In silico engineering of graphitic carbon nitride nanostructures through germanium mono-doping and codoping with transition metals (Ni, Pd, Pt) as sensors for diazinon organophosphorus pesticide pollutants

Abstract

The extensive use of pesticides has raised concerns about environmental contamination, which poses potential health risks to humans and aquatic life. Hence, the need for a healthy and friendly ecosystem initiated this study, which was modeled through profound density functional theory (DFT) at the B3LYP-D3(BJ)/def2svp level of theory to gain insights into the electronic characteristics of germanium-doped graphitic carbon nitride (Ge@C₃N₄) engineered with nickel group transition metals (Ni, Pt, and Pd) as sensors for diazinon (DZN), an organophosphorus pesticide pollutant. To effectively sense diazinon, this research employed a variety of methodologies, beginning with the analysis of electronic properties, intermolecular investigations, adsorption studies, and sensor mechanisms. These detailed assessments revealed insightful results, as clearly indicated by their narrow energy gap and other electronic properties. Noncovalent interactions characterized by van der Waals forces were revealed predominantly by quantum atoms in molecules (QTAIM) and noncovalent interaction (NCI) analyses. Furthermore, the results of the adsorption studies, which measured the strength of the interaction between the pesticide molecules and the nanostructures, revealed favorable results characterized by negative adsorption energies of − 1.613, − 1.613, and − 1.599 eV for DZN_Ge@C₃N₄, DZN_Ni_Ge@C₃N₄, and DZN_Pd_Ge@C₃N₄, respectively. The simulated mechanism through which diazinon is sensed revealed favorable results, as observed by the negative Fermi energy and fraction of electron transfer (∆N), as well as a high dipole moment. This study also revealed that the codoping influenced the behavior of the systems, revealing that DZN_Ni_Ge@C₃N₄ was the best sensing system because of its strongest adsorption (− 1.613 eV), highest dipole moment (8.348 D), most negative Fermi energy (− 1.300 eV), lowest work function (1.300 eV), and good ∆N (− 1.558) values. This study, therefore, proposes these nanostructures for further in vitro studies seeking to sense diazinon and other pesticides to maintain healthy ecosystems.

Introduction

With the IUPAC name O,O-diethyl O-[4-methyl-6-(propan-2-yl)pyrimidin-2-yl] phosphorothioate and a molecular formula of C₁₂H₂₁N₂O₃PS, diazinon (DZN) is an insecticide known as an organophosphate [1]. Its widespread application in agriculture is aimed at controlling insect infestations in various crops, including fruits, vegetables, nuts, and field crops. Organophosphorus pesticides (such as malathion, parathion, fenthion, dichlorvos, chlorpyrifos, and ethion) are esters originating from phosphoric acid and are commonly employed to manage insects and mites on vegetables [2, 3]. These pesticides, specifically organophosphorus pesticides, are recognized for their capacity to improve both the quality and quantity of crop yields in agriculture. As an acetylcholinesterase inhibitor, similar to other organophosphates, diazinon interferes with the breakdown of the neurotransmitter acetylcholine [4, 5]. This disruption leads to the accumulation of acetylcholine in nerve cell synapses, resulting in overstimulation of the nervous system in insects [6]. Additionally, it has been applied in nonagricultural settings, such as residential areas, for pest control in and around buildings [7]. However, the extensive use of pesticides raises concerns about environmental contamination, which poses potential health risks to humans and aquatic life because of their environmentally persistent properties [8,9,10]. In response to these concerns, several countries, including the United States, have implemented regulatory actions, restricting or outright banning the use of diazinon. In the U.S., for example, residential use was phased out in 2004, and diazinon was no longer registered for use on most crops [11, 12].

