Toxicological Impacts and Environmental Risk of Atrazine in the Presence of Graphene Family Nanomaterials (GFNs) on Chlorella sp

Toxicological Impacts and Environmental Risk of Atrazine in the Presence of Graphene Family Nanomaterials (GFNs) on Chlorella sp | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Toxicological Impacts and Environmental Risk of Atrazine in the Presence of Graphene Family Nanomaterials (GFNs) on Chlorella sp Abhrajit Debroy, Mrudula Pulimi, Amitava Mukherjee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5853275/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Atrazine is a widely used chlorine herbicide, but recent studies raised concerns about its environmental and human health risks. Graphene family nanomaterials (GFNs) have various applications and are often released into aquatic environments, impacting marine microflora. However, the combined effects of atrazine and GFNs on marine organisms like Chlorella sp. have not been thoroughly assessed. The physicochemical interactions between atrazine and GFNs were examined using Raman spectroscopy, electron microscopy, contact angle measurements, surface charge analyses, and chromatography. The contact angle analysis revealed a decline with increasing atrazine concentration, indicating enhanced hydrophilicity of the mixture. Key toxicity parameters, including growth inhibition, total reactive oxygen species (ROS) production, malondialdehyde (MDA) generation, photosynthetic efficiency, and antioxidant enzyme activity, were assessed for individual contaminants and their binary mixtures. ROS and antioxidant enzyme activity exhibited the most significant modulation in response to atrazine concentration. Low atrazine levels exacerbated toxicity by elevating oxidative stress markers (ROS and MDA) in mixtures with GFNs, whereas higher concentrations mitigated these effects by reducing ROS and MDA generation compared to individual exposures. The study also uses statistical tools to evaluate the interconnection between the biochemical parameters and the treatment groups. The results clearly show how the GFNs can reduce the harmful effects of atrazine in marine ecosystems. GFNs provide a surface for the adsorption of the atrazine molecules, thereby reducing their availability to the algal cells and reducing their toxic potential. This deepens our understanding of the environmental applications of the GFNs for mitigating the risk of emerging pollutants like atrazine. Graphene family nanomaterials atrazine mixture toxicity oxidative stress photosynthetic activities Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Pesticides play a critical role in contemporary agricultural practices aimed at meeting the demands of the increasing global population; however, their application leads to environmental contamination, particularly water pollution [ 1 ]. Atrazine (2-chloro-4-ethylamino-6-isopropylamino-1,3,5-triazine) (ATZ) is one of the most extensively utilized herbicides globally, owing to its great efficacy, cost-effectiveness, and versatile applications. It is marketed in over 100 countries, with an annual consumption of around 70,000 to 90,000 tons [ 2 ]. The extensive use of ATZ results in its flow into aquatic ecosystems. It possesses an extended half-life ranging from several days to years and a moderate aqueous solubility of 30 mg L − 1 . Atrazine's persistence results in its frequent detection in aquatic ecosystems at elevated quantities, reaching up to 30 µg L − 1 [ 3 , 4 ]. Given its established toxicological effects on non-target species, including toxigenicity, reproductive toxicity, and endocrine disruption [ 5 – 7 ], environmental and public health organizations flagged its risk in aquatic environment. ATZ may diminish cellular metabolism and affect the production of reactive oxygen species (ROS), thereby modifying antioxidant activity in fish [ 8 , 9 ], and crustaceans [ 10 , 11 ]. ATZ can impede algae development and photosynthesis [ 12 ]. In a previous study, the exposure of Raphidocelis subcapitata to 50–600 µg L − 1 ATZ for 72 hours led to a decreased carbon-to-nitrogen ratio, reflecting metabolic disruptions [ 13 ]. Similarly, Selenastrum capricornutum exhibited significant growth inhibition at ATZ concentrations of 0.076 and 0.023 mg L − 1 over 48 and 72 hours, respectively [ 14 ]. In another study, upon exposure of Chlorella vulgaris to ATZ and DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea) under 'standard' low light intensity, as stipulated by the OECD201 guideline, they showed a substantial decline in oxygen productivity and photosynthetic activity at short exposure times (> 1hr) [ 15 ]. All these studies prove the toxic potential of ATZ in the algal cells, in various aquatic environments. Graphene-family nanomaterials (GFNs) are analogous graphene-like materials with diverse sizes, layers, surface chemistries, and flaws. These encompass various nanomaterials, including few-layer graphene, graphene oxide (GO), graphene quantum dots, reduced graphene oxide (rGO), and multilayer graphene materials synthesized from graphene, GO, or other graphene derivatives as precursors [ 16 ]. The presence of robust sigma bonds connecting carbon atoms offers GFNs exceptional thermal stability, mechanical capabilities, radiation resistance, high-temperature resistance, and superior chemical stability. The extended π–π coupling leads to strong electrical conductivity and superior oxidation efficiency of GFNs. These outstanding qualities and low synthesis costs allow GFNs to become novel materials in the 21st century with quick development and great demand [ 17 ]. With increasing production and utilization, GFNs have infiltrated the environment by numerous means, suggesting potential harm to aquatic species, plants, or animals [ 18 ]. Recent investigations demonstrated that the doses of GFNs in the environment range from 0.001 to 10 mg/L, even higher [ 19 , 20 ]. Amongst the GFNs, shading impact and loss of available nutrients were revealed to be the primary harmful mechanism of GO to Chlorella pyrenoidosa [ 21 ]. GO at low concentration was reported to stimulate the upregulation of microcystin production of M. aeruginosa due to the metabolic alterations [ 22 ]. Also, graphene (at 1 mg/L concentration) diminished cell survival, reduced esterase activity, and impaired photosynthetic efficiency in the cells. Another recent research aimed to investigate the effects, both individually and collectively, of polystyrene microplastics (PSMPs, 1 µm) and nanoplastics that are (PSNPs, 50–100 nm), in conjunction with agriculturally pertinent GO, on the germination and growth of lettuce seeds ( Lactuca sativa ). The findings indicated that the interactions between PSMPs/PSNPs and GO combinations had both synergistic and antagonistic effects, contingent upon several toxicity markers. The biological mechanism responsible for the synergistic effects on the roots and shoots of seedlings entailed oxidative stress [ 23 ]. The study highlighted the role of mixture studies and how GO can help modify the toxic potential of PSMPs/PSNPs. Hence, it is pertinent to evaluate the impact of GFNs in combination with other pollutants on the marine ecosystem. In addition, literature confirmed the availability of GFNs and ATZ in the marine environment [ 2 , 24 ]. If these materials are coming into the environment, then their combinations can also be found in the natural environment. In this way, the current study is one of a kind since it is the first to establish the concentration-dependent changes in the toxic potential of ATZ in the presence of various GFNs. Microalgae have a remarkably rapid growth rate and can turn solar energy into chemical energy, stabilize atmospheric carbon dioxide, and function as primary producers. They also supply critical nutrients for terrestrial and aquatic creatures [ 25 ]. Microalgae are biomarkers of eutrophication and are efficiently used to monitor water quality [ 26 ]. Chlorella sp. are well-known unicellular green microalgae that usually thrive in aquatic settings. These green microalgae are regarded as key biological markers in aquatic settings and comprise a large portion of the food chain in aquatic ecosystems [ 25 ]. These species exhibit sensitivities to harmful compounds, endurance to high temperatures, and the capacity to proliferate in small quantities of nutrients. Hence, in the current study, Chlorella sp. was chosen as the indicator organism to determine the impacts of GFNs on the toxic potential of ATZ at environmentally relevant concentrations. A comprehensive literature review reveals a notable gap in the existing research on the effects of the mixture of ATZ and GFNs in marine algal species. Hence, the current work focuses on bridging the research gap by evaluating the impact of different GFNs on the increasing concentrations of ATZ in the marine environment. The current work postulates that ATZ produces toxicity in algal cells by forming oxidative stress while lowering photosynthetic efficacy. The use of GFNs in recent years has increased tremendously, which has resulted in their leaching into the aquatic environment. ATZ, too, has been one of the most frequently detected herbicides in aquatic environments. Recent research shows that the levels of GFNs in the aquatic environment can reach up to (1–1000 µ g/L) [ 27 ], and ATZ concentrations can reach up to 134 ng L − 1 [ 28 ]. Hence, there is a chance that ATZ can co-exist in aqueous environments, allowing their interaction. Authors hypothesized that including GFNs (GO, rGO, and graphene, at a concentration of 250 µg/L) would alter their potential toxicities by modulating their solubilities in the media. The chosen concentrations of GFNs for the study were fixed after carefully deliberating the available literature on the amount of GFNs present in the aquatic environment. ATZ is one of the most commonly used herbicides in the agricultural field [ 29 ]. The high demand results in huge production, and as a result, the runoffs from the agricultural land, result in their transport in the aquatic ecosystems. However, none previous studies have observed the impact of the GFNs and ATZ on aquatic organisms. Hence, the study's authors considered this combination and explored their interaction in the present study. The work proposed that adding GFNs at environmentally relevant concentrations to increasing concentrations of ATZ would result in the simultaneous interaction of ATZ with GFNs, thereby inducing them to form aggregates. The aggregate formation is increased with the increase in ATZ concentration, resulting in an antagonistic effect towards the algal cells. Whereas, at lower concentrations of ATZ in the media, the number of aggregates would be lower, implying more available free ATZ for algal interaction and an increase in their toxic potential compared to their pristine groups. Current research examined the toxic potential of ATZ at naturally relevant doses ranging from 25–100 µg/L in the marine microalga Chlorella sp. The study was performed in the well-defined artificial seawater (ASW) media. The ATZ-GFN interaction was examined using surface charge and hydrophobicity analyses. Additionally, the nominal concentration of ATZ was investigated using ultra-performance liquid chromatography (UPLC). The available concentration of ATZ at 72 h confirms the GFNs-ATZ interaction and adsorption of ATZ. The toxicological experiments were studied by assessing the growth inhibitory effect, generated oxidative stress, variations in SOD and catalase activities, and photosynthetic efficiencies as well. The changes in the physicochemical parameters of GFNs and ATZ were associated with the changes in the toxicological parameters. 2. Methodology 2.1. Material synthesis, and chemical preparation The current study incorporates chemicals details in the supporting information (M1S1). As an interaction medium, autoclaved ASW was used, similar with the past research [ 30 , 31 ]. The protocol to prepare ASW is outlined in the supporting information Tables S1–S4. The synthesis of GFNs followed established procedures as outlined in prior publications [ 31 – 33 ]. Further information can be found in the supporting information (M2S2). ATZ was purchased from was purchased from Sigma Aldrich. GFN suspensions were prepared at a concentration of 50 mg/L in milli-q water, as detailed by Lu et al. (2018) [ 34 ], utilizing ultra-sonication at a power of 130 W and frequency of 20 kHz (Sonics, USA) for a duration of 20 minutes. The working concentration for GFNs was set at 250 µg/L. A solution of ATZ at 500 mg/L was also prepared using acetone, following the method outlined by Flood et al. (2018) [ 35 ]. The chosen concentrations for ATZ use were 25, 50, 75, and 100 µg/L, selected based on concentrations relevant to the environment as indicated in prior research and the EC 50 values for the organisms being tested. 2.2. Materials characterization High Resolution Transmission Electron Microscopy (HR-TEM) analyses were conducted to observe the surface morphologies. To check the ID/IG ratio of the GFN, Raman spectroscopy was performed (Anton Paar GmbH). Additionally, the wettability (Model: DMs-401, Kyowa Interface Science Co., Ltd.; software- FAMAS) and surface charge (zeta potential) (90 Plus Particle Size Analyzer, Brookhaven Instruments Corp., USA) were also assessed for the pristine, as well as binary mixtures of the GFNs, and ATZ. To determine the ATZ concentration, ultra-performance liquid chromatography (UPLC) was utilized [ 36 ]. More information can be found in the supporting information (M3S3). 2.3. Test organism The marine microalga Chlorella sp., employed as a model organism in this study, was obtained from the Central Marine Fisheries Research Institute (CMFRI), Rameswaram, Tamil Nadu. Cultivation was carried out in 200 mL Erlenmeyer flasks containing natural seawater (NSW) supplemented with specific micronutrients (details in Supplementary Tables S2, S3, and S4) for a duration of 15–20 days. To ensure optimal growth, the cultures of algae were kept under a light cycle of 16 hours of exposure to white fluorescent light (with an intensity of 3000 lux provided by TLD Super80 linear-fluorescent lamps) within a chamber where the temperature was meticulously regulated to remain at 23 ± 2°C [ 37 ]. 2.4. Growth Inhibition Study 2.4.1. Experimental setup In this study, algal cells were sourced from the exponential growth and then the centrifugation was done for 10 minutes at a speed of 7000 rpm at 4 \(\:℃\) . After centrifugation, gathered pellets was re-suspended in the sterile ASW. Subsequently, a colorimeter was utilized for the adjustment of the optical density (OD) of 0.1 at 610 nm in ASW. Three distinct conditions were established to assess the GFNs-ATZ interaction and their mixtures and algae: (a) GFNs (pristine, 250 µg/L), and ATZ (25, 50, 75, and 100 µg/L), (b) binary mixtures of GFNs (250 µg/L) and ATZ (25, 50, 75, and 100 µg/L), and (c) control cells without any treatment. The room temperature (23 ± 2 \(\:℃\) ) was maintained for the interaction for 72 h; the interaction volume was selected at 10 mL, and the set-ups were kept in visible light (3000 lux). Another control set was kept with the maximum amount of acetone utilized in the present work and was evaluated for cell viability (Fig S1 ). Compared to the control group, there was a negligible and insignificant decline in cell viability. Thus, this setup was not pursued for further assays. The toxicity assessment in this study followed the OECD guidelines [ 38 ], and triplicates (n = 3) were conducted for each experimental condition. 2.4.2. EC 50 and cytotoxicity evaluation After the interaction time of 72 h, growth inhibitory effects were evaluated by counting the viable cells using a haemocytometer using the microscopic analysis (Zeiss Axiostar, USA) [ 39 ]. The specific protocol can be found in the supplementary information (M5S5). The EC 50 of ATZ (in the pristine form) was established through various concentrations, typical concentration-wise. Additionally, the EC 50 of ATZ in conjunction with GFNs at a concentration of 250 µg/L was also measured. Further details are included in the supplementary section (M5S5). 2.4.3. Model to predict binary toxicity The independent action model (Abbott's model), was utilized to assess the potential interaction in-between ATZ, and GFNs [ 40 ]. Further details about this approach are provided in the supplementary information (M5S5). The Abbott's model and the Ratio of Inhibition (R I ) are described as follows: \(\:{C}_{exp}=⌈\left(A+B\right)-\left(\frac{AB}{100}\right)⌉\) (i) \(\:{R}_{I}=\frac{{C}_{obs}}{{C}_{exp}}\) (ii) [A is the percentage of growth inhibition GFNs (pristine), B is the percentage of growth inhibition of ATZ, C exp is expected toxicity, and C obs is observed toxicity.] 2.4.4. Oxidative stress estimation Previously published protocol followed by Debroy et al., 2024, was used for the estimation of total ROS, and MDA content. Supplementary information contains the other details related to these procedures (M6S6, M7S7). DCFH-DA was used to estimate produced ROS, and TCA-TBA was used to estimate produced MDA content. 2.4.5. Photosynthetic activity estimation After 72 h of interaction, the control and the treated cells were placed in dark incubation for 15 min. The protocol followed by Lee et al., 2020, and Giri and Mukherjee, 2021 was modified, and followed to estimate the effective quantum yield of photosystem II (Y(II)), and electron transport rate (ETR) in the algal cells. Further details are available in the supplementary information (M8S8). 2.4.6. Antioxidant enzymes assessment After 72 h interaction, the cells were collected by centrifugation at 7000 rpm for 10 min at 4°C followed by washing with 0.5 M phosphate buffer saline (PBS), and homogenised. After that, the antioxidant enzymes such as, SOD, and catalase (CAT) activities were estimated by adopting the method used by [ 31 ]. Details are added in the supplementary information (M9S9). 