A method for predicting the migration rate of nano-metal oxides and antibiotics under combined conditions

Abstract

This invention discloses a method for predicting the migration rate of nano-metal oxides and antibiotics under combined conditions. First, the relationships between the absorbance of nano-metal oxides and antibiotics and their respective concentrations are obtained. Then, the standardized effluent concentrations P of the nano-metal oxides and antibiotics are calculated. NPs and P Ab Finally, a quadratic polynomial model relating the migration rates of nano-metal oxides and antibiotics to combined environmental condition variables was derived through response surface methodology (RSM) experiments. This method is the first to predict the migration behavior of nano-metal oxides and antibiotics under combined conditions, providing a new approach for studying pollutant migration in complex natural systems and showing broad application prospects in the design of remediation schemes for nano-metal oxide and antibiotic pollution in aquifers.

Description

Technical Field

[0001] This invention belongs to the technical field of pollutant migration, and more specifically, relates to a method for predicting the migration rate of nano-metal oxides and antibiotics under combined conditions. Background Technology

[0002] Nanotechnology is rapidly developing, and new applications of nanomaterials are constantly emerging. Due to increased exposure from consumer use and environmental release, the impact of nanoparticles on human health and the environment needs to be considered. Recent studies have shown that the small size and enhanced reactivity of nanoparticles may pose risks to human health, and that nanoparticles have a potentially destructive impact on the environment that cannot be ignored. Given the significant potential threat of nanoparticles to the soil-aquatic environment, research on the migration, retention, and deposition of nanoparticles in porous media is crucial.

[0003] With the development of modern synthetic chemical industry, various synthetic organic pollutants (SOPs), including antibiotics, pesticides, and detergents, have been detected in the aquatic environment. Antibiotics are secondary metabolites produced by microorganisms or other higher plants and animals that possess antipathogenic or other activities. Due to their excellent broad-spectrum bactericidal properties and economic efficiency, antibiotics are widely used to treat human diseases and in animal husbandry and aquaculture. However, the overuse of antibiotics leads to their release into soil and groundwater systems through various pathways; antibiotic pollution has been detected in wastewater discharge, surface water, soil, and groundwater systems. Furthermore, multiple studies have shown that antibiotics and their environmental residues have adverse effects on terrestrial and aquatic microorganisms, such as inhibiting bacterial community growth and increasing the formation of bacterial resistance. Given these potentially significant risks, research on the environmental fate and migration of antibiotics, particularly their fate and migration in soil and groundwater systems, is currently an urgent need.

[0004] Currently, numerous studies have investigated the influence of single physicochemical factors on pollutant transport behavior in porous media, such as the complexity of the porous matrix (e.g., particle and pore size distribution), the physicochemical conditions of water chemistry (e.g., ionic strength), pH value, and the effects of natural organic matter (NOM) or surfactants. However, these studies are largely limited to investigating the effect of single factors on single substances in porous media, and have not yet studied whether these conditions exhibit mutually reinforcing or antagonistic interactions under complex background conditions. Therefore, a method for predicting pollutant migration rates under complex background conditions is currently lacking. Summary of the Invention

[0005] To address the aforementioned technical problems, the present invention aims to provide a method for predicting the migration rate of nano-metal oxides and antibiotics under composite conditions.

[0006] A second objective of this invention is to provide a method for predicting the migration rate of nano-metal oxides and antibiotics under combined conditions, and its application in predicting pollutant migration.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0008] This invention claims protection for a method for predicting the migration rate of nano-metal oxides and antibiotics under combined conditions, comprising the following steps:

[0009] S1. Obtain the infrared characteristic peaks of nano-metal oxides and antibiotics. Under the setting of these two infrared characteristic peaks, measure the absorbance of nano-metal oxides and antibiotics under different concentration conditions. Obtain the standard curves of absorbance and concentration of nano-metal oxides and antibiotics, respectively. Combine them to obtain the relationship between absorbance of nano-metal oxides and concentration of nano-metal oxides and concentration of antibiotics, and the relationship between absorbance of antibiotics and concentration of nano-metal oxides and concentration of antibiotics.

