Method and system device for evaluating heavy metal or metalloid adsorption on iron mineral surface
By using density functional theory calculations and parameter extraction, the development challenges of heavy metal or metalloid adsorbent materials on the surface of iron minerals have been solved. This enables quantitative comparison and screening of adsorption capacity under different surface conditions, reducing trial-and-error costs and making it applicable to both devices and computer programs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SOUTH CHINA UNIV OF TECH
- Filing Date
- 2026-05-13
- Publication Date
- 2026-07-14
AI Technical Summary
In the existing technology, the development of heavy metal or metal-like adsorbent materials on the surface of iron minerals relies on experimental screening and empirical optimization. This has problems such as high trial and error costs, difficulty in accurately identifying microscopic active sites, and difficulty in separating and evaluating geometric and electronic structural factors. There is also a lack of a unified theoretical framework for systematic comparison and screening.
Density functional theory was used to construct multiple surface state models of iron minerals, identify candidate adsorption sites, and perform spin-polarized density functional theory calculations to extract local geometric parameters and spin-resolved electronic structure parameters. Combined with bonding strength parameters, a systematic evaluation of the adsorption capacity under different surface states was achieved.
It enables quantitative comparison of the adsorption capacity of heavy metals or metalloids under different surface conditions, reduces the trial and error cost in the material development process, provides guidance for surface control and theoretical design, and is applicable to device and computer program implementation.
Smart Images
Figure CN122392756A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of mineral interface chemistry and computational chemistry, specifically relating to a method and system for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals. Background Technology
[0002] Heavy metals and metalloid pollutants are widely found in groundwater, industrial wastewater, mine wastewater, and contaminated soil systems. Typical types include As, Cd, Pb, Zn, Cu, and Cr. Different pollutants can exist as cations, anions, oxyanions, or neutral hydrolyzable species, and their mobility, toxicity, and bioavailability in the environment are closely related to their adsorption and fixation behavior on mineral surfaces.
[0003] Iron minerals are widely used for the control of heavy metal or metalloid pollution due to their abundant surface active sites, strong interfacial coordination ability, and good environmental compatibility. In particular, hematite, maghemite, goethite, and magnetite are considered important pollutant adsorption carriers in both natural environments and engineering systems. However, the adsorption behavior of different pollutants on iron mineral surfaces is influenced by a combination of factors, including surface crystal faces, surface termination mechanisms, the degree of surface hydroxylation, and Fe spin states. The adsorption configuration and adsorption intensity may vary significantly under different surface states.
[0004] In current technologies, the development of heavy metal or metalloid adsorbents mainly relies on experimental screening and empirical optimization, which suffers from high trial-and-error costs, difficulty in accurately identifying microscopic active sites, and difficulty in separating and evaluating geometric and electronic structural factors. In recent years, density functional theory calculations have been widely used to study surface adsorption and interfacial reaction mechanisms, revealing adsorption configurations, adsorption energies, charge transfer, and electronic structure changes at the atomic scale, providing a theoretical basis for material design. However, existing theoretical studies on heavy metal or metalloid adsorption on iron mineral surfaces mostly focus on calculating the adsorption energy of a specific mineral, surface, or adsorbate, or perform qualitative analysis based solely on a single electronic structure descriptor. There is a lack of a method that, within a unified theoretical framework, simultaneously considers surface termination states, protonated states, local geometric site characteristics, spin-resolved electronic structure parameters, and bonding strength parameters, thereby comparing and screening the adsorption capacity of different heavy metals or metalloids on different iron mineral surfaces. Summary of the Invention
[0005] To address the shortcomings and deficiencies of existing technologies, the primary objective of this invention is to provide a method for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals. Based on density functional theory, this method calculates and screens the adsorption capacity of heavy metals or metalloids on the surface of iron minerals, enabling a systematic evaluation of adsorption capacity under different surface conditions and providing guidance for the surface control and theoretical design of iron mineral adsorption materials.
[0006] Another objective of this invention is to provide a system and apparatus for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals.
