Spectral Characterization
The inhomogeneous electron gas (the Born–Oppenheimer approximation) is a collection of interacting point electrons traveling quantum–mechanically in the potential region of a set of atomic nuclei that are considered to be static. The independent electron approximation, Hartree theory, and Hartree-Fock theory are the most common approximation schemes used to solve such models. However, over the last thirty years or so, another approach-Density Functional Theory (DFT)-has become increasingly popular for the solution of such problems. This approach has the advantage of being able to solve a wide variety of problems with high precision while still being computationally simple. Density functional theory can be used to measure the electronic structure of atoms, molecules, and solids (DFT). Its aim is to use quantum mechanics' fundamental laws to obtain a quantitative understanding of material properties.
Using traditional electronic structure approaches, the Schrödinger equation of N interacting electrons moving in an external, electrostatic potential is solved (typically the Coulomb potential generated by the atomic nuclei). However, there are major disadvantages to this approach: (1) the problem is nontrivial even for small numbers N, and the resulting wave functions are complicated objects; and (2) the computational effort increases exponentially with increasing N, rendering the description of larger systems prohibitive. DFT takes a different approach, using the one-body density as the fundamental variable instead of the many-body wave function. Density-functional theory is computationally feasible also for large systems since the density n(r) is a function of only three spatial coordinates (rather than the 3N coordinates of the wave function). A significant development influenced spectral analysis methodology, as the numerical approach was improved by Cooley and Tukey in 1965, known as the Fast Fourier Transformation. The continuum of power density or power range indicates the value of the Fourier process in spectral analysis. The function of autocorrelation of a continuous signal plays an important role as well. The Fourier transform of a finite continuous and their relation to infinite, continuous signals will provide the Dirichlet condition and zero means for the continuous operation. The experimental measures of the compound in our paper are limited to a limited amount of time. By multiplying the infinite continuous recording by a data window as defined, the record duration can be limited to a certain period T.
(1)
The window in fourier integral leads to the identity as
w(t) = 1 for |t| ≤ T/2 (2)
The Fourier transform of this product is the transformation of the infinite record which is paired with transformations of the window when multiplying the infinite record. The Convolution Theorem establishes this relationship, which affirms that:
F[f(t).w(t)] = F(α) * W(α) (3)
Discrete-time Fourier transform (DTFT) is helpful in calculating the diffracted wave information and the obtained peaks tell us all about the molecular design's properties without having to measure the molecular structure. The DTFT is in the direction of x, y, z for a given compound.
(4)
The p(x,y,x) gives the density distribution of the crystalline state of the compound and (a,b,c) represent the edge length in the (x,y,z) directions. The DTFT biosynthesis analysis of Cu2O as shown (Fig. 1) reveals a distinct difference in the spectral density peak for the three experiments. DTFT peaks shown above indicate that the peak value of 1100 is unique to the synthesized compound and that of the previous compound, the lower peak of 200 is. The sharp rise in maximum value also indicates that the compound is more active and reactive than the other compound.
UV-Vis Analysis
The optical properties of biomolecules loaded Cu2O NPs were analyzed through UV-Vis absorbance spectra. Fig. 2 shows the optical absorbance spectra of the Cu2O NPs. The formation of Cu2O NPs confirmed by the colour change of copper nitrate aqueous solution from bluish green to sea green when adding of Datura metel L. leaf extract shown (Fig. 3). The SPR absorbance peak was found at 790 nm by the oscillation of electrons on the surface of the Cu2O NPs, which is clearly revealing the reduction of Cu2O NPs. The formed Cu2O NPs absorbance peak at 790 nm shows the electronic d-d transitions making by the Cu2+ ions in d orbital, in this kind of absorption favored for the extended lifetime of photogenerated carriers [13]. The serious of alkaloids present on the Datura metel L. extract such as hyoscyamine, atropine is exhibited the characteristic absorption band at 200-350 nm in UV-Vis spectra corresponds to π-π* transition [14]. The bandgap energy of prepared Cu2O nanoparticles is 2.98 eV from Tauc’s plot analysis. This bandgap energy well suited for solar cell and optical device applications according to the report of earlier researcher. Awed et al., 2019 has achieved 2.98 eV for nonlinear optical susceptibility through annealing process and Singh et al., 2004 has attained the same bandgap energy for nanocrystalline CdTe film for electroluminescent display devices [15, 16].
