Biosynthesis and photocatalytic performance of Cumin extract-mediated ZnO nanoparticles with mechanistic investigation of degradation pathways

Abstract

The purpose of the study was to investigate novel and green-synthesized ZnO NPs derived from C. cyminum seeds for photocatalytic degradation of the Direct Yellow 86 dye under UV irradiation. The green catalyst cumin-derived Zinc Oxide NPs (GC-C-ZnO NPs) were characterized using Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscope (SEM), Transmission Electron Microscopy (TEM), and X-Ray Diffraction (XRD). A Box-Behnken Design (BBD) determines optimal conditions for DY86 removal, achieving 94% efficiency at pH 11.0, dye concentration of 30 mg L^-1, catalyst dose of 0.12 g L^-1, and a time of 30 min. The predictive model was statistically validated by analysis of variance (ANOVA), confirming a high correlation coefficient (R² 0.99). Significance was also verified by 0.99 and 0.98 values. The model and all parameters exhibited exceptional statistical significance (F-value 281.00, P-value < 0.0001). Liquid chromatography-mass spectrometry (LC-MS) analysis confirms diminished peaks at m/z 318.70 after photocatalysis by the GC-C-ZnO NPs. This investigation successfully advanced an efficient, eco-friendly photocatalytic system for enhanced water and wastewater purification. The study demonstrates that GC-C-ZnO NPs provide an efficient and eco-friendly photocatalytic system for the removal of organic dyes from textile wastewater.

Introduction

The use of synthetic dyes has become a significant problem due to the generation of large volumes of colored wastewater, which presents serious ecological and public health concerns^1,2. Azo dyes, such as Direct Yellow 86 (DY86), possess structural stability and biodestruction resistivity that render them recalcitrant and toxic to aquatic systems³. DY86 is a highly resistant dye used in the commercial production of textiles. It causes light blockage, interferes with the photosynthetic process, and generates byproducts that are harmful to aquatic life and humans⁴. Various removal methods, including coagulation, adsorption, membrane filtration, and biological degradation, have been employed for treating dye-contaminated effluents^5–7. However, they are often ineffective, uneconomical, or environmentally unfriendly. On the other hand, photocatalytic degradation has been proposed as an attractive process, as it can mineralize stable organic pollutants into less harmful products using light energy and appropriate photocatalysts⁸. Zinc Oxide (ZnO) is a well-known semiconductor photocatalyst framework due to its non-toxicity, high thermal stability, and pronounced absorbance in the UV region. Under UV radiation, ZnO-treated particles can generate reactive oxygen species (ROS), including hydroxyl radicals and superoxide ions, which can break down dye molecules and ultimately transform into harmless intermediates⁹.

The photocatalytic activity is greatly influenced by the morphology, dimensions, and crystallinity of the nanoparticles, which are also affected by the synthesis method¹⁰. The green synthesis method, which utilizes plant extracts, has attracted significant attention as an eco-friendly approach to synthesizing nanoparticles. Phytochemicals found in plants, such as flavonoids, terpenoids, and phenolics, perform as reducing and stabilizing agents in the preparation of metal and metal oxide nanoparticles¹¹. Cuminum cyminum (cumin) is a flowering plant with antioxidant and antimicrobial properties due to its rich phytochemical profile. Cumin (Cuminum cyminum L., family Apiaceae) is native to Egypt and Asia, and is now extensively cultivated in Iran¹². The seeds of C. cyminum serve as a promising natural precursor for the green synthesis of ZnO nanoparticles. Indeed, plant extracts offer a more economical alternative to synthetic chemicals, reducing both costs and consumption while supporting environmentally friendly agricultural practices¹³. Plant-mediated ZnO nanoparticles from Annona reticulata and Catharanthus pusillus display antibacterial, anticancer, and photocatalytic properties, demonstrating effective green synthesis methods^14–16. Based on our knowledge, there is no information in the literature concerning the use of C. cyminum originating from this type of specific geographical area, which undeniably supports the originality of this study. The novelty of this study was the green synthesis of ZnO NPs using C. cyminum from a specific, previously unreported geographical region, and in their first-time application as an efficient photocatalyst for the degradation of the DY86 dye. Therefore, this research aimed to assess the performance of GC-C-ZnO NPs as a new photocatalyst for the removal of the DY86 dye. The photocatalytic process was optimized using a BBD. Characterization of the photo catalyst and LC-MS analysis of intermediate products were also conducted.