[0010] The relationship is shown below:

[0011]

[0012] In the formula, and The absorbance at the infrared characteristic peaks of nano-metal oxides and antibiotics, respectively; C NPs and C Ab The concentrations of nano-metal oxides and antibiotics after passing through porous media are [M·L], respectively. -3 ]; α, β, ε, ∈, m, n, u, v are the corresponding standard curve coefficients and intercepts, respectively;

[0013] The nano-metal oxide is selected from nano-titanium dioxide; the antibiotic is a quinolone antibiotic.

[0014] S2. A mixed solution of nano-metal oxides and antibiotics under composite chemical conditions is passed through a column filled with porous media. The initial concentrations C0 of the nano-metal oxides and antibiotics before passing through the porous media are measured. NPs and C0 Ab The absorbance of metal oxides and antibiotics at the infrared characteristic peaks under combined chemical conditions was measured. and C is obtained through the relational calculation in step S1. NPs and C Ab Then, the standardized effluent concentration P of nano-metal oxides and antibiotics is calculated using the following relationship. NPs and P Ab ;

[0015]

[0016] In the formula, P NPs and P Ab These represent the standardized effluent concentrations of nano-metal oxides and antibiotics under combined chemical conditions; CO NPs and C0 Ab The initial concentrations [M·L] of nano-metal oxides and antibiotics measured before passing through porous media, respectively. -3 ];

[0017] S3. A response surface methodology (RSM) experiment was conducted using Expert Design software. The relationship between the migration rates of nano-metal oxides and antibiotics under combined chemical conditions and the independent variables was analyzed. The "Prob>F" value was evaluated; a value < 0.05 indicates that the model term composed of the independent variables is significant at the 95% confidence level. Significant model terms for the migration rates of nano-metal oxides and antibiotics under combined chemical conditions were obtained through the "Prob>F" value. A quadratic polynomial model for the migration rates of nano-metal oxides and antibiotics under combined chemical conditions was derived based on the following RSM model:

[0018]

[0019] Where, x i X is the dimensionless encoded value of the i-th independent variable. i It is the actual value of the independent variable, X0 is X i At the center point, ΔX is the step size change; Y is the mobility; β0, β i β j β ij It is the constant of the regression coefficient of the established model, x i and x j The independent variables are represented by coded values; the independent variables are the concentration of antibiotics, the concentration of Na ions in the solution, the flow rate of the solution, and the concentration of citric acid in the solution.

[0020] Preferably, the porous medium is quartz sand.

[0021] Preferably, the average diameter of the nano-metal oxide is 30-50 nm.

[0022] Preferably, the antibiotic is levofloxacin.

[0023] Preferably, the mixed solution of nano-metal oxides and antibiotics under the composite factor chemical conditions refers to the mixed solution in which the concentration of antibiotics, the concentration of Na ions in the solution, the flow rate of the solution, and the concentration of citric acid in the solution are controlled.

[0024] Preferably, it is selected from at least one of the following (a) to (d):

[0025] (a) The concentration of antibiotics is 10–40 mg·L⁻¹;

[0026] (b) The concentration of Na ions in the solution is 1–50 mM·L⁻¹;

[0027] (c) The flow rate of the solution is 1 to 4 mL·min⁻¹;

[0028] (d) The concentration of citric acid in the solution is 5–15 mg·L⁻¹.

[0029] Preferably, the chemical conditions of the composite factors in step S2 are converted into three levels, namely -1, 0, and +1; so that each factor is at one of the three levels, thereby obtaining the standardized effluent concentration P. NPs and P Ab The predicted value, in step S2, is used to calculate the standardized effluent concentration P of nano-metal oxides and antibiotics. NPs and P Ab and standardized effluent concentration P NPs and P Ab The goodness of fit of the predicted values ​​is determined by the coefficient of determination R. 2 Evaluate the accuracy of the model.

[0030] Preferably, when the probability of the F value occurring is less than 0.0001%, it indicates that the RSM model is sufficient.

