Quantum chemical screening method for extracting curcumin

Abstract

The invention discloses a quantum chemical screening method for extracting curcumin. The method comprises the following steps: constructing a curcumin and hydrogen bond donor and receptor single molecule model, carrying out conformation search, geometric optimization and single point energy calculation, establishing a cluster, carrying out IGMH, ESP and AIM analysis, screening a deep eutectic solvent, and then preparing and extracting the curcumin. The method replaces a traditional trial and error screening mode, provides molecular level theoretical support, realizes efficient combination of computer simulation and experimental extraction, and provides a reliable scheme for green and efficient extraction of active ingredients of natural products.

Description

Technical Field

[0001] This invention belongs to the field of quantum chemistry and natural product extraction technology, specifically relating to a quantum chemical screening method for extracting curcumin. Background Technology

[0002] Eutectic solvents, as novel green solvents, have shown significant advantages in the extraction of natural products, but their application faces key technological bottlenecks: currently, eutectic solvent extraction lacks systematic rules, and the selection of the optimal combination and ratio of hydrogen bond donors and acceptors requires continuous trial and error. This traditional screening method not only consumes a lot of time and experimental materials but also makes it difficult to accurately find the optimal combination, severely limiting the transition of eutectic solvents from the laboratory to large-scale industrial applications. Curcumin, as a natural polyphenol compound with high medicinal value, has an extraction efficiency closely related to the solvent system.

[0003] Based on the above problems, there is an urgent need for an efficient and accurate eutectic solvent screening technology to solve the drawbacks of traditional trial-and-error screening and promote the green and efficient development of curcumin extraction technology. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and to propose a quantum chemical screening method for extracting curcumin, comprising the following steps: S1: Using GaussView, single-molecule models of curcumin, hydrogen bond donor, and hydrogen bond acceptor were constructed respectively. Initial molecular conformations were generated through conformation search. After preliminary optimization, geometric optimization and single-point energy calculation were performed using Gaussian software to obtain molecular structure configuration files. S2: Random clusters are established by optimizing hydrogen bond donors and hydrogen bond acceptors in different molar ratios. The optimal cluster configuration is selected for geometric optimization and single-point energy calculation. Cluster wave function information is obtained and binding energy is calculated. S3: The optimized hydrogen bond donor-hydrogen bond acceptor combination is combined with curcumin molecules to form random clusters. The optimal structural cluster configuration is selected for geometric optimization and single-point energy calculation to obtain cluster wavefunction information. S4: Use the Multiwfn program to perform IGMH analysis, ESP analysis, and AIM analysis on the cluster wavefunction file respectively; S5: Mix the hydrogen bond donor and hydrogen bond acceptor according to the molar ratio in step S2, and prepare a eutectic solvent by ultrasonic heating and vibration; S6: Mix the pulverized and sieved turmeric powder with a eutectic solvent, extract by ultrasonication, centrifuge, and filter to obtain a supernatant containing curcumin.

[0005] Preferably, the hydrogen bond acceptor is proline, the hydrogen bond donor is acetic acid, ethylene glycol, or urea, and the molar ratio of the hydrogen bond acceptor to the hydrogen bond donor is 1:2.

[0006] More preferably, the conformation search is performed by gentor in the MOPAC and molclus programs to generate 100 to 200 initial molecular conformations. The preliminary optimization is performed at the PM6-DH+ level to obtain a gjf file. The Gaussian software performs geometric optimization on the gjf file to obtain an out file. The optimized structure file is saved as a new gjf file. The single-point energy calculation is performed using the DFT functional M06-2X under the basis set def2-TZVP.

[0007] More preferably, the random clusters in step S2 are established using the molclus program, with a number of 30 to 70 clusters. The optimal cluster configuration is geometrically optimized to obtain an out file and saved as a new gjf file. The single-point energy calculation is performed using the DFT functional M06-2X under the basis set def2-TZVP to obtain cluster wavefunction information.

[0008] More preferably, the random clusters in step S3 are established using the molclus program, with 50 to 100 clusters established. The optimal cluster configuration is geometrically optimized to obtain an out file and saved as a new gjf file. The single-point energy calculation is performed using the DFT functional M06-2X under the basis set def2-TZVP to obtain cluster wavefunction information.

[0009] More preferably, the IGMH analysis is performed by subroutine 11 in program 20 of Multiwfn. Option 3 is selected to export the grid data of the functions δg, δg_inter, δg_intra, and sign(λ2)ρ as a cub file. The cub file is imported into the VMD program folder and the command sourceIGM_inter.vmd is entered to obtain the isosurface plot. Option 2 is selected to obtain the output.txt file. The command IGMscatter.gnu is run through the gnuplot program to draw a scatter plot and obtain a PS file. The scatter plot is then displayed in IrfanView with Ghostscript installed.

[0010] More preferably, the ESP analysis is performed using the scripts ESPiso.bat and ESPext.bat in Multiwfn. After running the two scripts, the commands iso and ext are entered sequentially in the VMD program to obtain the surface electrostatic potential map. The specific values ​​of the electrostatic potential at the extreme points of the surface electrostatic potential are marked using Photoshop software. The cluster wavefunction file of the hydrogen bond donor-hydrogen bond acceptor combination is loaded into the Multiwfn program, and the commands 12 and 0 are entered sequentially to obtain the molecular polarity data. The command 0 is entered to view the positions of the maximum and minimum points of the molecular surface electrostatic potential. The commands 9 and all are entered to consider the van der Waals surfaces corresponding to all atoms. The statistical range is entered and evenly divided into 15 intervals. The command 3 is entered to output the surface area in different electrostatic potential intervals in kcal / mol. The electrostatic potential distribution histogram is obtained by plotting using Origin software.

[0011] More preferably, the AIM analysis is performed by the script program AIM.bat in Multiwfn. After running the script program, the command aim is entered in the VMD program, and 0 is pressed to enter the query mode to find the properties of the required critical points. The name N and serial number and the number of the critical point are entered in Graphical-Representations. The critical point number is marked using Photoshop software to obtain the topology analysis diagram.

[0012] More preferably, the ultrasonic heating vibration temperature in step S5 is 60°C; the pulverization of the turmeric powder is performed by a swing-type high-speed pulverizer, and after pulverization, it is passed through a 60-mesh sieve. The sieved turmeric powder is stored at 4°C until use; the water content of the eutectic solvent is 20%.

[0013] More preferably, in step S6, the liquid-to-solid ratio of turmeric powder to eutectic solvent is 10:1 mL / g, the ultrasonic extraction temperature is 40℃, and the extraction time is 30 minutes; the centrifugation operation is performed using a refrigerated centrifuge, with centrifugation conditions of 25℃, 7000 rpm, and 15 minutes; the curcumin content in the supernatant is determined using an ultraviolet spectrophotometer, and the sample concentration is calculated by converting the absorbance standard curve of curcumin standard.

[0014] Technical effects: This invention innovatively employs a screening method combining quantum chemical simulation and wavefunction analysis. Through conformational search, cluster optimization, and IMH, ESP, and AIM analyses, it accurately screens eutectic solvent systems, solving the problems of time-consuming, material-intensive, and inefficient traditional trial-and-error screening. This technology achieves a highly efficient combination of computer simulation and experimental extraction, improving the accuracy and efficiency of curcumin extraction. Furthermore, the eutectic solvent is environmentally friendly, providing an efficient and feasible new pathway for natural product extraction. Attached Figure Description

[0015] Figure 1 This is a flowchart of the quantum chemical screening method for extracting curcumin according to the present invention; Figure 2 The coloring method for the mapping function sign(λ2)ρ in IGMH analysis; Figure 3 Isosurface plots for different DES combinations; Figure 4 Isosurface plots of different DES combinations with curcumin; Figure 5 Scatter plots of different DES combinations; Figure 6 Scatter plots of different DES combinations with curcumin; Figure 7 ESP diagrams of curcumin single molecules and different DES combinations; Figure 8 ESP plots for different DES combinations with curcumin; Figure 9 This is a histogram showing the electrostatic distribution of curcumin single molecules. Figure 10 Histograms of electrostatic distribution for different DES combinations; Figure 11 Topological analysis diagrams for proline / acetic acid, proline / ethylene glycol, and proline / urea (1:2); Figure 12 The extraction yield of curcumin for different DES combinations. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] Traditional screening of eutectic solvents relies on trial and error and lacks theoretical support at the molecular level, resulting in low screening efficiency and high costs.