XRD Analysis
Crystallinity, size, and phase of the biosynthesized Cu2O NPs were determined through XRD analysis and their diffraction pattern shown (Fig. 4). Biosynthesized Cu2O NPs have characteristic diffraction peaks at 2θ angle 22.77°, 25.08°, 26.54°, 29.48°, 31.28°, 32.95°, 36.47°, 38.67°, 42.19°, 47.66°, 51.62°. Here, the observed diffraction reflections peaks at 29.48°, 36.47°, 42.19°, 51.62° indicates the presence of Cu2O NPs and indexed by Bragg’s reflections (110), (111), (200), and (211). According to the JCPDS Card No: 77-0199 the mentioned lattice planes such as (110), (111), (200) are exhibit primitive lattice structures of Cu2O NPs. Similar kinds of Cu2O NPS XRD diffractogram are reported by some other researchers for various plant extract [2, 6, 17]. The remaining unassigned peaks and background noises in the XRD pattern represented by the star symbol, which reveals the Datura metel L. biomolecules, encapsulated around the Cu2O NPs [18]. The prepared Cu2O NPs average crystallite size is 19.56 nm calculated from the result of XRD analysis using Debye – Scherer’s equation.
(5)
In this case, k is the dimensionless shape factor taken as 0.9, λ known as X-ray wavelength, β is line broadening at half the maximum intensity (FWHM) and θ is the Bragg angle. This result illustrated biomolecules are well bound with Cu2O nanoparticles during synthesis and Datura metel L. extract is one of the promising candidates for reduction and stabilization of Cu2O NPs.
Fourier Transform Infrared Spectroscopy Analysis
Phytochemicals present on the plant extract and the formation of Cu2O were identified through the FTIR spectra analysis. The IR spectra of Cu2O NPs compared with Datura metel L. is shown (Fig. 5). The FTIR spectrum (Fig. 5b) Datura metel L. leaf extract shows a broad absorption band at 3406 cm-1 which is due to the O-H stretching mode of phenol and alcohols. The peak at 2937 cm-1 indicates C-H stretching of alkyl groups and strong peaks 1651 cm-1 show the C=C stretching vibration of carboxylic groups. The peaks at 1546 cm-1 reveal that the C-N stretch of aliphatic amines and peaks at 1406, 1359, and 1317 cm-1 are representing C-C stretch (in-ring) of aromatics, N=O bending vibration of nitro compounds, C-N stretch of aromatic amines, respectively. The peaks that appeared at 1105, and 1068 cm-1 are belong to the C-N stretch of aliphatic amines. The peaks at 752, 621, and 526 cm-1 are show the existence of C-Cl stretch alkyl halides, C-H bends alkanes, and C-I stretches aliphatic iodo compounds. The FTIR spectrum of Cu2O NPs showed again the presence of O-H stretching mode of phenol and alcohols and C-H stretching of alkyl groups at 3404, and 1620 cm-1, which supports the idea of phenol, alcohols, and alkyl group, are free from Cu2O NPs formation. The peaks at 1409, 1359, and 1317 cm-1 on Cu2O NPs represent a diminishment of C-C stretch (in-ring) of aromatics, N=O bending vibration of nitro compounds, C-N stretch of aromatic amines, respectively. After bioreduction, peak shifts have occurred at 752 to 709 cm-1 on C-Cl alkyl halides, 621 to 650 cm-1 on C-H bend of alkanes, and 526 to 609 cm-1 on C-I stretch in aliphatic iodo compounds. Above mentioned biocompounds are acting as a capping agent as well as bound along with the Cu2O nanoparticles. The disappearance of peaks at 2937, 1546, 879 cm-1 and newly formed peaks at 1043, 999 cm-1 confirms the C-H stretching of alkyl, C-N stretch of aliphatic amines, N-H bending vibration of nitro compounds, and C-OH of carboxylic acid are responsible for structural changes on NPs formation. The strong peaks obtained at 819 cm-1 and the existence of new peaks at 499 cm-1 are correspond to the characteristic formation of Cu2O NPs (Fig. 5a) [19]. Particularly aliphatic amines in Datura metel L. leaf extract are mainly responsible for the reduction of copper ions into Cu2O NPs.