2.5. Ecological risk assessment In this research, the ecological risks of ATZ in the marine ecosystems were evaluated by using the risk quotient (RQ). The RQ is a standard method for assessing the risks posed by the contaminants or the materials in the aquatic environments [ 44 ]. The calculation of ATZ's RQ was carried out using the following specific equations. \(\:PNEC=\frac{{EC}_{50}}{AF}\) (iii) \(\:RQ=\frac{MEC}{PNEC}\) (iv) In this study, MEC (measured environmental concentration) were sourced from existing articles [ 45 ]. The PNEC (predicted no-effect concentration) was derived from the EC 50 values using an assessment factor (AF) set at 1000. This AF aligns with the guidelines from the European Chemical Agency and applies to the acute toxicity evaluations [ 46 ]. EC 50 values were determined both in the absence and presence of GFNs. 2.6. Analysis of data The experimental procedures, which includes toxicity testing, were performed in triplicate (n = 3). Results are shown as mean ± standard deviation. A normality test was performed followed by ANOVA (two-way) with a Bonferroni post-test with the help of GraphPad-Prism-8 was also performed to evaluate the statistically significance amid different test sets with respect to the control. Graphical representations were done by the help of both OriginPRO 2024b, and GraphPad-Prism-8. For the assessment of the biological parameters, cluster heatmaps, Pearson correlation matrix, and principal component analysis (PCA) analyses were also conducted. 3. Results 3.1. Characterization In Fig. 1 , HR-TEM images clearly revealed the structural features of GFNs. The GO’s sheet like structures were made up of multiple layers of partially oxidized graphite oxide. In contrast, the characterization of the rGO sheets were analyzed and revealed that, several layers organized in a stacked formation, displaying both folding, and wrinkling. The graphene nano-sheets exhibited sheet-like structures with formations of aggregates. Raman spectroscopy revealed that, amongst the pristine GFNs, rGO showed the highest ID/IG ratio, followed by GO, and graphene (Fig S2). The surface charges of GFNs were recorded in ASW medium (Table S5). The surface charges were − 15.5 ± 4.1, -25.2 ± 1.6, and − 7.3 ± 3.6 mV for GO, rGO, and graphene, respectively. However, after interacting with ATZ, all the mixtures exhibited a significant reduction in surface charge (Table S5). The contact angles (Table S6) were measured at 36.37 ± 2.87, 45.90 ± 1.77, and 21.00 ± 3.02 \(\:^\circ\:\) for GO, rGO, and graphene, respectively. After the interaction with ATZ, the values also decreased, particularly with the higher concentrations of ATZ. The observed concentrations of ATZ in the interaction medium were measured at both 0th, and 72 h timepoints (Table S7). At the initial point, ATZ concentrations (in the absence of GFNs) were recorded at 27.72 ± 1.66, 77.15 ± 15.91, 146.21 ± 2.34, and 142.65 ± 2.72 µg/L for the nominal concentrations of 25, 50, 75, and 100 µg/L, respectively. After 72 hours, a reduction in the measured concentrations within the mixtures suggested that ATZ was adsorbed onto the GFNs. 3.2. Changes in the cytotoxicity 3.2.1. EC 50 assessment The EC 50 concentrations (Table S8) of pristine forms of GFNs (250 µg/L), ATZ, and their mixture were tested towards Chlorella sp. Remarkably, the EC 50 values of ATZ were greater than before in GFN’s presence, with respect to pristine ATZ. The trend was noted as follows: graphene + ATZ > rGO + ATZ > GO + ATZ. 3.2.2. Inhibitory effects Figure 2 represents the growth inhibition patterns of the algal cells upon treatment with GO, rGO, graphene, ATZ and their combinations with various concentrations of ATZ. All the pristine GFNs treated cells showed a significant amount of growth inhibition in comparison with the control cells (p GO > graphene. The growth inhibition was detected to be concentration-dependent for pristine ATZ, with the highest concentration of ATZ showing the maximum inhibition in growth, i.e., \(\:\sim\) 25% was highly significant, when compared to the control treatment (p < 0.001). The addition of GFNs in the interaction medium containing ATZ resulted in a significant increase in growth inhibition at the lower concentration of 25 µg/L with respect to the pristine ATZ treatments ( \(\:\sim\) 20% for the mixture containing GO, \(\:\sim\) 23% for the one with rGO, and lastly \(\:\sim\) 13% for the one with graphene). This increment in the growth inhibition was observed to be highly significant for the mixture of GO (p < 0.001), slightly significant for the mixture with rGO (p 0.05) when compared with the pristine ATZ treatments. Similar results were observed for the binary mixtures consisting of 50 µg/L with the GFNs, where adding GFNs resulted in the increment of the growth inhibition compared to the pristine ATZ treatment of the same concentration. However, this increase was statistically insignificant (p > 0.05) for all the mixtures. Upon treatment with high concentrations of ATZ (75, and 100 µg/L) in the binary mixture with GFNs, the growth inhibition was noticed to decline with respect to the pristine ATZ treatments of the same concentrations. At 75 µg/L of ATZ, the observed reduction in growth inhibition for the mixture containing GFNs was statistically insignificant (p > 0.05), in comparison with the pristine ATZ treatments. However, the addition of GFNs (rGO and graphene) to the binary mixture comprising 100 µg/L resulted in a highly significant decline (p < 0.001) in growth inhibition in comparison to their pristine counterparts. For the mixture containing GO and ATZ of 100 µg/L, there was a decrease in the growth inhibition in comparison to the pristine ATZ treatment, but it was only slightly significant (p < 0.05). 3.2.3. Abbott’s model based prediction of the mixture effects The predicted toxicity of GFNs and ATZ combinations was obtained by using Abbott’s model (Table S9), known as the independent-action model. The findings revealed that the R I declined in a concentration wise manner for the mixtures, with high concentrations of ATZ resulting in the lowest R I values. When GFNs were combined with lower concentrations of ATZ, an additive effect was observed. Conversely, the mixtures of GFN with higher concentration of ATZ exhibited antagonistic effects. 3.3. Oxidative Stress Figure 3 (A i-iii) reveals the changes in the generation of total ROS in the algal cells upon treatment with GFNs, ATZ and their binary mixtures. All the pristine treatments of GFNs showed an increased total ROS generation compared to the control treatment. This was found to be statistically significant (p < 0.001). Also, the ATZ treatments displayed elevated levels of total ROS generation in a concentration-dependent manner, which revealed statistical significant in comparison with the control treatments (p < 0.001). These results were in accordance with the cell viability data. The ATZ-GFNs binary mixture with the lowest concentration of ATZ (25 µg/L) showed the maximum total ROS generation for all binary mixtures treatment groups. The maximum ROS production was observed in the mixture comprising rGO ( \(\:\sim\) 128%). This was observed to be statistically higher than the pristine ATZ treatment (p < 0.001). The same trend was found for the GO mixtures, where the ROS production increase was significant in comparison with the pristine ATZ (p 0.05). Similar results were observed for the binary mixture treatments comprised of the ATZ concentration of 50 µg/L with GFNs. The total ROS production was higher than their pristine ATZ treatments for all the mixtures, although this increase was insignificant (p > 0.05). However, the mixtures comprising GFNs at high concentrations of ATZ at 100 µg/L did show a decline in ROS generation compared to their pristine counterparts. This decline was also statistically significant (p < 0.001). This was irrespective of the different GFN used for the mixture. The binary mixtures of GO, rGO and ATZ at the concentration of 75 µg/L, however, displayed a decline in ROS generation, which is insignificant in comparison with the pristine ATZ treatments (p > 0.05), although for the mixture comprised of graphene; this decline was observed to be highly significant (p < 0.001). Figure 3 (B i-iii) reveals the impact on the MDA production levels upon the treatments with GFNs, ATZ, and their binary mixtures. The MDA generation results also show a similar trend, which was observed for cell viability and total ROS generation. All the pristine groups showed a significant increment in the MDA generation with respect to the control groups (p < 0.05). When treated with the binary mixtures of GFNs with ATZ at lower concentrations (25, and 50 µg/L), the MDA generation was observed to increase concerning their pristine ATZ treatments. However, this increment was not significant compared to the pristine counterparts (p > 0.05). The binary mixture comprising 75 µg/L and GFNs resulted in a decline in the MDA generation compared to their pristine treatments, even though the decline was insignificant (p > 0.05). The mixtures comprised of GO (p < 0.01) and graphene (p < 0.001) along ATZ with a concentration of 100 µg/L showed a significant decline in the MDA production with respect to their pristine ATZ treatments. It was noticed that the binary mixture comprising rGO with the same concentration of ATZ showed a decline in the generated MDA, which was insignificant (p > 0.05) in comparison with the ATZ pristine treatment. 3.4. Antioxidant enzyme activity Figure 4 (A i-iii) depicts the effects of GFNs, ATZ, and the binary mixtures on SOD enzyme activity in treated algal cells. All algal cells treated with GFNs (in the pristine forms) exhibited a significant enhancement in SOD activity compared to control cells (p < 0.001). SOD activity was observed to be concentration-dependent with pristine ATZ, where the highest ATZ concentration resulted in peak SOD activity, which was significantly elevated relative to the control treatment (p < 0.001). In mixtures containing the lowest ATZ concentration (25 µg/L), all combinations with GFNs demonstrated maximum SOD activity, with the mixture involving rGO showing the highest activity. This was statistically greater than the pristine ATZ treatment (p < 0.001). A similar pattern was noted for mixtures containing GO and graphene; each exhibited significant increases in enzyme activity compared to the pristine ATZ (p < 0.001). Comparable results were obtained for binary mixtures with an ATZ concentration of 50 µg/L, where SOD activity surpassed that of pristine ATZ treatments across all mixtures. However, this increase did not achieve statistical significance (p > 0.05). Conversely, mixtures comprising GFNs with higher ATZ concentrations (75, and 100 µg/L) exhibited a reduction in SOD activity compared to their pristine counterparts. This decline was statistically significant (p < 0.001) and consistent across all types of GFNs used. Figure 4 (B i-iii) reveals the presence of GFNs, ATZ, and their binary mixtures on CAT enzyme activity in the algal cells. Similar to SOD, all pristine GFNs-treated algal cells displayed a significant increase in CAT activity compared to control cells (p < 0.001). The activity of CAT was also concentration-dependent for pristine ATZ, with the maximum concentration yielding the highest CAT activity, which was greater than the control set, significantly (p < 0.001). For the lowest ATZ concentration (25 µg/L), the binary mixtures with GFNs exhibited the highest CAT activity, particularly those containing rGO. This activity was statistically more significant than that of the pristine ATZ treatment (p < 0.001). Mixtures with GO and graphene also showed significant increases in CAT enzyme activity compared to the pristine ATZ (p < 0.001). Similar patterns were observed for mixtures at the ATZ concentration of 50 µg/L; the CAT activity was enhanced in all mixtures compared to their pristine counterparts, although this was not statistically significant for the GO mixture (p > 0.05). In contrast, the rGO mixtures showed a significant increase (p < 0.01), while the graphene mixtures exhibited a highly significant rise in CAT enzyme activity relative to the pristine treatments (p < 0.001). However, mixtures containing GFNs with higher ATZ (75, and 100 µg/L) concentrations also demonstrated a significant decline in CAT activity compared to their pristine counterparts, consistently yielding statistically significant results (p < 0.001), independent of the GFN type utilized. 3.5. Photosynthetic parameters Figure 5 (A, C, E) and S3 (A i-iii) illustrates the effects of GFNs, ATZ, and their binary mixtures on the quantum yield of photosystem II (Y (II)) in algal cells subjected to treatment. Algal cells treated with pristine GFNs showed a substantial reduction in Y (II) in comparison with the control group (p < 0.001). The Y (II) revealed a concentration-wise relationship with ATZ in the pristine form, where the highest concentration of ATZ resulted in the most pronounced decline in Y (II), significantly lower than the control treatment (p < 0.001). At the lowest ATZ concentration (25 µg/L), all the GFN combinations resulted in minimal Y (II), with the rGO mixture yielding the lowest Y (II), which was significantly lower than the pristine ATZ treatment (p 0.05). For the binary mixtures with an ATZ concentration of 50 µg/L, Y (II) values were lower than those of pristine ATZ treatments across all combinations, although these reductions did not reach statistical significance (p > 0.05). In contrast, mixtures that combined GFNs with higher ATZ concentrations (75 µg/L) demonstrated an increase in Y (II) relative to their pristine counterparts, although these increases were statistically insignificant (p > 0.05) and consistent across all GFNs used. Moreover, for mixtures with the highest ATZ concentration (100 µg/L), increases in Y (II) were statistically significant (p 0.05). Figure 5 (B,D,F), and S3 (B i-iii) reveals the influence of GFNs, ATZ, and the binary mixtures on the electron transport rate (ETR) within algal cells. Consistent with Y (II), all algal cells treated with pristine GFNs experienced a significant reduction in ETR compared to the control cells (p < 0.001). The ETR was also dependent on ATZ concentration, with the highest concentration resulting in the lowest ETR values, significantly lower than the control treatment (p < 0.001). In mixtures containing the lowest ATZ concentration (25 µg/L), all combinations with GFNs exhibited minimal ETR, with the rGO mixture recording the lowest ETR, which was not significantly different from that of the pristine ATZ treatment (p > 0.05). Similar declines in ETR were observed for mixtures with GO and graphene; however, these declines remained statistically insignificant compared to the pristine ATZ (p > 0.05). For binary mixtures with an ATZ concentration of 50 µg/L, ETR decreased across all mixtures compared to pristine ATZ treatments, yet this decline did not achieve statistical significance (p > 0.05). Conversely, mixtures involving GFNs with elevated ATZ concentrations (75 µg/L) demonstrated increases in ETR relative to their pristine counterparts, although these increases remained statistically insignificant (p > 0.05) across all GFNs. Notably, mixtures featuring GFNs at the highest ATZ concentrations (100 µg/L) exhibited significant increases in ETR (p < 0.001) for those containing GO and graphene, while the increase for the rGO mixture was only slightly statistically significant (p < 0.05). 4. Discussion 4.1. Physico-chemical interactions The TEM images (Fig. 1 ) of the GFNs disclosed the layered, flaky appearance of the pristine GFNs. GO (Fig. Ai) shows folded, wrinkled, thin sheets layered together, characteristic of the defects caused by oxidation [ 47 ].The rGO sheets are more wrinkled and have a higher number of folds than the GO. This resulted from removal of oxygen groups, which causes internal stress and structural rearrangement, producing sharper edges [ 48 ]. The Graphene sheets show a smooth, multilayered structure with no visible folds or wrinkles. This results in a soft structure that reflects the organized sp 2 carbon structure [ 49 ]. GFNs, have demonstrated considerable promise for adsorbing ATZ, because of the strong π-π interactions between ATZ's aromatic rings and the sp 2 hybridized carbon structure of graphene, facilitating the effective adsorption of ATZ [ 50 ]. In another recent study, it was documented that surface modification of multiwall carbon nanotube with magnetite (Fe 3 O 4 ) resulted in the easier adsorption of the small molecular weight compounds such as pesticides like ATZ, due to electrostatic interactions between the negatively charged pesticide molecule and the positively charged metal ions [ 51 ]. The contact angle results indicate that graphene is the most hydrophobic among the various GFNs. In comparison, rGO is the most hydrophilic with the lowest contact angle, followed by GO, which has a higher contact angle than rGO but lower than graphene. This results from the various degrees of oxidation of the materials, leading to their various surface morphologies that cause the differences in their toxic potential. This is directly linked to their toxic potential as well. The adsorption of ATZ over the GFN surface might be due to the \(\:\pi\:-\pi\:\) interaction that helped create bonds with the GFN surface [ 52 ]. Zeta potential quantifies the surface electrical charge of particles suspended in a liquid medium. An elevated zeta potential indicates a stronger electrostatic repulsion among particles, which means less likelihood of aggregation. The adsorption of ATZ over the GFNs at higher concentrations of ATZ resulted in a lower zeta potential, indicating a decline in the colloidal stability of the mixture [ 53 ]. However, the interaction of GFNs with lower concentrations of ATZ led to more stable colloidal suspensions where the particles were evenly dispersed throughout the liquid phase. 