[0007] The objective of this invention is achieved through the following technical solution:
[0008] A method for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals includes the following steps:
[0009] S1. Construct multiple surface state models of the target mineral surface, determine candidate adsorption sites on each surface state model, and construct initial adsorption structure models of heavy metals or metal-like adsorbates respectively.
[0010] S2. Perform spin polarization density functional theory calculations on the initial adsorption structure model obtained in step S1 to obtain the stable adsorption structure and the corresponding adsorption energy.
[0011] S3. Based on the stable adsorption structure obtained in step S2, extract the local geometric parameters, spin-resolved electronic structure parameters, and bonding strength parameters of the surface metal atoms at the adsorption sites.
[0012] S4. Based on the adsorption energy, local geometric parameters, spin-resolved electronic structure parameters, and bonding strength parameters, the adsorption capacity of heavy metals or metalloids under different surface states is compared to obtain the target mineral surface states and corresponding advantageous adsorption configurations that are conducive to adsorption.
[0013] The flowchart of the above evaluation method for heavy metal or metalloid adsorption on the surface of iron minerals is as follows: Figure 1 As shown.
[0014] Furthermore, the target mineral in step S1 is an iron oxide or an iron hydroxyl oxide. Examples include hematite, maghemite, goethite, or magnetite.
[0015] Furthermore, the multiple surface state models mentioned in step S1 include two or more of the following: metal-terminated surface model, oxygen-terminated surface model, protonated metal-terminated surface model, and protonated oxygen-terminated surface model.
[0016] Furthermore, the candidate adsorption sites mentioned in step S1 include one or more of the following: surface low-coordination metal sites, adjacent metal site pairs, surface hydroxyl sites, and bridging oxygen-related sites.
[0017] Further, the heavy metal or metalloid adsorbate mentioned in step S1 includes one or more of the following: cations, anions, oxyanions, and neutral hydrolyzable species of metals such as As, Cd, Pb, Zn, Cu, or Cr. Preferably, it includes one or more of the following: cations, anions, oxyanions, or neutral hydrolyzable species of As(III), As(V), Cd(II), Pb(II), Zn(II), Cu(II), Cr(III), or Cr(VI). For example, the As(III) form can be constructed using H3AsO3 to establish the initial adsorption structure model, and the As(V) form can be constructed using H2AsO4. − or HAsO4 2− Construct an initial adsorption structure model; the Cr(VI) form can be HCrO4. - or CrO4 2- An initial adsorption structure model was constructed. Similar methods were used for other heavy metal or metalloid adsorbates, employing representative species of each metal species to construct the initial adsorption structure model.
[0018] Furthermore, the spin polarization density functional theory calculation in step S2 includes: using the PBE functional in the generalized gradient approximation to handle exchange correlation; using the projected fused wave (PAW) method to handle electron-ion interactions; applying DFT+U correction to the metal 3d electrons; and performing geometric optimization and electronic structure self-consistent calculation on the initial adsorption structure model under the set plane wave cutoff energy, k-point grid, and convergence criteria.
[0019] Furthermore, the adsorption energy in step S2 is calculated according to the following formula:
[0020]
[0021] Among them, E ads E is the adsorption energy. slab+ads E represents the total energy of the adsorption system. slab E represents the total energy of the clean surface model. adsorbate E represents the total energy of the isolated adsorbate; when adsorption is accompanied by ligand exchange and the formation of small molecules, the E... slab+ads This includes the energy contribution that generates the small molecule.
[0022] Furthermore, the local geometric parameters mentioned in step S3 include one or more of the following: coordination number of surface metal atoms, spacing between adjacent active metal sites, bridging bond angle, number of adjacent metal site pairs that can participate in cooperative coordination, surface hydroxyl information, and accessibility of surface active sites.
[0023] Furthermore, the spin-resolved electronic structure parameters mentioned in step S3 include one or more of the following: magnetic moments of surface metal atoms, majority spin d-band centers, minority spin d-band centers, Bader charge, and projected density of states.
[0024] The majority and minority spin d-band centers are calculated according to the following formulas:
[0025]
[0026] in, It represents the majority or minority of spin d-band centers, and DOS(E) is the density of d states of the surface metal atoms in the corresponding spin channel (majority or minority spin channel).
