting, yields the quantitative model of the pollutant reaction rate-structure relationship. The results show that this QSAR model exhibits good linear correlation (R0) in the CNT / PDS system. 2 =0.935), this model reflects the relationship between the reaction rate of pollutants and their molecular structure properties quite well.

[0119] Example 3

[0120] Descriptors for six contaminants, including phenol (PhOH), 4-chlorophenol (CP), guaiacol (MOP), 4-ethylguaiacol (4,E-MOP), 2,6-dimethylphenol (2,6-MPhOH), and bisphenol A (BPA), were obtained according to the method in 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 experiments were conducted in a 50 mL beaker with a reaction volume of 40 mL. 36 mL of an aqueous solution containing 5.2 μmol of pollutant was added to the beaker, followed by 4 mL of borate buffer containing 200 mmol / L borate to adjust the pH to 8.0. The beaker was then continuously stirred with a magnetic stirrer, and 20 mg of Co3O4 was added for pre-adsorption. After 30 min of pre-adsorption, 9.6 μmol of potassium persulfate (PMS) was added. Samples were taken from the solution at predetermined time points (t), and the pollutant concentration (c) in the solution after different reaction times was determined by high-performance liquid chromatography (HPLC). The experiments were repeated three 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, using the adsorbed pollutant concentration as the initial concentration c0, plot the curve of c0 / c versus t and perform linear fitting. The slope of the fitted curve is the second-order kinetic reaction rate constant k.

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

[0127] Figure 4 The results of QSAR model fitting are shown for different heterogeneous persulfate oxidation systems; among them, 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 ΔE of the pollutant is used. 2e1p Plotting lgk (the logarithm of the pollutant reaction rate constant) on the x-axis and linearly fitting the graph, the resulting linear relationship constitutes the quantitative model of the pollutant reaction rate-structure relationship. The results show that this QSAR model exhibits good linear correlation (R0) in the Co3O4 / PMS system. 2 =0.902), this model reflects the relationship between the reaction rate of pollutants and their molecular structure properties quite well.

[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 with MnFe2O4;

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

[0134] Calculation of pollutant reaction rate:

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

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

[0137] like Figure 4 As shown in Figure (b), the molecular descriptor ΔE of the pollutant is used. 2e1p Plotting lgk (the logarithm of the pollutant reaction rate constant) on the x-axis and linearly fitting the graph, the resulting linear relationship constitutes the quantitative model of the pollutant reaction rate-structure relationship. The results show that this QSAR model exhibits good linear correlation (R0) in the MnFe2O4 / PMS system. 2 =0.949), this model reflects the relationship between the reaction rate of pollutants and their molecular structure properties quite well.

[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 with CuO and PMS is replaced with PDS;

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

[0143] Calculation of pollutant reaction rate:

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

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

[0146] like Figure 4 As shown in Figure (c), the molecular descriptor ΔE of the pollutant is used.2e1p Plotting lgk (the logarithm of the pollutant reaction rate constant) on the x-axis and lgk on the y-axis, and performing linear fitting, yields the quantitative model of the pollutant reaction rate-structure relationship. The results show that this QSAR model exhibits good linear correlation (R0) in the CuO / PDS system. 2 =0.940), this model reflects the relationship between the reaction rate of pollutants and their molecular structure properties quite well.

[0147] Example 6

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

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

[0150] Descriptors for six contaminants, including phenol (PhOH), nitrophenol (NP), guaiacol (MOP), 2,6-dimethylphenol (2,6-MPhOH), p-hydroxybenzaldehyde (HBAl), and sulfonamide (SA), were obtained according to the method in 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 experiments were conducted in a 100 mL beaker with a reaction volume of 80 mL. 80 mL of pretreated wastewater was added to the beaker, along with 4 μmol of each pollutant. The beaker was continuously stirred with a magnetic stirrer, and 24 mg of CNT was added for pre-adsorption. After 30 min of pre-adsorption, 72 μmol of PDS was added. Samples were taken from the solution at predetermined time points (t), and the pollutant concentration (c) in the solution after different reaction times was determined by high-performance liquid chromatography (HPLC). The experiments were repeated three times, and the standard deviation is given in the form of error bars.