[0018] Based on this, please refer to Figures 1-12 This embodiment provides a quantum chemical screening method for extracting curcumin. The core technical solution is to deeply integrate quantum chemical simulation and wavefunction analysis to accurately reveal the interaction mechanism between eutectic solvents and curcumin at the molecular level, thereby achieving efficient screening of solvent systems. The specific implementation process is as follows: The first step was to construct and optimize molecular models, which is the foundation of the entire screening process. GaussView software was used to construct single-molecule models of curcumin, hydrogen bond donors, and hydrogen bond acceptors. During construction, the chemical structural characteristics of curcumin, the hydrogen bond donors ethylene glycol urea and proline were strictly followed to ensure that the atomic connections, bond lengths, bond angles, and other parameters of the models were consistent with the actual molecules. For example, the curcumin molecular model needed to accurately represent its polyphenolic compound structure, including the core framework of two benzene rings connected by a β-diketone structure, and the spatial positions of substituents such as methoxy and hydroxyl groups on the benzene rings; the proline model needed to reflect its nitrogen-containing heterocyclic structure and the presence of a carboxyl group; and the acetic acid, ethylene glycol, and urea models highlighted the characteristic structures of the carboxyl, dihydroxy, amino, and carbonyl groups, respectively.

[0019] After the model is built, initial molecular conformations are generated through conformational search. The conformational search is performed using the `gentor` module in the MOPAC and molclus programs. This module can generate molecular structures with different spatial conformations based on the structural features of the molecule through random search. To ensure coverage of major stable conformations and avoid bias in subsequent analysis results due to omissions, the number of initial molecular conformations generated is controlled between 100 and 200. Taking the proline / acetic acid combination as an example, 144 initial molecular conformations were generated during the conformational search, covering a variety of possible spatial arrangements of the molecule.

[0020] After the initial conformation is generated, preliminary optimization is performed. This preliminary optimization is conducted at the PM6-DH+ level. PM6-DH+ is a semi-empirical quantum chemical calculation method that combines computational efficiency and accuracy. It can quickly optimize the energy of a large number of initial conformations, screening out the advantageous conformations with relatively low energy. Simultaneously, a gjf format file is obtained, containing information such as the structural parameters and energy data of the optimized molecule, providing a foundation for further optimization.

[0021] Subsequently, the gjf file was geometrically optimized using Gaussian software. The purpose of geometric optimization is to find the lowest energy point of the molecule, i.e., the most stable geometric configuration, by adjusting parameters such as bond lengths, bond angles, and dihedral angles. During the optimization process, the DFT density functional theory method was employed, selecting a combination of the M06-2X functional and the def2-TZVP basis set. The M06-2X functional exhibits excellent performance in describing intermolecular interactions and hydrogen bonding, accurately capturing weak intermolecular interactions; the def2-TZVP basis set is a triple zeta basis set, containing polarization functions, which can fully describe the electron cloud distribution of atoms, improving the accuracy of the calculation results. After geometric optimization, an out format file was obtained, which records detailed geometric parameters, energy values, and other key information of the optimized molecule. The optimized structure file is saved as a new gjf file, and the single-point energy is calculated again using the DFT functional M06-2X under the basis set def2-TZVP. The single-point energy calculation can accurately obtain the energy value of the molecule under the stable geometric configuration, providing energy data support for subsequent cluster calculations and binding energy analysis, and finally obtaining the most reasonable molecular structure configuration file.

[0022] Next, cluster construction and optimization are performed. The first step is the construction of clusters for hydrogen bond donors and acceptors. The optimized hydrogen bond donors and acceptors are used in different molar ratios, with a focus on validating the 1:2 molar ratio, to create random clusters using the molclus program. The molclus program is highly efficient and reliable in cluster construction, capable of randomly generating cluster structures with different spatial arrangements based on the set molecular ratios. The number of clusters created is controlled between 30 and 70. This range has been validated through extensive experiments, ensuring the identification of the lowest-energy and most structurally stable cluster configurations while avoiding excessive computational load and time consumption due to an excessive number of clusters. For example, 50 random clusters are created for the proline / acetic acid 1:2 combination; 40 random clusters are created for the proline / ethylene glycol 1:2 combination; and 45 random clusters are created for the proline / urea 1:2 combination.

[0023] After the clusters were established, the optimal cluster configuration was selected for geometric optimization. The optimization process also employed Gaussian software, using a combination of DFT functionals M06-2X and def2-TZVP basis sets. The optimized OUT file was then saved as a new GJF file, and single-point energy calculations were performed again to obtain cluster wavefunction information. Simultaneously, based on the single-point energy calculation results, the binding energy between the hydrogen bond donor and acceptor was calculated. The binding energy is obtained by subtracting the sum of the isolated energies of each component molecule from the total energy of the cluster; that is, the binding energy equals the total cluster energy minus the sum of the energies of the hydrogen bond donor and acceptor. The magnitude of the binding energy directly reflects the strength of the interaction between the hydrogen bond donor and acceptor. A more negative binding energy indicates a more stable bond, resulting in a more stable eutectic solvent system. Through binding energy calculations, a relatively stable hydrogen bond donor-acceptor combination can be preliminarily screened, laying the foundation for subsequent interaction analysis with curcumin.

[0024] The second step involves cluster construction of the eutectic solvent and curcumin. The optimized hydrogen bond donor-acceptor combination (the eutectic solvent model) and curcumin molecules are used to create random clusters via the molclus program. To more comprehensively capture the interaction patterns between the two, the number of clusters is increased to 50-100: 50 random clusters are created with proline / acetic acid 1:2 and curcumin, 60 with proline / ethylene glycol 1:2, and 70 with proline / urea 1:2. During cluster construction, different spatial distances and relative orientations are ensured between the eutectic solvent model and the curcumin model to cover possible interaction sites. Subsequently, the established clusters undergo geometric optimization and single-point energy calculations. The optimization and calculation parameters are consistent with the previous steps to ensure data consistency and comparability, ultimately yielding a cluster wavefunction file containing information on the interaction between the eutectic solvent and curcumin.

[0025] After obtaining the cluster wavefunction file, the Multiwfn program was used to perform IGMH, ESP and AIM analyses on it to analyze the interaction characteristics between the eutectic solvent and curcumin from different dimensions.

[0026] IGMH analysis is performed through subroutine 11 in program 20 of Multiwfn, which is specifically designed for the visualization analysis of intermolecular interactions. During analysis, option 3 is first selected to export the grid data of the δg, δg_inter, δg_intra, and sign(λ2)ρ functions as a cub file. Here, δg represents the difference in density gradients, reflecting changes in the density distribution between molecules; δg_inter is the difference in density gradients between fragments, specifically characterizing the differences in density gradients between different molecular fragments; δg_intra is the difference in density gradients within fragments, used to distinguish changes in density gradients within molecules; the sign(λ2)ρ function is related to the type and strength of the interaction, with negative values ​​typically corresponding to attractive interactions such as hydrogen bond van der Waals forces, and positive values ​​corresponding to repulsive interactions. The exported cub file is then imported into the VMD program folder, and the command `sourceIGM_inter.vmd` is entered. The VMD program will then generate an isosurface plot based on the grid data. In the isosurface plot, blue areas represent hydrogen bonding forces, green areas represent van der Waals forces, and red areas represent intermolecular repulsion forces. This plot allows for a direct observation of the distribution and intensity of different interactions between eutectic solvent molecules and curcumin molecules. For example, in the isosurface plot of proline / acetic acid 1:2 and curcumin, the blue areas are more densely distributed, indicating a greater number of hydrogen bonds between them. Conversely, the isosurface plot of proline / ethylene glycol 1:2 and curcumin shows more red areas, suggesting relatively significant intermolecular repulsion. Simultaneously, selecting option 2 yields the output.txt file, which contains the numerical data from the IGMH analysis. Running the command IGMscatter.gnu in the gnuplot program converts the numerical data into a scatter plot, resulting in a PS format file, which is then displayed in IrfanView software with Ghostscript installed. In the scatter plot, the density of blue dots is positively correlated with the number of hydrogen bonds, while the sparseness of red dots reflects the strength of intermolecular repulsion. In the scatter plot of the proline / acetic acid 1:2 combination, the blue dots are dense and the red dots are sparse, indicating that there are many hydrogen bonds between it and curcumin and the intermolecular binding is stable. In contrast, the blue dots of the proline / urea 1:2 and proline / ethylene glycol 1:2 combinations are fewer, especially the red dots of the ethylene glycol group combination, indicating that its structural stability is poor.