[0027] Furthermore, the bonding strength parameter mentioned in step S3 includes COHP, ICOHP, or other parameters that can characterize the bonding strength between the adsorbate and the surface metal atoms.
[0028] Furthermore, the method for comparing the adsorption capacity of heavy metals or metalloids under different surface states in step S4 is as follows: taking the adsorption energy value as the standard, the surface state and configuration with the most negative value are the target mineral surface states and corresponding advantageous adsorption configurations that are conducive to adsorption; comparing the differences in local geometric parameters, spin-resolved electronic structure parameters and bonding strength parameters, the factors with larger differences are the dominant factors that determine the adsorption energy.
[0029] An evaluation system for the adsorption of heavy metals or metalloids on the surface of iron minerals includes a model building module, an adsorption energy calculation module, a geometric and electronic structure calculation module, and a comparison output module. The model building module is used to implement step S1 of the above evaluation method, the adsorption energy calculation module is used to implement step S2, the geometric and electronic structure calculation module is used to implement step S3, and the comparison output module is used to implement step S4.
[0030] An evaluation device for the adsorption of heavy metals or metalloids on the surface of iron minerals includes a processor and a memory. The memory stores the aforementioned evaluation system, and the processor executes the evaluation system to evaluate the adsorption of heavy metals or metalloids on the surface of iron minerals.
[0031] Compared with the prior art, the beneficial effects of the present invention are:
[0032] (1) This invention incorporates surface termination state and protonation state into a unified calculation framework, enabling quantitative comparison of different surface states and different heavy metal or metalloid adsorbate systems on the same iron mineral surface. This solves the problem in the prior art that it is difficult to systematically compare the effects of different surface termination states, different protonation states, and different adsorbate types on adsorption capacity.
[0033] (2) This invention not only considers the adsorption energy, but also introduces local geometric parameters, spin-resolved electronic structure parameters and bonding strength parameters, thereby distinguishing the relative roles of geometric structure effects and electronic structure effects in the adsorption process, providing a basis for explaining the differences in adsorption configuration and adsorption strength, and enabling a more comprehensive evaluation of adsorption capacity.
[0034] (3) The present invention can theoretically screen potential favorable surface states and potential favorable adsorption objects before the experiment, thereby reducing the trial and error costs in the material development process.
[0035] (4) This invention is applicable to devices, electronic devices and computer programs, and has good engineering feasibility and promotional value. Attached Figure Description
[0036] Figure 1 This is a flowchart of the method for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals according to the present invention.
[0037] Figure 2 This is a schematic diagram of the target mineral surface model under different surface conditions in Example 1.
[0038] Figure 3 This is a schematic diagram of the surface geometry of the target mineral under different surface conditions in Example 1.
[0039] Figure 4 This is a graph showing the comparison of the bonding strength of adsorption bonds under different surface states in Example 1. Detailed Implementation
[0040] The present invention will be further described in detail below with reference to the embodiments and accompanying drawings, but the embodiments of the present invention are not limited thereto.
[0041] Example 1
[0042] This embodiment uses the hematite (α-Fe2O3) (001) surface as the research object to quantitatively evaluate and screen the adsorption capacity of As(III) and As(V) under different surface termination and protonation states. The specific process steps are as follows:
[0043] S1. First, the bulk crystal structure of hematite is obtained and optimized. Based on the optimized bulk structure, a surface supercell model is constructed by cutting along the (001) crystal plane. In this embodiment, a (2×2) surface supercell is used, a 15 Å vacuum layer is set, and several atomic layers at the bottom are fixed to simulate bulk phase constraint. Based on this, four surface state models are constructed, namely the protonated O-terminated surface model (OHT, Figure 2 a) O-terminated surface model (OT, Figure 2 (b) Protonated Fe Termination Surface Model (FeHT) Figure 2c) and Fe-terminated surface model (FeT, Figure 2 (d). Subsequently, candidate adsorption sites were identified on four surface state models, including surface low-coordination Fe sites, adjacent Fe–Fe active site pairs, surface hydroxyl sites, and bridging oxygen-related sites. H3AsO3 was used as the representative species of As(III), and H2AsO4 was used as the representative species. − As representative species of As(V), H3AsO3 and H2AsO4 were constructed by introducing charge compensation of the ions. − This is the initial adsorption structure model of the adsorbate.