To maintain healthy ecosystems, several techniques and methods have been employed by several researchers to remediate pesticides, including diazinon. Some of these methods include biological and chemical methods such as bioremediation, phytoremediation, activated carbon, liquid chromatography, and mass spectrometry, as revealed by the review carried out by Lykogianni et al. [13]. All these methods have several shortcomings, with a high dependence on uncontrollable factors. As such, recent advances have ushered in the application of nanotechnology for the monitoring and control of toxic substances, including pesticides. In terms of investigating novel sensing adsorbents, density functional theory is a widely used modeling method for exploring the behavior of nanomaterials. Hence, researchers have adopted this computational tool to explore the behavior of nanomaterials for the sensing of toxic compounds such as drugs and pesticides. On the basis of pesticide sensing and adsorption, scholars have used different nanomaterials, such as nanoparticles [14,15,16,17,18], nanotubes [19,20,21,22,23,24,25,26], nanowires [27,28,29,30,31,32,33,34,35], quantum dots [36,37,38,39], 2D materials [40,41,42], nanosheets [43,44,45,46,47,48,49,50,51], nanocages [52,53,54,55,56], nanostructures [42,43,44,45,46], nanocomposites [57,58,59,60], nanoclusters [61,62,63,64], metal‒organic frameworks [51, 65, 66], and nanodots [67], and many studies have reported that introducing materials improves the behavior of nanomaterials [56, 68, 69]. For example, Luo et al. [70] reported a significant decrease in the energy gap of their systems after doping atoms on g-C₃N₄ for the sensing of hydrogen gas. Owing to these insightful results, studies in which various dopants are added to enhance the behavior of their investigated nanomaterials are common. Most of these nanomaterials are built on different materials, including graphene and graphene-based materials [16, 71,72,73,74,75,76,77,78,79]. These studies reported insightful results ranging from their good electronic properties to their adsorption behavior. However, there is still much to explore due to the dynamic nature of nanomaterials.

This finding simply suggests more advances and calls for more applications in various nano research explorations, including pesticide sensing. Although graphitic carbon nitride has been widely explored [59, 79,80,81,82,83,84,85,86] and some have specifically focused on diazinon [60, 82, 87,88,89,90], studies in which germanium is used to engineer graphene carbon nitrides are rare even when Laumier et al. [19], and several scholars have previously reported the insightful behavior of Ge and other elements. Some studies have investigated other nanomaterials, such as carbon nitride and boron nitride, for use in pesticide sensing [91,92,93,94,95,96,97,98,99,100,101,102]. Hence, the present study investigated a more insightful dimension by probing the electronic behavior of Ge-doped graphitic carbon (Ge@C₃N₄) for diazinon sensing, with a critical interest in the influence of the functionalization of Ni, Pd, and Pt on the system.

We utilized advanced theoretical calculations employing density functional theory (DFT) at the B3LYP-D3(BJ)/def2svp level of theory to gain insights into the electronic characteristics of Ge@C₃N₄ and doped transition metals such as Ni, Pt, and Pd. The objective of this study was to investigate the promising sensing capabilities of Ge@C₃N₄-based diazinon sensors. Analyzing the electronic properties of sensor materials is crucial for understanding their sensor behavior. Consequently, we conducted a detailed assessment of the electronic properties, including the analysis of frontier molecular orbitals (HOMO and LUMO energies). This provided comprehensive information on the sensitivity and conductivity of the studied material. Natural bond orbital analysis was employed to examine both intermolecular and intramolecular charge transfer between the investigated nanomaterials and diazinon. After optimizing the geometric structure, we thoroughly evaluated the changes in the electronic properties, specifically bond lengths, before and after the adsorption of the diazinon pesticide. This discussion covers the adsorption capacity and sensor mechanism of the Ge@C₃N₄ surface and its doped elements.