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

[0154] Plot the curve of pollutant concentration c versus reaction time t, as follows: Figure 5 As shown in Figure (a). Figure 5 Removal curves and QSAR model fitting results for pollutants in simulated wastewater prepared for secondary effluent and mixed pollutants from wastewater treatment plants; among them, Figure 5 Figure (a) shows the pollutant removal curves in the secondary effluent of the wastewater treatment plant and the simulated wastewater prepared with mixed pollutants. Figure 5Figure (b) shows the fitting results of the QSAR model. It can be seen that the removal rates of mixed pollutants in actual water differ significantly. Then, using the adsorbed pollutant concentration as the initial concentration c0, a c0 / c versus t curve was plotted and linearly fitted. The slope of the fitted curve is the second-order kinetic reaction rate constant k.

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

[0156] like Figure 5 As shown in Figure (b), the molecular descriptor ΔE of the pollutant is used. 2e1p Plotting lgk (the logarithm of the pollutant reaction rate constant) on the x-axis and linearly fitting the graph, the resulting linear relationship constitutes the quantitative model of the pollutant reaction rate-structure relationship. The results show that this QSAR model exhibits good linear correlation (R0) in real water and mixed pollutant systems. 2 =0.986), indicating that the model remains applicable under actual conditions of wastewater and multiple pollutants coexisting, and has good potential for practical application.

[0157] Comparative Example

[0158] The molecular descriptor most frequently used in existing construction models has the 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 has very low correlation. HOMO The established model has a linear correlation of only 0.653, based on ΔE. 1p The established model has a linear correlation (linear fit correlation) of only 0.102, such as Figure 6 As shown.

[0159] Figure 6 This represents the fitting results of the QSAR model constructed based on other descriptors under the CNT / PDS architecture. Figure 6 (a) is the highest occupied orbital energy (E) HOMO The fitting results of the QSAR model constructed using descriptors are as follows: Figure 6 (b) is based on the proton ionization energy (ΔE) 1p The fitting results of the QSAR model constructed using ) as descriptors.

[0160] The descriptions of the above embodiments are merely illustrative of the methods and core ideas of the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope 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) Obtain the descriptor: S1-1) Pollutant molecule structure optimization and energy calculation: The initial structure of the pollutant molecule is constructed in GaussView software. The structure is optimized using low-to-medium precision functionals and basis sets. The single-point energy of the optimized structure is calculated using high-precision functionals and basis sets. The single-point energy is denoted as E1. S1-2) Pollutant ion structure optimization and energy calculation: The initial structure of the positive ion generated by the pollutant molecule losing 2 electrons and 1 proton was constructed in GaussView software. The structure was optimized using low-to-medium precision functionals and basis sets. The single-point energy of the optimized structure was calculated using high-precision functionals and basis sets. The obtained single-point energy is denoted as E2. S1-3) Calculation of the descriptor: The descriptor is the energy difference between the pollutant molecule after losing 2 electrons and 1 proton and its initial state, which is ΔE. 2e1p =E2-E1; S2) Construction of QSAR model: S2-1) Conduct pollutant removal experiments: An aqueous solution containing the pollutant is stirred and mixed with a catalyst for pre-adsorption. The pre-adsorbed mixture was mixed with persulfate, and samples were taken from the solution according to the reaction time t. The concentration of pollutants c in the solution after different reaction times was determined by high performance liquid chromatography. S2-2) Calculation of pollutant reaction rate: Plot the curve of the pollutant concentration c versus the reaction time t. Then, using the adsorbed pollutant concentration as the initial concentration c0, plot the curve of c0 / c versus t and perform linear fitting. The slope of the fitted curve is the second-order kinetic reaction rate constant k. S2-3) Model Construction: The molecular descriptor ΔE of the pollutant 2e1p Plotting the pollutant reaction rate constant k as the x-axis and the logarithm lgk of the pollutant reaction rate constant k as the y-axis, and performing linear fitting, the resulting linear relationship is the quantitative model of pollutant reaction rate-structure.

2. 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 aniline substances.

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

4. The construction method according to claim 1, characterized in that, In step S2-1), the persulfate includes potassium persulfate or potassium perhydrosulfate.

5. A quantitative device for the reaction rate and structure of pollutants obtained by the construction method according to any one of claims 1 to 4.

6. The quantitative device for pollutant reaction rate and structure according to claim 5, characterized in that, Used to predict pollutant reaction rates.

7. The quantitative device for determining the reaction rate and structure of pollutants according to claim 5, characterized in that, Used to predict the reaction rate of pollutants in heterogeneous persulfate oxidation systems.

8. The quantitative device for pollutant reaction rate and structure according to claim 7, 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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