[0044] S2. The initial adsorption structure model described above is imported into a density functional theory (DFT) calculation program. Spin-polarized DFT is used for geometric optimization and self-consistent electronic structure calculations. In the specific implementation, the exchange-correlation functional is in PBE form, and the electron-ion interaction is performed using the PAW method. DFT+U correction is applied to Fe 3d electrons, with an effective U value set to 4. The plane wave cutoff energy is set to 500 eV, the surface model uses a 3×3×1 k-point grid, and the electron convergence criterion is set to 10. -5 eV, with the ion relaxation convergence standard set to 0.02 eV / Å, and the initial magnetic moment of Fe atoms set to ±5 μB. Calculate the total energy of each adsorption system, the total energy of the clean surface model, and the total energy of isolated arsenic species, and calculate the adsorption energy according to the following formula:
[0045]
[0046] Among them, E ads E is the adsorption energy. slab+ads E represents the total energy of the adsorption system. slab E represents the total energy of the clean surface model. adsorbate This represents the total energy of the isolated adsorbate.
[0047] The results show that the dominant adsorption states of H3AsO3 on the four surface state models (OHT, OT, FeHT, and FeT) have adsorption energies of -1.22 eV, -1.69 eV, -1.21 eV, and -2.62 eV, respectively; H2AsO4 − The adsorption energies of the dominant adsorbed states on the four surface state models are -2.54 eV, -2.88 eV, -2.65 eV and -3.51 eV, respectively.
[0048] S3. Based on the optimized stable adsorption structure of S2, local geometric parameters, spin-resolved electronic structure parameters, and bonding strength parameters of the surface Fe at the adsorption sites are extracted. The local geometric parameters include: coordination number of surface Fe, spacing between adjacent active Fe–Fe sites, Fe–O–Fe bridging angle, number of adjacent Fe site pairs capable of cooperative coordination, surface hydroxyl information, and accessibility of surface active sites. For example... Figure 3 As shown, Figure 3 In the diagram, a and b represent OHT and OT, respectively, and the difference between the Fe-O-Fe bond angle and the Fe-Fe spacing at the O site on the surface is small. Figure 3 In this context, c and d represent FeHT and FeT, respectively. FeHT has only two tricoordinate Fe sites on its surface, surrounded by a dense hydroxyl network. FeT has four tricoordinate Fe sites on its surface, with smaller spacing between adjacent Fe-Fe atoms and no hydroxyl network, which is more conducive to a multidentate complex configuration. Therefore, OHT and OT have small geometric differences, but larger geometric differences compared to FeHT and FeT, and FeHT and FeT have even larger geometric differences. The spin-resolved electronic structure parameters include: the magnetic moment of the surface Fe, the majority spin d-band center (d_ma), the minority spin d-band center (d_mi), the Bader charge, and the projected density of states characteristics. By calculating the surface Fe magnetic moment, the first layer Fe magnetic moment of OHT is 4.20 μB, indicating a high-spin state; the first layer Fe magnetic moment of OT is 2.74 μB, indicating a low-spin state. The magnetic moment changes of FeHT and FeT compared to OHT are not significant. The majority spin d-band centers and minority spin d-band centers are calculated using the following formulas:
[0049]
[0050] in, It represents the majority or minority of spin d-band centers, and DOS(E) is the density of d states of the surface metal atoms in the corresponding spin channel (majority or minority spin channel).
[0051] The bonding strength parameters include the Fe-O bond COHP and ICOHP parameters between the adsorbate and the surface Fe, such as... Figure 4 As shown, Figure 4 In Figure 'a', we see COHP images of the first layer Fe-O bond formed by the adsorption configuration of H3AsO3 in OHT and OT. It can be seen that the bonding mainly originates from the Fe 3d-O 2p orbitals, and the antibonding state occupancy near the Fermi level in OT is significantly reduced compared to OHT. Figure 4Table b shows the spin-resolved ICOHP data for all Fe-O bonds formed between surface Fe and O in H3AsO3 on the four surfaces OHT, OT, FeHT, and FeT. Combined with the small geometric difference obtained from S3, it can be concluded that for OHT and OT, the majority spin-electronic structure is the dominant factor in the difference in adsorption energy between the two. Figure 4 As can be seen from b, the ICOHP adsorption on FeHT is far inferior to that on OHT, but the adsorption energy on FeHT is close to that on OHT; the ICOHP adsorption on FeT is close to that on OT, with no obvious advantage, but its adsorption energy is much greater than that on OHT.
