Density Functional Theory Studies on Boron Nitride and Silicon Carbide Nanoclusters Functionalized with Amino Acids for Organophosphorus Pesticide Adsorption

Abstract

This study compares the properties of B₁₂N₁₂ and Si₁₂C₁₂ nanoclusters functionalized with tyrosine in the adsorption of organophosphorus pesticides, focusing on adsorption energy and electronic stability. The results indicate that B₁₂N₁₂/tyrosine exhibits more negative adsorption energies than Si₁₂C₁₂/tyrosine, suggesting stronger interactions and higher adsorption stability. Additionally, B₁₂N₁₂ demonstrates higher ionization energy and chemical hardness, enhancing its electronic stability during the adsorption process. In contrast, Si₁₂C₁₂ has higher electrophilicity and maximum electron transfer capacity, leading to greater variability in adsorption energy and more flexible electronic structure adjustments. These findings suggest that B₁₂N₁₂ nanoclusters have greater potential and application value as adsorption materials, particularly when modified with tyrosine. B₁₂N₁₂/tyrosine demonstrates higher stability and predictability in pesticide adsorption, making it more suitable for related applications.

1. Introduction

Nanozymes exhibit excellent stability and enhanced catalytic activity, providing a solid foundation for their applications in environmental pollutant monitoring and remediation [1]. The use of nanomaterials as novel carriers for enzyme delivery and the regulation of enzyme activity has sparked significant interest in the field of nanobiotechnology for biomedical applications [2]. Currently, nanostructure-based drug delivery systems represent one of the most compelling areas of research. Among these, boron nitride nanoclusters show promise as drug carriers for targeted delivery systems.

Previous studies have investigated the interaction mechanisms between the drug Anagrelide (AG) and B₁₂N₁₂, as well as Al- and Ga-doped B₁₂N₁₂ nanocages in both gaseous and aqueous media using DFT with the B3LYP/6-31G(d,p) method [3]. Additionally, a comprehensive computational study was conducted using density functional theory (DFT) to investigate the adsorption of the drug molecule penicillamine (PCA) on Al- and Ga-doped B₁₂N₁₂ nanoclusters in aqueous and chloroform environments. The PCA molecule interacts effectively with Al- and Ga-doped clusters through its four nucleophilic sites: amine, carbonyl, hydroxyl, and thiol groups. The computational results indicate that the most stable adsorption complex is achieved when the PCA molecule is adsorbed through its amine group. The adsorption of the PCA molecule reduces the HOMO-LUMO gap and overall hardness of the doped clusters, confirming an increase in the reactivity of the considered clusters for drug delivery purposes [4].

Based on Celaya et al.’s research findings, boron nitride and some of its mixed nanoclusters have the potential to serve as nanocarriers for the targeted delivery of the drug melphalan [5]. Using density functional theory (DFT) calculations, the interactions between Cu/Zn-doped boron nitride nanocages and the anticancer drug mercaptopurine (MP) were investigated, leading to the design of a novel drug carrier. The adsorption of MP on Cu/Zn-doped boron nitride nanocages was found to be appropriate [6]. Hachem et al. employed density functional theory (DFT) calculations using the PBE1 functional to investigate the structural, electronic, and spectral properties of B₁₆N₁₆, B₁₅GeN₁₆, and B₁₅SiN₁₆ nanocages modified with sulfasalazine (SSZ). When SSZ is adsorbed onto the B₁₆N₁₆, B₁₅GeN₁₆, and B₁₅SiN₁₆ nanocages through its pyridine ring, the system attains its most stable state. Furthermore, the adsorption of SSZ via the SO₂ group on the B₁₆N₁₆, B₁₆GeN₁₆, and B₁₆SiN₁₆ nanocages results in lower binding energy and an increased dipole moment, both of which can enhance the sensitivity of the B₁₆GeN₁₆ nanocage to the drug [7]. Recently, boron nitride (BN) nanoparticles have been identified as promising candidates for drug delivery systems. Cao et al.’s studies utilized density functional theory (DFT) and time-dependent density functional theory (TDDFT) models with B3LYP and B3PW91 functionals to investigate the loading of curcumin (CUR) in various potential states onto platinum-functionalized B₁₂N₁₂ nanocages, aiming to enhance its solubility and stability. The strong binding of CUR to Pt-B12N12 can be attributed to the substantial charge transfer from CUR to the cage, resulting in significant changes in the dipole moment and energy gap [8].

In Hasan et al.’s studies, the adsorption mechanisms of the Favipiravir (FPV) molecule on the pristine, Zn-functionalized, and Ni-functionalized B₁₂N₁₂ (BN, Zn f-BN, and Ni f-BN) nanocages’ exterior surfaces were investigated using DFT/QTAIM methods and the B3LYP/6-31G(d,p) method. Adsorption energy data indicate that the functionalized BN adsorbents can very effectively adsorb the FPV drug compared to the pristine adsorbents. The reduction in the HOMO-LUMO gap by up to 67.79% suggests that the drug can be promisingly detected through the generated electrical signal in the case of f-BN nanocages [9]. Hossain et al.’s research using the DFT B3LYP/6-31G (d,p) level of theory revealed that the Ni-B12N12 nanocage is the most prominent nanostructure for forming drug delivery complexes to transport chlorocholine (CM) drugs. This nanocage exhibits the most stable adsorption of CM drugs in both gas and aqueous media. The interaction between the CM drug and the nanostructure was also confirmed through frontier molecular orbital and QTAIM analyses [10].