Materials and methods

Materials

Zinc nitrate hexahydrate (Zn(NO₃)₂.6H₂O) was obtained from Sigma Aldrich. Ethanol and deionized water performed as solvents. The native seeds of C. cyminum were utilized to synthesize ZnO nanoparticles using a facile and eco-friendly approach. The dye used in the photocatalytic degradation experiments was Direct Yellow 86 (DY 86), prepared at varying concentrations of 10, 30, and 50 mg L^-1. All solutions were made with deionized water to ensure purity.

Preparation of C. cyminum extract

To remove surface contaminants, 20 g of C. cyminum seeds were rinsed with deionized water. The seeds were then dried and ground into a coarse powder at room temperature (25^°C). This powdered C. cyminum was added to 200 mL of deionized water and boiled at 100^°C for 1 h to extract the bioactive compounds. After that, the mixture was allowed to cool at 25^°C, and the aqueous extract was filtered using a Whatman filter with a pore size of 0.45 μm to obtain a clear solution¹⁷. This solution was then used in the synthesis procedure.

Synthesis of GC-C-ZnO NPs

A 0.01 M aqueous solution of Zn(NO₃)₂.6H₂O was prepared by dissolving it in 250 mL of deionized water. Then, 4 mL of the prepared C. cyminum extract was added to the Zinc nitrate hexahydrate solution and stirred (60^°C for 1 h). After time, a white precipitate formation indicated the successful synthesis of GC-C-ZnO NPs. The suspension was decanted into a 50 mL Falcon tube and centrifuged at 3000 rpm for 20 min to remove solid nanoparticles. The solid was then washed several times with distilled water (DW) to remove residual impurities. Finally, the catalyst was dried in an oven at 60^°C and calcined at 400^°C for 4 h to enhance crystallinity.

Photocatalytic activity

Photocatalytic degradation of DY86 was carried out in a 500 mL Plexiglas batch reactor under magnetic stirring, with a 7 W UV-C lamp (254 nm) positioned 3 cm from the reactor wall. Stock dye solutions (1000 mg L^-1) were diluted to 10, 30, and 50 mg L^-1 for testing. During the experiments, 5 mL aliquots were withdrawn at 30 min intervals and centrifuged at 5000 rpm for 5 min to remove the catalyst before measuring the residual dye concentration at 768 nm using a DR6000 UV-VIS spectrophotometer (Hatch Company, USA).

Experimental design and statistical analysis

The color removal efficiency was calculated based on Eq. 1. The study was designed using the BBD using Design of Experiments software (Ver. 13). A total of 29 experiments were conducted to investigate the effects of various variables. The variables investigated were pH (3 to 11), catalyst dosage (0.5 to 3.5 g L^-1), dye concentration (10 and 50 mg L^-1), and contact time (5 to 55 min). ANOVA analysis was also performed to determine the effect of variables and interactions.

1

Efficiency and characterization

The GC-C-ZnO NPs were characterized using SEM (TESCAN, MIRA III, Czech Republic (, TEM (CM120, Netherlands (, XRD (Bruker AXS-D8 diffractometer, USA), and FTIR (Shimadzu FTIR-8400 spectrometer, Japan) techniques. The intermediate products of photocatalytic degradation were analyzed using LC-MS. After the reaction, 5 mL aliquots were collected and filtered through a 0.22 μm PTFE syringe filter to remove GC-C-ZnO NPs. The filtered samples were injected into an Agilent 1260 Infinity LC system coupled with an Agilent 6120 Quadrupole LC-MS using a C18 column (150 × 4.6 mm, 5 μm). The mass spectrometer was operated in positive ion mode over a range of m/z 50–1000 to identify intermediate products.