[0031] Furthermore, this invention claims protection for the application of a method for predicting the migration rate of nano-metal oxides and antibiotics under combined conditions in predicting pollutant migration.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] This invention provides a method for predicting the migration rate of nano-metal oxides and antibiotics under combined conditions. First, the mathematical relationship between the absorbance of the nano-metal oxide or antibiotic and its concentration is determined, and based on this, the standardized effluent concentration P under the combined chemical conditions is calculated. NPs and P Ab Response surface methodology (RSM) experiments were conducted using Expert Design software to determine the standardized effluent concentrations (P0) of nano-metal oxides and antibiotics. NPs and P AbThe invention identifies significant model terms and derives a quadratic polynomial model for the migration rates of nano-metal oxides and antibiotics under combined conditions. This invention can better quantitatively predict the migration capabilities of nano-metal oxides and antibiotics under combined conditions. This method is the first to propose predicting the migration behavior of nano-metal oxides and antibiotics under combined conditions, providing new insights into the study of pollutant migration in complex natural systems and showing broad application prospects in the design of remediation schemes for nano-metal oxide and antibiotic pollution in aquifers. Attached Figure Description

[0034] Figure 1 Scanning electron microscope (SEM) images of TiO2 nanoparticles and levofloxacin. Among them, Figure 1 (a) and (b) in the image are scanning electron microscope (SEM) images of TiO2 nanoparticles; Figure 1 (c) and (d) in the image are scanning electron microscope images of levofloxacin; Figure 1 (e) and (f) are scanning electron microscope images of a mixture of TiO2 nanoparticles and levofloxacin.

[0035] Figure 2 Fourier transform infrared spectroscopy results of TiO2 nanoparticles, levofloxacin and their mixtures under different hydrochemical conditions.

[0036] Figure 3 The 3D response surface and 2D contour plot of TiO2 nanoparticle mobility are used as functions of AB, AC, and AD, respectively. Figure 3 In the figure, (a) represents the 3D response surface of TiO2 nanoparticle mobility as a function of AB; Figure 3 In the figure, (b) represents the 3D response surface of TiO2 nanoparticle mobility as a function of AC; Figure 3 In the figure, (c) represents the 3D response surface of TiO2 nanoparticle mobility as a function of AD; Figure 3 In the diagram, (d) represents a 2D contour plot of the mobility of TiO2 nanoparticles as a function of AB. Figure 3 In the diagram, (e) represents a 2D contour plot of the mobility of TiO2 nanoparticles as a function of AC. Figure 3 In the diagram, (f) represents a 2D contour plot of the mobility of TiO2 nanoparticles as a function of AD.

[0037] Figure 4 The 3D response surface and 2D contour plot of levofloxacin mobility are used as functions of AB and AD, respectively. Figure 4 In the figure, (a) represents the 3D response surface of levofloxacin mobility as a function of AB; Figure 4 In (b), the 3D response surface of levofloxacin mobility is used as a function of AD; Figure 4(c) in the figure represents a 2D contour plot of levofloxacin mobility as a function of AB. Figure 4 In the diagram, (d) represents a 2D contour plot of levofloxacin mobility as a function of AD.

[0038] Figure 5 This section compares the migration rates of TiO2 nanoparticles and levofloxacin between experimental and model predictions. Figure 5 (a) in the figure shows a comparison between the experimental values ​​and the model predictions of the migration rate of TiO2 nanoparticles; Figure 5 (b) in the figure shows the comparison between experimental and model-predicted migration rates of levofloxacin. Detailed Implementation

[0039] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but the embodiments do not limit the present invention in any way. Unless otherwise specified, the reagents, methods and equipment used in the present invention are conventional reagents, methods and equipment in this technical field.

[0040] In addition, unless otherwise specified, all reagents and materials used in the following examples are commercially available.

[0041] Titanium dioxide nanoparticles (TiO2 nanoparticles) were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd., with an average diameter of 40 nm.

[0042] Levofloxacin (LEV) was purchased from Shanghai Maclean Biochemical Technology Co., Ltd.

[0043] Quartz sand (QS) was purchased from Longxin Water Purification Materials Co., Ltd., with a particle size of 0.45-0.6mm.