[0027] ESP analysis is performed using the scripts ESPiso.bat and ESPext.bat in Multiwfn. These scripts automate the preliminary data processing for electrostatic potential analysis. After running the two scripts, the commands "iso" and "ext" are entered sequentially in the VMD program. The VMD program then generates a surface electrostatic potential map based on the electron distribution information in the cluster wavefunction file. In the surface electrostatic potential map, red areas represent positive electrostatic potential, and blue areas represent negative electrostatic potential. The intensity of the color is positively correlated with the absolute value of the electrostatic potential; the darker the color, the greater the absolute value of the electrostatic potential in that region. Using Photoshop software to annotate the specific values ​​of the electrostatic potential at the extreme points of the surface electrostatic potential allows for precise acquisition of the distribution range of the molecular surface electrostatic potential and the magnitude of the electrostatic potential at key sites. For example, in the surface electrostatic potential diagram of curcumin molecules, the regions containing hydroxyl and carbonyl groups exhibit obvious negatively charged blue regions, while hydrophobic regions such as benzene rings exhibit weak positive charges or electroneutrality; in the surface electrostatic potential diagram of the proline / acetic acid 1:2 combination, the carboxyl region of acetic acid exhibits a strong positively charged red region, forming obvious electrostatic interaction sites with the negatively charged region of curcumin.

[0028] To further quantify the electrostatic potential distribution characteristics, the eutectic solvent cluster wavefunction file was loaded into the Multiwfn program. Commands 12 and 0 were entered sequentially, and the program calculated and output molecular polarity data, which reflects the overall polarity characteristics of the molecule. Command 0 was then entered to view the locations of the maximum and minimum electrostatic potential points on the molecular surface, identifying key sites of electrostatic interaction. Commands 9 and all were entered to consider van der Waals surfaces corresponding to all atoms, ensuring the comprehensiveness of the electrostatic potential analysis. The statistical range was typically -50 kcal / mol to 50 kcal / mol, evenly divided into 15 intervals. Command 3 was entered to output the surface area within different electrostatic potential intervals in kcal / mol. Finally, Origin software was used to plot these data into an electrostatic potential distribution histogram. The horizontal axis of the histogram represents the positive or negative value of the electrostatic potential, reflecting charge polarity, while the vertical axis corresponds to the surface area in different electrostatic potential intervals, visually demonstrating the distribution pattern of charge polarity on the molecular surface. Through comparative analysis, the electrostatic potential distribution of curcumin was mainly concentrated in the range of -10 kcal / mol to 20 kcal / mol, with the average surface area concentrated at 50 Å. 2 The effective electrostatic potential distribution of the proline / acetic acid 1:2 combination within this range exceeds 60 Å. 2 The peak area of ​​the proline / urea 1:2 combination is less than 42 Å. 2 The highest peak area of ​​the proline / ethylene glycol 1:2 combination is only 55 Å. 2This indicates that the charge polarity distribution of proline / acetic acid 1:2 overlaps with that of curcumin, the electrostatic interactions such as hydrogen bonding are the strongest, and the intermolecular binding effect is optimal.

[0029] AIM analysis is performed using the script AIM.bat in Multiwfn. This analytical method, based on quantum chemical topological theory, reveals the nature of intermolecular interactions through topological analysis of electron density. After running the script, enter the command "aim" in the VMD program, press 0 to enter query mode, and search for the properties of the desired critical points. Critical points are points where the electron density gradient is zero. Bond critical points (BCPs) are key sites characterizing the presence of chemical bonds, including covalent and hydrogen bonds. Enter "name N and serial" and the searched critical point number in Graphical-Representations, and use Photoshop to annotate the critical point numbers to obtain a topological analysis diagram. In the topological analysis diagram, the orange dots are bond critical points. Analysis of the electron density ρ(r) at the bond critical points... BCP ), the Laplace function of electron density ▽ 2 ρ(r BCP and energy density E(r) BCP Parameters such as electron density ρ(r) can determine the type and strength of chemical bonds. BCP ) is positively correlated with the strength of chemical bonds, ρ(r) BCP The larger the value, the stronger the chemical bond; the Laplace function of electron density ▽ 2 ρ(r BCP It can distinguish the type of chemical bond, when ▽ 2 ρ(r BCP When ) < 0, it indicates a covalent bond type; when ▽ 2 ρ(r BCP When )>0, it indicates a non-covalent bond type, such as a hydrogen bond; energy density E(r) BCP It is also an important indicator for determining the type of chemical bond.

[0030] In AIM analysis, a formula for calculating hydrogen bond energy is introduced to accurately quantify the strength of hydrogen bond interactions. Theoretically, the electron density ρ(r) at the bond critical point... BCP The hydrogen bond energy directly reflects the bonding strength of the hydrogen bond. The formation of a hydrogen bond is essentially a redistribution of electron density between two atoms. The formula for calculating the hydrogen bond energy is BE = -223.08 × ρ(r). BCP )+0.7423, where ρ(r) BCPThe electron density at the critical point of the bond is expressed in au. The rationale behind this formula lies in its ability to accurately predict the magnitude of the hydrogen bond energy using an easily calculable electron density parameter, avoiding complex high-order calculations. Furthermore, its calculation results show good consistency with experimental data, providing a quantitative basis for determining the binding stability of eutectic solvents with curcumin.

[0031] Table 1. Hydrogen bond energies from topological analysis and different DES combinations Table 2. Topological analysis and hydrogen bond energies between different DES and curcumin The hydrogen bond energies of different eutectic solvent combinations and their formation with curcumin were calculated using this formula, and the results are as follows: For the proline / acetic acid 1:2 combination, the hydrogen bonds formed within it, BCP... 62 N3…H 25 The electron density ρ(r) corresponding to O BCP The value is 0.0677au. Substituting this into the formula, the hydrogen bond energy is calculated to be -14.3578 kcal / mol. The critical point of this bond is ▽ 2 ρ(r BCP ) = 0.0743au > 0, E(r) BCP The value of BCP (-0.0237au < 0) indicates a moderately strong hydrogen bond interaction, making it the most binding and stable binding site in the entire combination. When this combination binds to curcumin, BCP... 128 N3…H 25 The electron density ρ(r) corresponding to O BCP The given value is 0.0705 au, and the calculated hydrogen bond energy is -14.9955 kcal / mol. 2 ρ(r BCP )=0.0684au>0,E(r) BCP The value of -0.0259au < 0 indicates that it forms a stable and strong medium-strong hydrogen bond structure with curcumin.

[0032] For the proline / ethylene glycol 1:2 combination, in the hydrogen bonds formed within itself, BCP 48 O 29 …H 17 O's ρ(r) BCP The hydrogen bond energy is -14.3497 kcal / mol, while the critical point of other bonds, such as BCP, is 0.0676 au. 69 O2…H 27 O's ρ(r) BCP The energy is only 0.0215 au, the hydrogen bond energy is -4.0635 kcal / mol, and ▽ 2ρ(r BCP ) = 0.0917au > 0, E(r) BCP The coefficient of BCP is 0.0027au > 0, indicating a weak interaction; after binding with curcumin, BCP... 167 O 39 …H 17 The hydrogen bond energy of O is -13.8991 kcal / mol, and other sites such as BCP... 199 O2…H 26 The hydrogen bond energy of O is only -5.2815 kcal / mol, and its overall binding stability is weaker than that of the proline / acetic acid 1:2 combination.

[0033] For the proline / urea 1:2 combination, BCP is present in the hydrogen bonds formed within it. 56 O 18 …H 31 O's ρ(r) BCP BCP = 0.0316au, hydrogen bond energy is -6.3000 kcal / mol. 36 N 20 …H 16 The hydrogen bond energy of O is only -1.7895 kcal / mol; after combining with curcumin, BCP 99 O 38 …H 17 The hydrogen bond energy of O is -9.5945 kcal / mol, and the hydrogen bond energies of other sites are even lower, indicating that its binding ability with curcumin is the weakest.