[0052] S4. After comparing the adsorption energy, local geometric parameters, spin-resolved electronic structure parameters, and bonding strength parameters under four surface state models, the method described in this invention can simultaneously achieve the following functions:
[0053] First, the adsorption capacity of arsenic under different surface states was quantitatively ranked based on adsorption energy; H3AsO3 and H2AsO4 − The adsorption capacity from strongest to weakest is: FeT > OT > FeHT ≈ OHT.
[0054] Secondly, it can distinguish the relative contributions of geometric structure and spin-electronic structure to adsorption energy; based on the previous analysis, it can be concluded that geometric structure plays a decisive role, while spin-electronic structure plays a fine-tuning role.
[0055] Third, it outputs the target surface states and corresponding dominant adsorption configurations that are favorable for arsenic adsorption. Using adsorption energy values as the standard, the surface state and configuration with the most negative values represent the target surface state and dominant adsorption configuration. For the adsorption of H3AsO3 on the four surface state models OHT, OT, FeHT, and FeT, the target surface state is FeHT, and the dominant adsorption configuration is a bidentate binuclear configuration.
[0056] Example 2
[0057] This embodiment is used to illustrate the applicability of the method of the present invention to other iron minerals and other heavy metals or metalloid adsorbates.
[0058] When the target iron mineral surface is selected as maghemite, goethite or magnetite, the corresponding surface state model can be established according to the same steps S1 as in Example 1 based on the corresponding crystal plane structure.
[0059] When the adsorbate is selected as Cd(II), Pb(II), Zn(II), Cu(II), Cr(III), As(V), or Cr(VI), an initial adsorption structure model can be established according to the charge type and species morphology of the adsorbate. The specific construction method is as follows:
[0060] For cations such as Cd(II), Pb(II), Zn(II), Cu(II) or Cr(III), the candidate adsorption sites identified on the surface state model can preferentially be constructed near low-coordination metal sites, surface hydroxyl sites and bridging oxygen-related sites to build a direct coordination adsorption model.
[0061] For oxyanions such as As(V) or Cr(VI), an inner-layer complexation model formed by surface hydroxyl ligand exchange can be preferentially constructed; for neutral species such as As(III), a direct coordination or ligand exchange adsorption model can be constructed based on the properties of the surface sites.
[0062] Subsequently, the spin polarization density functional theory calculations in step S2 were performed according to the method in Example 1. Based on the adsorption energy, local geometric parameters, spin-resolved electronic structure parameters, and bonding strength parameters, the adsorption capacity of different adsorbate systems under different iron mineral surface states was quantitatively compared and screened.
[0063] Example 3
[0064] A system and apparatus for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals. The system includes a model building module, an adsorption energy calculation module, a geometric and electronic structure calculation module, and a comparison output module. The model building module is used to implement step S1 of the evaluation method in Example 1 or Example 2, the adsorption energy calculation module is used to implement step S2, the geometric and electronic structure calculation module is used to implement step S3, and the comparison output module is used to implement step S4.
[0065] The device includes a processor and a memory. The memory stores the aforementioned system. The processor executes the system program to quantitatively compare and screen the adsorption capacity of different adsorbate systems under different iron mineral surface conditions.
[0066] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.
Claims
1. A method for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals, characterized in that... The process includes the following steps: S1. Construct multiple surface state models of the target mineral surface, determine candidate adsorption sites on each surface state model, and construct initial adsorption structure models of heavy metals or metal-like adsorbates respectively. S2. Perform spin polarization density functional theory calculations on the initial adsorption structure model obtained in step S1 to obtain the stable adsorption structure and the corresponding adsorption energy. S3. Based on the stable adsorption structure obtained in step S2, extract the local geometric parameters, spin-resolved electronic structure parameters, and bonding strength parameters of the surface metal atoms at the adsorption sites. S4. Based on the adsorption energy, local geometric parameters, spin-resolved electronic structure parameters, and bonding strength parameters, the adsorption capacity of heavy metals or metalloids under different surface states is compared to obtain the target mineral surface states and corresponding advantageous adsorption configurations that are conducive to adsorption.