4.2. Algal toxicity In a previous study, the measurements of chlorophyll a, chlorophyll b, and carotenoids in Chlorella vulgaris were used to assess the toxic impacts of ATZ at concentrations of 10, 1, 0.1, and 0.01 mg L − 1 . Based on the results obtained, it was deduced that ATZ exerts a toxic impact on Chlorella vulgaris at the concentrations tested [ 2 ]. Similar patterns were observed in another study where the addition of ATZ led to the limitation of the population growth of Chlamydomonas reinhardtii through the decline in the photosynthetic parameters [ 54 ]. Among GFNs, GO exhibits the highest antibacterial efficacy, primarily due to its superior ability to generate ROS, which is brought forward by the inactivation of proteins and lipids [ 55 , 56 ]. Also, in another study, exposure to GO led to elevated mortality rates in zebrafish and negatively impacted reproductive performance, with pronounced effects observed through trophic transfer at exposure concentrations of 100.0, 200.0, 400.0, and 800.0 ng L − 1 [ 57 ]. Furthermore, the antibacterial properties of GO and rGO are influenced by exposure duration and sample concentration. The growth inhibition observed in mixtures of GFNs with low ATZ concentrations was higher than that seen with pristine ATZ treatments. These mixtures enhanced hydrophobicity and dispersibility, promoting more effective interactions between the algae and GFNs, thereby enhancing their joint toxicity. Conversely, at higher concentrations of ATZ, an increase in hydrophilicity and a greater tendency for agglomeration were observed, which likely reduced their potential toxicity [ 58 ]. Abbott's model demonstrated the additive nature of the interactions in the mixture of ATZ, and GFNs with low doses of ATZ. Nevertheless, the combinations comprising of the nanomaterials and elevated amounts of ATZ demonstrated an antagonistic relationship. This discovery could be ascribed to improved sorption of ATZ, resulting in heightened sedimentation, and agglomeration, diminishing their bio-availability to the algal cells. ROS is generated in all living cells as a by-product of cellular metabolism, which includes respiration and photosynthesis (Scheme 1 ). These radicals generally include superoxide (O 2 − ) and hydroxyl radicals (OH.), as well as non-radical molecules like hydrogen peroxide (H 2 O 2 ). Nonetheless, during environmental stress, the overproduction of reactive oxygen species (ROS) can disturb this equilibrium, resulting in oxidative damage to the cells. In a previous study, ATZ treatment (77.6 \(\:\mu\:\) g L − 1 ) of juvenile algal cells ( C. reinhardtii ) resulted in a rapid and significant increase in H 2 O 2 production. The hydrogen peroxide levels generated by those cells two hours post-treatment rose approximately 16-fold compared to the control [ 59 ]. Similar patterns were noted in the present study, where treating algal cells with pristine ATZ generated substantial amounts of ROS, which was statistically significant in comparison with the control-treated cells. Adding GFNs to the lower concentrations of ATZ in the treatment media resulted in the escalated generation of ROS. This could be attributed to the defects in the sp 2 hybridized structure of the GFNs, which creates more wrinkles and sharp edges that can cut through the algal cells [ 60 ]. Additionally, by interrupting the electron transport and interfering with photosynthesis, ATZ can produce ROS. The divergent effects of ATZ-GFN interactions at varying doses arise from alterations in bioavailability, oxidative stress, and adsorption kinetics. At lower ATZ levels, well-dispersed GFNs promote ATZ absorption, magnifying ROS generation, oxidative damage, and photosynthetic inhibition. Conversely, elevated ATZ concentrations facilitate GFN aggregation and sedimentation, diminishing cellular interactions and toxicity. Additionally, antioxidant defence systems may be engaged at greater ATZ levels, decreasing oxidative damage. Competitive binding on the surfaces of GFNs may restrict ATZ bioavailability, hence modifying its toxicity profile. These findings underscore the necessity for concentration-dependent risk evaluations in environmental research. MDA is an established biomarker that detects the levels of oxidative stress generation within the cells. Encountering MDA might intensify oxidative stress by facilitating the buildup of intracellular reactive oxygen species and impairing mitochondrial function. This establishes a feedback loop wherein ROS-induced lipid peroxidation produces MDA, further exacerbating ROS generation and cellular injury [ 61 ]. The association of ROS with phospholipids in the cell membrane induces lipid peroxidation, leading to increased MDA levels. As a result, the porousness and fluidity of the cell membrane are diminished, resulting in structural and functional deficits in algae [ 62 ]. Following the similar pattern noted for the growth inhibitory effects, generated total ROS, and MDA also demonstrated a concentration-wise elevation for the cells, treated with pristine ATZ. In a previous study, a mixture of ATZ (10, and 100 µg/L) and 100 nm-sized orange fluorescent PS-NH 2 particles generated extensive amounts of MDA in the treated algal cells of Chlorella vulgaris [ 53 ]. The combination of GFN-ATZ with low concentrations of ATZ (25, and 50 \(\:\mu\:\) g/L) yielded a higher degree of ROS and MDA production than the pristine treated sets. In comparison, the combinations with larger doses of ATZ demonstrated showed diminished ROS and MDA generation. The addition of GFNs to lower concentrations of ATZ increases toxicity because GFNs enhance the bioavailability of ATZ by acting as carriers, facilitating its uptake by algal cells. Antioxidant enzyme activity plays a crucial role in mitigating oxidative stress, a condition caused by an imbalance between the production of ROS and the cellular capacity to neutralize them. Enzymes, such as SOD and CAT, form a critical defense mechanism, scavenging ROS to prevent oxidative damage to lipids, proteins, and DNA. However, oxidative stress ensues when ROS levels overwhelm the antioxidant defense—due to environmental stressors like pollution, herbicides, or UV radiation—leading to cellular dysfunction and potential death. Persistent oxidative stress might disrupt metabolic processes, thereby impairing photosynthesis and accelerating ageing or disease progression in plants, algae, and other organisms [ 63 ]. Following the growth inhibitory trend and the oxidative stress parameters such as ROS and MDA generation, SOD and CAT enzyme activities also tend to increase upon the treatment of the algal cells with ATZ in a dose-dependent manner. In a previous study, ATZ was shown to increase the gene expression of the number of genes such as MSD 5 – gene encoding for the mitochondrial isoform of manganese superoxide dismutase; CAT 1 – gene encoding for catalase; FSD 1 – gene encoding for iron superoxide dismutase; MSD 3 – gene encoding for the chloroplastic isoform of manganese superoxide dismutase; APX 1 – gene encoding for ascorbate peroxidase, which are important genes in Chlamydomonas reinhardtii that are responsible for the antioxidant enzymes production [ 59 ]. The combination of low concentrations of ATZ, and GFNs led to an increase in SOD and CAT levels compared to those observed with pristine ATZ. On the other hand, at high ATZ concentration, the binary mixture with GFNs resulted in a notable decline in the SOD and CAT content. GFNs impact photosynthesis in algal cells by providing a shading effect that shields the light from entering the cells and impairs photosynthetic activity [ 21 ]. Hence, they can induce a toxic impact on the algal cells by impairing their photosynthetic machinery. The differences in chlorophyll fluorescence might be considered indicative of reactions of microalgae to environmental stresses. Following the patterns seen in growth inhibition, total ROS accumulation, and MDA content, the photosynthetic parameters (including Y(II) and ETR) likewise revealed a concentration dependent decline in response to pristine ATZ exposure. Any disruption that results in inactivation damage of PS II or the induction of persistent quenching results in a reduction of Fv/Fm and rETR [ 64 ]. In another work, the inhibitory effects of ATZ and its two primary derivatives on the carbon fixation ability of P. tricornutum Pt-1 after 0.5-, 1-, 2-, 4- and 7-day incubation were indicated using Chl a concentration and chlorophyll fluorescence parameters (Fv/Fm, and rETR) correspondingly [ 65 ]. GFNs enhance the bioavailability of ATZ by acting as carriers, facilitating its uptake by algal cells, at lower concentrations of ATZ. This results in greater disruption of the photosynthetic electron transport chain, further inhibiting Y(II) and reducing ETR. At elevated ATZ levels, GFNs adsorb and immobilize excess ATZ molecules on their surface through \(\:\pi\:-\pi\:\) interactions and hydrogen bonding. This reduces the concentration of free ATZ available to interact with photosystem II, thereby mitigating its inhibitory effects and partially restoring Y(II) and ETR. Figure 6 reveals the correlation between the various biological indicators evaluated for the mixtures of GFN, and ATZ at multiple concentrations in the indicator organism Chlorella sp. The clustered heatmap revealed that the total ROS production, and MDA generation are linked together, and the SOD activity is linked with both of these and was grouped for all the mixture, and pristine categories of the GFNs (Fig. 6 Ai-iii). This shows the direct correlation between total ROS and MDA production with SOD activity, which shows that an increase in ROS increases MDA production, which, in turn, enhances the SOD activity within the treated algal cells. The increment in these parameters leads to an increase in oxidative stress, which causes an increase in growth inhibition. This is indicated by grouping the growth inhibition parameter with the oxidative stress parameters, which shows their direct interdependence among them. Furthermore, group formation was observed among the growth inhibition, photosynthetic parameters, and oxidative stress, specifically the ROS and MDA content, for all of the GFN and ATZ treatment groups. This demonstrates that the excessive production of ROS and lipid peroxidation resulted in the disruption of photosynthesis and the enhancement of growth inhibition. 4.3. Modelling Pearson modelling (Fig. 6Bi–iii) demonstrated a positive correlation between the total ROS and MDA content and the growth inhibition in the treatment groups at the p < 0.001 significance level. At the p < 0.001 significance level, a negative correlation was observed between the growth inhibition and ETR, Y II (PS II). Furthermore, the total ROS generation was correlated with ETR, and Y II (PS II) negatively, at a significant level of p < 0.01 for entire treatment setups. Identical correlation was detected between MDA content, and ETR, Y II (PS II) at the significance level of p < 0.001 for the GFN treatment groups. This pattern indicates that the oxidative damage surge ought to be one of the rationales for the delayed electron transport and decrement in Y II of the PS II system. The PCA analysis aimed to ascertain the correlation between the impacts of GFNs addition on the ATZs at different concentrations and their impacts on the varied biochemical parameters in the algal cells (Fig. 7 ). For Fig. 7 Ai, the principal components PC1 and PC2 account for 97.67% of the total data variance, indicating a high level of variability captured. The scatter plot illustrates that cell damage parameters such as growth inhibition, total ROS, and CAT activity are predominantly associated with Component 1, suggesting their direct contribution to cellular injury. Additionally, Component 1 includes parameters like MDA production and encompasses treatments involving pristine GO, ATZ, and their binary mixtures at lower concentrations. Component 2, on the other hand, primarily features SOD activity, positioned opposite photosynthetic parameters such as Y(II) and ETR, which are grouped under Component 4 along with the control treatment. Components 1 and 2 represent the biochemical parameters that indicate toxicity. Treatment groups containing ATZ at 25 µg/L and binary mixtures of GO and ATZ at 75, and 100 µg/L are situated in Component 3. This component appears independent and lacks association with biochemical parameters, indicating that these treatments do not correlate with toxicity indicators. Similar results were observed for rGO, shown in Fig. 7 Aii, where the principal components PC1 and PC2 account for 96.63%, and graphene, shown in Fig. 7 Aiii, where the principal components PC1 and PC2 account for 97.53%. The PCA data determines the direct correlation between the toxicity parameters such as growth inhibition, total ROS, MDA generation and antioxidant enzyme activities with higher concentrations of ATZ and binary combinations of GFNs with lower concentrations of ATZ. This clearly shows the interconnection of the biochemical parameters with the various treatment groups, thereby highlighting their relationship. In addition, the PCA biplots also reveal a strong association between elevated oxidative stress markers—such as ROS and lipid peroxidation—and increased cellular mortality, indicating that oxidative damage is a major driver of cytotoxicity. Simultaneously, the analysis highlights the involvement of antioxidant enzymes (e.g., superoxide dismutase, catalase) as crucial components of the algal stress response, working to counteract the harmful effects of redox imbalance. Photosynthetic parameters, including electron transport rate and maximum quantum yield of PS-II (Y-II), also align closely with oxidative stress indicators in the PCA space, suggesting that the photosynthetic apparatus is particularly vulnerable to oxidative perturbations. Moreover, PCA effectively distinguishes treatment groups based on their unique biochemical response profiles, revealing that pollutant mixtures at lower concentrations of ATZ elicit more pronounced physiological disturbances than individual exposures. In contrast, at higher concentrations of ATZ, the mixture shows less harmful impacts on the algae. Thus, PCA emerges as a robust multivariate tool for identifying key biomarkers and deciphering complex contaminant mixtures' additive or antagonistic toxic effects, providing valuable insights into the mechanistic basis of mixture toxicity in aquatic phototrophs. 4.4. Mechanisms of toxicity In addition, the surface charge of the GFNs was decreased upon the adsorption of the ATZ compared to the pristine GFNs. It revealed that the aggregation was high upon ATZ adsorption over GFNs. In addition, the highest adsorption of ATZ at 100 µg/L with 250 µg/L of GFNs revealed that the aggregation happened due to the higher adsorption, which revealed the lower availability of GFNs in the media. The lower concentration of ATZ in the mixture with GFNs revealed the greater availability of the GFNs in the media due to the lower adsorption of ATZ over the GFNs. Higher aggregation also led to the more hydrophilic nature of the medium compared to the pristine GFNs. Additionally, due to the aggregation and hydrophilic nature of the medium, it showed less algal growth inhibition and less oxidative stress generation. The presence of GFNs with lower concentrations of ATZ results in better stability of ATZ, which causes the extended toxic impact of these combinations, in comparison to the pristine ATZ. However, the opposite effects were observed in the combinations of GFNs with higher concentrations of ATZ. This was due to the excessive aggregation of GFNs with ATZ that causes them to settle down thereby declining their interactions with the algal cells and causing to reduce the toxicity of the contaminants. In a previous study by Jiao et al., (2019), it was observed that the high aromaticity of GBCs (graphene-based composites) facilitates strong π–π stacking interactions between the triazine rings of ATZ molecules and the abundant aromatic domains on the GBC surfaces, playing a crucial role in the adsorption and immobilization of ATZ [ 66 ]. In addition to these interactions, hydrogen bonding is considered a dominant supramolecular force in this system, mainly due to the prevalence of N–H···O type bonds. The triazine units in atrazine can engage with electron-donating and electron-accepting functional groups, such as carboxyl and hydroxyl groups, commonly present on the GBC surfaces, thereby enhancing the binding affinity and stability of the pollutant-composite complex. This explains the type of bonding that GFNs possibly use to bind ATZ. With the increase in the concentration of ATZ in the medium, the effective bonds between GFNs and ATZ increase, covering the surface of GFNs, thereby completely covering them and causing them to become less available for interaction with algal cells. A previous study by Harshkova et al., (2021), shows the mechanism of toxicity induction by ATZ in green algae, Chlamydomonas reinhardtii , is through induction of oxidative stress in chloroplast and inhibition of the photosynthetic electron transport [ 59 ]. The disruption in the energies of the chloroplast indirectly influenced respiration, which in turn caused lower photosynthetic efficiency that led to a decline in cell growth. Also, in another study, it was observed that ATZ was found to inhibit light absorption per reaction centre (ABS/RC) and electron transport efficiency (FEo) in microalgae Chlorella sp. while exerting a minimal effect on energy dissipation (FDo). This imbalance between light harvesting and energy utilization ultimately led to the overall suppression of photosynthetic activity [ 54 ]. Thus, it can be concurred from the literature that the inhibition of photosynthetic efficiency in combination with the oxidative stress generated within the chloroplast results in the overall decline in the cell viability, which also supports the production of antioxidant enzyme. At lower concentrations, ATZ in the presence of GFNs induces elevated oxidative stress and reduced photosynthetic efficiency in Chlorella sp., owing to greater bioavailability of the contaminants. Conversely, higher ATZ concentrations in combination with GFNs lead to decreased oxidative stress and improved photosynthetic parameters. This is likely due to the aggregation of ATZ with GFNs, limiting their interaction with algal cells. 