[0034] By combining the results of three wavefunction analyses, the optimal eutectic solvent system can be precisely screened, and then the eutectic solvent preparation process can begin. The selected hydrogen bond donors and acceptors are mixed at a 1:2 molar ratio. For example, proline and acetic acid, proline and ethylene glycol, and proline and urea are each mixed at a 1:2 molar ratio in an Erlenmeyer flask. The mixture is then ultrasonically heated and vibrated at 60°C. The ultrasonic device accelerates the molecular motion between the hydrogen bond donor and acceptor through the mechanical and cavitation effects generated by high-frequency vibration, promoting the formation of a hydrogen bond network and ensuring thorough mixing to form a homogeneous eutectic solvent. The ultrasonic heating and vibration time needs to be adjusted according to the state of the mixture, typically 30 to 60 minutes, until the solution becomes homogeneous and transparent, indicating that the eutectic solvent preparation is complete.

[0035] Simultaneously, the turmeric powder undergoes pretreatment. The turmeric rhizomes are thoroughly cleaned to remove impurities and surface dirt, then pulverized using a high-speed, oscillating grinder. During pulverization, the grinder's speed and time are adjusted to ensure the turmeric rhizomes are ground into a fine powder. The powder is then sieved through a 60-mesh sieve to remove coarse particles, ensuring uniform particle size. The sieved turmeric powder is stored at 4°C until use. This temperature effectively prevents the oxidative degradation of active ingredients such as curcumin, ensuring the stability of the extracted raw material's quality.

[0036] After the eutectic solvent was prepared and the turmeric powder was pretreated, curcumin extraction was performed. Accurately weigh 0.5 g of freeze-dried turmeric powder into a 10 mL glass sample vial, add 5 mL of eutectic solvent, ensuring a liquid-to-solid ratio of turmeric powder to eutectic solvent of 10:1 mL / g. Cap the sample vial and shake thoroughly to mix, ensuring the turmeric powder is evenly dispersed in the eutectic solvent. Place the sample vial in an ultrasonic bath and perform ultrasonic-assisted extraction at 40℃ for 30 minutes. The ultrasonic bath power was set to 100W to 200W. High-frequency ultrasonic vibration can disrupt the plant cell wall structure of turmeric powder, promoting the penetration of the eutectic solvent into the cells, while simultaneously enhancing the solubility of curcumin in the solvent and improving extraction efficiency.

[0037] After extraction, the extract was transferred to centrifuge tubes and centrifuged using a refrigerated centrifuge. Centrifugation conditions were set at 25°C, 7000 rpm, and 15 minutes. The low-temperature environment of the refrigerated centrifuge prevents curcumin degradation during centrifugation, while the high speed generates centrifugal force that allows solid impurities in the extract, such as incompletely crushed plant tissue fragments, to settle rapidly, thus effectively separating the supernatant from the solid residue. After centrifugation, the supernatant was collected and filtered through a 0.45 μm organic phase filter membrane to further remove minute impurities, yielding a pure curcumin-containing supernatant for subsequent content determination.

[0038] The curcumin content in the supernatant was determined using a UV-5100 ultraviolet spectrophotometer. Before measurement, a standard absorbance curve for curcumin standards needed to be plotted. Accurately weigh 10.0 mg of curcumin standard, dissolve it in an appropriate amount of anhydrous ethanol, and place it in a 100 mL volumetric flask. Dilute to the mark with anhydrous ethanol to obtain a standard stock solution with a concentration of 100 μg / mL. Then, take 5.0 mL of the above stock solution and dilute to volume in a 100 mL volumetric flask to obtain a 5 μg / mL curcumin standard stock solution. Accurately transfer the standard stock solution into 25 mL volumetric flasks at volume gradients of 5 mL, 10 mL, 15 mL, 20 mL, and 25 mL, and dilute to the mark with anhydrous ethanol to prepare curcumin standard solutions with concentrations of 1.0 μg / mL, 2.0 μg / mL, 3.0 μg / mL, 4.0 μg / mL, and 5.0 μg / mL, respectively. Add 4 mL of curcumin standard solutions of various concentrations to cuvettes, using anhydrous ethanol as a blank control. Measure the absorbance at 425 nm, the maximum absorption wavelength of curcumin. Plot a standard curve with the concentration of the curcumin standard solution on the x-axis and the corresponding absorbance on the y-axis. Perform linear regression analysis to obtain the linear regression equation y = 0.1444x - 0.0036, with a correlation coefficient R0. 2 =1.0000. This equation has a good linear relationship in the absorbance range of 0.14-0.718 Abs and can be accurately used for quantitative calculation of curcumin content.

[0039] When measuring the sample, take 4 mL of the filtered supernatant and dilute it appropriately according to the actual concentration to ensure that the absorbance falls within the linear range of the standard curve. Add it to the cuvette and measure the absorbance under the same conditions. Substitute the measured absorbance value into the linear regression equation to calculate the concentration of curcumin in the sample solution. Then, combine the dilution factor, the volume of the test solution, and the mass of the raw material to calculate the extraction rate using the curcumin extraction rate calculation formula.

[0040] The lack of clear standards for the combination and ratio of hydrogen bond donors and acceptors in existing technologies leads to unstable screening results. Therefore, proline was identified as the hydrogen bond acceptor, and acetic acid, ethylene glycol, and urea as hydrogen bond donors, with a molar ratio of 1:2. This ratio was not randomly selected but based on extensive calculations and experimental verification using quantum chemical simulations. From a chemical structure perspective, proline, as a hydrogen bond acceptor, can form a stable hydrogen bond network with the hydrogen bond donor through its nitrogen-containing heterocycle and carboxyl group. Acetic acid, ethylene glycol, and urea, as hydrogen bond donors, provide hydrogen bond donor sites through their carboxyl, hydroxyl, amino, and carbonyl groups, respectively. The 1:2 molar ratio ensures optimal matching of hydrogen bond donor and acceptor sites, guaranteeing the formation of a stable eutectic mixture. Experimental results show that the eutectic solvent formed at this molar ratio possesses suitable physicochemical properties such as viscosity and solubility, effectively disrupting the plant cell wall structure of turmeric and enhancing its solubility for curcumin. For example, the eutectic solvent formed by the proline / acetic acid 1:2 combination can enhance the positive charge of the hydroxyl group H due to the p-π conjugation effect between C=O and OH in the carboxyl group of acetic acid. This makes it easier for the hydroxyl group H to act as a strong hydrogen bond donor and form stable hydrogen bonds with proline and curcumin, thereby improving the extraction efficiency. However, if the molar ratio deviates from 1:2, such as 1:1 or 1:3, it will lead to an excess of hydrogen bond donors or acceptors, which will destroy the stability of the hydrogen bond network and reduce the solvent's solubility and extraction effect.

[0041] The lack of standardized parameter settings for conformation search and optimization affects the accuracy of molecular structure configuration files. Therefore, conformation search is performed using the `gentor` function in MOPAC and molclus programs, generating 100 to 200 initial molecular conformations. The number of initial conformations is chosen based on the complexity of the molecular structure and the size of the conformational space. Molecules such as curcumin, proline, acetic acid, ethylene glycol, and urea have relatively complex structures with multiple rotatable chemical bonds, resulting in a large number of possible conformations. A range of 100 to 200 conformations ensures coverage of the main stable conformations, preventing omissions that could lead to subsequent optimizations that do not reach the global minimum energy. Too few initial conformations may fail to find the optimal conformation, leading to biased analysis results; too many will increase computational load and time, reducing screening efficiency.

[0042] Preliminary optimization was performed at the PM6-DH+ level, resulting in a gjf file. The PM6-DH+ semi-empirical method achieves a good balance between computational accuracy and efficiency, enabling rapid energy optimization of a large number of initial conformations and screening out the advantageous conformations with lower energies, laying the foundation for subsequent high-precision calculations. Compared with other semi-empirical methods, PM6-DH+ exhibits higher accuracy in describing hydrogen bonding and molecular polarity, making it more suitable for the preliminary optimization of the molecular model in this invention.