Methods

In this investigation, we employed density functional theory (DFT) calculations at the B3LYP-D3(BJ)/def2svp level of theory to assess the efficiency of electronically optimized nanocomposites in detecting diazinon pesticide. The optimization procedure for DZN_Ge@C₃N₄, which incorporated Pt, Pd, and Ni dopants, was systematically carried out via Gaussian 16 [103] and GaussView 6.0.16 [104]. The selection of this computational approach is crucial because it facilitates a comprehensive exploration of molecular properties and interactions, offering valuable insights into electronic structures, reactivity, and bonding mechanisms. To effectively sense diazinon, a variety of methodologies, beginning with the analysis of frontier molecular orbital (FMO) and natural bond orbital (NBO) methods, coupled with evaluations of density of states (DOS) plotted via OriginPro 2018, were used [105]. These procedures provided in-depth insights into the electronic characteristics governing the behavior of the nanocomposites under investigation. The visualization of isosurfaces for the highest molecular orbital and lowest unoccupied molecular orbital (HOMO–LUMO) was facilitated via Chemcraft software version 1.644, accessible at http://www.chemcraftprog.com. To bolster scientific validity, this study utilized the Multiwfn package 3.7 [106] to explore the quantum theory of atoms-in-molecules (QTAIM), which offers valuable insights into intermolecular interactions. A thorough investigation of noncovalent interactions (NCIs) was carried out, elucidating the nonbonding forces influencing molecular assemblies via the visual molecular dynamics (VMD) software package version 1.9.4 [107]. Additionally, the research has extended its scope to encompass studies on adsorption energy and a comprehensive exploration of sensor mechanisms. This holistic analytical framework collectively advances our understanding of the potential of Ge-doped graphitic carbon (Ge@C₃N₄) for sensing, positioning this study at the forefront of scientific inquiry.

Results and discussion

Geometry optimization

Geometry optimization is a structural analysis generally used to ascertain stable geometric structures for calculating bond lengths within systems [107,108,109]. Optimization before and after interaction ensures the conformation of the complexes under investigation. The geometries of the investigated systems are shown in Fig. 1, while their bond length results, as discussed herein, are presented in the supporting information (see Table S1). This analysis shows the bond lengths of diazinon (DZN) before and after interaction with Ge@C₃N₄ doped with metals (Ni, Pd, Pt). For the Ge54–C14 bond_, DZN_Ge@C₃N₄ exhibited a bond length of 2.162 Å before interaction and 2.241 Å after interaction; DZN_Pt_Ge@C₃N₄ exhibited a bond length of 2.023 Å before interaction and 1.612 Å after interaction; DZN_Pd_Ge@C₃N₄ exhibited a bond length of 2.018 Å before interaction and 2.022 Å after interaction; and DZN_Ni_Ge@C₃N₄ exhibited a bond length of 2.017 Å before interaction and 2.022 Å after interaction. The DZN_Pt_Ge@C₃N₄ complex demonstrated the shortest bond length, suggesting that it had the lowest tendency to undergo structural rearrangement. Additionally, for the Ge54–C15 bond, DZN_Ge@C₃N₄ has a bond length of 2.044 Å before interaction and 2.037 Å after interaction, whereas DZN_Pt_Ge@C₃N₄ has a bond length of 2.005 Å before interaction and 1.632 Å after interaction; moreover, the DZN_Pd_Ge@C₃N₄ system has a bond length of 2.005 before interaction and 2.008 Å after interaction, and finally, DZN_Ni_Ge@C₃N₄ has a bond length of 1.998 before interaction and 2.001 Å after interaction. Additionally, for the Ge54–C16 bond, DZN_Ge@C₃N₄ had a bond length of 2.044 Å before the interaction and 2.079 Å after the interaction, DZN_Pt_Ge@C₃N₄ had a bond length of 2.005 Å before the interaction and 2.004 Å after the interaction, and DZN_Pd_Ge@C₃N₄ had a bond length of 2.017 Å before the interaction and 2.021 Å after the interaction. Finally, DZN_Ni_Ge@C₃N₄ exhibited a bond length of 1.998 Å before interaction; upon interaction, the bond length was 2.002 Å. Hence, DZN_Pt_Ge@C₃N₄ is theoretically explained to have the most stable configuration owing to the shortest bond lengths observed within the system.