4.5. Risk assessment The RQ analysis is an effective method for the analysis of the environmental risk of herbicide, ATZ, in aquatic environment. As per the previous investigations, ATZ was typically exposed at a level of ng/L to µg/L (Table 1 ). In the current study, to predict the environmental risk of ATZ in the marine, RQ was calculated as per the acute toxicity assessment towards marine Chlorella sp. The noted concentrations of ATZ in the marine were also taken from the previous literature [ 67 ]. The finding (Table 1 ) demonstrates that at the given experimental conditions, such as maintenance in ASW at pH 7, room temperature (23 ± 2 \(\:℃\) ) for the interaction time of 72 h, the interaction volume of 10 mL, kept under visible light (3000 lux), shows that in the absence of GFNs (250 µg/L), ATZ poses a higher risk. In the presence of GFNs, ATZ is becoming less harmful to the marine environment. Table 1 Evaluated RQ of ATZ in the marine Chlorella sp. in the presence and absence of GFNs. Sl No. Source Available Concentration of Atrazine (ng/L) RQ of Atrazine RQ of GO + Atrazine RQ of rGO + Atrazine RQ of Graphene + Atrazine Lower range Higher range Lower range Higher range Lower range Higher range Lower range Higher range Lower range Higher range 1 North Sea, German Bight nd 110 nd 0.1594 nd 0.0063 nd 0.0038 nd 0.0027 2 Western Baltic Sea 2 8 0.0029 0.0116 0.0001 0.0005 0.0001 0.0003 nd 0.0002 3 North Sea, Irish Sea, English Channel nd 8.3 nd 0.0120 nd 0.0005 nd 0.0003 nd 0.0002 4 North Sea, German Bight 3.3 37 0.0048 0.0536 0.0002 0.0021 0.0001 0.0013 0.0001 0.0009 5 Baltic Sea, Poland 1.8 9 0.0026 0.0130 0.0001 0.0005 0.0001 0.0003 nd 0.0002 6 Coastal lagoon, Northern Adriatic, Italy 2.4 8.2 0.0035 0.0119 0.0001 0.0005 0.0001 0.0003 0.0001 0.0002 ATZ concentrations were taken from (Nödler et al., 2013) 5. Conclusions and Environmental Implications This study highlights the role of GFNs in modulating the toxicity of ATZ in Chlorella sp., emphasizing concentration-dependent effects. At lower ATZ concentrations, GFNs exacerbated toxicity by enhancing growth inhibition, reactive oxygen species (ROS) production, and malondialdehyde (MDA) levels (~ 10% increase), while reducing photosynthetic efficiency (~ 25% decline). These effects stem from the synergistic interaction between GFNs' nano-blade properties and ATZ-induced oxidative stress. In contrast, higher ATZ concentrations promoted GFN aggregation and sedimentation, reducing cellular interactions and mitigating toxicity (~ 12% decline). Risk quotient analysis suggests minimal environmental risk at high ATZ but low GFN concentrations. This study underscores the complex environmental dynamics governing contaminant toxicity in aquatic systems and provides a foundation for future research on ecological risk assessments and trophic-level impacts. 6. Future perspectives Further research is needed to evaluate the environmental fate and ecotoxicological implications of ATZ and GFNs under realistic environmental conditions. Studies should focus on how natural variables such as organic matter, microbial activity, and photodegradation influence their interactions and toxicity. Long-term exposure assessments and multi-trophic level studies will provide a more comprehensive understanding of ecosystem risks. Exploring potential mitigation strategies, such as functionalized nanomaterials or bioremediation approaches, could help minimize adverse impacts on primary producers and aquatic biodiversity. Abbreviations AF - Assessment factor ASW – Artificial sea water ATZ – Atrazine CAT – Catalase ETR – Electron transport rate FTIR - Fourier Transform Infrared GFNs – Graphene family nanomaterials GO – Graphene oxide MDA - Malondialdehyde MEC – Measured environmental concentration NSW – Natural sea water PBS - Phosphate buffer saline PCA - Principal Component Analysis PNEC – Predicted no-effect concentration PSMPs – Polystyrene microplastics PSNPs – Polystyrene nanoplastics rGO – reduced graphene oxide ROS – Reactive oxygen species RQ – Risk quotient SOD - Superoxide dismutase UPLC - Ultra Performance Liquid Chromatography Declarations Ethics, Consent to Participate, and Consent to Publish declarations: Not applicable Availability of data and material: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests Funding: Not applicable Authors' contributions: Abhrajit Debroy: Conceptualization, Investigation, Methodology, Visualization, Formal analysis, Writing-Original Draft; Mrudula Pulimi: Conceptualization, Formal analysis; Amitava Mukherjee: Conceptualization, Methodology, Formal analysis, Supervision, Project administration, Writing- Review and editing. Acknowledgements: The authors would like to acknowledge Vellore Institute of Technology (VIT), Vellore for High-resolution transmission electron microscopy (HR-TEM), and Ultra-performance liquid chromatography (UPLC) facilities used in this study. References Hu N, Xu Y, Sun C, et al (2021) Removal of atrazine in catalytic degradation solutions by microalgae Chlorella sp. and evaluation of toxicity of degradation products via algal growth and photosynthetic activity. Ecotoxicol Environ Saf 207:111546. https://doi.org/10.1016/j.ecoenv.2020.111546 Matejczyk M, Kondzior P, Ofman P, et al (2023) Atrazine toxicity in marine algae Chlorella vulgaris, in E. coli lux and gfp biosensor tests. Arch Environ Prot 49:87–99. https://doi.org/10.24425/aep.2023.147331 Taverna ME, Busatto CA, Lescano MR, et al (2018) Microparticles based on ionic and organosolv lignins for the controlled release of atrazine. J Hazard Mater 359:139–147, https://doi.org/10.1016/j.jhazmat.2018.07.010 Kabra AN, Ji MK, Choi J, et al (2014) Toxicity of atrazine and its bioaccumulation and biodegradation in a green microalga, Chlamydomonas mexicana. Environ Sci Pollut Res 12270–12278. https://doi.org/10.1007/s11356-014-3157-4 Rodríguez-Robledo V, Vendrell A, García-Cifuentes A, et al (2022) Determination of atrazine and propazine metabolites deemed endocrine disruptors in human seminal plasma by LC–ESI-MS/MS. Chem Biol Technol Agric 9:22, https://doi.org/10.1186/s40538-022-00285-4 Blahova J, Cocilovo C, Plhalova L, et al (2020) Embryotoxicity of atrazine and its degradation products to early life stages of zebrafish (Danio rerio). Environ Toxicol Pharmacol 77:103370, https://doi.org/10.1016/j.etap.2020.103370 Wang S, Bryan C, Xie J, et al (2022) Atrazine exposure in zebrafish induces aberrant genome-wide methylation. Neurotoxicol Teratol 92:107091, doi: 10.1016/j.ntt.2022.107091 Owolabi OD, Omotosho JS (2017) Atrazine-mediated oxidative stress responses and lipid peroxidation in the tissues of Clarias gariepinus. Iran J Toxicol 11:29–38, Doi: 10.29252/arakmu.11.2.29 Santos TG, Martinez CBR (2012) Atrazine promotes biochemical changes and DNA damage in a Neotropical fish species. Chemosphere 89:1118–1125, https://doi.org/10.1016/j.chemosphere.2012.05.096 Schmidt AM, Sengupta N, Saski CA, et al (2017) RNA sequencing indicates that atrazine induces multiple detoxification genes in Daphnia magna and this is a potential source of its mixture interactions with other chemicals. Chemosphere 189:699–708, https://doi.org/10.1016/j.chemosphere.2017.09.107 Stara A, Kouba A, Velisek J (2018) Biochemical and histological effects of sub-chronic exposure to atrazine in crayfish Cherax destructor. Chem Biol Interact 291:95–102, https://doi.org/10.1016/j.cbi.2018.06.012 Zhu X, Sun Y, Zhang X, et al (2016) Herbicides interfere with antigrazer defenses in Scenedesmus obliquus. Chemosphere 162:243–251, https://doi.org/10.1016/j.chemosphere.2016.07.087 Religia P, Kato Y, Fukushima EO, et al (2019) Atrazine exposed phytoplankton causes the production of non-viable offspring on Daphnia magna. Mar Environ Res 145:177–183, https://doi.org/10.1016/j.marenvres.2019.02.007 Zhao F, Li Y, Huang L, et al (2018) Individual and combined toxicity of atrazine, butachlor, halosulfuron-methyl and mesotrione on the microalga Selenastrum capricornutum. Ecotoxicol Environ Saf 148:969–975. https://doi.org/10.1016/j.ecoenv.2017.11.069 Camuel A, Guieysse B, Alcántara C, Béchet Q (2017) Fast algal eco-toxicity assessment: Influence of light intensity and exposure time on Chlorella vulgaris inhibition by atrazine and DCMU. Ecotoxicol Environ Saf 140:141–147. https://doi.org/10.1016/j.ecoenv.2017.02.013 Bianco A, Cheng H-M, Enoki T, et al (2013) All in the graphene family–A recommended nomenclature for two-dimensional carbon materials. Carbon 65:1–6, https://doi.org/10.1016/j.carbon.2013.08.038 Ding X, Pu Y, Tang M, Zhang T (2022) Environmental and health effects of graphene-family nanomaterials: Potential release pathways, transformation, environmental fate and health risks. Nano Today 42:101379. https://doi.org/10.1016/j.nantod.2022.101379 Zhang Y, Duan X, Bai L, Quan X (2020) Effects of nanomaterials on metal toxicity: Case study of graphene family on Cd. Ecotoxicol Environ Saf 194:110448. https://doi.org/10.1016/j.ecoenv.2020.110448 Ali J, Li Y, Shang E, et al (2023) Aggregation of graphene oxide and its environmental implications in the aquatic environment. Chinese Chem Lett 34:107327. https://doi.org/10.1016/j.cclet.2022.03.050 Pires A, Figueira E, Silva MSS, et al (2022) Effects of graphene oxide nanosheets in the polychaete Hediste diversicolor: Behavioural, physiological and biochemical responses. Environ Pollut 299:118869 Zhao J, Cao X, Wang Z, et al (2017) Mechanistic understanding toward the toxicity of graphene-family materials to freshwater algae. Water Res 111:18–27. https://doi.org/10.1016/j.watres.2016.12.037 Wu S, Ji X, Li X, et al (2022) Mutual impacts and interactions of antibiotic resistance genes, microcystin synthetase genes, graphene oxide, and Microcystis aeruginosa in synthetic wastewater. Environ Sci Pollut Res 29:3994–4007, doi: 10.1007/s11356-021-15627-2. Yuan X, Zhang F, Wang Z (2024) Impacts of Micro/Nanoplastics Combined with Graphene Oxide on Lactuca sativa Seeds: Insights into Seedling Growth, Oxidative Stress, and Antioxidant Gene Expression. Plants 13:. https://doi.org/10.3390/plants13243466 Pretti C, Oliva M, Pietro R Di, et al (2014) Ecotoxicity of pristine graphene to marine organisms. Ecotoxicol Environ Saf 101:138–145. https://doi.org/10.1016/j.ecoenv.2013.11.008 Barari F, Eydi M, Bonyadi Z (2024) Heliyon Recent progress on the toxic effects of microplastics on Chlorella sp . in aquatic environments. Heliyon 10:e32881. https://doi.org/10.1016/j.heliyon.2024.e32881 Khalil S, Mahnashi MH, Hussain M, et al (2021) Saudi Journal of Biological Sciences Exploration and determination of algal role as Bioindicator to evaluate water quality – Probing fresh water algae. Saudi J Biol Sci 28:5728–5737. https://doi.org/10.1016/j.sjbs.2021.06.004 Bi C, Junaid M, Liu Y, et al (2022) Graphene oxide chronic exposure enhanced perfluorooctane sulfonate mediated toxicity through oxidative stress generation in freshwater clam Corbicula fluminea. Chemosphere 297:134242. https://doi.org/10.1016/j.chemosphere.2022.134242 Thomas MC, Flores F, Kaserzon S, et al (2020) Toxicity of ten herbicides to the tropical marine microalgae Rhodomonas salina. Sci Rep 10:1–16. https://doi.org/10.1038/s41598-020-64116-y Bhatt P, Sethi K, Gangola S, et al (2022) Modeling and simulation of atrazine biodegradation in bacteria and its effect in other living systems. J Biomol Struct Dyn 40:3285–3295, https://doi.org/10.1080/07391102.2020.1846623 Natarajan L, Annie Jenifer M, Peijnenburg WJGM, Mukherjee A (2023) Algal extracellular polymeric substances (algal-EPS) for mitigating the combined toxic effects of polystyrene nanoplastics and nano-TiO2 in Chlorella sp. Nanotoxicology 0:1–14. https://doi.org/10.1080/17435390.2023.2179438 Debroy A, Roy N, Giri S, et al (2024) EPS-corona formation on graphene family nanomaterials (GO, rGO and graphene) and its role in mitigating their toxic effects in the marine alga Chlorella sp. Environ Pollut 341:632014. https://doi.org/10.1016/j.envpol.2023.123015 Chia JSY, Tan MTT, Khiew PS, et al (2014) A novel one step synthesis of graphene via sonochemical-assisted solvent exfoliation approach for electrochemical sensing application. Chem Eng J 249:270–278. https://doi.org/10.1016/j.cej.2014.03.081 Yang ZZ, Zheng Q Bin, Qiu HX, et al (2015) A simple method for the reduction of graphene oxide by sodium borohydride with CaCl2 as a catalyst. Xinxing Tan Cailiao/New Carbon Mater 30:41–47. https://doi.org/10.1016/S1872-5805(15)60174-3 Lu J, Zhu X, Tian S, et al (2018) Graphene oxide in the marine environment: Toxicity to Artemia salina with and without the presence of Phe and Cd2+. Chemosphere 211:390–396. https://doi.org/10.1016/j.chemosphere.2018.07.140 Flood S, Burkholder JA, Cope G (2018) Assessment of atrazine toxicity to the estuarine phytoplankter, Dunaliella tertiolecta (Chlorophyta), under varying nutrient conditions. Environ Sci Pollut Res 25:11409–11423. https://doi.org/10.1007/s11356-018-1310-1 Jacomini AE, Bonato PS, Avelar WEP (2003) HPLC method for the analysis of atrazine in freshwater bivalves. J Liq Chromatogr Relat Technol 26:1885–1894. https://doi.org/10.1081/JLC-120021757 Thiagarajan V, M. P, S. A, et al (2019) Diminishing bioavailability and toxicity of P25 TiO2 NPs during continuous exposure to marine algae Chlorella sp. Chemosphere 233:363–372. https://doi.org/10.1016/j.chemosphere.2019.05.270 OECD (2011) Test No. 201: Freshwater Alga and Cyanobacteria, Growth Inhibition Test, OECD Guidelines for the Testing of Chemicals, Section 2, OECD Publishing, Paris, Rex M C, Debroy A, Mukherjee A (2024) The impact of nTiO2 and GO (graphene oxide), and their combinations, on freshwater Chlorella sp.: a comparative study in lake water and BG-11 media. Environ Sci Process Impacts. https://doi.org/10.1039/d4em00041b Giri S, Debroy A, Nag A, Mukherjee A (2024) Evaluating the role of soil EPS in modifying the toxicity potential of the mixture of polystyrene nanoplastics and xenoestrogen, Bisphenol A (BPA) in Allium cepa L. J Hazard Mater 477:135252. https://doi.org/10.1016/j.jhazmat.2024.135252 Debroy A, Saravanan JS, Joyce Nirmala M, et al Algal Eps Modifies the Toxicity Potential of the Mixture of Polystyrene Nanoplastics (Psnps) and Flame Retardant, Triphenyl Phosphate in Freshwater Microalgae Chlorella Sp. Chemosphere, 143471, https://doi.org/10.1016/j.chemosphere.2024.143471 Lee JW, Lee SH, Han JW, Kim GH (2020) Early Light-Inducible Protein (ELIP) Can Enhance Resistance to Cold-Induced Photooxidative Stress in Chlamydomonas reinhardtii. Front Physiol 11:. https://doi.org/10.3389/fphys.2020.01083 Giri S, Mukherjee A (2021) Ageing with algal EPS reduces the toxic effects of polystyrene nanoplastics in freshwater microalgae Scenedesmus obliquus. J Environ Chem Eng 9:105978. https://doi.org/10.1016/j.jece.2021.105978 Smith PN, Armbrust KL, Brain RA, et al (2021) Assessment of risks to listed species from the use of atrazine in the USA: a perspective. J Toxicol Environ Heal - Part B Crit Rev 24:223–306. https://doi.org/10.1080/10937404.2021.1902890 De Caroli Vizioli B, Silva da Silva G, Ferreira de Medeiros J, Montagner CC (2023) Atrazine and its degradation products in drinking water source and supply: Risk assessment for environmental and human health in Campinas, Brazil. Chemosphere 336:. https://doi.org/10.1016/j.chemosphere.2023.139289 Cui H, Chen B, Jiang Y, et al (2021) Toxicity of 17 disinfection by-products to different trophic levels of aquatic organisms: ecological risks and mechanisms. Environ Sci Technol 55:10534–10541 Mouhat F, Coudert F-X, Bocquet M-L (2020) Structure and chemistry of graphene oxide in liquid water from first principles. Nat Commun 11:1566, https://doi.org/10.1038/s41467-020-15381-y Kahsay MH, Belachew N, Tadesse A, Basavaiah K (2020) Magnetite nanoparticle decorated reduced graphene oxide for adsorptive removal of crystal violet and antifungal activities. RSC Adv 10:34916–34927, https://doi.org/10.1039/D0RA07061K Sinha S, Warner JH (2021) Recent progress in using graphene as an ultrathin transparent support for transmission electron microscopy. Small Struct 2:2000049, https://doi.org/10.1002/sstr.202000049 Wang Q, Peng L, Wang P, et al (2024) Changes of atrazine dissipation and microbial community under coexistence of graphene oxide in river water. J Hazard Mater 462:132708, https://doi.org/10.1016/j.jhazmat.2023.132708 Pereira HA, da Boit Martinello K, Vieira Y, et al (2023) Adsorptive behavior of multi-walled carbon nanotubes immobilized magnetic nanoparticles for removing selected pesticides from aqueous matrices. Chemosphere 325:. https://doi.org/10.1016/j.chemosphere.2023.138384 Gupta B, Kumar N, Panda K, et al (2017) Role of oxygen functional groups in reduced graphene oxide for lubrication. Sci Rep 7:1–14. https://doi.org/10.1038/srep45030 Khoshnamvand M, You D, Xie Y, et al (2024) Presence of humic acid in the environment holds promise as a potential mitigating factor for the joint toxicity of polystyrene nanoplastics and herbicide atrazine to Chlorella vulgaris: 96-H acute toxicity. Chemosphere 357:. https://doi.org/10.1016/j.chemosphere.2024.142061 Sun C, Xu Y, Hu N, et al (2020) To evaluate the toxicity of atrazine on the freshwater microalgae Chlorella sp. using sensitive indices indicated by photosynthetic parameters. Chemosphere 244:. https://doi.org/10.1016/j.chemosphere.2019.125514 Ojha A, Samriti, Thakur S, Prakash J (2023) Graphene family nanomaterials as emerging sole layered nanomaterials for wastewater treatment: Recent developments, potential hazards, prevention and future prospects. Environ Adv 13:100402. https://doi.org/10.1016/j.envadv.2023.100402 Shi L, Chen J, Teng L, et al (2016) The Antibacterial Applications of Graphene and Its Derivatives. Small 12:4165–4184. https://doi.org/10.1002/smll.201601841 Hashemi E, Giesy JP, Liang Z, et al (2024) Impacts of graphene oxide contamination on a food web: Threats to somatic and reproductive health of organisms. Ecotoxicol Environ Saf 285:117032. https://doi.org/10.1016/j.ecoenv.2024.117032 Tsavatopoulou VD, Manariotis ID (2022) Chlorococcum sp. and mixotrophic algal biofilm growth in horizontal and vertical–oriented surfaces using wastewater and synthetic substrate. Biomass Convers Biorefinery 1–16, https://doi.org/10.1007/s13399-022-02752-2 Harshkova D, Majewska M, Pokora W, et al (2021) Diclofenac and atrazine restrict the growth of a synchronous Chlamydomonas reinhardtii population via various mechanisms. Aquat Toxicol 230:. https://doi.org/10.1016/j.aquatox.2020.105698 Zhao J, Wang Z, White JC, Xing B (2014) Graphene in the aquatic environment: Adsorption, dispersion, toxicity and