Soltani et al.’s studies reported the adsorption of neutral and zwitterionic forms of serine on pristine and Pt-modified B₁₂N₁₂ fullerenes using density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations. The binding energies of serine on the fullerene surface were investigated through its hydroxyl (-OH), carboxyl (-COOH), and amine (-NH2) functional groups. According to the analysis using the M06-2X functional, the binding energy of the zwitterionic form of serine on the B₁₂N₁₂ fullerene is less stable than that of the neutral form. The results indicate that the most stable chemisorption state of serine occurs through its amino group interacting with the Pt-modified B₁₂N₁₂ fullerene. Additionally, the conductivity of the B₁₂N₁₂ and Pt-modified B₁₂N₁₂ fullerenes is influenced by band gap changes when serine is adsorbed on their external surfaces [11].

In Feng et al.’s studies, computational simulation methods were employed to investigate the removal of organic pollutants from wastewater. Researchers selected two types of nanoporous boron nitride (BN) materials, B₁₆N₁₆ and B₁₂N₁₂, as adsorbents. They conducted a comprehensive evaluation of pure boron nitride adsorbents and glycine-functionalized boron nitride adsorbents (B₁₆N₁₆(Glycine)₂/B₁₂N₁₂(Glycine)₂) to understand the mechanisms and adsorption capacities for removing two different emerging pollutants. Particularly, the study examined the adsorption capacity and potential changes in spectral and electronic properties (including frontier molecular orbitals, energy gap (ΔE_GAP), chemical softness (σ), and hardness (η)) of the pure and functionalized boron nitride adsorbents before and after the adsorption process. The results indicated that the functionalization of nanospheres of boron nitride with glycine enhances the affinity for pollutant adsorption [12].

BN-based materials exhibit satisfactory adsorption capacities for inorganic pollutants, such as heavy metal ions, and organic pollutants, including dyes and pharmaceutical molecules. The interaction mechanisms between pollutants and BN-based materials primarily involve surface complexation, π–π stacking, and electrostatic interactions [13]. Onsori and Alipour’s research utilizing density functional theory (DFT) calculations explored the reactivity and electronic sensitivity of synthesized B₁₂N₁₂ nanoclusters towards the anticancer drug cisplatin (CP). Upon adsorption of the CP drug, the conduction level of the BN nanoclusters stabilizes significantly, and the valence state shifts to higher energy levels. As a result, the HOMO-LUMO gap decreases notably. Consequently, the BN nanoclusters transform into semiconductors with higher conductivity following the adsorption process. The increase in conductivity can generate electrical signals that facilitate the detection of CP drugs. Furthermore, UV-vis calculations indicate the emergence of a strong peak in the visible region after CP drug adsorption, which aids in drug detection [14]. Kamali et al.’s research aimed to investigate the adsorption of the drug metformin (MF) on fullerene-like nanoclusters of boron nitride (B₂₄N₂₄), aluminum nitride (Al₂₄N₂₄), aluminum phosphide (Al₂₄P₂₄), and boron phosphide (B₂₄P₂₄) in both gas and solvent (water) phases using the B3PW91/6-311G(d,p) theoretical level. A comparison of the HOMO-LUMO energy gaps in the pure nanoclusters indicated that the electronic properties of B₂₄N₂₄ nanoclusters can be enhanced by doping with aluminum and phosphorus atoms, compared to the other nanoclusters studied. A thermodynamic analysis revealed that the interaction between MF and the nanoclusters is exothermic and spontaneous [15].

Zhiani employed quantum mechanical methods to investigate the adsorption and binding characteristics of five different amino acids—namely alanine (Ala), arginine (Arg), asparagine (Asn), histidine (His), and cysteine (Cys)—on the surfaces of graphene (Gra) and boron nitride (BN) nanosheets from a molecular perspective. Density functional theory (DFT) and DFT-D3 calculations were used to study the electronic properties and dispersion interactions of the amino acid/adsorbent complexes. The calculations indicated that the polarity of BN nanosheets provides a stronger affinity for the amino acids [16]. In Ghasemi et al.’s studies, density functional theory (DFT) calculations were conducted using B3LYP-D and PW91-D functionals to examine the adsorption behavior and detection of metformin on the outer surface of both pure and nitrogen-doped boron nitride (BN) fullerenes. The results indicated that the NH group of the metformin single bond can chemisorb onto the boron atoms of B₁₂N₁₂ and B₁₆N₁₆ fullerenes. The GeB₁₁N₁₂ biosensor demonstrates significant potential for the determination of metformin in environmental systems [17].