Fig. 1

Geometry of the investigated systems presented using Chemcraft

Electronic properties

HOMO–LUMO analysis

The analysis of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) is a molecular assessment used to qualitatively determine the molecular excitation capacities and electron transport characteristics of the examined molecules [110]. The HOMO indicates the electron-donating ability, whereas the LUMO designates the electron-attracting capacity, also known as the electrophilic capacity, of the synthesized compounds [56, 62]. As presented in Table 1, the highest HOMO energy among the doped surfaces was observed for Ge@C₃N₄ at − 5.388 eV, whereas the other surfaces presented values of − 5.034 eV, − 5.089 eV, and − 4.299 eV for Ni_Ge@C₃N₄, Pt_Ge@C₃N₄, and Pd_Ge@C₃N₄, respectively. Conversely, the highest LUMO peak was observed for Ge@C₃N₄ at − 3.129 eV, with other surfaces displaying values of − 2.748 eV, − 2.667 eV, and − 2.640 eV for Ni_Ge@C₃N₄, Pt_Ge@C₃N₄, and Pd_Ge@C₃N₄, respectively.

Table 1 HOMO‒LUMO and energy gap analysis of the studied systems

When these surfaces interact with the pollutant DZN, both the HOMO and LUMO exhibit distinct energy levels. Discrepancies in these energy levels are evidenced by the presence of an energy gap, a crucial parameter that provides valuable insights into the stability and reactivity of the investigated nanostructures. Considering the energy gap and the justification for this observation, it is imperative to highlight that, according to the literature, a high energy gap implies low chemical reactivity, whereas a low energy gap signifies high reactivity [51]. The results revealed that the pollutant diazinon (DZN) possesses a high energy gap of 7.504 eV, whereas the doped surfaces exhibit energy gaps ranging from 1.551 eV to 2.422 eV. The introduction of diazinon onto different doped surfaces led to a reduction in the energy gap for all systems with codopants. Specifically, the energies of the DZN_Pt_Ge@C₃N₄, DZN_Pd_Ge@C₃N₄, and DZN_Ni_Ge@C₃N₄ systems decrease to 0.735 eV, 2.367 eV, and 2.340 eV, respectively. Interestingly, DZN_Ge_@C₃N₄ (without codopant) was observed with an increased energy gap (2.449 eV). The decrease in the energy gap of the codoped systems emphasized the importance of transition metals in influencing the sensing behavior of engineered graphitic carbon nitride. The flow of electrons from the HOMO to the LUMO, which is revealed by the isorsurfaces, is clearly visualized and presented in Fig. 2. Figure 2 shows that the major transfer of electrons is influenced mostly by the transition metal functional groups, as observed by the density of the isorsurfaces. This justifies the importance of adding functionals to nanostructures.

Fig. 2

Isorfaces showing the HOMO–LUMO contributions of the investigated surfaces and their interactions

Natural bond orbital (NBO) analysis

NBOs are localized few-centered orbitals that usually describe the Lewis-like bonding structural pattern of electron pairs (or individual electrons in the open shell) in a desireably close form. NBO (natural bond orbital) analysis has proven to be a valuable tool in the interpretation of computational solutions derived from Schrödinger's wave equation, specifically in the realm of chemical bonding. Furthermore, it offers an alternative method for characterizing noncovalent interactions by delineating the functional groups involved in a molecular aggregate [93, 108]. NBO analysis is a comprehensive method for interpreting the numerical content of quatum chemistry in conventional language. By effectively investigating both intra- and intermolecular bonding and interactions, charge transfer and conjugative interactions within molecular systems can be explored [90]. The following section presents various NBOs from diverse systems employed in this research, accompanied by their corresponding transitions, perturbation energies, and diagonal elements. The NBO parameters obtained as discussed herein are presented in Table S2. Notably, transitions for the DZN_Ge@C₃N₄ system involve ${\uppi }^{*}\to {\uppi }^{*}$, ${\uppi }^{*}\to {LP}^{*}$. ${\uppi }^{*}\to {\uppi }^{*}$, as shown by the E⁽²⁾ value of 254.96 kcal, contributes significantly to the system stability, and the other adsorbed bate‒adsorbent interactions result in a lower E⁽²⁾ value (178.42 kcal). For DZN_Ni_Ge@C₃N_4, the bond transitions observed for the adsorbate-adsorbent were both ${\uppi }^{*}\to {\uppi }^{*}$ bonds, with the highest energy observed at 120.87 kcal and the other at 199.68 kcal. For the interaction DZN_Pd_Ge@C₃N_4, the bond transitions observed in this system are ${\uppi }^{*}\to {\uppi }^{*}$ and ${\uppi }^{*}\to {LP}^{*}$. The ${\uppi }^{*}\to {\uppi }^{*}$ transition contributes significantly to the stability of the system, as indicated by E⁽²⁾, which is 163.95 kcal, whereas the other transition has an E⁽²⁾ of 134.97 kcal. Finally, the ${\uppi }^{*}\to {LP}^{*}$ values of the DZN_Pt_Ge@C3N4 system exhibit bond transitions of 226.76 kcal/mol and 327.15 kcal/mol, respectively. DZN_Pt_Ge@C₃N_4, with an E⁽²⁾ of 327.15 kcal/mol for ${\uppi }^{*}\to {LP}^{*}$, is more chemically stable than other adsorbate‒adsorbent interactions, as the E⁽²⁾ value surpasses that of other interactions. According to a literature review, the complex with the least energy stabilization energy (E⁽²⁾) is said to be more reactive [111].