transformation. Environ Sci Technol 48:9995–10009. https://doi.org/10.1021/es5022679 Cheng J, Wang F, Yu D-F, et al (2011) The cytotoxic mechanism of malondialdehyde and protective effect of carnosine via protein cross-linking/mitochondrial dysfunction/reactive oxygen species/MAPK pathway in neurons. Eur J Pharmacol 650:184–194. https://doi.org/https://doi.org/10.1016/j.ejphar.2010.09.033 Yang W, Gao P, Nie Y, et al (2021) Comparison of the effects of continuous and accumulative exposure to nanoplastics on microalga Chlorella pyrenoidosa during chronic toxicity. Sci Total Environ 788:147934 Bhattacharyya A, Chattopadhyay R, Mitra S, Crowe SE (2014) Oxidative stress: An essential factor in the pathogenesis of gastrointestinal mucosal diseases. Physiol Rev 94:329–354. https://doi.org/10.1152/physrev.00040.2012 Murchie EH, Lawson T (2013) Chlorophyll fluorescence analysis: A guide to good practice and understanding some new applications. J Exp Bot 64:3983–3998. https://doi.org/10.1093/jxb/ert208 Yang L, Zhang Y (2020) Effects of atrazine and its two major derivatives on the photosynthetic physiology and carbon sequestration potential of a marine diatom. Ecotoxicol Environ Saf 205:111359. https://doi.org/10.1016/j.ecoenv.2020.111359 Jiao W-B, Zhang Y-Q, Yu K, et al (2019) Porous graphitic biomass carbons as sustainable adsorption and controlled release carriers for atrazine fixation. ACS Sustain Chem Eng 7:20180–20189, https://doi.org/10.1021/acssuschemeng.9b06269 Nödler K, Licha T, Voutsa D (2013) Twenty years later - Atrazine concentrations in selected coastal waters of the Mediterranean and the Baltic Sea. Mar Pollut Bull 70:112–118. https://doi.org/10.1016/j.marpolbul.2013.02.018 Scheme 1 Scheme 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SIFNAtz14.03.2511am.docx Scheme1.tif Scheme 1: Correlation, and interconnection of ROS generation and MDA production Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5853275","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448572220,"identity":"802bfcc7-78d6-41a6-9b7e-1294c1f649f0","order_by":0,"name":"Abhrajit Debroy","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Abhrajit","middleName":"","lastName":"Debroy","suffix":""},{"id":448572221,"identity":"50c656f8-f200-49e4-8146-8e78f644ed18","order_by":1,"name":"Mrudula Pulimi","email":"","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Mrudula","middleName":"","lastName":"Pulimi","suffix":""},{"id":448572222,"identity":"aa322af4-3ac2-4f87-9112-ecdd6d37029a","order_by":2,"name":"Amitava Mukherjee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYFACxgYgYcPABuFJEKWlEagnjYGNjXgtYGsOM8CsIQz4px1uf/Cz7Xxin3wD44cfDBZ5BLVI3E5sbOxtu53YxsbALNnDIFFM2BqglgaeM7eNQX6RBhqR2EBIhzzIlj9nzoG0MP8mSosBUEszT8UBOaAWNuJsMQRqmS1TkQzUkthm2WNAhBa52+kPPr4xsOORbz58+MaPijrCWpAAKH4MSFA/CkbBKBgFowA3AACtdzZKKpK9OAAAAABJRU5ErkJggg==","orcid":"","institution":"Vellore Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Amitava","middleName":"","lastName":"Mukherjee","suffix":""}],"badges":[],"createdAt":"2025-01-18 06:38:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5853275/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5853275/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81684766,"identity":"81777100-e512-4f35-99d6-28b4bf0b2486","added_by":"auto","created_at":"2025-04-30 10:08:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11950627,"visible":true,"origin":"","legend":"\u003cp\u003eHR-TEM images of (A-i) GO, (A-ii) rGO, and (A-iii) graphene\u003c/p\u003e","description":"","filename":"Fig1TEM.png","url":"https://assets-eu.researchsquare.com/files/rs-5853275/v1/1c5e26c551358f6dedb298e4.png"},{"id":81684254,"identity":"19a99417-d631-4908-b91e-0a835cab7218","added_by":"auto","created_at":"2025-04-30 10:00:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1268218,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences in growth inhibition for 25, 50, 75, and 100 µg/L of ATZ with 250 µg/L of (Ai) GO, (Aii) rGO, and (Aiii) graphene in the pristine and binary mixtures. The level of significance for algal cells treated with ATZ and GFNs with respect to control is labelled with ‘***’ (p \u0026lt; 0.001), ‘α, β, χ, δ’ suggests a significant difference between pristine and binary mixtures ATZ and GFNs with pristine ATZ treatment groups (α = p \u0026lt; 0.001, β = p \u0026lt; 0.001, χ = p \u0026lt; 0.05, and δ = no significance).\u003c/p\u003e","description":"","filename":"Fig2AtzUpdated.png","url":"https://assets-eu.researchsquare.com/files/rs-5853275/v1/bd76251b9947ccbda9c3027f.png"},{"id":81684253,"identity":"b929d8d1-d71a-4a26-bc6f-672b52e0f425","added_by":"auto","created_at":"2025-04-30 10:00:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2438301,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences in total ROS generation 25, 50, 75, and 100 µg/L of ATZ with 250 µg/L of (Ai) GO, (Aii) rGO, and (Aiii) graphene in the pristine and binary mixtures; and the differences in total MDA generation for 25, 50, 75, and 100 µg/L of ATZ with 250 µg/L of (Bi) GO (Bii) rGO, and (Biii) graphene in the pristine and binary mixtures. The level of significance for algal cells treated with ATZ and GFNs with respect to control is labelled with ‘***’ (p \u0026lt; 0.001), ‘α, β, δ’ suggests a significant difference between pristine and binary mixtures ATZ and GFNs with pristine ATZ treatment groups (α = p \u0026lt; 0.001, β = p \u0026lt; 0.001 and δ = no significance).\u003c/p\u003e","description":"","filename":"Fig3Oxidativestress.png","url":"https://assets-eu.researchsquare.com/files/rs-5853275/v1/2c2390d8d4e8529600de05b7.png"},{"id":81684768,"identity":"9864cad9-c900-4d33-8661-0b5563e0a831","added_by":"auto","created_at":"2025-04-30 10:08:20","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2676103,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences in total SOD generation 25, 50, 75, and 100 µg/L of ATZ with 250 µg/L of (Ai) GO, (Aii) rGO, and (Aiii) graphene in the pristine and binary mixtures; and the differences in total MDA generation for 25, 50, 75, and 100 µg/L of ATZ with 250 µg/L of (Bi) GO (Bii) rGO, and (Biii) graphene in the pristine and binary mixtures. The level of significance for algal cells treated with ATZ and GFNs with respect to control is labelled with ‘***’ (p \u0026lt; 0.001), ‘α, β, δ’ suggests a significant difference between pristine and binary mixtures ATZ and GFNs with pristine ATZ treatment groups (α = p \u0026lt; 0.001, β = p \u0026lt; 0.001 and δ = no significance).\u003c/p\u003e","description":"","filename":"Fig4SodCatalase.png","url":"https://assets-eu.researchsquare.com/files/rs-5853275/v1/394654459e7456090799bc5b.png"},{"id":81684789,"identity":"9e798de9-0fb7-4cc0-b27c-584c9486ab72","added_by":"auto","created_at":"2025-04-30 10:08:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":26574757,"visible":true,"origin":"","legend":"\u003cp\u003eThe differences in effective quantum yield of PS (II) (Y(II)) for 25, 50, 75, and 100 µg/L of ATZ with 250 µg/L of (A) GO, (C) rGO, and (E) graphene in the pristine and binary mixtures; and the differences in electron transport rate (ETR) for 25, 50, 75, and 100 µg/L of ATZ with 250 µg/L of (B) GO, (D) rGO, and (F) graphene in the pristine and binary mixtures.\u003c/p\u003e","description":"","filename":"Fig5PAM.png","url":"https://assets-eu.researchsquare.com/files/rs-5853275/v1/cc98625c710c081b30be6f7b.png"},{"id":81684272,"identity":"464766a2-897d-42c8-a4c4-374095ef99b5","added_by":"auto","created_at":"2025-04-30 10:00:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":37266092,"visible":true,"origin":"","legend":"\u003cp\u003eThe cluster heat map (A-i, A-ii, and A-iii) and correlation analysis plots (B-i, B-ii, and B-iii) illustrate the connections among biological indicators across all treatment groups. The presence of \"*\" denotes statistical significance compared to the control group (p \u0026lt; 0.05), indicating a noteworthy difference.\u003c/p\u003e","description":"","filename":"Fig6Heatmapcorrelation.png","url":"https://assets-eu.researchsquare.com/files/rs-5853275/v1/d77e1a1d5a5eccd3cf271a41.png"},{"id":81685298,"identity":"84eedd90-38ae-4aec-ae7d-003d51484fb0","added_by":"auto","created_at":"2025-04-30 10:16:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":5823220,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) of (A-i) GO, (A-ii) rGO, (A-iii) graphene in their pristine, and mixture forms with ATZ\u003c/p\u003e","description":"","filename":"Fig7PCA.png","url":"https://assets-eu.researchsquare.com/files/rs-5853275/v1/50966f6b68593faa89fee24d.png"},{"id":81684260,"identity":"37bccfc8-2624-4cbf-b31f-0f81ffe5e5a4","added_by":"auto","created_at":"2025-04-30 10:00:20","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":829949,"visible":true,"origin":"","legend":"","description":"","filename":"SIFNAtz14.03.2511am.docx","url":"https://assets-eu.researchsquare.com/files/rs-5853275/v1/75e2b63158b5ab3ab79cc3e6.docx"},{"id":81685297,"identity":"ed392d76-b855-4d12-95e2-ec8274df9ec3","added_by":"auto","created_at":"2025-04-30 10:16:20","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1252380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScheme 1: \u003c/strong\u003eCorrelation, and interconnection of ROS generation and MDA production\u003c/p\u003e","description":"","filename":"Scheme1.tif","url":"https://assets-eu.researchsquare.com/files/rs-5853275/v1/33ca2e824af40bbbdfc3fa31.tif"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eToxicological Impacts and Environmental Risk of Atrazine in the Presence of Graphene Family Nanomaterials (GFNs) on Chlorella sp\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePesticides play a critical role in contemporary agricultural practices aimed at meeting the demands of the increasing global population; however, their application leads to environmental contamination, particularly water pollution [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Atrazine (2-chloro-4-ethylamino-6-isopropylamino-1,3,5-triazine) (ATZ) is one of the most extensively utilized herbicides globally, owing to its great efficacy, cost-effectiveness, and versatile applications. It is marketed in over 100 countries, with an annual consumption of around 70,000 to 90,000 tons [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The extensive use of ATZ results in its flow into aquatic ecosystems. It possesses an extended half-life ranging from several days to years and a moderate aqueous solubility of 30 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Atrazine's persistence results in its frequent detection in aquatic ecosystems at elevated quantities, reaching up to 30 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Given its established toxicological effects on non-target species, including toxigenicity, reproductive toxicity, and endocrine disruption [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], environmental and public health organizations flagged its risk in aquatic environment. ATZ may diminish cellular metabolism and affect the production of reactive oxygen species (ROS), thereby modifying antioxidant activity in fish [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and crustaceans [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. ATZ can impede algae development and photosynthesis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In a previous study, the exposure of \u003cem\u003eRaphidocelis subcapitata\u003c/em\u003e to 50\u0026ndash;600 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e ATZ for 72 hours led to a decreased carbon-to-nitrogen ratio, reflecting metabolic disruptions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Similarly, \u003cem\u003eSelenastrum capricornutum\u003c/em\u003e exhibited significant growth inhibition at ATZ concentrations of 0.076 and 0.023 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e over 48 and 72 hours, respectively [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In another study, upon exposure of \u003cem\u003eChlorella vulgaris\u003c/em\u003e to ATZ and DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea) under 'standard' low light intensity, as stipulated by the OECD201 guideline, they showed a substantial decline in oxygen productivity and photosynthetic activity at short exposure times (\u0026gt;\u0026thinsp;1hr) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. All these studies prove the toxic potential of ATZ in the algal cells, in various aquatic environments.\u003c/p\u003e \u003cp\u003eGraphene-family nanomaterials (GFNs) are analogous graphene-like materials with diverse sizes, layers, surface chemistries, and flaws. These encompass various nanomaterials, including few-layer graphene, graphene oxide (GO), graphene quantum dots, reduced graphene oxide (rGO), and multilayer graphene materials synthesized from graphene, GO, or other graphene derivatives as precursors [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The presence of robust sigma bonds connecting carbon atoms offers GFNs exceptional thermal stability, mechanical capabilities, radiation resistance, high-temperature resistance, and superior chemical stability. The extended π\u0026ndash;π coupling leads to strong electrical conductivity and superior oxidation efficiency of GFNs. These outstanding qualities and low synthesis costs allow GFNs to become novel materials in the 21st century with quick development and great demand [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. With increasing production and utilization, GFNs have infiltrated the environment by numerous means, suggesting potential harm to aquatic species, plants, or animals [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Recent investigations demonstrated that the doses of GFNs in the environment range from 0.001 to 10 mg/L, even higher [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Amongst the GFNs, shading impact and loss of available nutrients were revealed to be the primary harmful mechanism of GO to \u003cem\u003eChlorella pyrenoidosa\u003c/em\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. GO at low concentration was reported to stimulate the upregulation of microcystin production of \u003cem\u003eM. aeruginosa\u003c/em\u003e due to the metabolic alterations [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Also, graphene (at 1 mg/L concentration) diminished cell survival, reduced esterase activity, and impaired photosynthetic efficiency in the cells. Another recent research aimed to investigate the effects, both individually and collectively, of polystyrene microplastics (PSMPs, 1 \u0026micro;m) and nanoplastics that are (PSNPs, 50\u0026ndash;100 nm), in conjunction with agriculturally pertinent GO, on the germination and growth of lettuce seeds (\u003cem\u003eLactuca sativa\u003c/em\u003e). The findings indicated that the interactions between PSMPs/PSNPs and GO combinations had both synergistic and antagonistic effects, contingent upon several toxicity markers. The biological mechanism responsible for the synergistic effects on the roots and shoots of seedlings entailed oxidative stress [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The study highlighted the role of mixture studies and how GO can help modify the toxic potential of PSMPs/PSNPs. Hence, it is pertinent to evaluate the impact of GFNs in combination with other pollutants on the marine ecosystem. In addition, literature confirmed the availability of GFNs and ATZ in the marine environment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. If these materials are coming into the environment, then their combinations can also be found in the natural environment. In this way, the current study is one of a kind since it is the first to establish the concentration-dependent changes in the toxic potential of ATZ in the presence of various GFNs.\u003c/p\u003e \u003cp\u003eMicroalgae have a remarkably rapid growth rate and can turn solar energy into chemical energy, stabilize atmospheric carbon dioxide, and function as primary producers. They also supply critical nutrients for terrestrial and aquatic creatures [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Microalgae are biomarkers of eutrophication and are efficiently used to monitor water quality [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. \u003cem\u003eChlorella\u003c/em\u003e sp. are well-known unicellular green microalgae that usually thrive in aquatic settings. These green microalgae are regarded as key biological markers in aquatic settings and comprise a large portion of the food chain in aquatic ecosystems [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These species exhibit sensitivities to harmful compounds, endurance to high temperatures, and the capacity to proliferate in small quantities of nutrients. Hence, in the current study, \u003cem\u003eChlorella\u003c/em\u003e sp. was chosen as the indicator organism to determine the impacts of GFNs on the toxic potential of ATZ at environmentally relevant concentrations.