[0044] Example 1: Characterization of TiO2 nanoparticles and levofloxacin

[0045] Titanium dioxide nanoparticles were used as the studied nano-metal oxide, levofloxacin as the studied antibiotic, and quartz sand as the porous medium. The surface morphology of TiO2 nanoparticles, LEV, and mixtures of TiO2 nanoparticles and LEV were observed by scanning electron microscopy.

[0046] like Figure 1 The image shows scanning electron microscope (SEM) images of TiO2 nanoparticles, LEV, and their mixtures. The images show that the TiO2 nanoparticles have an uneven surface, exhibit a spherical structure, are prone to aggregation, and have a large specific surface area. The LEV, on the other hand, exhibits a plate-like structure with a relatively smooth surface and is relatively dispersed. Observation of the mixture reveals that the spherical clusters and the flat, smooth structure have partially aggregated.

[0047] The effects of different background solutions (citric acid (CA)-NaCl solution (citric acid concentration 10 mg / L, NaCl solution concentration 10 mg / L), citric acid (CA) solution (citric acid concentration 10 mg / L), NaCl solution (NaCl solution concentration 10 mg / L), and aqueous solution) on TiO2 nanoparticles and LEV functional groups were analyzed by Fourier transform infrared spectroscopy.

[0048] like Figure 2 The figure shows the Fourier transform infrared spectra of TiO2 nanoparticles, LEV and their mixtures under different background solutions.

[0049] First, the absorption peak of TiO2 nanoparticles is at 470 cm⁻¹. -1 There is a significant fluctuation at one point, but in TiO2-LEV-H2O and TiO2-LEV-Na + In the system, this absorption peak exhibits a significant redshift, all around 610 cm⁻¹. -1 Observed Ti 3+ Fourier transform infrared (FTIR) results indicate that Ti-O oxides contain additional unsaturated sites (TiO2) due to the incorporation of -OH groups. 3+ There are weak complex vibrations between them, and then between citric acid (CA) and citric acid (CA)-Na. + As the absorption peak in the mixed solution gradually weakens, we can infer that the adsorption of TiO2 with water gradually decreases, and more of it undergoes a complexation reaction with citric acid to form a stable complex.

[0050] Subsequently, in TiO2-LEV-CA-Na + The absorption peak in the system has changed significantly compared to before, mainly at 1360 cm⁻¹. -1 1700cm -1 2360cm -1 and 3060cm -1 Significant fluctuations were observed at and near the same wavelength, particularly at 1210 cm⁻¹. -1 It has a weak absorption band, of which 1360cm -1 1700cm -1 and 1210cm -1 These three bands are characteristic of deprotonated citrate, with the first two prominent bands assigned to the carboxylate ion (COO). - The asymmetric and symmetric stretching motions of the groups, while the weaker bands are referred to as the bending mode of the carboxylate groups, at 1700 cm⁻¹. -1 It could also be a result of citrate adsorbing onto TiO2 nanoparticles or the LEV surface via carbonyl oxygen, but considering the 2360 cm⁻¹... -1 and 3060cm-1 Fluctuations were also observed at this location, and no or very weak fluctuations were observed under other conditions. Therefore, it is speculated that this is because the carbonyl oxygen or hydrogen atom forms a special chemical bond with the nitrogen atom in LEV, resulting in absorption characteristics that are different from those of ordinary carbonyl groups.

[0051] Example 2: A method for predicting the migration rate of nano-metal oxides and antibiotics under combined conditions.

[0052] (1) Scan the infrared characteristic peaks of nano-metal oxides and antibiotics. Under the setting of these two infrared characteristic peaks, measure the absorbance of nano-metal oxides and antibiotics under different concentration conditions. Obtain the standard curves of absorbance and concentration of nano-metal oxides and antibiotics, respectively. Combine them to obtain the relationship between absorbance of nano-metal oxides and concentration of nano-metal oxides and concentration of antibiotics, and the relationship between absorbance of antibiotics and concentration of nano-metal oxides and concentration of antibiotics. The relationship is as follows:

[0053]

[0054] In the formula, and The absorbance at the infrared characteristic peaks of nano-metal oxides and antibiotics, respectively; C NPs and C Ab The concentrations of nano-metal oxides and antibiotics after passing through porous media are [M·L], respectively. -3 ]; α, β, ε, ∈, m, n, u, v are the corresponding standard curve coefficients and intercepts, respectively.