Wu et al.’s theoretical studies have demonstrated that incorporating carbon into (BN)₁₂ fullerene makes the hydrogenation reaction on carbon-doped B₁₁N₁₂C clusters thermodynamically favorable and kinetically feasible under ambient conditions. Without the use of metal catalysts, carbon atoms can serve as active centers for dissociating H₂ molecules and provide free hydrogen atoms for further hydrogenation on B₁₁N₁₂C fullerene, thereby reducing material costs in practical hydrogen storage applications [18]. Esrafili’s studies utilizing density functional theory (DFT) investigated the adsorption and catalytic decomposition of N₂O molecules on boron nitride nanocages resembling fullerenes (B₁₂N₁₂). The research revealed that the electron-donating properties of the nanocage play a crucial role in the adsorption and activation of N₂O. By incorporating carbon atoms into the B₁₂N₁₂ cluster, the results demonstrated that B₁₁N₁₂C or B₁₂N₁₁C exhibits stronger adsorption of N₂O compared to the original B₁₂N₁₂ [19]. Yoosefian et al. calculated the drug-loading capacity of carboxylated carbon nanotubes functionalized with benzylhydrazine for use as nanocarriers for levodopa. In this context, it is crucial to evaluate all adsorption characteristics of the most stable conformer of benzylhydrazine molecules on carboxylated carbon nanotubes. To determine the minimum energy conformer of benzylhydrazine, first-principles quantum mechanical calculations were conducted on the molecular structure, and a conformational analysis of 512 possible isomers was performed. A novel and easily prepared formulation of benzylhydrazine/carboxylated carbon nanotube conjugates was developed, demonstrating high drug-loading efficiency for levodopa, intended for the treatment of Parkinson’s disease [20].

This study aims to investigate the stability and electronic structural properties of B₁₂N₁₂ and Si₁₂C₁₂ nanoclusters functionalized with amino acids during the adsorption of organophosphorus (OP) pesticides. By analyzing adsorption energy, ionization energy, chemical hardness, electronegativity, and other electronic structural parameters, we seek to elucidate the superior performance of B₁₂N₁₂ nanoclusters with tyrosine in the adsorption process and compare it with the stability and electronic structural adaptability of Si₁₂C₁₂ nanoclusters with tyrosine. Specifically, this research employs systematic density functional theory (DFT) calculations to explore the energy changes and electronic characteristics of these nanoclusters when adsorbing OP pesticides, thereby determining the potential application value of B₁₂N₁₂ nanoclusters functionalized with amino acids in practical scenarios.

2. Computational Details

The theoretical analysis and methods employed in this work utilized density functional theory (DFT). Energy calculations and geometry optimizations were conducted using the Dmol³ program package within the Material Studio software [21,22]. The 3D structures of eight pesticides—parathion (Par), coumaphos (Cou), diazinon (Dia), naled (Nal), trichlorfon (Tri), sulfotep (Sul), isazofos (Isa), and chlorethoxyfos (Chl)—were obtained from PubChem. These organophosphate insecticides function as EC 3.1.1.7 (acetylcholinesterase) inhibitors. Interaction energies for the binding of these eight pesticides with various configurations of B₁₂N₁₂ and Si₁₂C₁₂ nanoclusters, modified with tyrosine at different positions (Tyr124 and Tyr337) in the crystal structure of human acetylcholinesterase (PDB: 4PQE), were calculated. The X-ray crystal structure of human acetylcholinesterase (EC 3.1.1.7) (PDB: 4PQE) was retrieved from the RCSB Protein Data Bank (www.rcsb.org, accessed on 13 June 2021) as the starting geometry.

The generalized gradient approximation (GGA) using the Perdew–Burke–Ernzerhof (PBE) functional [23] was selected to compute the exchange–correlation functional of the Hamiltonian operator, utilizing a double numerical plus D-functions (DND) basis set. For adsorption complexes, the basis set incorporated the relativistic effective core potential (ECP) with small-core pseudopotentials. Furthermore, the conductor-like screening model (COSMO) [24,25] was employed to account for solvent effects under aqueous conditions, with the dielectric constant ε set to 78.54, corresponding to water. The self-consistent field (SCF) convergence criteria were established at 1.0 × 10⁻⁵ Ha for energy and 1.0 × 10⁻³ Ha/Å for forces. A smearing value of 0.005 Ha was applied for electronic occupation to facilitate convergence. The total energy for each optimized structure was calculated, followed by a frontier molecular orbital (FMO) analysis to determine the HOMO and LUMO energies. The ionization potential and electron affinity calculated using the HOMO and LUMO with the PBE functional are only qualitatively accurate. Further refinement and additional computational methods may be necessary for quantitatively precise results.

Conceptual density functional theory (CDFT) [26,27] offered insights into valuable descriptors, including the following:

Energy gap (GAP): GAP = E_LUMO − E_HOMO.

Ionization potential (I): I = −E_HOMO.

Electron affinity (A): A = −E_LUMO.

Electronegativity (χ): χ = (I + A)/2.

Chemical hardness (η): η = (I − A)/2.