\u003c/p\u003e \u003cp\u003eA comprehensive literature review reveals a notable gap in the existing research on the effects of the mixture of ATZ and GFNs in marine algal species. Hence, the current work focuses on bridging the research gap by evaluating the impact of different GFNs on the increasing concentrations of ATZ in the marine environment. The current work postulates that ATZ produces toxicity in algal cells by forming oxidative stress while lowering photosynthetic efficacy. The use of GFNs in recent years has increased tremendously, which has resulted in their leaching into the aquatic environment. ATZ, too, has been one of the most frequently detected herbicides in aquatic environments. Recent research shows that the levels of GFNs in the aquatic environment can reach up to (1\u0026ndash;1000 \u0026micro; g/L) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and ATZ concentrations can reach up to 134 ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Hence, there is a chance that ATZ can co-exist in aqueous environments, allowing their interaction. Authors hypothesized that including GFNs (GO, rGO, and graphene, at a concentration of 250 \u0026micro;g/L) would alter their potential toxicities by modulating their solubilities in the media. The chosen concentrations of GFNs for the study were fixed after carefully deliberating the available literature on the amount of GFNs present in the aquatic environment. ATZ is one of the most commonly used herbicides in the agricultural field [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The high demand results in huge production, and as a result, the runoffs from the agricultural land, result in their transport in the aquatic ecosystems. However, none previous studies have observed the impact of the GFNs and ATZ on aquatic organisms. Hence, the study's authors considered this combination and explored their interaction in the present study. The work proposed that adding GFNs at environmentally relevant concentrations to increasing concentrations of ATZ would result in the simultaneous interaction of ATZ with GFNs, thereby inducing them to form aggregates. The aggregate formation is increased with the increase in ATZ concentration, resulting in an antagonistic effect towards the algal cells. Whereas, at lower concentrations of ATZ in the media, the number of aggregates would be lower, implying more available free ATZ for algal interaction and an increase in their toxic potential compared to their pristine groups. Current research examined the toxic potential of ATZ at naturally relevant doses ranging from 25\u0026ndash;100 \u0026micro;g/L in the marine microalga \u003cem\u003eChlorella\u003c/em\u003e sp. The study was performed in the well-defined artificial seawater (ASW) media. The ATZ-GFN interaction was examined using surface charge and hydrophobicity analyses. Additionally, the nominal concentration of ATZ was investigated using ultra-performance liquid chromatography (UPLC). The available concentration of ATZ at 72 h confirms the GFNs-ATZ interaction and adsorption of ATZ. The toxicological experiments were studied by assessing the growth inhibitory effect, generated oxidative stress, variations in SOD and catalase activities, and photosynthetic efficiencies as well. The changes in the physicochemical parameters of GFNs and ATZ were associated with the changes in the toxicological parameters.\u003c/p\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Material synthesis, and chemical preparation\u003c/h2\u003e \u003cp\u003eThe current study incorporates chemicals details in the supporting information (M1S1). As an interaction medium, autoclaved ASW was used, similar with the past research [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The protocol to prepare ASW is outlined in the supporting information Tables S1\u0026ndash;S4. The synthesis of GFNs followed established procedures as outlined in prior publications [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Further information can be found in the supporting information (M2S2). ATZ was purchased from was purchased from Sigma Aldrich.\u003c/p\u003e \u003cp\u003eGFN suspensions were prepared at a concentration of 50 mg/L in milli-q water, as detailed by Lu et al. (2018) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], utilizing ultra-sonication at a power of 130 W and frequency of 20 kHz (Sonics, USA) for a duration of 20 minutes. The working concentration for GFNs was set at 250 \u0026micro;g/L. A solution of ATZ at 500 mg/L was also prepared using acetone, following the method outlined by Flood et al. (2018) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The chosen concentrations for ATZ use were 25, 50, 75, and 100 \u0026micro;g/L, selected based on concentrations relevant to the environment as indicated in prior research and the EC\u003csub\u003e50\u003c/sub\u003e values for the organisms being tested.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Materials characterization\u003c/h2\u003e \u003cp\u003eHigh Resolution Transmission Electron Microscopy (HR-TEM) analyses were conducted to observe the surface morphologies. To check the ID/IG ratio of the GFN, Raman spectroscopy was performed (Anton Paar GmbH). Additionally, the wettability (Model: DMs-401, Kyowa Interface Science Co., Ltd.; software- FAMAS) and surface charge (zeta potential) (90 Plus Particle Size Analyzer, Brookhaven Instruments Corp., USA) were also assessed for the pristine, as well as binary mixtures of the GFNs, and ATZ. To determine the ATZ concentration, ultra-performance liquid chromatography (UPLC) was utilized [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. More information can be found in the supporting information (M3S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Test organism\u003c/h2\u003e \u003cp\u003eThe marine microalga \u003cem\u003eChlorella\u003c/em\u003e sp., employed as a model organism in this study, was obtained from the Central Marine Fisheries Research Institute (CMFRI), Rameswaram, Tamil Nadu. Cultivation was carried out in 200 mL Erlenmeyer flasks containing natural seawater (NSW) supplemented with specific micronutrients (details in Supplementary Tables S2, S3, and S4) for a duration of 15\u0026ndash;20 days. To ensure optimal growth, the cultures of algae were kept under a light cycle of 16 hours of exposure to white fluorescent light (with an intensity of 3000 lux provided by TLD Super80 linear-fluorescent lamps) within a chamber where the temperature was meticulously regulated to remain at 23\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Growth Inhibition Study\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Experimental setup\u003c/h2\u003e \u003cp\u003eIn this study, algal cells were sourced from the exponential growth and then the centrifugation was done for 10 minutes at a speed of 7000 rpm at 4\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:℃\\)\u003c/span\u003e\u003c/span\u003e. After centrifugation, gathered pellets was re-suspended in the sterile ASW. Subsequently, a colorimeter was utilized for the adjustment of the optical density (OD) of 0.1 at 610 nm in ASW. Three distinct conditions were established to assess the GFNs-ATZ interaction and their mixtures and algae: (a) GFNs (pristine, 250 \u0026micro;g/L), and ATZ (25, 50, 75, and 100 \u0026micro;g/L), (b) binary mixtures of GFNs (250 \u0026micro;g/L) and ATZ (25, 50, 75, and 100 \u0026micro;g/L), and (c) control cells without any treatment. The room temperature (23\u0026thinsp;\u0026plusmn;\u0026thinsp;2 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:℃\\)\u003c/span\u003e\u003c/span\u003e) was maintained for the interaction for 72 h; the interaction volume was selected at 10 mL, and the set-ups were kept in visible light (3000 lux). Another control set was kept with the maximum amount of acetone utilized in the present work and was evaluated for cell viability (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Compared to the control group, there was a negligible and insignificant decline in cell viability. Thus, this setup was not pursued for further assays. The toxicity assessment in this study followed the OECD guidelines [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and triplicates (n\u0026thinsp;=\u0026thinsp;3) were conducted for each experimental condition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. EC\u003csub\u003e50\u003c/sub\u003e and cytotoxicity evaluation\u003c/h2\u003e \u003cp\u003eAfter the interaction time of 72 h, growth inhibitory effects were evaluated by counting the viable cells using a haemocytometer using the microscopic analysis (Zeiss Axiostar, USA) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The specific protocol can be found in the supplementary information (M5S5). The EC\u003csub\u003e50\u003c/sub\u003e of ATZ (in the pristine form) was established through various concentrations, typical concentration-wise. Additionally, the EC\u003csub\u003e50\u003c/sub\u003e of ATZ in conjunction with GFNs at a concentration of 250 \u0026micro;g/L was also measured. Further details are included in the supplementary section (M5S5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. Model to predict binary toxicity\u003c/h2\u003e \u003cp\u003eThe independent action model (Abbott's model), was utilized to assess the potential interaction in-between ATZ, and GFNs [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Further details about this approach are provided in the supplementary information (M5S5). The Abbott's model and the Ratio of Inhibition (R\u003csub\u003eI\u003c/sub\u003e) are described as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{exp}=\u0026lceil;\\left(A+B\\right)-\\left(\\frac{AB}{100}\\right)\u0026rceil;\\)\u003c/span\u003e \u003c/span\u003e (i)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{R}_{I}=\\frac{{C}_{obs}}{{C}_{exp}}\\)\u003c/span\u003e \u003c/span\u003e (ii)\u003c/p\u003e \u003cp\u003e[A is the percentage of growth inhibition GFNs (pristine), B is the percentage of growth inhibition of ATZ, C\u003csub\u003eexp\u003c/sub\u003e is expected toxicity, and C\u003csub\u003eobs\u003c/sub\u003e is observed toxicity.]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4. Oxidative stress estimation\u003c/h2\u003e \u003cp\u003ePreviously published protocol followed by Debroy et al., 2024, was used for the estimation of total ROS, and MDA content. Supplementary information contains the other details related to these procedures (M6S6, M7S7). DCFH-DA was used to estimate produced ROS, and TCA-TBA was used to estimate produced MDA content.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.5. Photosynthetic activity estimation\u003c/h2\u003e \u003cp\u003eAfter 72 h of interaction, the control and the treated cells were placed in dark incubation for 15 min. The protocol followed by Lee et al., 2020, and Giri and Mukherjee, 2021 was modified, and followed to estimate the effective quantum yield of photosystem II (Y(II)), and electron transport rate (ETR) in the algal cells. Further details are available in the supplementary information (M8S8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.6. Antioxidant enzymes assessment\u003c/h2\u003e \u003cp\u003eAfter 72 h interaction, the cells were collected by centrifugation at 7000 rpm for 10 min at 4\u0026deg;C followed by washing with 0.5 M phosphate buffer saline (PBS), and homogenised. After that, the antioxidant enzymes such as, SOD, and catalase (CAT) activities were estimated by adopting the method used by [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Details are added in the supplementary information (M9S9).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Ecological risk assessment\u003c/h2\u003e \u003cp\u003eIn this research, the ecological risks of ATZ in the marine ecosystems were evaluated by using the risk quotient (RQ). The RQ is a standard method for assessing the risks posed by the contaminants or the materials in the aquatic environments [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The calculation of ATZ's RQ was carried out using the following specific equations.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:PNEC=\\frac{{EC}_{50}}{AF}\\)\u003c/span\u003e \u003c/span\u003e (iii)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:RQ=\\frac{MEC}{PNEC}\\)\u003c/span\u003e \u003c/span\u003e (iv)\u003c/p\u003e \u003cp\u003eIn this study, MEC (measured environmental concentration) were sourced from existing articles [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The PNEC (predicted no-effect concentration) was derived from the EC\u003csub\u003e50\u003c/sub\u003e values using an assessment factor (AF) set at 1000. This AF aligns with the guidelines from the European Chemical Agency and applies to the acute toxicity evaluations [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. EC\u003csub\u003e50\u003c/sub\u003e values were determined both in the absence and presence of GFNs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Analysis of data\u003c/h2\u003e \u003cp\u003eThe experimental procedures, which includes toxicity testing, were performed in triplicate (n\u0026thinsp;=\u0026thinsp;3). Results are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. A normality test was performed followed by ANOVA (two-way) with a Bonferroni post-test with the help of GraphPad-Prism-8 was also performed to evaluate the statistically significance amid different test sets with respect to the control. Graphical representations were done by the help of both OriginPRO 2024b, and GraphPad-Prism-8. For the assessment of the biological parameters, cluster heatmaps, Pearson correlation matrix, and principal component analysis (PCA) analyses were also conducted.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Characterization\u003c/h2\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, HR-TEM images clearly revealed the structural features of GFNs. The GO\u0026rsquo;s sheet like structures were made up of multiple layers of partially oxidized graphite oxide. In contrast, the characterization of the rGO sheets were analyzed and revealed that, several layers organized in a stacked formation, displaying both folding, and wrinkling. The graphene nano-sheets exhibited sheet-like structures with formations of aggregates. Raman spectroscopy revealed that, amongst the pristine GFNs, rGO showed the highest ID/IG ratio, followed by GO, and graphene (Fig S2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe surface charges of GFNs were recorded in ASW medium (Table S5). The surface charges were \u0026minus;\u0026thinsp;15.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1, -25.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6, and \u0026minus;\u0026thinsp;7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6 mV for GO, rGO, and graphene, respectively. However, after interacting with ATZ, all the mixtures exhibited a significant reduction in surface charge (Table S5). The contact angles (Table S6) were measured at 36.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87, 45.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77, and 21.00\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:^\\circ\\:\\)\u003c/span\u003e\u003c/span\u003e for GO, rGO, and graphene, respectively. After the interaction with ATZ, the values also decreased, particularly with the higher concentrations of ATZ.\u003c/p\u003e \u003cp\u003eThe observed concentrations of ATZ in the interaction medium were measured at both 0th, and 72 h timepoints (Table S7). At the initial point, ATZ concentrations (in the absence of GFNs) were recorded at 27.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66, 77.15\u0026thinsp;\u0026plusmn;\u0026thinsp;15.91, 146.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.34, and 142.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.72 \u0026micro;g/L for the nominal concentrations of 25, 50, 75, and 100 \u0026micro;g/L, respectively. After 72 hours, a reduction in the measured concentrations within the mixtures suggested that ATZ was adsorbed onto the GFNs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Changes in the cytotoxicity\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. EC\u003csub\u003e50\u003c/sub\u003e assessment\u003c/h2\u003e \u003cp\u003eThe EC\u003csub\u003e50\u003c/sub\u003e concentrations (Table S8) of pristine forms of GFNs (250 \u0026micro;g/L), ATZ, and their mixture were tested towards \u003cem\u003eChlorella\u003c/em\u003e sp. Remarkably, the EC\u003csub\u003e50\u003c/sub\u003e values of ATZ were greater than before in GFN\u0026rsquo;s presence, with respect to pristine ATZ. The trend was noted as follows: graphene\u0026thinsp;+\u0026thinsp;ATZ\u0026thinsp;\u0026gt;\u0026thinsp;rGO\u0026thinsp;+\u0026thinsp;ATZ\u0026thinsp;\u0026gt;\u0026thinsp;GO\u0026thinsp;+\u0026thinsp;ATZ.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Inhibitory effects\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e represents the growth inhibition patterns of the algal cells upon treatment with GO, rGO, graphene, ATZ and their combinations with various concentrations of ATZ. All the pristine GFNs treated cells showed a significant amount of growth inhibition in comparison with the control cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The pattern, as observed amongst the pristine GFNs, was rGO\u0026thinsp;\u0026gt;\u0026thinsp;GO\u0026thinsp;\u0026gt;\u0026thinsp;graphene. The growth inhibition was detected to be concentration-dependent for pristine ATZ, with the highest concentration of ATZ showing the maximum inhibition in growth, i.e., \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sim\\)\u003c/span\u003e\u003c/span\u003e 25% was highly significant, when compared to the control treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe addition of GFNs in the interaction medium containing ATZ resulted in a significant increase in growth inhibition at the lower concentration of 25 \u0026micro;g/L with respect to the pristine ATZ treatments (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sim\\)\u003c/span\u003e\u003c/span\u003e 20% for the mixture containing GO, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sim\\)\u003c/span\u003e\u003c/span\u003e 23% for the one with rGO, and lastly \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sim\\)\u003c/span\u003e\u003c/span\u003e 13% for the one with graphene). This increment in the growth inhibition was observed to be highly significant for the mixture of GO (p \u0026lt; 0.001), slightly significant for the mixture with rGO (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and not significant for the one containing graphene (p \u0026gt; 0.05) when compared with the pristine ATZ treatments. Similar results were observed for the binary mixtures consisting of 50 \u0026micro;g/L with the GFNs, where adding GFNs resulted in the increment of the growth inhibition compared to the pristine ATZ treatment of the same concentration. However, this increase was statistically insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) for all the mixtures.