[0055] Specifically, in this embodiment, the nano-metal oxide is TiO2 nanoparticles, the antibiotic is LEV, the two infrared characteristic peaks are 287nm and 339nm, α=0.0721, β=-0.0018, ε=0.0164, ε₀=-0.0055, m=0.0256, n=-0.0019, u=0.0174, v=-0.0095.

[0056] Finally, by combining the equations, we obtain the following relationships: the absorbance of the nano-metal oxide with its concentration and the antibiotic concentration, and the absorbance of the antibiotic with its concentration and the nano-metal oxide concentration.

[0057]

[0058] In the formula, A 287 and A 339 The absorbance at the infrared characteristic peaks of nano-metal oxides and antibiotics, respectively; and C LEVThe concentrations of nano-metal oxides and antibiotics after passing through porous media are [M·L], respectively. -3 ].

[0059] (2) A mixed solution of nano-metal oxides and antibiotics under composite chemical conditions was passed into a column filled with porous media, and the initial concentrations C0 of the nano-metal oxides and antibiotics before passing through the porous media were measured. NPs and C0 Ab The absorbance of metal oxides and antibiotics at the infrared characteristic peaks under combined chemical conditions was measured. and C is obtained through the relational calculation in step (1). NPs and C Ab Then, the standardized effluent concentration P of nano-metal oxides and antibiotics is calculated using the following relationship. NPs and P Ab ;

[0060]

[0061] In the formula, P NPs and P Ab These represent the standardized effluent concentrations of nano-metal oxides and antibiotics under combined chemical conditions; CO NPs and C0 Ab The initial concentrations [M·L] of nano-metal oxides and antibiotics before passing through the porous medium are respectively. -3 ].

[0062] In this embodiment, the porous medium is quartz sand.

[0063] In this embodiment, during the specific test, 100 mg·L⁻¹ ultrapure water was used to prepare the solution. -1 The TiO2 nanoparticles and LEV stock solutions were prepared. Before use, the TiO2 and LEV suspensions were sonicated for 30 min to ensure uniform dispersion of the nanoparticles, and then diluted to the required concentration for the experiment. Specific conditions under the combined chemical conditions are shown in items A, B, C, and D of Table 1 below, where item A represents the concentration of the antibiotic (mg·L⁻¹). -1 ), Item B is the concentration of Na ions in the solution (mM·L). -1 ), Item C is the flow rate of the solution (mL·min) -1 Option D represents the concentration of citric acid in the solution (mg·L). -1 ).

[0064] Table 1. Experimental design matrix and related results for TiO2 nanoparticles and LEV mobility.

[0065]

[0066] (3) Response surface methodology (RSM) experiments were conducted using Expert Design software; the chemical conditions of the composite factors in step S2 were transformed into three levels, namely -1, 0, and +1; so that each factor was at one of the three levels, and the standardized effluent concentration P was obtained. NPs and P Ab The predicted value, in step S2, is used to calculate the standardized effluent concentration P of nano-metal oxides and antibiotics. NPs and P Ab and standardized effluent concentration P NPs and P Ab The goodness of fit of the predicted values ​​is determined by the coefficient of determination R. 2 Evaluate the accuracy of the model;

[0067] The DESIGN-EXPERT software was used for experimental design, data regression, and graphical analysis. A total of 29 experiments were conducted using RSM to investigate the effects of antibiotic concentration, solution ionic strength, solution flow rate, and citric acid on the penetration rate of nano-titanium dioxide and levofloxacin in the column.

[0068] The relationship between the migration rates of nano-metal oxides and antibiotics under combined chemical conditions and independent variables was analyzed. The "Prob>F" value was evaluated; a value <0.05 indicates that the model term composed of independent variables is significant at the 95% confidence level. Significant model terms for the migration rates of nano-metal oxides and antibiotics under combined chemical conditions were obtained through the "Prob>F" value. A quadratic polynomial model for the migration rates of nano-metal oxides and antibiotics under combined chemical conditions was derived based on the following RSM model:

[0069] Using RSM, a quadratic polynomial condition is used as a component of the independent variable to create an authoritative RSM model of the reaction prior to its occurrence and to analyze the relationship between the condition and the independent variable. RSM includes a set of precise procedures for determining a set of test factors for the independent variable and for enhancing the relationship between the measured reaction and the independent variable under optimized conditions.