2. The method for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals according to claim 1, characterized in that: The target mineral in step S1 is iron oxide or iron hydroxyl oxide, and the multiple surface state models include two or more of the following: metal-terminated surface model, oxygen-terminated surface model, protonated metal-terminated surface model, and protonated oxygen-terminated surface model.
3. The method for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals according to claim 1, characterized in that: The candidate adsorption sites mentioned in step S1 include one or more of the following: surface low-coordination metal sites, adjacent metal site pairs, surface hydroxyl sites, and bridging oxygen-related sites. The heavy metal or metalloid adsorbate includes one or more of the following: cations, anions, oxygen-containing anions, and neutral hydrolyzable species of metals such as As, Cd, Pb, Zn, Cu, or Cr.
4. The method for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals according to claim 1, characterized in that: The spin polarization density functional theory calculation in step S2 includes: using the PBE functional in the generalized gradient approximation to handle exchange correlation; using the projected fused wave method to handle electron-ion interactions; applying DFT+U correction to the metal 3d electrons; and performing geometric optimization and electronic structure self-consistent calculation on the initial adsorption structure model under the set plane wave cutoff energy, k-point grid, vacuum layer thickness, and convergence criteria.
5. The method for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals according to claim 4, characterized in that: The adsorption energy in step S2 is calculated according to the following formula: , of which E ads E is the adsorption energy. slab+ads E represents the total energy of the adsorption system. slab E represents the total energy of the clean surface model. adsorbate E represents the total energy of the isolated adsorbate; when adsorption is accompanied by ligand exchange and the formation of small molecules, the E value is... slab+ads This includes the energy contribution that generates the small molecule.
6. The method for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals according to claim 1, characterized in that: The local geometric parameters mentioned in step S3 include one or more of the following: coordination number of surface metal atoms, spacing between adjacent active metal sites, bridging bond angle, number of adjacent metal site pairs that can participate in cooperative coordination, surface hydroxyl information, and accessibility of surface active sites; The spin-resolved electronic structure parameters include one or more of the following: magnetic moments of surface metal atoms, majority spin d-band centers, minority spin d-band centers, Bader charge, and projected density of states; the bonding strength parameters include COHP, ICOHP, or other parameters that can characterize the bonding strength between the adsorbate and surface metal atoms.
7. The method for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals according to claim 6, characterized in that: The majority and minority spin d-band centers are calculated according to the following formulas: ,in, This indicates the majority or minority of spin d-band centers, and DOS(E) is the density of d states of the metal atoms in the lower surface layer of the corresponding spin channel.
8. The method for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals according to claim 1, characterized in that: The method for comparing the adsorption capacity of heavy metals or metalloids under different surface states in step S4 is as follows: using the adsorption energy value as the standard, the surface state and configuration with the most negative value are the target mineral surface states and corresponding advantageous adsorption configurations that are conducive to adsorption; compare the differences in local geometric parameters, spin-resolved electronic structure parameters and bonding strength parameters, and the factors with larger differences are the dominant factors that determine the adsorption energy.
9. A system for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals, characterized in that: It includes a model building module, an adsorption energy calculation module, a geometric and electronic structure calculation module, and a comparison output module; the model building module is used to implement step S1 of the evaluation method according to any one of claims 1-8, the adsorption energy calculation module is used to implement step S2, the geometric and electronic structure calculation module is used to implement step S3, and the comparison output module is used to implement step S4.
10. A device for evaluating the adsorption of heavy metals or metalloids on the surface of iron minerals, characterized in that: It includes a processor and a memory, wherein the memory stores the evaluation system of claim 9, and the processor executes the evaluation system to evaluate the adsorption of heavy metals or metalloids on the surface of iron minerals.