\u003c/p\u003e \u003cp\u003eUpon treatment with high concentrations of ATZ (75, and 100 \u0026micro;g/L) in the binary mixture with GFNs, the growth inhibition was noticed to decline with respect to the pristine ATZ treatments of the same concentrations. At 75 \u0026micro;g/L of ATZ, the observed reduction in growth inhibition for the mixture containing GFNs was statistically insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), in comparison with the pristine ATZ treatments. However, the addition of GFNs (rGO and graphene) to the binary mixture comprising 100 \u0026micro;g/L resulted in a highly significant decline (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in growth inhibition in comparison to their pristine counterparts. For the mixture containing GO and ATZ of 100 \u0026micro;g/L, there was a decrease in the growth inhibition in comparison to the pristine ATZ treatment, but it was only slightly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Abbott\u0026rsquo;s model based prediction of the mixture effects\u003c/h2\u003e \u003cp\u003eThe predicted toxicity of GFNs and ATZ combinations was obtained by using Abbott\u0026rsquo;s model (Table S9), known as the independent-action model. The findings revealed that the R\u003csub\u003eI\u003c/sub\u003e declined in a concentration wise manner for the mixtures, with high concentrations of ATZ resulting in the lowest R\u003csub\u003eI\u003c/sub\u003e values. When GFNs were combined with lower concentrations of ATZ, an additive effect was observed. Conversely, the mixtures of GFN with higher concentration of ATZ exhibited antagonistic effects.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Oxidative Stress\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(A i-iii) reveals the changes in the generation of total ROS in the algal cells upon treatment with GFNs, ATZ and their binary mixtures. All the pristine treatments of GFNs showed an increased total ROS generation compared to the control treatment. This was found to be statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Also, the ATZ treatments displayed elevated levels of total ROS generation in a concentration-dependent manner, which revealed statistical significant in comparison with the control treatments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results were in accordance with the cell viability data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ATZ-GFNs binary mixture with the lowest concentration of ATZ (25 \u0026micro;g/L) showed the maximum total ROS generation for all binary mixtures treatment groups. The maximum ROS production was observed in the mixture comprising rGO (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sim\\)\u003c/span\u003e\u003c/span\u003e 128%). This was observed to be statistically higher than the pristine ATZ treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The same trend was found for the GO mixtures, where the ROS production increase was significant in comparison with the pristine ATZ (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, for graphene, the difference was not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Similar results were observed for the binary mixture treatments comprised of the ATZ concentration of 50 \u0026micro;g/L with GFNs. The total ROS production was higher than their pristine ATZ treatments for all the mixtures, although this increase was insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eHowever, the mixtures comprising GFNs at high concentrations of ATZ at 100 \u0026micro;g/L did show a decline in ROS generation compared to their pristine counterparts. This decline was also statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This was irrespective of the different GFN used for the mixture. The binary mixtures of GO, rGO and ATZ at the concentration of 75 \u0026micro;g/L, however, displayed a decline in ROS generation, which is insignificant in comparison with the pristine ATZ treatments (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), although for the mixture comprised of graphene; this decline was observed to be highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (B i-iii) reveals the impact on the MDA production levels upon the treatments with GFNs, ATZ, and their binary mixtures. The MDA generation results also show a similar trend, which was observed for cell viability and total ROS generation. All the pristine groups showed a significant increment in the MDA generation with respect to the control groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eWhen treated with the binary mixtures of GFNs with ATZ at lower concentrations (25, and 50 \u0026micro;g/L), the MDA generation was observed to increase concerning their pristine ATZ treatments. However, this increment was not significant compared to the pristine counterparts (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The binary mixture comprising 75 \u0026micro;g/L and GFNs resulted in a decline in the MDA generation compared to their pristine treatments, even though the decline was insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The mixtures comprised of GO (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and graphene (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) along ATZ with a concentration of 100 \u0026micro;g/L showed a significant decline in the MDA production with respect to their pristine ATZ treatments. It was noticed that the binary mixture comprising rGO with the same concentration of ATZ showed a decline in the generated MDA, which was insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) in comparison with the ATZ pristine treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Antioxidant enzyme activity\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (A i-iii) depicts the effects of GFNs, ATZ, and the binary mixtures on SOD enzyme activity in treated algal cells. All algal cells treated with GFNs (in the pristine forms) exhibited a significant enhancement in SOD activity compared to control cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). SOD activity was observed to be concentration-dependent with pristine ATZ, where the highest ATZ concentration resulted in peak SOD activity, which was significantly elevated relative to the control treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn mixtures containing the lowest ATZ concentration (25 \u0026micro;g/L), all combinations with GFNs demonstrated maximum SOD activity, with the mixture involving rGO showing the highest activity. This was statistically greater than the pristine ATZ treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A similar pattern was noted for mixtures containing GO and graphene; each exhibited significant increases in enzyme activity compared to the pristine ATZ (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Comparable results were obtained for binary mixtures with an ATZ concentration of 50 \u0026micro;g/L, where SOD activity surpassed that of pristine ATZ treatments across all mixtures. However, this increase did not achieve statistical significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Conversely, mixtures comprising GFNs with higher ATZ concentrations (75, and 100 \u0026micro;g/L) exhibited a reduction in SOD activity compared to their pristine counterparts. This decline was statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and consistent across all types of GFNs used.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (B i-iii) reveals the presence of GFNs, ATZ, and their binary mixtures on CAT enzyme activity in the algal cells. Similar to SOD, all pristine GFNs-treated algal cells displayed a significant increase in CAT activity compared to control cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The activity of CAT was also concentration-dependent for pristine ATZ, with the maximum concentration yielding the highest CAT activity, which was greater than the control set, significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFor the lowest ATZ concentration (25 \u0026micro;g/L), the binary mixtures with GFNs exhibited the highest CAT activity, particularly those containing rGO. This activity was statistically more significant than that of the pristine ATZ treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mixtures with GO and graphene also showed significant increases in CAT enzyme activity compared to the pristine ATZ (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similar patterns were observed for mixtures at the ATZ concentration of 50 \u0026micro;g/L; the CAT activity was enhanced in all mixtures compared to their pristine counterparts, although this was not statistically significant for the GO mixture (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, the rGO mixtures showed a significant increase (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while the graphene mixtures exhibited a highly significant rise in CAT enzyme activity relative to the pristine treatments (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, mixtures containing GFNs with higher ATZ (75, and 100 \u0026micro;g/L) concentrations also demonstrated a significant decline in CAT activity compared to their pristine counterparts, consistently yielding statistically significant results (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), independent of the GFN type utilized.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Photosynthetic parameters\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (A, C, E) and S3 (A i-iii) illustrates the effects of GFNs, ATZ, and their binary mixtures on the quantum yield of photosystem II (Y (II)) in algal cells subjected to treatment. Algal cells treated with pristine GFNs showed a substantial reduction in Y (II) in comparison with the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Y (II) revealed a concentration-wise relationship with ATZ in the pristine form, where the highest concentration of ATZ resulted in the most pronounced decline in Y (II), significantly lower than the control treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the lowest ATZ concentration (25 \u0026micro;g/L), all the GFN combinations resulted in minimal Y (II), with the rGO mixture yielding the lowest Y (II), which was significantly lower than the pristine ATZ treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Similar trends emerged for the treatments with the mixtures containing GO and graphene; however, the reductions in Y (II) were statistically insignificant when compared to the pristine ATZ (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). For the binary mixtures with an ATZ concentration of 50 \u0026micro;g/L, Y (II) values were lower than those of pristine ATZ treatments across all combinations, although these reductions did not reach statistical significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, mixtures that combined GFNs with higher ATZ concentrations (75 \u0026micro;g/L) demonstrated an increase in Y (II) relative to their pristine counterparts, although these increases were statistically insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and consistent across all GFNs used. Moreover, for mixtures with the highest ATZ concentration (100 \u0026micro;g/L), increases in Y (II) were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for those involving GO and graphene, whereas the increase for the rGO mixture was statistically insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (B,D,F), and S3 (B i-iii) reveals the influence of GFNs, ATZ, and the binary mixtures on the electron transport rate (ETR) within algal cells. Consistent with Y (II), all algal cells treated with pristine GFNs experienced a significant reduction in ETR compared to the control cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The ETR was also dependent on ATZ concentration, with the highest concentration resulting in the lowest ETR values, significantly lower than the control treatment (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIn mixtures containing the lowest ATZ concentration (25 \u0026micro;g/L), all combinations with GFNs exhibited minimal ETR, with the rGO mixture recording the lowest ETR, which was not significantly different from that of the pristine ATZ treatment (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Similar declines in ETR were observed for mixtures with GO and graphene; however, these declines remained statistically insignificant compared to the pristine ATZ (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). For binary mixtures with an ATZ concentration of 50 \u0026micro;g/L, ETR decreased across all mixtures compared to pristine ATZ treatments, yet this decline did not achieve statistical significance (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Conversely, mixtures involving GFNs with elevated ATZ concentrations (75 \u0026micro;g/L) demonstrated increases in ETR relative to their pristine counterparts, although these increases remained statistically insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) across all GFNs. Notably, mixtures featuring GFNs at the highest ATZ concentrations (100 \u0026micro;g/L) exhibited significant increases in ETR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for those containing GO and graphene, while the increase for the rGO mixture was only slightly statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Physico-chemical interactions\u003c/h2\u003e \u003cp\u003eThe TEM images (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) of the GFNs disclosed the layered, flaky appearance of the pristine GFNs. GO (Fig. Ai) shows folded, wrinkled, thin sheets layered together, characteristic of the defects caused by oxidation [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].The rGO sheets are more wrinkled and have a higher number of folds than the GO. This resulted from removal of oxygen groups, which causes internal stress and structural rearrangement, producing sharper edges [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The Graphene sheets show a smooth, multilayered structure with no visible folds or wrinkles. This results in a soft structure that reflects the organized sp\u003csup\u003e2\u003c/sup\u003e carbon structure [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. GFNs, have demonstrated considerable promise for adsorbing ATZ, because of the strong π-π interactions between ATZ's aromatic rings and the sp\u003csup\u003e2\u003c/sup\u003e hybridized carbon structure of graphene, facilitating the effective adsorption of ATZ [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In another recent study, it was documented that surface modification of multiwall carbon nanotube with magnetite (Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e) resulted in the easier adsorption of the small molecular weight compounds such as pesticides like ATZ, due to electrostatic interactions between the negatively charged pesticide molecule and the positively charged metal ions [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The contact angle results indicate that graphene is the most hydrophobic among the various GFNs. In comparison, rGO is the most hydrophilic with the lowest contact angle, followed by GO, which has a higher contact angle than rGO but lower than graphene. This results from the various degrees of oxidation of the materials, leading to their various surface morphologies that cause the differences in their toxic potential. This is directly linked to their toxic potential as well. The adsorption of ATZ over the GFN surface might be due to the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pi\\:-\\pi\\:\\)\u003c/span\u003e\u003c/span\u003e interaction that helped create bonds with the GFN surface [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Zeta potential quantifies the surface electrical charge of particles suspended in a liquid medium. An elevated zeta potential indicates a stronger electrostatic repulsion among particles, which means less likelihood of aggregation. The adsorption of ATZ over the GFNs at higher concentrations of ATZ resulted in a lower zeta potential, indicating a decline in the colloidal stability of the mixture [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. However, the interaction of GFNs with lower concentrations of ATZ led to more stable colloidal suspensions where the particles were evenly dispersed throughout the liquid phase.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Algal toxicity\u003c/h2\u003e \u003cp\u003eIn a previous study, the measurements of chlorophyll a, chlorophyll b, and carotenoids in \u003cem\u003eChlorella vulgaris\u003c/em\u003e were used to assess the toxic impacts of ATZ at concentrations of 10, 1, 0.1, and 0.01 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Based on the results obtained, it was deduced that ATZ exerts a toxic impact on \u003cem\u003eChlorella vulgaris\u003c/em\u003e at the concentrations tested [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Similar patterns were observed in another study where the addition of ATZ led to the limitation of the population growth of \u003cem\u003eChlamydomonas reinhardtii\u003c/em\u003e through the decline in the photosynthetic parameters [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Among GFNs, GO exhibits the highest antibacterial efficacy, primarily due to its superior ability to generate ROS, which is brought forward by the inactivation of proteins and lipids [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Also, in another study, exposure to GO led to elevated mortality rates in zebrafish and negatively impacted reproductive performance, with pronounced effects observed through trophic transfer at exposure concentrations of 100.0, 200.0, 400.0, and 800.0 ng L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Furthermore, the antibacterial properties of GO and rGO are influenced by exposure duration and sample concentration. The growth inhibition observed in mixtures of GFNs with low ATZ concentrations was higher than that seen with pristine ATZ treatments. These mixtures enhanced hydrophobicity and dispersibility, promoting more effective interactions between the algae and GFNs, thereby enhancing their joint toxicity. Conversely, at higher concentrations of ATZ, an increase in hydrophilicity and a greater tendency for agglomeration were observed, which likely reduced their potential toxicity [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Abbott's model demonstrated the additive nature of the interactions in the mixture of ATZ, and GFNs with low doses of ATZ. Nevertheless, the combinations comprising of the nanomaterials and elevated amounts of ATZ demonstrated an antagonistic relationship. This discovery could be ascribed to improved sorption of ATZ, resulting in heightened sedimentation, and agglomeration, diminishing their bio-availability to the algal cells.