Density Functional Theory Analysis
The optical properties of a molecule or crystal are among the most useful classes of properties that can predict distinctive characteristics. These can be used to locate wavelengths of optical radiation based on its electronic structure either in the absorption or emission spectrum. DFT lets us calculate these properties, related to electron motion evolution under electric field control. DFT is the theory of differential and functional functions. The spectrum data are shown (Fig. 6). NUM is first standardized with [01] which Gaussian has reoriented to speed up the two calculations of electron energy models. The internal nuclear energy has been measured using the spectral data measurement. The next step is to measure every single electron transaction using the Hamiltonian Fock matrix. Once we know the propagation of the electron, we measure the angular momentum of the electrons that are then used to detect an electron's energy gap.
The energy of the synthesized compound stabilized over some time to a constant value of 16.6378 eV and remained the same indicating compound stability. As shown in Fig. 6. The initial fluctuation is caused by the excitation of the electron that tends to return the electron to its normal condition in time. The oscillation reaction shows that the compound is erratic, but as the proposed compound can be shown, its behavior is very stable.
Computational modeling
Calculation of the thermodynamic and surface characteristics of Cu2O thin films at all temperatures was carried out using MERA software with periodic boundary conditions along with a, b and c axes of Cu2O unit cell like it were described in [20, 21] and applied in studying organic, inorganic and combined systems in [20-37].
The MOPS algorithm has been used to model oxyhydrate gel formation [20, 25, 29], crystal structures of triosmium clusters [21, 23, 24, 28, 30, 33, 35], organic molecule complexation during chemical reactions [31], protein affinity [34], and crystal structures and interaction energies of gas hydrates [26, 37]. Calculated energies, thermodynamic properties (such as enthalpies, entropies, and Gibbs-free energies), modeled structures of complexes, crystals, and clusters, and predicted yields, rates, and regio- and stereospecificity of reactions were all in good agreement with experimental results.
The structures hyoscyamine and atropine were optimized at the DFT B3LYP 6-311G (d,p) level of theory. Then, the UV-Vis spectrum was calculated using TD DFT B3LYP 6-311G (d,p) which shows the absorption band is 253.3 nm that in good agreement with the experimental data.
The structure of cuprite [38] (Crystallography Open Database ID 1000063) was taken as the initial structure for the computer simulation of Cu2O nanoparticles (cubic syngony, space group Pn m, a = b =c = 4.252(2), α = β = γ = 90°).
1000 multiplications of the crystal cell in random directions were performed to the composition Cu2474O1237 (this composition corresponds to the experimental size of the particles) and the structure with the minimum energy is chosen. Calculation using the Bragg’s equation showed that the resulting modelled nanoparticles should have diffraction reflections peaks at 2θ angle 29.69°, 36.57°, 42.49°, 52.69°, 61.65°, 69.90°, 73.86°, 77.74°, 85.35°. The first four reflections are in good agreement with the experimental diffractogram and correspond to reflections (110), (111), (200), and (211) that show a good quality of the simulation.
The initial structure of atropine and hyoscyamine in an aqueous solution (they are similar, since atropine is a racemate and hyoscyamine is an L-isomer of the same compound) modeled within the MOPS software [20, 25, 29] with the continual account of the solvent influence shown (Fig. 7a). The structure contains the intramolecular hydrogen bond =O…H-O with a length of 2.22 Å.
Subsequently, the modeling of the complex of this nanoparticle with hyoscyamine and with atropine was carried out. The calculated Gibbs free energy of the complex formation is -179.4 kJ/mol. The structure and conformation of atropine (hyoscyamine) remain almost unchanged during the formation of the complex. The complex is formed by three short contacts (Fig. 7b). Two of them are carried out by carbonyl oxygen with two copper atoms of nanoparticles (2.16 and 2.19 Å). These bifurcate interactions become possible due to a defect in the surface of the nanoparticles, when two copper atoms at once turn out to be with a lack of valence. The third contact is the hydrogen bond of the hydroxyl hydrogen of atropine with the oxygen of the nanoparticle surface (2.09 Å). The intramolecular hydrogen bond =O…H-O is retained, but slightly extended to 2.37 Å.