[0070]

[0071] Where x i X is the dimensionless encoded value of the i-th independent variable; i It is the actual value of the independent variable; X0 is X i The value at the center point; ΔX is the step size change. The behavior of the system is defined by an empirical second-order polynomial model; Y is the predicted response value; β0, β i β j β ij It is the constant of the regression coefficient of the established model, x i and x jThe independent variables are represented by coded values; the independent variables are the concentration of antibiotics, the concentration of Na ions in the solution, the flow rate of the solution, and the concentration of citric acid in the solution.

[0072] like Figure 3 , 4 As shown, the response surface methodology is used to predict the optimal combination of control variables to study which conditions best control the migration of nanomaterials and antibiotics in groundwater amidst complex environmental variables. Based on the absolute values ​​of the regression equation coefficients, the order of influence of each factor on TiO2 and LEV is consistent: concentration ratio has the greatest impact, followed by ionic strength and flow rate, which have the least impact.

[0073] Based on the results above, we can infer that when TiO2 migrates in porous media, increasing the LEV concentration significantly promotes migration, while increasing the ionic strength and flow rate have slight promoting effects. Increasing the citric acid concentration, however, inhibits TiO2 migration. When LEV migrates in porous media, it reaches saturation at a TiO2 / LEV ratio of approximately 2 / 5, at which point the promoting effect on LEV migration is highest. Increasing the ionic strength, flow rate, and citric acid concentration all promote its migration. These conclusions are consistent with the single-factor analysis, demonstrating the accuracy and stability of the experiment.

[0074] Contour lines and response surfaces obtained from the multinomial regression equations of quadratic models can provide in-depth insights into the interaction effects of the factors under study. They can be used to predict and optimize response values, as well as analyze the interaction between any two factors. Figure 3 , 4 As shown, the smoothness of the surface reflects the magnitude and importance of the interaction; a greater curvature indicates a stronger interaction. This can be seen from the comparison within the salient model terms. Figure 3 (a)~(c) and Figure 4 The response surface plots in (a) to (b) show that Figure 3 The surface of (b) is the steepest, and the surface of (a) is the gentlest. Figure 4 The surface of (a) is steeper than that of (b). Therefore, we infer that the interaction between concentration ratio and flow rate has the greatest impact on the migration of TiO2 in porous media, followed by the interactions between concentration ratio and citric acid and ionic strength, respectively, with the weakest interaction being between ionic strength and flow rate. The interaction between concentration ratio and ionic strength has the greatest impact on the migration of LEVs in porous media, followed by the interaction between concentration ratio and citric acid, while the interaction between flow rate and citric acid has the least impact on LEV migration.

[0075] The standardized effluent concentration P was obtained using the above response surface model. NPs and P AbThe experimental and predicted values; the goodness of fit between the experimental and predicted values ​​is determined by the coefficient of determination (R²). 2 The evaluation was conducted using an analysis of variance of the response surface model, demonstrating that the derived model is effective for nano-metal oxides and antibiotic-normalized effluent concentrations P. NPs and P Ab The sufficiency of the relationship with complex environmental condition variables.

[0076] Figure 5 Experimental and predicted values ​​of the penetration rates of nano-titanium dioxide and levofloxacin are presented. The goodness of fit between the predicted and actual results is determined by the coefficient of determination (R²). 2 The value is used for evaluation, and a higher value indicates a better fit. and R LEV 2 =0.981), indicating that the experimental results and predictions of the penetration rates of nano-titanium dioxide and levofloxacin are in good agreement, demonstrating that the model has very high accuracy.

[0077] Model obtained The value is 295.30, F LEV The value is 50.55, indicating that the regression model has high significance, and the probability of the "model F-value" occurring due to noise is less than 0.0001%. All these results indicate that the regression model has good adequacy.