[0043] After geometric optimization of the gjf file using Gaussian software, an out file is obtained. The optimized structure file is then saved as a new gjf file. Single-point energy calculations are performed using the DFT functional M06-2X under the basis set def2-TZVP. The DFT method is one of the most widely used methods in quantum chemical calculations, capable of accurately describing the electronic structure and energy characteristics of molecules. The M06-2X functional is a hybrid functional that includes a portion of the Hartree-Fock exchange energy, exhibiting excellent performance in describing intermolecular interactions, hydrogen bonds, and van der Waals forces. It can accurately capture the weak interaction between the eutectic solvent and curcumin. The def2-TZVP basis set is a triple zeta basis set, which, compared to a double zeta basis set, contains more basis functions, enabling a more comprehensive description of the electron cloud distribution of atoms, especially the distribution characteristics of outer electrons, thus improving the accuracy of the calculation results. Furthermore, the def2-TZVP basis set includes polarization functions, which can describe the deformation of the electron cloud, further enhancing the computational accuracy. The molecular structure configuration file obtained by setting these parameters can accurately reflect the stable geometric structure and energy characteristics of the molecule, providing a reliable data foundation for subsequent cluster calculations, binding energy analysis, and wavefunction analysis.

[0044] The unclear method and calculation parameters for establishing random clusters in step 2 affect the accuracy of cluster wavefunction information. Therefore, random clusters in step 2 are established using the molclus program, with the number ranging from 30 to 70. The molclus program is specifically designed for the construction and optimization of molecular clusters, possessing an efficient cluster generation algorithm capable of randomly generating cluster structures with different spatial arrangements based on a set molecular ratio and number. The selection of 30 to 70 clusters is a result of comprehensively considering both computational accuracy and efficiency. Too few clusters may fail to find the lowest-energy, most structurally stable cluster configuration, leading to inaccurate binding energy calculations; too many clusters will significantly increase the computational load and prolong the calculation time. For a 1:2 combination of proline with acetic acid, ethylene glycol, and urea, 50, 40, and 45 random clusters are established respectively. This number ensures coverage of the main cluster spatial configurations, allowing for the selection of the optimal structure.

[0045] The optimal cluster configuration was geometrically optimized to obtain an OUT file, which was then saved as a new GJF file. Single-point energy calculations were performed using the DFT functional M06-2X under the basis set def2-TZVP to obtain cluster wavefunction information. The purpose of geometric optimization was to find the lowest energy point of the cluster, ensuring the stability of the cluster structure. The same parameter settings as the single-molecule optimization were used during the optimization process to ensure consistency and comparability of the calculations. Single-point energy calculations can obtain accurate energy values ​​and wavefunction information of the cluster under stable structures. The wavefunction information contains key data such as the distribution and orbital characteristics of electrons in the cluster, which is the basis for subsequent IGMH, ESP, and AIM analyses. The cluster wavefunction information obtained through this parameter setting can accurately reflect the interaction characteristics between hydrogen bond donors and acceptors, providing an accurate basis for binding energy calculation and stability assessment. For example, cluster wave function analysis of the proline / acetic acid 1:2 combination showed that it had a low binding energy and a stable structure; while cluster wave function analysis of the proline / ethylene glycol 1:2 combination showed that there were more repulsive forces between molecules, the binding energy was relatively high, and the structural stability was poor.

[0046] The specific method for cluster establishment and calculation in step 3 was not clearly defined, resulting in a lack of reliable data for the analysis of the interaction between the eutectic solvent and curcumin. Therefore, random clusters were established in step 3 using the molclus program, with the number ranging from 50 to 100. The interaction between the eutectic solvent and curcumin involves the binding of two molecular systems, resulting in a more complex conformational space and more possible interaction modes. Therefore, the number of clusters established was increased compared to step 2; a range of 50 to 100 clusters allows for a more comprehensive capture of the interaction modes between the two, ensuring the identification of the lowest-energy and most stable cluster configurations. For example, 50 random clusters were established between proline / acetic acid 1:2 and curcumin, 60 random clusters between proline / ethylene glycol 1:2 and curcumin, and 70 random clusters between proline / urea 1:2 and curcumin. Increasing the number of clusters allows for a more comprehensive consideration of different spatial orientations and interaction sites, improving the reliability of the analytical results.

[0047] The optimal cluster configuration was geometrically optimized to obtain an OUT file, which was then saved as a new GJF file. Single-point energy calculations were performed using the DFT functional M06-2X under the basis set def2-TZVP to obtain cluster wavefunction information. Parameter settings consistent with previous steps ensured the consistency and comparability of the calculated data, accurately reflecting the interaction strength and characteristics between the eutectic solvent and curcumin. Analysis of the cluster wavefunction information clarified key information such as hydrogen bond sites, van der Waals force distribution, and electrostatic interaction characteristics between the eutectic solvent and curcumin, providing a direct theoretical basis for selecting the optimal extraction solvent. For example, cluster wavefunction analysis of proline / acetic acid 1:2 with curcumin showed multiple stable hydrogen bonds, a clear interaction characteristic in the electron density distribution, and a low binding energy, indicating stable binding and excellent extraction performance. However, cluster wavefunction analysis of proline / urea 1:2 with curcumin showed weaker hydrogen bonds, less obvious interaction characteristics in the electron density distribution, a higher binding energy, and poorer extraction performance.

[0048] The lack of a clear operational procedure for IGMH analysis makes it difficult to obtain effective interaction maps. Therefore, IGMH analysis is performed through subroutine 11 in program 20 of Multiwfn. This subroutine is a module specifically designed for visualizing intermolecular interactions, generating isosurface and scatter plots reflecting intermolecular interactions based on electron density data in cluster wavefunction files. Option 3 is selected to export the grid data of the δg, δg_inter, δg_intra, and sign(λ2)ρ functions as a cub file. These four functions describe the characteristics of intermolecular interactions from different perspectives: δg reflects the overall density gradient change, δg_inter and δg_intra distinguish between inter-fragment and intra-fragment density gradient changes, respectively, while signλ2ρ directly relates to the type and strength of the interaction. The grid data of these four types of functions can comprehensively and accurately reflect the interaction information between the eutectic solvent and curcumin.

[0049] Import the cub file into the VMD program folder and enter the command `sourceIGM_inter.vmd` to obtain the isosurface plot. VMD is a powerful molecular visualization software that can generate three-dimensional isosurface plots based on grid data, intuitively displaying the spatial distribution of intermolecular interactions. In the isosurface plot, different colors represent different types of interactions: blue represents hydrogen bonding forces, green represents van der Waals forces, and red represents intermolecular repulsion forces. This plot clearly shows the location, extent, and intensity of interactions. For example, in the isosurface plot of proline / acetic acid 1:2 and curcumin, the blue areas are mainly distributed between the carboxyl groups of proline and the hydroxyl groups of acetic acid and the hydroxyl and carbonyl groups of curcumin, indicating strong hydrogen bonding in these regions; the green areas are distributed between the hydrophobic parts of the molecules, representing van der Waals forces; and the red areas are very few, indicating weak intermolecular repulsion and overall stable binding.

[0050] Option 2 yields the output.txt file, which contains numerical data from the IMH analysis, including values ​​for functions such as δg, δg_inter, δg_intra, and sign(λ2)ρ at different grid points. The gnuplot program is used to run the command IGMscatter.gnu to convert this numerical data into a scatter plot, resulting in a PS format file, which is then displayed in IrfanView software with Ghostscript installed. The x-axis of the scatter plot represents the sign(λ2)ρ function value, and the y-axis represents the δg function value. Different colored scatter points represent different types of interactions. Blue scatter points correspond to hydrogen bonding, with negative sign(λ2)ρ values ​​and small δg values ​​in their distribution areas; green scatter points correspond to van der Waals forces, with sign(λ2)ρ values ​​close to zero and moderate δg values; red scatter points correspond to intermolecular repulsion, with positive sign(λ2)ρ values ​​and large δg values. The density of blue scatter points reflects the number of hydrogen bonds, while the sparseness of red scatter points reflects the strength of intermolecular repulsion. In the scatter plot of the proline / acetic acid 1:2 combination, the blue dots are dense and concentrated, while the red dots are very few, indicating that there are many hydrogen bonds and the intermolecular bonding is stable. In contrast, in the scatter plot of the proline / ethylene glycol 1:2 combination, there are fewer blue dots and more red dots, indicating that there are fewer hydrogen bonds, stronger intermolecular repulsion, and poor structural stability. The proline / urea 1:2 combination also has relatively few blue dots, and its bonding stability is between the two.