Topology analysis

QTAIM

The quantum theory of atoms in molecules (QTAIM) analysis, which originated from Bader’s theory [112], is utilized to gather information about bond types and spatial interactions between adsorbate-adsorbent surfaces. According to QTAIM theory, a bond path connects the nuclei of interacting atoms at topological points known as bond critical points (BCPs). The BCP is a spatial point where the Laplacian electron density equals zero, ∇2ρ(r) = 0. Several topological parameters are studied at BCPs, including the electron density ρ(r), Laplacian electron density ∇2ρ(r), Lagrangian kinetic energy G(r), potential electron energy density V(r), total electron energy density H(r), eigenvalues (λ1, λ2, λ3) of the Hessian matrix, and bond ellipticity ɛ [26]. These parameters are tabulated in Table 2. With the electron density values ρ(r), one can predict the strength of the studied interactions, where higher ρ(r) values (ρ > 0.1 a.u.) indicate stronger covalent interactions, and lower ρ(r) values (ρ < 0.1 a.u.) suggest weak noncovalent interactions. The analysis results show p(r) values ranging from 0.100 to 0.905 a.u., indicating strong covalent interactions for all the systems. DZN_Pd_Ge@C₃N₄ exhibited the most covalent interactions, with a p(r) of 0.905 a.u.

Table 2 Parameters of quantum atoms in molecules investigating the intermolecular behavior of the adsorbed systems

The Laplacian electron density ∇2ρ(r), derived as the sum of Hessian eigenvalues (λ1, λ2, λ3), provides insights into the electron density distribution. ∇2ρ (r) reveals strong shared-shell interatomic interactions with a local concentration of electron density distribution at the critical point when ∇2ρ(r) < 0. Conversely, weak closed-shell (CS) interactions are characterized by local depletion when ∇2ρ(r) > 0. Greater electron density at bond critical points (BCPs) signifies greater structural stability, highlighting the importance of understanding the stability and reactivity of sensor materials for the sensor community [113]. In the obtained H(r) calculation results, negative values were observed for Ge54–N75, H₉₂–C₁₄, and N76–O80, suggesting covalent interactions at these points. In contrast, other forms of bond formation presented positive values, indicating robust electrostatic bonding. The results for λ1, λ2, and λ3 from the QTAIM studies were subsequently analyzed via noncovalent interaction (NCI) analysis, revealing a strong correlation between these two studies.

NCI analysis

Noncovalent interaction (NCI) analysis was conducted to clarify and quantitatively assess both inter- and intramolecular interactions involving DZN_Ge@C₃N₄ and doped metal atoms (lead (Pb), nickel (Ni), and platinum (Pt)). NCI, which is distinct from covalent bonding interactions, does not necessitate the exchange of electrons; instead, it rationalizes more distributed variations in electromagnetic interactions between molecules or within a molecule. Various dimensions or categories of noncovalent contacts are generally recognized, including electrostatic interactions, π effects, van der Waals forces, and hydrophobic effects. The formation of NCIs is undoubtedly studied in terms of the release of chemical energy, which typically ranges from 1 to 5 kcal/mol (1000–5000 cal per 6.02 × 10²³ molecules).