Softness (S): S = 1/2η.

Electrophilicity index (ω): ω = χ²/2η.

Maximum amount of electronic charge (ΔN_max): ΔN_max = χ/η.

The AutoDock Vina Extended SAMSON Extension was used to dock the organophosphorus pesticides with the AChE enzyme. In this docking procedure, the search domain was based on the entire size of the AChE enzyme. The grid’s center was set to ensure the grid box covered the entire enzyme, with default values used for other parameters. From the first 200 docking poses, the best candidate was selected according to the standard scoring function (Figure 1a,b) [28].

The molecular modeling involved combining Tyr124 and Tyr337 from the crystallographic structure of AChE with eight different OPs. After docking AChE with the OPs, the structures of Tyr124_OP and Tyr337_OP, which form hydrogen bonds with OP, were extracted and used as the starting structures for modification with nanoclusters, followed by structural optimization (Figure 2 and Figure 3). The distance between the hydrogen atom on the amino group of tyrosine (Tyr337) and the nitrogen atom of the B12N12 nanocluster ranged from 2.8 to 3.5 Å. Functionalizing B12N12 (boron nitride fullerene) with the amino acid tyrosine is particularly effective due to several chemical characteristics. B12N12’s high surface reactivity and the electron deficiency of its boron atoms make it highly receptive to nucleophilic attack by tyrosine’s phenolic hydroxyl group. The ability of tyrosine to form hydrogen bonds with the nitrogen atoms in B12N12 and engage in π–π interactions with the boron nitride rings further stabilizes the functionalization. Additionally, the polar nature of both tyrosine and B12N12 allows for favorable dipole–dipole interactions, while their size compatibility ensures effective bonding.

The total energy of the optimized structures of the B₁₂N₁₂ and Si₁₂C₁₂ nanoclusters functionalized with Tyr124_OP and Tyr337_OP, after subtracting the energy of the nanocluster_Tyr124 (or Tyr337) and OP, represents the reaction energy of the biomimetic nanozyme adsorption of OP at the hydrogen bonding sites of AChE and OP. This adsorption reaction is described as follows:

nanocluster_Tyr124 (or Tyr337) + OP → nanocluster_Tyr124 (or Tyr337)_OP

The energy change in the adsorption reaction, ΔE, can be expressed as follows:

ΔE = E nanocluster_Tyr124 (or Tyr337)_OP − (E nanocluster_Tyr124 (or Tyr337) + E OP)

3. Results and Discussion

3.1. Adsorption Energies

Table 1. The adsorption energies.

	B12N12_Tyr124	B12N12_Tyr337	Si12C12_Tyr124	Si12C12_Tyr337
parathion (Par)	−34.67	−29.92	4.17	34.13
coumaphos (Cou)	−30.83	−32.07	31.24	6.05
diazinon (Dia)	−34.41	−34.01	1.85	−11.05
naled (Nal)	−31.98	−26.89	8.74	11.41
trichlorfon (Tri)	−35.43	−34.70	−27.55	−9.59
sulfotep (Sul)	−34.33	−34.93	3.70	−8.79
isazofos (Isa)	−53.45	−34.63	2.41	−9.79
chlorethoxyfos (Chl)	−48.39	−32.94	6.30	5.72

shows the OP pesticide adsorption energies of the B₁₂N₁₂ and Si₁₂C₁₂ nanoclusters functionalized with Tyr124 and Tyr337. The adsorption energies for the B₁₂N₁₂ nanoclusters with Tyr337 are generally more negative, indicating higher adsorption stability, whereas the adsorption energy variation for Si₁₂C₁₂ is larger, indicating less consistency. These results suggest that B₁₂N₁₂ nanoclusters have greater potential and application value as adsorption materials, particularly when functionalized with Tyr337, due to their higher adsorption stability and consistency. Most of the organophosphorus (OP) pesticides have negative adsorption energies with B₁₂N₁₂_Tyr337, indicating an exothermic and stable adsorption process. For example, the OP pesticide Dia has an adsorption energy of −34.01 eV, Isa −34.63 eV, and Tri −34.70 eV. One pesticide, in particular, shows notably large negative adsorption energy with B₁₂N₁₂_Tyr337: the OP pesticide Sul has an adsorption energy of −34.93 eV, indicating very high stability. The energy variation for different pesticides adsorbed with B₁₂N₁₂_Tyr337 is minimal, indicating consistent adsorption capability across different OP pesticides. The adsorption energy varies from approximately −26.89 eV (OP pesticide Nal) to −34.93 eV (OP pesticide Sul), suggesting stable and consistent adsorption processes.

In contrast, for the Si₁₂C₁₂ nanoclusters functionalized with Tyr337, the adsorption energy varies widely, with some positive and some negative values, indicating less consistency during the adsorption process. For example, the OP pesticide Par has a positive adsorption energy of 34.13 eV, indicating an endothermic and unstable adsorption process, while the OP pesticide Dia has a negative adsorption energy of −11.05 eV. The OP pesticide Sul has an adsorption energy of −8.79 eV, which is more stable than Par but less stable than Tri, which has an adsorption energy of −9.59 eV. The pesticide with the maximum stability on Si₁₂C₁₂_Tyr337 is Chl, with an adsorption energy of −48.39 eV.