[0078] Tables 2 and 3 below show the LEV ANOVA analysis data obtained by RSM and the TiO2 ANOVA analysis data obtained by RSM, respectively.

[0079] In this case, A, B, C, D, AB, AC, AD, and A 2 These are significant model terms for the penetration rate of nano-titanium dioxide in this study: A, B, D, AB, AD, A 2 and B 2 This was a significant model term for the penetration rate of levofloxacin in this study, while other factors were not significant (P>0.05).

[0080] Table 2. LEV ANOVA analysis data obtained from RSM

[0081]

[0082]

[0083] Table 3. TiO2 ANOVA analysis data obtained from RSM

[0084] source sum of squares Degrees of freedom Mean Square F value p-value Model 3507.97 14 250.57 295.30 <0.0001 A-LEV concentration 1799.23 1 1799.23 2120.44 <0.0001 B-NaCl concentration 22.63 1 22.63 26.67 0.0001 C-flow rate 13.79 1 13.79 16.25 0.0012 D-CA concentration 28.39 1 28.39 33.46 <0.0001 AB 6.73 1 6.73 7.93 0.0138 AC 24.38 1 24.38 28.74 0.0001 AD 17.39 1 17.39 20.50 0.0005 BC 0.0109 1 0.0109 0.0129 0.9112 BD 0.5407 1 0.5407 0.6372 0.4381 CD 0.1276 1 0.1276 0.1504 0.7040 <![CDATA[A 2 ]]> 301.78 1 301.78 355.65 <0.0001 <![CDATA[B 2 ]]> 0.0002 1 0.0002 0.0002 0.9885 <![CDATA[C 2 ]]> 0.2538 1 0.2538 0.2992 0.5930 <![CDATA[D 2 ]]> 0.3431 1 0.3431 0.4043 0.5351 residual 11.88 14 0.8485 underfit 11.78 10 1.18 47.12 0.0010 Pure error 0.1000 4 0.0250 sum 3519.85 28

[0085] Based on the correlation coefficients of the migration rates of metal oxides and antibiotics under combined conditions obtained using DESIGN-EXPERT software, a quadratic polynomial model relating the migration rates of nano-metal oxides and antibiotics to the combined environmental condition variables was derived:

[0086] Y TiO2 =9.05+15.26A+1.52B+1.34C-2.05D+1.12AB+2.31AC-2.02AD-0.045BC-0.3279BD+0.1728CD+7.91A 2 +0.0097B 2

[0087] -0.2293C 2 +0.2304D 2

[0088] Y LEV =95.06-4.93A+1.57B+0.332C+3.89D+1.46AB-0.4102AC+1.45AD-0.2613BC-0.7714BD+0.0356CD-9.5A 2

[0089] -2.19B 2 +0.3761C 2 +0.8703D 2

[0090] In the formula, A, B, C, D, and Y LEV The concentrations of LEV (mg·L) are respectively -1 ), NaCl concentration (mM·L) -1 Solution flow rate (ml·min) -1 ), citric acid concentration (mg·L) -1 The migration rates of TiO2 (%) and LEV (%).

[0091] The foregoing examples are merely illustrative, used to explain some features of the method described in this invention. The appended claims are intended to claim the broadest possible scope, and the embodiments presented herein are demonstrated by the applicant's actual experimental results. Therefore, the applicant intends that the appended claims are not limited by the selection of examples illustrating the features of the invention. Some numerical ranges used in the claims also include sub-ranges within them, and variations within these ranges should also be interpreted as being covered by the appended claims where possible.