The visualization of both intramolecular and intermolecular interactions within and between complex molecules revealed numerous noteworthy zones, as permitted by the created isosurfaces. These zones encompass van der Waals contacts, steric repulsion, and strong attractive attractions. The NCI method, utilized to analyze weak noncovalent interactions, involves plotting the reduced density gradient (RDG) and the product of the second eigenvalue of the Hessian matrix (λ2) with the electron density ρ (sign (λ2) ρ). These weak interactions were visually represented via NCI 3D iso-surface plots and 2D-reduced density gradient (RDG) scatter plots (Fig. 3). On the 2D-RDG scatter plots, interactions manifest as spikes. Specifically, attractive hydrogen bonding interactions are indicated by the sign (λ2) ρ < 0, strongly repulsive/steric forces are represented by the sign (λ2) ρ > 0, and relatively weak van der Waals (vdW) interactions are denoted by the sign (λ2) ρ ≈ 0 [114]. On the 3D iso-surface plots, the color range from a blue‒green‒red scale provides informative insights into interaction types, with blue representing hydrogen bonding, green indicating weak van der Waals (vdW) interactions, and red indicating repulsive forces.

Fig. 3

RDG plots showing the prevalence of noncovalent interactions characterized by van der Waals interactions

Sensor mechanism

The mechanism of detection and possibly control of the environment exposed to Diazinon, which is an organophosphorus pesticide employing metal (Ni, Pd, Pt)-encapsulated Ge-doped graphitic carbon (Ge@C₃N₄), encompasses the reactivity, adsorption potentials, etc., utilizing different parameters such as the Fermi energy level, charge transfer, work function, electron transfer, dipole moment, electrical conductivity, etc. The detection of the pesticide is solely dependent on the interaction between the studied metal-doped nanocomposites and the diazinon pesticide, which influences or does not influence the general structure and configuration of the studied material. The mechanism of the sensor activity of the systems was calculated via density functional theory (DFT) at the B3LYP-D3(BJ)/def2svp level of theory, as presented in Table 3.

Table 3 Results of the calculated sensor mechanism parameters (dipole moment, adsorption energy, Fermi energy, work function, charge transfer, and fraction of electron transfer)

Adsorption studies

Adsorption energy is a crucial parameter in designing sensor materials and is calculated by taking the difference between the energy of the adsorbing interaction and the energy of the diazinon surface. This energy is significant enough to explain the mechanism of adsorption and factors that may contribute to the energy values in the detection of the DZN pesticide [110]. It also measures the strength of the interaction between the pesticide molecules and the surface of the sensor material, contributing to the sensitivity, reproducibility, and selectivity of the sensor. The adsorption energy for this study was calculated via Eq. (1) and is presented in Table 3.

$$Eads = E_{complex} {-} \, (E_{surf} + \, E_{adsorb} )$$

(1)

where E_complex is the energy of the adsorbent (metal (Ni, Pd, Pt)-encapsulated Ge-doped graphitic carbon (Ge@C₃N₄)) and DZN, E_adsorb is the energy of the adsorbate DZN, and E_surf is the energy of the noninteracting surfaces. The negative adsorption energy exhibited by three negative adsorption energies (DZN_Ge@C₃N₄, DZN_Ni_Ge@C₃N₄, and DZN_Pd_Ge@C₃N₄), which is synonymous with chemisorption, suggested greater adsorption stability than that of DZN_Pt_Ge@C₃N_4, with a high positive value explaining physisorption. The literature has reported that a higher negative adsorption energy signifies a more favorable system for the adsorption of the pesticide diazinon [115]. Notably, the systems performed well in terms of adsorption energy, with values of − 1.613 eV, − 1.613 eV, and − 1.599 eV for DZN_Ge@C₃N₄, DZN_Ni_Ge@C₃N₄, and DZN_Pd_Ge@C₃N_4, respectively. However, DZN_Pt_Ge@C₃N₄ was observed to have a positive adsorption energy (7.999 eV), suggesting weak adsorption. The positive adsorption observed for DZN_Pt_Ge@C₃N₄ suggests further exploration.