Therefore, the adsorption energies of the OP pesticides on the B₁₂N₁₂ nanoclusters with Tyr337 are generally more negative than those of the Si₁₂C₁₂ nanoclusters with Tyr337, indicating higher stability. Most of the OP pesticides exhibit negative adsorption energies on the B₁₂N₁₂ nanoclusters with Tyr337, while several have positive energies on the Si₁₂C₁₂ nanoclusters with Tyr337.

3.2. HSAB Parameters

The ionization energy (I) of B₁₂N₁₂_Tyr337 (5.395 eV) is slightly higher than that of Si₁₂C₁₂_Tyr337 (5.208 eV), suggesting that B₁₂N₁₂_Tyr337 has greater electronic stability (

Table 2. The HSAB parameters before adsorption ^a.

	GAP (eV)	I (eV)	A (eV)	χ (eV)	η (eV)	S (eV−1)	ω (eV)	ΔNmax
B12N12_Tyr337	3.747	5.395	1.648	3.522	1.874	0.267	3.310	1.880
Si12C12_Tyr337	2.128	5.208	3.080	4.144	1.064	0.470	8.070	3.895
parathion (Par)	2.417	6.174	3.757	4.966	1.209	0.414	10.201	4.109
coumaphos (Cou)	3	6.038	3.038	4.538	1.500	0.333	6.864	3.025
diazinon (Dia)	4.112	6.173	2.061	4.117	2.056	0.243	4.122	2.002
naled (Nal)	4.091	6.957	2.866	4.912	2.046	0.244	5.897	2.401
trichlorfon (Tri)	4.783	7.007	2.224	4.616	2.392	0.209	4.454	1.930
sulfotep (Sul)	5.39	6.085	0.695	3.390	2.695	0.186	2.132	1.258
isazofos (Isa)	4.629	6.125	1.496	3.811	2.315	0.216	3.137	1.646
chlorethoxyfos (Chl)	4.028	6.310	2.282	4.296	2.014	0.248	4.582	2.133

^a The variables include GAP (eV), the energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO); I (eV), ionization energy required to remove an electron from a molecule; A (eV), electron affinity indicating the energy change when an electron is added; χ (eV), electronegativity representing the ability to attract electrons; η (eV), chemical hardness measuring resistance to electron distribution changes; S (eV⁻¹), chemical softness as the inverse of hardness; ω (eV), electrophilicity index indicating the tendency to accept electrons; and ΔN_max, the maximum amount of electronic charge transfer.

). This aligns with the more stable adsorption energies observed for B₁₂N₁₂_Tyr337. Additionally, the chemical hardness of B₁₂N₁₂_Tyr337 (1.874 eV) is higher than that of Si₁₂C₁₂_Tyr337 (1.064 eV), indicating a greater resistance to electron transfer. Correspondingly, B₁₂N₁₂_Tyr337 exhibits more negative adsorption energies when adsorbing pesticides, reflecting its more stable electronic structure. The higher electrophilicity index (ω) of Si₁₂C₁₂_Tyr337 (8.070 eV) compared to B₁₂N₁₂_Tyr337 (3.310 eV) implies that Si₁₂C₁₂_Tyr337 more readily accepts electrons during adsorption. Consequently, Si₁₂C₁₂_Tyr337 shows greater variability in adsorption energy. The maximum amount of electronic charge transfer (ΔN_max) indicates the maximum amount of electronic charge a molecule can accept during a reaction. Si₁₂C₁₂_Tyr337’s ΔN_max (3.895) is significantly higher than that of B₁₂N₁₂_Tyr337 (1.880), suggesting that Si₁₂C₁₂_Tyr337 can accommodate more electrons (Table 2). In summary, the B₁₂N₁₂_Tyr337 nanoclusters, due to their higher ionization energy and chemical hardness, demonstrate more stable adsorption energies, indicating greater electronic stability during adsorption. In contrast, Si₁₂C₁₂_Tyr337, with higher electrophilicity and maximum electron transfer, shows more variability in adsorption energy, indicating greater flexibility in adjusting its electronic structure during adsorption.

The reactivity parameters of the pesticides in Table 2 provide insights into their chemical behaviors and interactions with nanoclusters. The energy gap (GAP) indicates the stability and reactivity of the molecules; a smaller GAP, like that of parathion (Par) at 2.417 eV, suggests higher reactivity compared to a larger GAP, such as trichlorfon (Tri) at 4.783 eV, indicating lower reactivity. Ionization energy (I) reflects the energy required to remove an electron, with higher values like naled (Nal) at 6.957 eV indicating more stability. Electron affinity (A) shows the energy change when an electron is added; parathion (Par) has a high electron affinity of 3.757 eV, suggesting it can easily gain electrons. Electron affinity is the energy change when an electron is added to a neutral atom or molecule in the gas phase to form a negative ion. A positive EA value indicates an exothermic process where energy is released, meaning the negative ion is more stable than the neutral atom. Conversely, a negative EA value signifies an endothermic process where energy is required, suggesting the neutral atom does not favor electron addition and the negative ion is less stable. Most atoms have positive EAs, indicating they release energy when gaining an electron, leading to a more stable state. However, some atoms, particularly noble gases, have negative EAs due to their stable electron configurations, requiring energy input to add an electron [29].