Results and discussion

GC-C-ZnO NPs characterization

To confirm the successful synthesis and understand the structural, morphological, and chemical properties of GC-C-ZnO NPs, the nanoparticles were characterized using a combination of complementary techniques, including SEM, TEM, XRD, FTIR, and LC-MS analysis. The FTIR pattern of the GC-C-ZnO NPs is shown in Fig. 1a. A strong peak at approximately 600 cm^-1 is attributed to the stretching vibration of Zn–O, indicating the successful formation of ZnO NPs¹⁸. Peaks around 1400 cm^-1 correspond to the C = O stretching of the amide band. The peaks observed in the range of 1050–1100 cm^-1 result from the stretching vibrations of C–OH bonds, likely contributed by other organic compounds present in the plant extract used for synthesis. This observation aligns with the FTIR spectrum of the C. cyminum^19,20. Additionally, the peak near 1600 cm⁻¹ can be attributed to the bending vibration of O–H bonds, suggesting the presence of water molecules or phenolic compounds of plant origin²¹. A broad peak around 3420 cm⁻¹ is related to the O–H stretching vibrations of alcohol and hydroxyl groups, indicating the presence of alcoholic or phenolic compounds such as cuminyl alcohols in the sample. These functional groups also demonstrate the capping and stabilizing effects of phytochemicals during synthesis that play a role in the photocatalytic performance of the NPs²². The optical absorption spectrum (Fig. 1b) reveals a broad absorption peak centered at approximately 3.5 eV, and the estimated optical band gap energy of 3.3 eV for the ZnO photocatalyst is consistent with the characteristic wide band gap of wurtzite ZnO structure, making it suitable for UV-light-driven photocatalytic applications.

Fig. 1.

(a) FTIR pattern and (b) energy band gap of the GC-C-ZnO NPs.

Characterization of the GC-C-ZnO NPs in terms of morphology and size distribution was performed using Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM), as presented in Fig. 2a,b. Based on Fig. 2a, GC-C-ZnO NPs are spherical in shape and well dispersed. Figure 2a indicates the mean diameter was found to be lower than 100 nm, which confirms the nanostructure of the synthesized catalyst.

Fig. 2.

(a) SEM, (b) TEM and (c) particle size distribution of the GC-C-ZnO NPs.

The TEM images provide direct evidence of the structural features and uniformity of the nanoparticles, which are crucial for assessing their quality. As shown in Fig. 2b, the particles primarily exhibit a globular shape with a uniform dispersion. The results indicate that the average particle size was 58.5 nm [Fig. 2c], and there is no significant agglomeration observed. This lack of agglomeration suggests that the nanoparticles are stable. Additionally, it indicates that the phytochemicals from the C. cyminum extract act as capping agents, effectively binding the nanoparticles²².

Figure 3 presents the XRD pattern of the synthesized ZnO nanoparticles, displaying characteristic diffraction peaks at 2θ = 31.7°, 34.4°, 36.2°, 47.5°, 56.6°, 62.8°, 66.4°, 67.9°, and 69.1°, corresponding to the (100), (002), (101), (102), (110), (103), (200), (112), and (201) planes of hexagonal wurtzite ZnO structure, respectively. All peaks are in good agreement with JCPDS Card No. 00-055-0664, confirming the formation of pure phase wurtzite ZnO without any detectable impurities. The sharp and intense diffraction peaks suggest high crystallinity of the synthesized nanoparticles.

Fig. 3.

XRD plot of the GC-C-ZnO NPs.

BBD and ANOVA analysis

In this research, the BBD method was used to analyze and optimize parameters. The optimal conditions for DY86 dye removal via BBD are presented in Table 1.

Table 1.

The optimal conditions of DY86 dye removal.