Fermi energy

The Fermi energy is the peak energy level at zero temperature occupied by electrons and is significant in defining the electronic structure of the interface between the nanomaterial and the pesticide in the process of charge transfer [74, 116]. A higher Fermi energy indicates that electrons may move to a higher energy state and higher thermal conductivity, contributing to the electronic structure of the studied nanocomposite after the adsorption of the diazinon pesticide [117]. After the adsorption of the diazinon pesticide on the studied metal-doped surfaces DZN_Ge@C₃N₄, DZN_Ni_Ge@C₃N₄, DZN_Pd_Ge@C₃N₄, and DZN_Pt_Ge@C₃N₄, the Fermi energy level was calculated and is presented in Table 3 via Eq. 2.

$${\text{E}}_{FL} ={E}_{HOMO+}\left(\frac{{\text{E}}_{\text{LUMO}}- {\text{E}}_{\text{HOMO}} }{2}\right)$$

(2)

The results revealed that DZN_Ge@C₃N₄, DZN_Ni_Ge@C₃N₄, DZN_Pd_Ge@C₃N₄, and DZN_Pt_Ge@C₃N₄ presented Fermi energies of − 1.519 eV, − 1.300 eV, − 1.331 eV, and − 1.345, respectively. A Fermi energy < 0 indicates that the material falls below the conduction band and lacks electrons, leading to the transfer of electrons between the nanomaterial and the sensing pesticide. Additionally, the negative energy level potentially enhances the adsorption of pollutants by metal (Ni, Pd, Pt)-encapsulated Ge-doped graphitic carbon nanomaterials.

Dipole moment

The dipole moment is referred to as a measure of electrical charge separation and is significant in sensor materials because it can influence behavior, electrostatic interactions, the electronic structure, and the distribution of charges. This can also influence the performance and reactivity of the sensor material. The higher values derived from the dipole moment suggest a greater binding strength of the material to the diazinon pesticide, which may enhance the sensitivity [117,118,119]. The results in Table 3 show that the DZN_Ge@C₃N₄ system had the lowest dipole moment of 1.3189, whereas the DZN_Ni_Ge@C₃N₄ system had the highest dipole moment of 8.3479, and the dipole moments of DZN_Pd_Ge@C₃N₄ and DZN_Pt_Ge@C₃N₄ were 7.0217 and 5.9176 D, respectively. The higher dipole moments in DZN_Ni_Ge@C₃N₄, DZN_Pd_Ge@C₃N₄, and DZN_Pt_Ge@C₃N₄ suggest a high separation tendency of the electrical charge between the complexes and the pesticide diazinon.

Charge transfer (Qt)

In the development of sensors for the detection of pollutants, the mechanism of pesticide sensing involves noticeable interactions between metal-doped surfaces and the DZN pesticide. There is always acceptance or donation of electrons when a nanocomposite adsorbs a pesticide, which subsequently results in the transfer of charges between the molecules. The transfer of charges between molecules greatly influences the conductivity of electrons in the system as a result of an alteration of their structural properties [71, 74, 116]. The charge can be obtained by determining the difference between the charge on the metal (Ni, Pd, Pt)-encapsulated Ge-doped graphitic carbon (Ge@C₃N₄) and that of the diazinon pesticide molecule. The results calculated through Eq. 3 and presented in Table 3 show that the charge transfer for DZN_Ni_Ge@C₃N₄ was the highest (1.5887 e), whereas DZN_Ge@C₃N₄ presented the lowest charge transfer values (−0.1495 e). However, the other systems, DZN_Pd_Ge@C₃N₄ and DZN_Pt_Ge@C₃N_4, had comparable values of 1.1906 and 1.2865 e, respectively. The charge transfer values are possibly influenced by the doped metals, leading to a change in the electronic properties of the bare surface.

$${\text{Qt}} = {\text{Qadsorption }}{-}{\text{Qisolated}}$$

(3)

where Qadsorption is the charge of the individual meta-doped surfaces and where Qisolated is the charge after interaction with the DZN pesticide.