Electronegativity (χ) reflects the ability to attract electrons, with parathion (Par) having a high value of 4.966 eV, indicating strong electron attraction. Chemical hardness (η) measures the resistance to electron distribution changes, where higher values like sulfotep (Sul) at 2.695 eV suggest greater resistance. Chemical softness (S) is the inverse of hardness; higher softness values like Si12C12_Tyr337 at 0.470 eV⁻¹ indicate greater reactivity. The electrophilicity index (ω) shows the tendency to accept electrons; parathion (Par) has a high electrophilicity of 10.201 eV, suggesting strong electron acceptance capability. The ΔN_max represents the maximum electronic charge a system can accept, with parathion (Par) having the highest value at 4.109, indicating a high capacity to gain electrons. Comparing these parameters helps in understanding the relative stability and reactivity of the pesticides, where parathion (Par) stands out for its high reactivity and electrophilicity, while trichlorfon (Tri) and sulfotep (Sul) exhibit higher stability.

A study explored the adsorption of glycine amino acid and its zwitterionic form onto hexagonal boron nitride (h-BN) and silicon carbide (h-SiC) sheets using density functional theory (DFT) calculations. The results showed that glycine and its conformers tend to chemisorb onto the h-SiC surface more strongly compared to h-BN, indicating that h-SiC has higher affinity due to stronger interaction energies [30]. Another study examined the adsorption of various organic molecules on boron nitride nanosheets, showing that functionalization with specific molecules can significantly enhance binding energies. This finding supports the hypothesis that modifying nanoclusters with amino acids can improve their interaction with target molecules, such as organophosphorus pesticides [31]. Research into the electronic properties of boron nitride and its adsorption sites indicated that modifications on the nanosheet surfaces, such as with sulfur compounds, can enhance adsorption through non-covalent interactions. These modifications affect the electronic affinity and reactivity of the nanosheets, which is crucial for applications in pesticide binding and detection [30,31].

3.3. Linear Correlation

The linear correlation between the HSAB parameters and ΔE for nanoclusters functionalized with Tyr124 is low, indicating that nanoclusters functionalized with Tyr337 exhibit relatively more stable structures when adsorbing OP pesticides (Figure 4). Compared to Tyr124 functionalization, the adsorption energies of nanoclusters functionalized with Tyr337 are generally more predictable, particularly for B₁₂N₁₂ nanoclusters. This suggests that Tyr337 may provide a more stable structural environment for adsorbing different OP pesticides. The energy gap (GAP) represents the energy difference between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO). A smaller GAP indicates higher reactivity. Generally, a smaller GAP may lead to higher adsorption energy (more negative values) due to easier electron transfer. The results show that molecules with smaller GAP values (e.g., Par and Dia) have higher adsorption energies on B₁₂N₁₂_Tyr337. Ionization energy (I) indicates the minimum energy required to remove an electron from a molecule. Lower ionization energy means the molecule loses electrons more easily. The OP pesticides with lower ionization energies (e.g., Sul) have higher adsorption energies on B₁₂N₁₂_Tyr337, indicating a negative correlation between ionization energy and adsorption energy. Electron affinity (A) indicates the energy change when a molecule accepts an electron. Higher electron affinity means the molecule accepts electrons more easily. The OP pesticides with higher electron affinities (e.g., Par) have higher adsorption energies on B₁₂N₁₂_Tyr337, suggesting a positive correlation between electron affinity and adsorption energy.

Electronegativity (χ) measures the ability of a molecule to attract electrons. Higher electronegativity means the molecule attracts electrons more easily. The OP pesticides with higher electronegativities (e.g., Par) have higher adsorption energies on B₁₂N₁₂_Tyr337, indicating a positive correlation between electronegativity and adsorption energy. Chemical hardness (η) measures the resistance of a molecule to electron transfer. Higher chemical hardness means electron transfer is more difficult. The OP pesticides with lower chemical hardness (e.g., Par) have higher adsorption energies on B₁₂N₁₂_Tyr337, indicating a negative correlation between chemical hardness and adsorption energy. Softness (S), the inverse of chemical hardness, indicates the ease of electron transfer. The OP pesticides with higher softness (e.g., Par) have higher adsorption energies on B₁₂N₁₂_Tyr337, suggesting a positive correlation between softness and adsorption energy. The electrophilicity index (ω) is a comprehensive measure of a molecule’s ability to accept electrons. The OP pesticides with higher electrophilicity indices (e.g., Par) have higher adsorption energies on B₁₂N₁₂_Tyr337, indicating a positive correlation between the electrophilicity index and adsorption energy. The OP pesticides with higher the maximum amount of electronic charge transfer (ΔN_max) values (e.g., Par) have higher adsorption energies on B₁₂N₁₂_Tyr337, suggesting a positive correlation between ΔN_max and adsorption energy.