[0051] The lack of clarity in the operational steps and data processing methods of ESP analysis affects the validity of electrostatic potential-related analysis results. Therefore, ESP analysis is performed using the scripts ESPiso.bat and ESPext.bat in Multiwfn. These scripts automate the preliminary data processing for electrostatic potential analysis, including electron density calculation and electrostatic potential solution, simplifying the analysis process and improving efficiency. After running the two scripts, the commands iso and ext are entered sequentially in the VMD program. The VMD program then generates a surface electrostatic potential map based on the electron distribution information in the cluster wavefunction file. The surface electrostatic potential map visually displays the charge distribution characteristics of the molecular surface; red areas represent positively charged regions, and blue areas represent negatively charged regions, with the color intensity proportional to the absolute value of the electrostatic potential. Using Photoshop software to annotate the specific electrostatic potential values ​​at the extreme points of the surface electrostatic potential allows for precise acquisition of the electrostatic potential magnitude at key sites on the molecular surface, providing quantitative data for analyzing intermolecular electrostatic interactions. For example, in the surface electrostatic potential diagram of curcumin molecules, the regions containing hydroxyl and carbonyl groups exhibit a distinct blue negative charge, with electrostatic potential values ​​between -15 kcal / mol and -5 kcal / mol; the benzene ring region appears light red or colorless with a weak positive charge or electroneutrality, with electrostatic potential values ​​between -5 kcal / mol and 5 kcal / mol; the methoxy region appears light red with a weak positive charge, with electrostatic potential values ​​between 5 kcal / mol and 10 kcal / mol. In the surface electrostatic potential diagram of the proline / acetic acid 1:2 combination, the carboxyl region of acetic acid exhibits a distinct red positive charge, with electrostatic potential values ​​between 10 kcal / mol and 20 kcal / mol. This region forms a strong electrostatic attraction with the negatively charged region of curcumin, promoting the formation of hydrogen bonds; the nitrogen-containing heterocyclic region of proline exhibits a blue negative charge and can form an auxiliary electrostatic interaction with the weakly positively charged region of curcumin.

[0052] The eutectic solvent cluster wavefunction file was loaded into the Multiwfn program, and the molecular polarity data was obtained by inputting commands 12 and 0 sequentially. The electrostatic surface area of ​​curcumin molecules ranged from -10 kcal / mol to 20 kcal / mol, indicating strong polarity. This is related to the presence of multiple polar groups (hydroxyl, carbonyl, and methoxy) in its molecular structure. Within this range, the electrostatic surface area of ​​the proline / acetic acid 1:2 combination was also large, showing a high degree of polarity matching with curcumin, which is conducive to electrostatic interactions and hydrogen bond formation between the two.

[0053] Enter command 0 to view the locations of the maxima and minima of the electrostatic potential on the molecular surface. The maxima are typically located on positively charged groups of the molecule, such as the carboxyl hydrogen in acetic acid and the amino hydrogen in proline. The minima are typically located on negatively charged groups of the molecule, such as the hydroxyl oxygen and carbonyl oxygen in curcumin and the heterocyclic nitrogen in proline. The locations of these extrema clearly define the key sites for intermolecular electrostatic interactions, and the distance and relative position between the maxima and minima directly affect the strength of the electrostatic interactions.

[0054] Input commands 9 and ALL consider the van der Waals surfaces corresponding to all atoms, ensuring that the electrostatic potential analysis covers the entire molecular surface and avoids missing key areas. The input statistical range is typically -50 kcal / mol to 50 kcal / mol, evenly divided into 15 intervals. This range covers the electrostatic potential distribution of most molecules, and the 15 intervals accurately reflect the detailed characteristics of the electrostatic potential distribution. Input command 3 outputs the surface area within different electrostatic potential intervals in kcal / mol, and uses Origin software to plot the electrostatic potential distribution histogram. The horizontal axis of the histogram represents the electrostatic potential value, and the vertical axis represents the surface area of ​​the corresponding interval, visually displaying the distribution pattern of the electrostatic potential on the molecular surface. By comparing and analyzing the electrostatic potential distribution histograms of different eutectic solvent combinations with curcumin, the degree of charge polarity matching between the two can be determined. The electrostatic potential distribution of curcumin is mainly concentrated in the -10 kcal / mol to 20 kcal / mol interval, which has the largest surface area. The proline / acetic acid 1:2 combination has the largest peak area in this interval, exceeding 60 Å. 2 This indicates that its charge polarity distribution overlaps most significantly with that of curcumin, resulting in the strongest electrostatic interaction; the highest peak area of ​​the proline / ethylene glycol 1:2 combination in this range is approximately 55 Å. 2 The proline / urea 1:2 combination is only 42 Å. 2 This indicates that its charge polarity matching with curcumin is low, and its electrostatic interaction is weak.

[0055] The execution process and critical point analysis methods for AIM analysis are unclear, making it difficult to obtain accurate topological analysis results. Therefore, AIM analysis is performed using the script AIM.bat in Multiwfn. This script can automatically complete the topological analysis of electron density, including critical point search and property calculation, ensuring the accuracy and efficiency of the analysis process. After running the script, enter the command "aim" in the VMD program and press 0 to enter the search mode to find the properties of the required critical points. A critical point is the point where the electron density gradient is zero. Based on its topological properties, it can be classified into bond critical points, ring critical points, and cage critical points. Among these, the bond critical point (BCP) is crucial for characterizing intermolecular interactions.

[0056] In the Graphical-Representations field, enter the name N and serial number, along with the critical point number you've searched for. Use Photoshop to annotate the critical point numbers to obtain the topological analysis diagram. In the topological analysis diagram, the orange dots represent bond critical points. Analyze the electron density ρ(r) at these bond critical points. BCP ), the Laplace function of electron density ▽ 2 ρ(r BCP and energy density E(r) BCP Parameters such as electron density ρ(r) can reveal the essence of intermolecular interactions. BCP ρ(r) is the core parameter reflecting the strength of the interaction. BCP The larger the value of ∠, the greater the overlap of the electron clouds between the two atoms, and the stronger the interaction; the Laplace function of electron density ∠. 2 ρ(r BCP It can distinguish the type of interaction, when ▽ 2 ρ(r BCP When ∠<0, it indicates that the electron density is concentrated at the bond critical point, belonging to the covalent bond type of interaction; when ∠ ... 2 ρ(r BCP When )>0, it indicates that the electron density is dispersed at the bond critical point, belonging to non-covalent bond types of interactions such as hydrogen bonds and van der Waals forces; energy density E(r) BCP E(r) is the sum of kinetic energy density and potential energy density. BCP When E(r) < 0, it indicates a non-covalent interaction; when E(r) < 0, it indicates a non-covalent interaction. BCP When )>0, it indicates covalent interaction.

[0057] To accurately quantify the strength of hydrogen bonds, the hydrogen bond energy calculation expression BE = -223.08 × ρ(r) is introduced. BCP )+0.7423, where ρ(r) BCP The electron density at the critical point of the bond is ρ(r), expressed in au. The derivation of this formula is based on quantum chemical theory and statistical analysis of extensive experimental data, possessing a solid theoretical foundation and practical basis. From the perspective of quantum chemical theory, the formation of a hydrogen bond is essentially a redistribution of electron density between the donor and acceptor, where ρ(r) represents the electron density at the critical point of the bond. BCP It directly reflects the bonding strength of hydrogen bonds and is the core physical quantity characterizing hydrogen bonding.

[0058] The rationality and effectiveness of this formula have been fully verified by experimental data. For example, for the methanol-acetone hydrogen bond system with known hydrogen bond energies, its critical point electron density ρ(r) can be obtained through quantum chemical calculations. BCPThe calculated hydrogen bond energy obtained by substituting the values ​​into the formula deviates from the experimentally measured value by less than 5%. For water-water hydrogen bond systems, the calculated results also show good agreement with the hydrogen bond energy data reported in the literature. In this invention, the formula can be obtained by using the easily calculated bond critical point electron density ρ(r). BCP This method allows for rapid and accurate prediction of the hydrogen bond energy between eutectic solvents and curcumin, providing a quantitative basis for assessing the stability of their bond. For example, in the hydrogen bond formed between proline / acetic acid 1:2 and curcumin, BCP... 128 N3…H 25 O's ρ(r) BCP The hydrogen bond energy is calculated to be -14.9955 kcal / mol, with a ratio of 0.0705 au. This indicates a strong and stable hydrogen bond. Meanwhile, the BCP formed by proline / urea in a 1:2 ratio with curcumin... 157 O2…H 25 O's ρ(r) BCP The hydrogen bond energy is calculated to be -3.8489 kcal / mol, with a value of 0.0206 au. This indicates that the hydrogen bond is weak and the bonding stability is poor.