RUN	pH	Concentration (mg L-1)	Dosage (g L-1)	Time (min)	Removal (%)
1	7	30	0.06	5	1
2	7	50	0.09	5	9
3	11	30	0.09	55	81
4	3	50	0.09	30	56.5
5	11	30	0.12	30	94
6	11	30	0.06	30	59
7	7	30	0.09	30	24.5
8	3	30	0.09	55	61
9	7	30	0.09	30	22.5
10	7	30	0.06	55	12.5
11	3	10	0.09	30	68
12	3	30	0.12	30	53
13	7	30	0.12	55	28
14	7	10	0.09	55	38
15	7	50	0.12	30	17
16	11	10	0.09	30	89
17	7	30	0.09	30	25
18	3	30	0.09	5	37
19	7	30	0.09	30	23
20	7	10	0.06	30	20
21	11	30	0.09	5	70
22	3	30	0.06	30	51
23	11	50	0.09	30	74
24	7	50	0.06	30	7
25	7	50	0.09	55	16
26	7	10	0.12	30	39.5
27	7	10	0.09	5	16
28	7	30	0.12	5	11
29	7	30	0.09	30	23

Equation 2 was employed to depict the association between independent variables and DY86 dye removal.

2

The ANOVA results are exhibited in Table 2 to present the interaction between parameters and response. The F-value and P-values of the quadratic model reveal the significance of each parameter, with higher F-values and lower P-values meaning significant terms²³.

Table 2.

ANOVA analysis for the DY86 dye degradation.

Source	Sum of squares	df	Mean square	F-value	p-value	Sig
Model	19848.91	14	1417.78	281.00	< 0.0001	Significant
A-pH	1645.02	1	1645.02	326.03	< 0.0001
B-Concentration	690.08	1	690.08	136.77	< 0.0001
C-Dosage	705.33	1	705.33	139.79	< 0.0001
D-Time	713.02	1	713.02	141.32	< 0.0001
AB	3.06	1	3.06	0.6070	0.4489
AC	272.25	1	272.25	53.96	< 0.0001
AD	42.25	1	42.25	8.37	0.0118
BC	22.56	1	22.56	4.47	0.0529
BD	56.25	1	56.25	11.15	0.0049
CD	7.56	1	7.56	1.50	0.2411
A2	13350.06	1	13350.06	2645.92	< 0.0001
B2	38.28	1	38.28	7.59	0.0155
C2	143.03	1	143.03	28.35	0.0001
D2	254.05	1	254.05	50.35	< 0.0001
Residual	70.64	14	5.05
Lack of fit	65.94	10	6.59	5.61	0.0555	Not significant
Pure error	4.70	4	1.18
Cor total	19919.55	28
R2	0.9965
Adjusted R2	0.9929
Predicted R2	0.9806
Adeq precision	57.7230

The F-value of 281.00 shows significance, with 0.01% noise chance and P-values < 0.0001. In this model, all parameters (A: pH, B: dye concentration, C: catalyst dosage, and D: time) were significant (P-value < 0.0001). The lack of fit F-value of 5.61 suggests no significant errors, with a 5.55% chance that the F-value is due to noise. The quadratic model defined for 99.65% of the response variability (R² 0.99), with only 0.35% considered negligible. Significance was proved by R²_adj 0.99 and R²_prd 0.98 values. The Adeq precision of 57.72 is a good signal for navigating the design space, as it calculates response values with an average prediction error ratio over than 4²⁴. This model shows the relationship between independent parameters and DY86 dye degradation.

Figure 4a–d shows the adequacy of the regression model to remove DY86 dye using the photocatalytic process. The normal probability in Fig. 4a indicates that the result is approximately normally distributed. Figure 4b shows that there is an excellent agreement between experimental measurements and predicted values. Figure 4c illustrates the model’s reliability, displaying a consistent data distribution within the interval of -3.93 to + 3.93, which corresponds to the thresholds to detect lower to higher outliers²⁵.

Fig. 4.

Normal plot of residuals (a) normal probability (b) predicted vs. actual values (c) residuals vs. run number (d) residuals vs. predicted.

Figure 4d exhibits a uniform distribution of residuals, which spreads above and below the baseline, meaning a random spread without a trend.

Interaction and optimization of process

The interaction of different parameters to degrade the DY86 dye was illustrated in Fig. 5a–d. As shown in Fig. 5a, the photocatalytic performance is markedly affected by the solution pH. The degradation efficiency improved with increasing pH, achieving its highest value under alkaline conditions (pH 11). This enhancement is related to the higher concentration of hydroxyl ions (OH^-) that can be oxidized to generate highly reactive hydroxyl radicals (^•OH), responsible for degrading contaminants. Several studies have documented that photocatalytic efficiency is often enhanced in alkaline conditions^26,27. In contrast, at pH < 6, the photocatalytic activity is decreased, which means that protonation of the catalyst surface suppresses the generation of ROS^28,29. In this condition, charge separation of photogenerated electron-hole pairs is inhibited³⁰. In Fig. 5a, increasing the dye concentration beyond 40 mg L^-1 led to a noticeable decline in degradation efficiency. This behavior can be associated with high dye concentration, which reduces photon availability for catalyst activation³¹.