Consequently, the B₁₂N₁₂ and Si₁₂C₁₂ nanoclusters with Tyr337 exhibit a linear relationship between adsorption energies and several HSAB parameters (e.g., GAP, I, A, χ, η, S, ω, and ΔN_max) when adsorbing the OP pesticides. These parameters collectively influence the stability and energy changes during the adsorption process. Notably, for the B₁₂N₁₂ nanoclusters with Tyr337, the adsorption energies show higher stability, consistent with the trends in HSAB parameters. These results also suggest that B₁₂N₁₂ nanoclusters have greater potential and application value as adsorption materials.

3.4. The Changes in HSAB Parameters after Adsorption

The GAP values decrease after adsorption (

Table 3. The HSAB parameters after adsorption.

	GAP (eV)	I (eV)	A (eV)	χ (eV)	η (eV)	S (eV−1)	ω (eV)	ΔNmax
B12N12_Tyr337_Par	1.726	5.449	3.723	4.586	0.863	0.579	12.185	5.314
B12N12_Tyr337_Cou	2.398	5.404	3.006	4.205	1.199	0.417	7.374	3.507
B12N12_Tyr337_Dia	3.479	5.625	2.146	3.886	1.740	0.287	4.339	2.234
B12N12_Tyr337_Nal	2.42	5.460	3.040	4.250	1.210	0.413	7.464	3.512
B12N12_Tyr337_Tri	3.094	5.377	2.283	3.830	1.547	0.323	4.741	2.476
B12N12_Tyr337_Sul	3.6	5.370	1.770	3.570	1.800	0.278	3.540	1.983
B12N12_Tyr337_Isa	3.745	5.518	1.773	3.646	1.873	0.267	3.549	1.947
B12N12_Tyr337_Chl	3.187	5.447	2.260	3.854	1.594	0.314	4.659	2.418
Si12C12_Tyr337_Par	1.741	5.530	3.789	4.660	0.871	0.574	12.470	5.353
Si12C12_Tyr337 _Cou	2.097	5.217	3.120	4.169	1.049	0.477	8.286	3.976
Si12C12_Tyr337 _Dia	2.155	5.219	3.064	4.142	1.078	0.464	7.959	3.844
Si12C12_Tyr337 _Nal	1.861	5.230	3.369	4.300	0.931	0.537	9.933	4.621
Si12C12_Tyr337_Tri	1.588	4.861	3.273	4.067	0.794	0.630	10.416	5.122
Si12C12_Tyr337_Sul	1.521	4.884	3.363	4.124	0.761	0.657	11.179	5.422
Si12C12_Tyr337_Isa	2.141	5.226	3.085	4.156	1.071	0.467	8.065	3.882
Si12C12_Tyr337_Chl	2.096	5.213	3.117	4.165	1.048	0.477	8.276	3.974

), indicating significant changes in the electronic structure during adsorption, leading to a reduced energy gap. The GAP of B₁₂N₁₂_Tyr337 decreases from 3.747 eV to 1.726 eV (B₁₂N₁₂_Tyr337_Par), and the GAP of Si₁₂C₁₂_Tyr337 decreases from 2.128 eV to 1.741 eV (Si₁₂C₁₂_Tyr337_Par).

The ΔGAP (eV) values for the B₁₂N₁₂_Tyr337 and Si₁₂C₁₂_Tyr337 nanoclusters are presented in Figure 5. The results reveal significant differences in the adsorption characteristics and stability between the two types of nanoclusters. The ΔGAP values for the B₁₂N₁₂_Tyr337 nanoclusters range from 0.002 eV to 2.021 eV. The highest ΔGAP is observed for B₁₂N₁₂_Tyr337_Par with a value of 2.021 eV, indicating a substantial energy change upon modification. Conversely, the lowest ΔGAP is noted for B₁₂N₁₂_Tyr337_Isa at 0.002 eV, suggesting minimal energy change. Overall, the average ΔGAP for the B₁₂N₁₂_Tyr337 nanoclusters is relatively high, indicating a stable interaction when they are modified with Tyr337.

For the Si₁₂C₁₂_Tyr337 nanoclusters, the ΔGAP values vary from −0.027 eV to 0.607 eV. The maximum ΔGAP is observed for Si₁₂C₁₂_Tyr337_Sul at 0.607 eV, signifying a moderate energy change. The minimum ΔGAP is recorded for Si₁₂C₁₂_Tyr337_Dia at −0.027 eV, indicating a slight decrease in energy. The average ΔGAP values for the Si₁₂C₁₂_Tyr337 nanoclusters are lower than those of B₁₂N₁₂_Tyr337, implying less stability.