Claims

1. A method for predicting the migration rate of nano-metal oxides and antibiotics under composite conditions, characterized in that, Includes the following steps: S1. Obtain the infrared characteristic peaks of the nano-metal oxide and the antibiotic. Under these two infrared characteristic peak settings, measure the absorbance of the nano-metal oxide and the antibiotic under different concentration conditions. Obtain the standard curves for the absorbance and concentration of the nano-metal oxide and the antibiotic, respectively. Combine these to obtain the relationship between the absorbance of the nano-metal oxide and the concentration of the nano-metal oxide and the concentration of the antibiotic, and the relationship between the absorbance of the antibiotic and the concentration of the nano-metal oxide and the concentration of the antibiotic. The relationships are shown below: In the formula, and The absorbance at the infrared characteristic peaks of nano-metal oxides and antibiotics, respectively; C NPs and C Ab The concentrations of nano-metal oxides and antibiotics after passing through porous media, respectively; α, β, , m, n, u, and v are the corresponding standard curve coefficients and intercepts, respectively; The nano-metal oxide is selected from nano-titanium dioxide; the antibiotic is a quinolone antibiotic. S2. A mixed solution of nano-metal oxides and antibiotics under composite chemical conditions was passed into a column filled with porous media. The initial concentrations C0 of the nano-metal oxides and antibiotics before passing through the porous media were measured. NPs and C0 Ab The absorbance of nano-metal oxides and antibiotics at the infrared characteristic peaks under combined chemical conditions was measured. and C is obtained through the relational calculation in step S1. NPs and C Ab Then, the standardized effluent concentration P of nano-metal oxides and antibiotics is calculated using the following relationship. NPs and P Ab ; In the formula, P NPs and P Ab These represent the standardized effluent concentrations of nano-metal oxides and antibiotics under combined chemical conditions; CO NPs and C0 Ab The initial concentrations of nano-metal oxides and antibiotics were measured before they passed through the porous medium, respectively. S3. A response surface methodology (RSM) experiment was conducted using Expert Design software. The relationship between the migration rates of nano-metal oxides and antibiotics under combined chemical conditions and the independent variables was analyzed. The "Prob>F" value was evaluated; a value < 0.05 indicates that the model terms composed of independent variables are significant at the 95% confidence level. Significant model terms for the migration rates of nano-metal oxides and antibiotics under combined chemical conditions were obtained through the "Prob>F" value. A quadratic polynomial model for the migration rates of nano-metal oxides and antibiotics under combined chemical conditions was derived based on the following RSM model: Where, x i X is the dimensionless encoded value of the i-th independent variable. i It is the actual value of the independent variable, X0 is X i The value at the center point, ΔX is the step size change; Y is the mobility; β0, β i β ii β ij It is the constant of the regression coefficient of the established model, x i and x j The independent variables are represented by coded values; the independent variables are the concentration of antibiotics, the concentration of Na ions in the solution, the flow rate of the solution, and the concentration of citric acid in the solution.

2. The method according to claim 1, characterized in that, The porous medium is quartz sand.

3. The method according to claim 1, characterized in that, The porous medium has a particle size of 0.45–0.6 mm.

4. The method according to claim 1, characterized in that, The average diameter of the nano-metal oxide is 30–50 nm.

5. The method according to claim 1, characterized in that, The antibiotic in question is levofloxacin.

6. The method according to claim 1, characterized in that, The aforementioned mixed solution of nano-metal oxides and antibiotics under the combined chemical conditions refers to a mixed solution of nano-metal oxides and antibiotics in which the concentration of antibiotics, the concentration of Na ions in the solution, the flow rate of the solution, and the concentration of citric acid in the solution are controlled.

7. The method according to claim 6, characterized in that, Choose from at least one of the following (a) to (d): (a) The concentration of antibiotics is 10–40 mg / L. -1 ; (b) The concentration of Na ions in the solution is 1–50 mM·L -1 ; (c) The flow rate of the solution is 1–4 mL / min. -1 ; (d) The concentration of citric acid in the solution is 5–15 mg·L⁻¹ -1 .

8. The method according to claim 1, characterized in that, The composite factors in step S2 are converted into three levels under chemical conditions: -1, 0, and +1; thus, each factor is at one of the three levels, obtaining the standardized effluent concentration P. NPs and P Ab The predicted value, in step S2, is used to calculate the standardized effluent concentration P of nano-metal oxides and antibiotics. NPs and P Ab and standardized effluent concentration P NPs and P Ab The goodness of fit of the predicted values ​​is determined by the coefficient of determination R. 2 Evaluate the accuracy of the model.

9. The method according to claim 1, characterized in that, When the probability of the F value is less than 0.0001%, it indicates that the RSM model is sufficient.

Images