Fig. 5.

3D plots of DY86 dye removal at different parameters. (a) Dye concentration and pH, (b) Catalyst dosage and pH, (c) Catalyst dosage and dye concentration, (d) Time and pH, and (e) Perturbation plot. (The image was created using Design-Expert^® software (Version 13, available at https://www.statease.com/).

At pH 6–7, photon penetration and competitive adsorption between dye molecules and hydroxyl radicals on the catalyst surface decreased. In Fig. 5b, the interaction between pH and catalyst dosage reveals that the optimal photocatalytic response occurs at a catalyst loading of approximately 0.08–0.10 g L^-1. Beyond this threshold, the degradation efficiency tends to plateau or slightly decrease, possibly due to particle agglomeration and light scattering effects, which hinder the effective utilization of incident photons^32,33. Similar studies align with the findings of the current research. For example, Adaileh et al. found that degradation rates increased with catalyst dosage up to 0.3 g, highlighting an optimal range for the reaction³³. Similarly, research on the degradation of pharmaceutical residues identified an optimum catalyst concentration of 150 mg L^-134. As a result, dye degradation decreases beyond an optimal point due to physical effects that limit a catalyst’s light absorption. High catalyst concentrations increase solution turbidity, causing light scattering that prevents photons from reaching the catalyst’s surface. This “light-scattering effect” reduces the photocatalyst’s activation and overall efficiency³⁵. Figure 5c illustrates the combined effect of dye concentration and catalyst dosage. Increasing the dye concentration from 10 to 50 mg L^-1 leads to a decline in degradation percentage, particularly at lower catalyst loadings. The high number of dye molecules may compete for limited active sites on the catalyst surface, reducing light penetration and the production of reactive species (^•OH and ^•O_₂^⁻)³⁶. Conversely, an increase in catalyst dosage from 0.06 to 0.12 g L^-1 enhances the degradation rate due to the availability of more active sites and higher photon absorption capacity³⁷. However, beyond a certain threshold, this enhancement becomes less pronounced, likely due to light scattering and aggregation effects at higher catalyst concentrations. Figure 5d displays the interactive effect of pH and reaction time. Degradation efficiency peaks at 25–30 min, after which improvements are minimal, indicating a quasi-steady state. At acidic conditions (pH < 5), protonation of the catalyst surface may suppress the adsorption of dye anions, while at basic conditions (pH > 9), excessive hydroxyl ions can lead to charge repulsion and decreased interaction between dye molecules and the catalyst surface. Overall, these findings confirm that the GC-C-ZnO NPs exhibit strong photocatalytic activity under operating conditions (catalyst dosage 0.12 g L^-1, pH 11, dye concentration 30 mg L^-1, and time 30 min). The degradation of DY86 dye reached 94% under these conditions.

The perturbation plot (Fig. 5e) illustrates the relative sensitivity of photocatalytic degradation efficiency to each experimental factor at the reference point. The plot reveals that pH (A) exhibits the steepest positive slope, indicating it is the most influential parameter on the degradation performance, with efficiency increasing dramatically from approximately 35% to 72% as pH shifts from its lowest to highest level. In contrast, dye concentration (B) shows a moderate negative slope, suggesting that higher dye concentrations lead to decreased degradation efficiency, likely due to reduced light penetration and active site saturation. Reaction time (D) demonstrates a slight positive effect with a relatively flat response, indicating that within the studied time range, its impact on degradation is minimal. Interestingly, catalyst dosage (C) displays a curved profile with minimal overall change, suggesting a complex non-linear relationship where both very low and very high dosages may not significantly enhance the photocatalytic performance. These findings highlight that pH optimization should be prioritized for maximizing the photocatalytic degradation efficiency of the ZnO catalyst.