[0059] The topological parameters and hydrogen bond energy data obtained through AIM analysis can reveal the nature of the interaction between eutectic solvents and curcumin at the atomic level, providing the most direct theoretical support for screening the optimal solvent system. The order of interaction strength and stability between different eutectic solvent combinations and curcumin is: proline / acetic acid 1:2 > proline / ethylene glycol 1:2 > proline / urea 1:2. This is consistent with the results of IMH and ESP analyses, fully verifying the reliability and accuracy of the screening method.

[0060] The unclear ultrasonic heating vibration parameters and turmeric powder pretreatment method affect the preparation effect and extraction efficiency of the eutectic solvent. Therefore, the ultrasonic heating vibration temperature in step 5 is set at 60℃, which is the optimal temperature determined through extensive experimental optimization. If the temperature is too low, such as below 40℃, the molecular motion rate of hydrogen bond donors and acceptors is slow, resulting in low hydrogen bond network formation efficiency and requiring a longer time to form a homogeneous eutectic solvent. If the temperature is too high, such as above 80℃, some hydrogen bond donors, such as acetic acid, may volatilize, or hydrogen bond donors and acceptors may decompose, disrupting the formation of the eutectic solvent and potentially increasing energy consumption and experimental costs. At 60℃, the molecular motion rate is sufficiently fast to promote rapid hydrogen bond network formation while avoiding solvent volatilization and decomposition, ensuring the quality and stability of the eutectic solvent.

[0061] The pulverization of turmeric powder is performed using a high-speed oscillating pulverizer. This type of pulverizer is characterized by high pulverization efficiency and uniform particle size, enabling the rapid grinding of turmeric roots and stems into fine powder. After pulverization, the powder is passed through a 60-mesh sieve. The 60-mesh sieve has an aperture of 0.25mm, which effectively removes coarse particles, ensuring that the particle size of the turmeric powder is uniform and below 0.25mm. Uniform particle size increases the contact area between the turmeric powder and the eutectic solvent, promoting solvent penetration into the cells and improving the extraction efficiency of curcumin. If the particle size is too large, the solvent will have difficulty penetrating into the cells, resulting in lower extraction efficiency. If the particle size is too small, it may cause the turmeric powder to agglomerate, which also affects the contact area and extraction effect.

[0062] After sieving, the turmeric powder is stored at 4℃ until use. This temperature effectively inhibits the activity of enzymes in the turmeric powder, preventing oxidative degradation and hydrolysis of curcumin, and ensuring the quality stability of the extracted raw material. Curcumin is a polyphenol compound that is sensitive to oxygen and temperature. It is easily oxidized and degraded at room temperature or high temperature, leading to a decrease in its content and loss of activity. The low temperature environment of 4℃ can significantly slow down the rate of oxidative degradation and extend the shelf life of the turmeric powder.

[0063] The water content of the eutectic solvent was set at 20%, chosen based on its solubility and extraction efficiency. Appropriate water content can adjust the viscosity of the eutectic solvent, reducing its viscosity value and improving its fluidity and permeability to turmeric powder. Simultaneously, water can synergistically enhance the solubility of curcumin. Experimental results show that at a water content of 20%, the viscosity of the eutectic solvent is suitable, typically between 100 mPa·s and 500 mPa·s, exhibiting good fluidity and rapid penetration into the turmeric powder, resulting in the strongest solubility for curcumin. If the water content is too low, such as below 10%, the solvent viscosity is high, resulting in poor fluidity, weak permeability, and low extraction efficiency. If the water content is too high, such as above 30%, it will dilute the concentration of the eutectic solvent, disrupt the hydrogen bond network structure, reduce its solubility for curcumin, and may also increase the difficulty of subsequent centrifugation and filtration.

[0064] Unclear parameter settings during the extraction process led to unstable curcumin extraction yield and purity. Therefore, in step 6, 0.5g of freeze-dried curcumin powder was accurately weighed and placed in a 10mL glass sample vial, and 5mL of eutectic solvent was added to ensure a liquid-to-solid ratio of 10:1mL / g. The determination of the liquid-to-solid ratio is based on a balance between solvent usage and extraction efficiency. If the liquid-to-solid ratio is too low, such as 5:1mL / g, the solvent usage is insufficient to fully dissolve the curcumin in the curcumin powder, resulting in low extraction efficiency. If the liquid-to-solid ratio is too high, such as 15:1mL / g, excessive solvent usage, while increasing the extraction rate, increases solvent costs and the workload of subsequent concentration and purification, reducing economic efficiency. A liquid-to-solid ratio of 10:1mL / g ensures a high extraction rate while controlling solvent usage, achieving the optimal balance between extraction efficiency and economic efficiency.

[0065] Ultrasonic extraction was performed in an ultrasonic bath with an ultrasonic power of 150W and a duration of 30 minutes. The selection of ultrasonic power and time was based on the disruptive effect of ultrasound on plant cell walls and the dissolution rate of curcumin. Too low an ultrasonic power (below 50W) resulted in weak mechanical and cavitation effects, making it difficult to effectively disrupt plant cell walls, leading to a slow dissolution rate of curcumin and low extraction efficiency. Too high an ultrasonic power (above 250W) could cause a rapid increase in the temperature of the extraction solution, leading to oxidative degradation of curcumin and potentially damaging the ultrasonic equipment. Too short an ultrasonic time (below 15 minutes) resulted in insufficient extraction and incomplete dissolution of curcumin. Too long an ultrasonic time (above 45 minutes) did not significantly improve extraction efficiency but increased energy consumption and experimental time. The optimal ultrasonic time of 30 minutes and ultrasonic power of 150W effectively disrupted plant cell walls while ensuring complete dissolution of curcumin without oxidative degradation, achieving a balance between extraction efficiency and product quality.

[0066] Centrifugation was performed using a refrigerated centrifuge, with the centrifugation conditions set at 25°C, 7000 rpm, and 15 minutes. The low-temperature environment of 25°C in the refrigerated centrifuge prevents curcumin from undergoing oxidative degradation due to temperature increases during centrifugation, ensuring the stability of curcumin. The selection of centrifugation speed and time was based on the sedimentation efficiency of solid impurities. A speed of 7000 rpm generates sufficient centrifugal force to rapidly sediment solid impurities in the extract, such as plant cell wall fragments and undissolved curcumin powder particles, within 15 minutes, achieving effective separation of the supernatant from the solid residue. If the speed is too low (below 5000 rpm) or the time is too short (below 10 minutes), solid impurities will not settle completely, resulting in a higher concentration of impurities in the supernatant and affecting the accuracy of subsequent content determination. If the speed is too high (above 9000 rpm) or the time is too long (above 20 minutes), although the separation effect can be improved, it will increase equipment energy consumption and operating costs.

[0067] The curcumin content in the supernatant was determined using a UV-5100 ultraviolet spectrophotometer. This model of ultraviolet spectrophotometer features high sensitivity, good stability, and high measurement accuracy, enabling accurate determination of the absorbance of low-concentration curcumin solutions. The measurement wavelength was selected as 425 nm, which is the maximum absorption wavelength of curcumin. At this wavelength, the linear relationship between the absorbance and concentration of curcumin is good, resulting in the highest measurement sensitivity and effectively avoiding interference from other impurities. The sample solution concentration was calculated by converting the absorbance standard curve of curcumin standards. The standard curve was plotted strictly according to standard methods to ensure the accuracy of the concentration conversion.

[0068] Experimental results show that the proline / acetic acid 1:2 eutectic solvent system selected using the method of this invention achieves an extraction yield of 63.19 mg / g for curcumin, significantly higher than the extraction yields of approximately 52.37 mg / g for proline / ethylene glycol 1:2 and approximately 45.82 mg / g for proline / urea 1:2, fully demonstrating the effectiveness and superiority of this screening method. Furthermore, compared to traditional organic solvent extraction methods such as ethanol extraction, which yields approximately 30.5 mg / g, the extraction efficiency of this invention is significantly improved. Moreover, the eutectic solvent possesses advantages such as low volatility, low toxicity, and biodegradability, making it more environmentally friendly and safer, aligning with the development trend of green extraction.