Work function (ɸ)

The minimum energy required for electrons to escape from the conduction band to the free energy state of a material is referred to as the work function and can be generated as in Eq. (4). This function is important for characterizing the surface energy and electronic structure of the sensor of a material by measuring the adsorption properties and determining how electrons move within the system. A higher work function can explain the applicability of the material as an insulator or semiconductor, whereas a lower value indicates electron emission in the system resulting from possible thermal excitation upon adsorption of the pesticide [116, 120]. The work functions calculated for this study are presented in Table 3, and from the results, the work functions decreased in the order DZN_Ni_Ge@C₃N₄ < DZN_Pd_Ge@C₃N₄ < DZN_Pt_Ge@C₃N₄ < DZN_Ge@C₃N_4, with values of 1.300 eV, 1.331 eV, 1.345 eV, and 1.519 eV, respectively. This result explains why metal-doped systems have greater potential for transferring electrons between interacting molecules owing to their smaller work function; however, the bare nanocomposite is likely to perform better as a semiconductor than the other systems.

$$\phi = - {\text{Ef}}$$

(4)

Fraction of electron transfer (∆N)

Electron transfer refers to the relocation of electrons from a molecule or atom to another chemical species. The fraction of electron transfer as a pesticide detection mechanism is important for predicting the sensitivity and response of a sensor to a pesticide [107,108,109]. In this study, this parameter was calculated for the interaction of codoped transition metals (Ni, Pd, Pt) and Ge-doped graphitic carbon (Ge@C₃N₄) with the diazinon pesticide via Eq. (5). The literature suggests that an FET value < 0 indicates the electron acceptance potential of the sensor, whereas FET values > 0 indicate electron donation. The results of this analysis presented in Table 3 show that the fraction of electron transfer falls between 0.036 and 4.074. The positive values suggest that the sensor selectivity toward the diazinon pesticide results from the higher electron donation potential of the sensor material. Furthermore, this leads to the distribution of charges in the system and subsequently affects the electronic structure of the system (increasing the reactivity of the sensor toward the pesticide).

(5)

Conclusions

The extensive use of pesticides has raised concerns about environmental contamination, which poses potential health risks to humans and aquatic life. In response to these concerns, several countries, including the United States, have implemented regulatory actions, restricting or outright banning the use of diazinon. Hence, the motivation for this study stems from the quest to maintain healthy ecosystems. This study utilized advanced theoretical calculations employing density functional theory (DFT) at the B3LYP-D3(BJ)/def2svp level of theory to gain insights into the electronic characteristics of Ge@C₃N₄ and doped transition metals such as Ni, Pt, and Pd, as well as their sensing behavior. This allows us to comprehensively explore the molecular properties and interactions of the investigated systems, offering valuable insights into their electronic structures, reactivities, and bonding mechanisms. To effectively sense diazinon, this research employed a varied range of methodologies beginning with the analysis of electronic properties (such as FMO, NBO, and DOS), intermolecular investigations (QTAIM and NCI), adsorption studies, and sensor mechanisms. These detailed assessments revealed insightful results, as clearly indicated by their narrow energy gap and other electronic properties. Noncovalent interactions characterized by van der Waals forces were revealed predominantly by QTAIM and NCI analysis.

Furthermore, the adsorption studies, which measured the strength of the interaction between the pesticide molecules and the surface of the sensor material, contributing to the sensitivity, reproducibility, and selectivity of the sensor, revealed favorable results characterized by negative adsorption energies of − 1.613 eV, − 1.613 eV, and − 1.599 eV for DZN_Ge@C₃N₄, DZN_Ni_Ge@C₃N₄, and DZN_Pd_Ge@C₃N_4, respectively. Although some results are not quite consistent across the various analyses, DZN_Ni_Ge@C3N4 and DZN_Pd_Ge@C3N4 are more consistent across all the parameters and are therefore associated with more insightful results. For example, both are observed to exhibit strong adsorption mechanisms and high separation tendencies, explained by the consistency in their adsorption energy and polarity values (dipole moment). This study, therefore, proposes these nanostructures for further in vitro studies aimed at sensing diazinon and other pesticides to maintain healthy ecosystems.