LC-MS analysis of by-products

To evaluate the degradation intermediates of DY86 dye, LC-MS analyses were performed, as depicted in Fig. 6. The chromatographic profiles obtained before and after the photocatalytic treatment, revealing substantial structural transformations of the parent dye molecule. The LC-MS spectra of the DY86 dye before photocatalytic treatment show intense peaks at m/z 318.70, 274.70, 340.70, 362.70, 437.70, etc., assigned to the molecular ion of the parent dye and major aromatic structures [Fig. 6a]. After the process [Fig. 6b], those parent-ion peaks are largely diminished, and new peaks appear (e.g., m/z 321.00, 365.50, 391.80, 413.60, as well as several peaks between m/z ~ 500–650), consistent with the formation of degradation intermediates. Based on the literature, the mechanisms of dye degradation are proposed via initial attack of reactive oxygen species (particularly ^•OH radicals) at the − N = N − linkages and electron‐rich aromatic rings, leading to − N = N − bond cleavage³⁸. Subsequent deamination, desulfonation, hydroxylation, and ring‐opening steps lead to smaller substituted aromatic compounds (e.g., phenolics, quinones), which further oxidize toward aliphatic acids and eventual mineralization. The presence of peaks corresponding to partially oxidized aromatic fragments (those with added hydroxyl groups or loss of sulfonate/amino moieties) supports intermediate stages³⁹. The disappearance of parent molecular ions confirms high conversion of dye molecules⁴⁰.

Fig. 6.

LC-MS spectra of DY86 before and after photocatalytic process using GC-C-ZnO NPs.

Proposed degradation mechanism

The observed transformation pattern follows Eqs. 3–10 and shown in Fig. 7: Firstly, DY86 dye molecules are adsorbed onto the GC-C-ZnO NPs surface. With UV irradiation, photo-generated ROS, mainly ^•OH and O₂^•– radicals, are generated via GC-C-ZnO NPs. ^•OH radicals are produced via the reaction of H⁺ with H₂O and OH⁻ ions. O₂^•– radicals are also produced through reaction between e⁻ and O₂, which converts into HO₂^{• 41,42}. These interactions lead to cleavage of DY86 dye molecules through ROS attacks on the bonds and electron-rich aromatic rings. After the dye degradation, the parent molecular ion decreases in intensity, and new ions at lower or varied m/z appear, as shown in the LC-MS analysis.

3

4

5

6

7

8

9

10

Fig. 7.

Photocatalytic degradation mechanism of DY86 dye using GC-C-ZnO NPs.

Conclusion

In this study, novel ZnO NPs derived from C. cyminum seeds were successfully synthesized to degrade DY86 dye using the photocatalytic process under UV irradiation. The characterization of the GC-C-ZnO NPs was conducted by XRD, FTIR, SEM, and TEM methods. The BBD model was utilized to explore the optimization of process variables. The optimum values were obtained at pH 11.0, initial dye concentration of 30.0 mg L^-1, catalyst dose of 0.12 g L^-1, and reaction time of 30.0 min with degradation efficiency of 94%. The statistical significance of the model was assessed using ANOVA. The high correlation coefficient (R² 0.99) demonstrated excellent correlation between the experimental and predicted responses, confirming the reliability of the developed model. The F-value of 281.00 shows significance, with 0.01% noise chance and P-values < 0.0001. All parameters were also significant (P-value < 0.0001). LC-MS analyses of DY86 dye showed intense peaks at m/z 318.70 and others before treatment. After photocatalysis, these peaks diminished, and new peaks indicated degradation intermediates. Accordingly, this investigation aims to improve the development of an eco-friendly photocatalyst process for environmental applications.

Author contributions

AmSh and FM performed the experiments. MKh and MM was a major contributor in Methodology and Supervision. MKh and FS were a major contributor in writing the manuscript. MM and MY interpreted the data. All authors designed the experiments and analyzed the experimental results. Mkh edited the manuscript. All authors read and approved the final manuscript.

Data availability

The data that support the findings of this study are available within the article.

Declarations

Competing interests

The authors declare no competing interests.