The ΔGAP values for the B₁₂N₁₂_Tyr337 nanoclusters are generally higher and more consistent compared to those of the Si₁₂C₁₂_Tyr337 nanoclusters. This indicates that the B₁₂N₁₂_Tyr337 nanoclusters exhibit more significant energy changes, associated with higher stability and stronger interactions with adsorbed species. In contrast, the Si₁₂C₁₂_Tyr337 nanoclusters show a broader variation and lower ΔGAP values, suggesting less stable interactions. The higher stability of the B₁₂N₁₂_Tyr337 nanoclusters, as evidenced by their ΔGAP values, suggests they have greater potential as adsorption materials. Their consistent and substantial energy changes upon modification highlight their suitability for applications requiring stable and robust adsorption properties. The variability and lower stability of the Si₁₂C₁₂_Tyr337 nanoclusters indicate they may be less effective for such purposes.

The analysis demonstrates that B₁₂N₁₂ nanoclusters, particularly when modified with Tyr337, show greater potential and application value as adsorption materials due to their higher stability and stronger interactions. This makes them more promising candidates for practical applications where stable adsorption is crucial compared to Si₁₂C₁₂_Tyr337 nanoclusters.

The ionization energy (I) changes slightly after adsorption. The ionization energy of B₁₂N₁₂_Tyr337 increases from 5.395 eV to 5.449 eV (B₁₂N₁₂_Tyr337_Par), and that of Si₁₂C₁₂_Tyr337 increases from 5.208 eV to 5.530 eV (Si₁₂C₁₂_Tyr337_Par). This suggests an enhancement in the stability of the electronic structure during adsorption. The electron affinity (A) also changes significantly after adsorption. The electron affinity of B₁₂N₁₂_Tyr337 increases from 1.648 eV to 3.723 eV (B₁₂N₁₂_Tyr337_Par), and that of Si₁₂C₁₂_Tyr337 increases from 3.080 eV to 3.789 eV (Si₁₂C₁₂_Tyr337_Par). This indicates that the system becomes more electron-accepting after adsorption.

The adsorption process also results in an increased electronegativity and electrophilicity index and decreased chemical hardness, indicating that the electronic structure of the nanoclusters becomes more flexible and reactive during adsorption. Moreover, B₁₂N₁₂ exhibits greater chemical hardness and stability after adsorption, while Si₁₂C₁₂ shows higher electrophilicity and electron transfer capacity. The electronegativity (χ) increases after adsorption, indicating a stronger attraction for electrons. The electronegativity of B₁₂N₁₂_Tyr337 increases from 3.522 eV to 4.586 eV (B₁₂N₁₂_Tyr337_Par), and that of Si₁₂C₁₂_Tyr increases from 4.144 eV to 4.660 eV (Si₁₂C₁₂_Tyr337_Par). The chemical hardness (η) generally decreases after adsorption, making the system more prone to electron transfer. The chemical hardness of B₁₂N₁₂_Tyr337 decreases from 1.874 eV to 0.863 eV (B₁₂N₁₂_Tyr337_Par), and that of Si₁₂C₁₂_Tyr337 decreases from 1.064 eV to 0.871 eV (Si₁₂C₁₂_Tyr337_Par). The softness (S) increases, indicating a greater ability to respond to external electrons. The softness of B₁₂N₁₂_Tyr337 increases from 0.267 eV⁻¹ to 0.579 eV⁻¹ (B₁₂N₁₂_Tyr337_Par), and that of Si₁₂C₁₂_Tyr337 increases from 0.470 eV⁻¹ to 0.574 eV⁻¹ (Si₁₂C₁₂_Tyr337_Par). The electrophilicity index (ω) significantly increases after adsorption, indicating a greater tendency to accept electrons. The electrophilicity index of B₁₂N₁₂_Tyr337 increases from 3.310 eV to 12.185 eV (B₁₂N₁₂_Tyr337_Par), and that of Si₁₂C₁₂_Tyr337 increases from 8.070 eV to 12.470 eV (Si₁₂C₁₂_Tyr337_Par). The maximum amount of electronic charge transfer (ΔN_max) also increases, indicating that the system can accept more electrons. The ΔN_max of B₁₂N₁₂_Tyr337 increases from 1.880 to 5.314 (B₁₂N₁₂_Tyr337_Par), and that of Si₁₂C₁₂_Tyr337 increases from 3.895 to 5.353 (Si₁₂C₁₂_Tyr337_Par).

4. Conclusions

This study demonstrates that B₁₂N₁₂_Tyr337 nanoclusters exhibit stronger interactions when adsorbing OP pesticide molecules compared to Si₁₂C₁₂_Tyr337. B₁₂N₁₂_Tyr337 possesses higher ionization energy and chemical hardness, indicating greater electronic stability. The adsorption energies of B₁₂N₁₂_Tyr337 are more negative and stable, suggesting that B₁₂N₁₂_Tyr337 provides a more stable structural environment for OP pesticide adsorption. In contrast, although Si₁₂C₁₂_Tyr337 has a higher electron acceptance capability, its adsorption energies vary more significantly, indicating lower consistency. Therefore, B₁₂N₁₂ nanoclusters modified with amino acid tyrosine have greater potential and value for applications as adsorption and biosensing materials.