[0069] While various wastewater treatment methods and prediction models have been proposed in the existing technology, a systematic control strategy and accurate prediction methods are still lacking for photovoltaic cell production wastewater with high ammonia nitrogen and low carbon-to-nitrogen ratios, especially for continuous flow processes that rely on hydraulic retention time gradient control to achieve stable partial nitrification. The method and model system involved in this invention are entirely built around the water quality characteristics of photovoltaic wastewater and the actual needs of biological treatment processes. Its core innovation lies in combining a gradient hydraulic retention time control strategy with integrated machine learning prediction, forming a closed-loop system from process control to intelligent prediction. This method not only focuses on actual photovoltaic wastewater treatment scenarios, but also ensures that all control parameters are derived from online monitoring indicators of the continuous flow reactor, eliminating the need for complex chemical additives or reliance on non-water quality-related theoretical models. The constructed Stacking integrated machine learning model uses input features selected from conventional and easily monitored process parameters. The model's structural design and weight fusion mechanism are specifically optimized for the nonlinear and multi-factor coupling characteristics of partial nitrification systems, ensuring prediction accuracy and real-time performance in industrial environments. Furthermore, the intelligent control logic proposed in this invention strictly follows the process mechanism, prioritizing hydraulic retention time as the control variable, and combining it with fine-tuning of dissolved oxygen and pH to achieve rapid and stable control of the effluent quality ratio in a real wastewater treatment system. This entire method demonstrates a high degree of pertinence and integration from process principle to engineering implementation, and is fundamentally different from research based solely on theoretical simulation or general solvent design in terms of technical path, application objects, and engineering implementation.

[0070] In summary, this invention establishes a complete eutectic solvent screening system by organically combining quantum chemical simulation and wavefunction analysis. From molecular model construction, conformation optimization, cluster calculation to wavefunction analysis, each step has clearly defined and reasonable parameters, ensuring the accuracy and reliability of the screening results. The optimal eutectic solvent system screened by this method can significantly improve the extraction efficiency of curcumin, reduce extraction costs, and minimize environmental impact. It provides a universal method for the green and efficient extraction of active ingredients from natural products, possessing significant theoretical and practical application value.

[0071] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A quantum chemical screening method for extracting curcumin, characterized in that, Includes the following steps: S1: Using GaussView, single-molecule models of curcumin, hydrogen bond donor, and hydrogen bond acceptor were constructed respectively. Initial molecular conformations were generated through conformation search. After preliminary optimization, geometric optimization and single-point energy calculation were performed using Gaussian software to obtain molecular structure configuration files. S2: Random clusters are established by optimizing hydrogen bond donors and hydrogen bond acceptors in different molar ratios. The optimal cluster configuration is selected for geometric optimization and single-point energy calculation. Cluster wave function information is obtained and binding energy is calculated. S3: The optimized hydrogen bond donor-hydrogen bond acceptor combination is combined with curcumin molecules to form random clusters. The optimal structural cluster configuration is selected for geometric optimization and single-point energy calculation to obtain cluster wavefunction information. S4: Use the Multiwfn program to perform IGMH analysis, ESP analysis, and AIM analysis on the cluster wavefunction file respectively; S5: Mix the hydrogen bond donor and hydrogen bond acceptor according to the molar ratio in step S2, and prepare a eutectic solvent by ultrasonic heating and vibration; S6: Mix the pulverized and sieved turmeric powder with a eutectic solvent, extract by ultrasonication, centrifuge, and filter to obtain a supernatant containing curcumin.

2. The quantum chemical screening method for extracting curcumin according to claim 1, characterized in that, The hydrogen bond acceptor is proline, and the hydrogen bond donor is acetic acid, ethylene glycol, or urea. The molar ratio of the hydrogen bond acceptor to the hydrogen bond donor is 1:

2.

3. The quantum chemical screening method for extracting curcumin according to claim 1, characterized in that, The conformation search is performed using the gentor function in the MOPAC and molclus programs, generating 100 to 200 initial molecular conformations. The preliminary optimization is performed at the PM6-DH+ level to obtain a gjf file. The Gaussian software performs geometric optimization on the gjf file to obtain an out file. The optimized structure file is saved as a new gjf file. The single-point energy calculation is performed using the DFT functional M06-2X under the basis set def2-TZVP.

4. The quantum chemical screening method for extracting curcumin according to claim 1, characterized in that, The random clusters mentioned in step S2 are established using the molclus program, with a number of 30 to 70 clusters. The optimal cluster configuration is obtained by geometric optimization to get an out file and saved as a new gjf file. The single-point energy calculation is performed using the DFT functional M06-2X under the basis set def2-TZVP to obtain cluster wavefunction information.

5. The quantum chemical screening method for extracting curcumin according to claim 1, characterized in that, The random clusters mentioned in step S3 are established using the molclus program, with a number of 50 to 100 clusters. The optimal cluster configuration is obtained by geometric optimization to get an out file and saved as a new gjf file. The single-point energy calculation is performed using the DFT functional M06-2X under the basis set def2-TZVP to obtain cluster wavefunction information.

6. The quantum chemical screening method for extracting curcumin according to claim 1, characterized in that, The IGMH analysis is performed through subroutine 11 in program 20 of Multiwfn. Option 3 is selected to export the grid data of the δg, δg_inter, δg_intra, and sign(λ2)ρ functions as a cub file. The cub file is imported into the VMD program folder and the command sourceIGM_inter.vmd is entered to obtain the isosurface plot. Option 2 is selected to obtain the output.txt file. The command IGMscatter.gnu is run through the gnuplot program to draw a scatter plot and obtain a PS file. The scatter plot is then displayed in IrfanView with Ghostscript installed.

7. The quantum chemical screening method for extracting curcumin according to claim 1, characterized in that, The ESP analysis was performed using the scripts ESPiso.bat and ESPext.bat in Multiwfn. After running the two scripts, the commands iso and ext were entered sequentially in the VMD program to obtain the surface electrostatic potential map. The specific values ​​of the electrostatic potential at the extreme points of the surface electrostatic potential were marked using Photoshop software. The cluster wavefunction file of the hydrogen bond donor-hydrogen bond acceptor combination was loaded into the Multiwfn program, and the commands 12 and 0 were entered sequentially to obtain the molecular polarity data. The command 0 was entered to view the positions of the maximum and minimum points of the molecular surface electrostatic potential. The commands 9 and all were entered to consider the van der Waals surfaces corresponding to all atoms. The statistical range was entered and evenly divided into 15 intervals. The command 3 was entered to output the surface area in different electrostatic potential intervals in kcal / mol. The electrostatic potential distribution histogram was obtained by plotting using Origin software.

8. The quantum chemical screening method for extracting curcumin according to claim 1, characterized in that, The AIM analysis is performed by the script program AIM.bat in Multiwfn. After running the script program, enter the command aim in the VMD program, press 0 to enter the query mode to find the properties of the required critical points, enter name N and serial and the index of the critical point found in Graphical-Representations, and use Photoshop software to mark the index of the critical point to obtain the topology analysis diagram.

9. The quantum chemical screening method for extracting curcumin according to claim 1, characterized in that, In step S5, the ultrasonic heating vibration temperature is 60℃; the pulverization of the turmeric powder is performed by a swing-type high-speed pulverizer, and after pulverization, it is passed through a 60-mesh sieve. The sieved turmeric powder is stored at 4℃ until it is used; the water content of the eutectic solvent is 20%.

10. The quantum chemical screening method for extracting curcumin according to claim 1, characterized in that, In step S6, the liquid-to-solid ratio of turmeric powder to eutectic solvent is 10:1 mL / g, the ultrasonic extraction temperature is 40℃, and the extraction time is 30 minutes. The centrifugation operation is performed using a refrigerated centrifuge at 25℃, 7000 rpm, and 15 minutes. The curcumin content in the supernatant is determined using a UV spectrophotometer, and the sample concentration is calculated by converting the absorbance standard curve of curcumin standard.

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