\u003c/p\u003e \u003cp\u003eROS is generated in all living cells as a by-product of cellular metabolism, which includes respiration and photosynthesis (Scheme \u003cspan refid=\"Sch1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These radicals generally include superoxide (O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e) and hydroxyl radicals (OH.), as well as non-radical molecules like hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e). Nonetheless, during environmental stress, the overproduction of reactive oxygen species (ROS) can disturb this equilibrium, resulting in oxidative damage to the cells. In a previous study, ATZ treatment (77.6 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003eg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) of juvenile algal cells (\u003cem\u003eC. reinhardtii\u003c/em\u003e) resulted in a rapid and significant increase in H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e production. The hydrogen peroxide levels generated by those cells two hours post-treatment rose approximately 16-fold compared to the control [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Similar patterns were noted in the present study, where treating algal cells with pristine ATZ generated substantial amounts of ROS, which was statistically significant in comparison with the control-treated cells. Adding GFNs to the lower concentrations of ATZ in the treatment media resulted in the escalated generation of ROS. This could be attributed to the defects in the sp\u003csup\u003e2\u003c/sup\u003e hybridized structure of the GFNs, which creates more wrinkles and sharp edges that can cut through the algal cells [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Additionally, by interrupting the electron transport and interfering with photosynthesis, ATZ can produce ROS. The divergent effects of ATZ-GFN interactions at varying doses arise from alterations in bioavailability, oxidative stress, and adsorption kinetics. At lower ATZ levels, well-dispersed GFNs promote ATZ absorption, magnifying ROS generation, oxidative damage, and photosynthetic inhibition. Conversely, elevated ATZ concentrations facilitate GFN aggregation and sedimentation, diminishing cellular interactions and toxicity. Additionally, antioxidant defence systems may be engaged at greater ATZ levels, decreasing oxidative damage. Competitive binding on the surfaces of GFNs may restrict ATZ bioavailability, hence modifying its toxicity profile. These findings underscore the necessity for concentration-dependent risk evaluations in environmental research. MDA is an established biomarker that detects the levels of oxidative stress generation within the cells. Encountering MDA might intensify oxidative stress by facilitating the buildup of intracellular reactive oxygen species and impairing mitochondrial function. This establishes a feedback loop wherein ROS-induced lipid peroxidation produces MDA, further exacerbating ROS generation and cellular injury [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The association of ROS with phospholipids in the cell membrane induces lipid peroxidation, leading to increased MDA levels. As a result, the porousness and fluidity of the cell membrane are diminished, resulting in structural and functional deficits in algae [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Following the similar pattern noted for the growth inhibitory effects, generated total ROS, and MDA also demonstrated a concentration-wise elevation for the cells, treated with pristine ATZ. In a previous study, a mixture of ATZ (10, and 100 \u0026micro;g/L) and 100 nm-sized orange fluorescent PS-NH\u003csub\u003e2\u003c/sub\u003e particles generated extensive amounts of MDA in the treated algal cells of \u003cem\u003eChlorella vulgaris\u003c/em\u003e [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The combination of GFN-ATZ with low concentrations of ATZ (25, and 50 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003eg/L) yielded a higher degree of ROS and MDA production than the pristine treated sets. In comparison, the combinations with larger doses of ATZ demonstrated showed diminished ROS and MDA generation. The addition of GFNs to lower concentrations of ATZ increases toxicity because GFNs enhance the bioavailability of ATZ by acting as carriers, facilitating its uptake by algal cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAntioxidant enzyme activity plays a crucial role in mitigating oxidative stress, a condition caused by an imbalance between the production of ROS and the cellular capacity to neutralize them. Enzymes, such as SOD and CAT, form a critical defense mechanism, scavenging ROS to prevent oxidative damage to lipids, proteins, and DNA. However, oxidative stress ensues when ROS levels overwhelm the antioxidant defense\u0026mdash;due to environmental stressors like pollution, herbicides, or UV radiation\u0026mdash;leading to cellular dysfunction and potential death. Persistent oxidative stress might disrupt metabolic processes, thereby impairing photosynthesis and accelerating ageing or disease progression in plants, algae, and other organisms [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Following the growth inhibitory trend and the oxidative stress parameters such as ROS and MDA generation, SOD and CAT enzyme activities also tend to increase upon the treatment of the algal cells with ATZ in a dose-dependent manner. In a previous study, ATZ was shown to increase the gene expression of the number of genes such as MSD 5 \u0026ndash; gene encoding for the mitochondrial isoform of manganese superoxide dismutase; CAT 1 \u0026ndash; gene encoding for catalase; FSD 1 \u0026ndash; gene encoding for iron superoxide dismutase; MSD 3 \u0026ndash; gene encoding for the chloroplastic isoform of manganese superoxide dismutase; APX 1 \u0026ndash; gene encoding for ascorbate peroxidase, which are important genes in \u003cem\u003eChlamydomonas reinhardtii\u003c/em\u003e that are responsible for the antioxidant enzymes production [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The combination of low concentrations of ATZ, and GFNs led to an increase in SOD and CAT levels compared to those observed with pristine ATZ. On the other hand, at high ATZ concentration, the binary mixture with GFNs resulted in a notable decline in the SOD and CAT content.\u003c/p\u003e \u003cp\u003eGFNs impact photosynthesis in algal cells by providing a shading effect that shields the light from entering the cells and impairs photosynthetic activity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Hence, they can induce a toxic impact on the algal cells by impairing their photosynthetic machinery. The differences in chlorophyll fluorescence might be considered indicative of reactions of microalgae to environmental stresses. Following the patterns seen in growth inhibition, total ROS accumulation, and MDA content, the photosynthetic parameters (including Y(II) and ETR) likewise revealed a concentration dependent decline in response to pristine ATZ exposure. Any disruption that results in inactivation damage of PS II or the induction of persistent quenching results in a reduction of Fv/Fm and rETR [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. In another work, the inhibitory effects of ATZ and its two primary derivatives on the carbon fixation ability of \u003cem\u003eP. tricornutum\u003c/em\u003e Pt-1 after 0.5-, 1-, 2-, 4- and 7-day incubation were indicated using Chl a concentration and chlorophyll fluorescence parameters (Fv/Fm, and rETR) correspondingly [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. GFNs enhance the bioavailability of ATZ by acting as carriers, facilitating its uptake by algal cells, at lower concentrations of ATZ. This results in greater disruption of the photosynthetic electron transport chain, further inhibiting Y(II) and reducing ETR. At elevated ATZ levels, GFNs adsorb and immobilize excess ATZ molecules on their surface through \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pi\\:-\\pi\\:\\)\u003c/span\u003e\u003c/span\u003e interactions and hydrogen bonding. This reduces the concentration of free ATZ available to interact with photosystem II, thereby mitigating its inhibitory effects and partially restoring Y(II) and ETR.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e reveals the correlation between the various biological indicators evaluated for the mixtures of GFN, and ATZ at multiple concentrations in the indicator organism \u003cem\u003eChlorella\u003c/em\u003e sp. The clustered heatmap revealed that the total ROS production, and MDA generation are linked together, and the SOD activity is linked with both of these and was grouped for all the mixture, and pristine categories of the GFNs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e Ai-iii). This shows the direct correlation between total ROS and MDA production with SOD activity, which shows that an increase in ROS increases MDA production, which, in turn, enhances the SOD activity within the treated algal cells. The increment in these parameters leads to an increase in oxidative stress, which causes an increase in growth inhibition. This is indicated by grouping the growth inhibition parameter with the oxidative stress parameters, which shows their direct interdependence among them. Furthermore, group formation was observed among the growth inhibition, photosynthetic parameters, and oxidative stress, specifically the ROS and MDA content, for all of the GFN and ATZ treatment groups. This demonstrates that the excessive production of ROS and lipid peroxidation resulted in the disruption of photosynthesis and the enhancement of growth inhibition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Modelling\u003c/h2\u003e \u003cp\u003ePearson modelling (Fig.\u0026nbsp;6Bi\u0026ndash;iii) demonstrated a positive correlation between the total ROS and MDA content and the growth inhibition in the treatment groups at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 significance level. At the p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 significance level, a negative correlation was observed between the growth inhibition and ETR, Y II (PS II). Furthermore, the total ROS generation was correlated with ETR, and Y II (PS II) negatively, at a significant level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for entire treatment setups. Identical correlation was detected between MDA content, and ETR, Y II (PS II) at the significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for the GFN treatment groups. This pattern indicates that the oxidative damage surge ought to be one of the rationales for the delayed electron transport and decrement in Y II of the PS II system.\u003c/p\u003e \u003cp\u003eThe PCA analysis aimed to ascertain the correlation between the impacts of GFNs addition on the ATZs at different concentrations and their impacts on the varied biochemical parameters in the algal cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). For Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eAi, the principal components PC1 and PC2 account for 97.67% of the total data variance, indicating a high level of variability captured. The scatter plot illustrates that cell damage parameters such as growth inhibition, total ROS, and CAT activity are predominantly associated with Component 1, suggesting their direct contribution to cellular injury. Additionally, Component 1 includes parameters like MDA production and encompasses treatments involving pristine GO, ATZ, and their binary mixtures at lower concentrations. Component 2, on the other hand, primarily features SOD activity, positioned opposite photosynthetic parameters such as Y(II) and ETR, which are grouped under Component 4 along with the control treatment. Components 1 and 2 represent the biochemical parameters that indicate toxicity. Treatment groups containing ATZ at 25 \u0026micro;g/L and binary mixtures of GO and ATZ at 75, and 100 \u0026micro;g/L are situated in Component 3. This component appears independent and lacks association with biochemical parameters, indicating that these treatments do not correlate with toxicity indicators. Similar results were observed for rGO, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e Aii, where the principal components PC1 and PC2 account for 96.63%, and graphene, shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e Aiii, where the principal components PC1 and PC2 account for 97.53%. The PCA data determines the direct correlation between the toxicity parameters such as growth inhibition, total ROS, MDA generation and antioxidant enzyme activities with higher concentrations of ATZ and binary combinations of GFNs with lower concentrations of ATZ. This clearly shows the interconnection of the biochemical parameters with the various treatment groups, thereby highlighting their relationship.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, the PCA biplots also reveal a strong association between elevated oxidative stress markers\u0026mdash;such as ROS and lipid peroxidation\u0026mdash;and increased cellular mortality, indicating that oxidative damage is a major driver of cytotoxicity. Simultaneously, the analysis highlights the involvement of antioxidant enzymes (e.g., superoxide dismutase, catalase) as crucial components of the algal stress response, working to counteract the harmful effects of redox imbalance. Photosynthetic parameters, including electron transport rate and maximum quantum yield of PS-II (Y-II), also align closely with oxidative stress indicators in the PCA space, suggesting that the photosynthetic apparatus is particularly vulnerable to oxidative perturbations. Moreover, PCA effectively distinguishes treatment groups based on their unique biochemical response profiles, revealing that pollutant mixtures at lower concentrations of ATZ elicit more pronounced physiological disturbances than individual exposures. In contrast, at higher concentrations of ATZ, the mixture shows less harmful impacts on the algae. Thus, PCA emerges as a robust multivariate tool for identifying key biomarkers and deciphering complex contaminant mixtures' additive or antagonistic toxic effects, providing valuable insights into the mechanistic basis of mixture toxicity in aquatic phototrophs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Mechanisms of toxicity\u003c/h2\u003e \u003cp\u003eIn addition, the surface charge of the GFNs was decreased upon the adsorption of the ATZ compared to the pristine GFNs. It revealed that the aggregation was high upon ATZ adsorption over GFNs. In addition, the highest adsorption of ATZ at 100 \u0026micro;g/L with 250 \u0026micro;g/L of GFNs revealed that the aggregation happened due to the higher adsorption, which revealed the lower availability of GFNs in the media. The lower concentration of ATZ in the mixture with GFNs revealed the greater availability of the GFNs in the media due to the lower adsorption of ATZ over the GFNs. Higher aggregation also led to the more hydrophilic nature of the medium compared to the pristine GFNs. Additionally, due to the aggregation and hydrophilic nature of the medium, it showed less algal growth inhibition and less oxidative stress generation. The presence of GFNs with lower concentrations of ATZ results in better stability of ATZ, which causes the extended toxic impact of these combinations, in comparison to the pristine ATZ. However, the opposite effects were observed in the combinations of GFNs with higher concentrations of ATZ. This was due to the excessive aggregation of GFNs with ATZ that causes them to settle down thereby declining their interactions with the algal cells and causing to reduce the toxicity of the contaminants.\u003c/p\u003e \u003cp\u003eIn a previous study by Jiao et al., (2019), it was observed that the high aromaticity of GBCs (graphene-based composites) facilitates strong π\u0026ndash;π stacking interactions between the triazine rings of ATZ molecules and the abundant aromatic domains on the GBC surfaces, playing a crucial role in the adsorption and immobilization of ATZ [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. In addition to these interactions, hydrogen bonding is considered a dominant supramolecular force in this system, mainly due to the prevalence of N\u0026ndash;H\u0026middot;\u0026middot;\u0026middot;O type bonds. The triazine units in atrazine can engage with electron-donating and electron-accepting functional groups, such as carboxyl and hydroxyl groups, commonly present on the GBC surfaces, thereby enhancing the binding affinity and stability of the pollutant-composite complex. This explains the type of bonding that GFNs possibly use to bind ATZ. With the increase in the concentration of ATZ in the medium, the effective bonds between GFNs and ATZ increase, covering the surface of GFNs, thereby completely covering them and causing them to become less available for interaction with algal cells.\u003c/p\u003e \u003cp\u003eA previous study by Harshkova et al., (2021), shows the mechanism of toxicity induction by ATZ in green algae, \u003cem\u003eChlamydomonas reinhardtii\u003c/em\u003e, is through induction of oxidative stress in chloroplast and inhibition of the photosynthetic electron transport [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The disruption in the energies of the chloroplast indirectly influenced respiration, which in turn caused lower photosynthetic efficiency that led to a decline in cell growth. Also, in another study, it was observed that ATZ was found to inhibit light absorption per reaction centre (ABS/RC) and electron transport efficiency (FEo) in microalgae Chlorella sp. while exerting a minimal effect on energy dissipation (FDo). This imbalance between light harvesting and energy utilization ultimately led to the overall suppression of photosynthetic activity [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Thus, it can be concurred from the literature that the inhibition of photosynthetic efficiency in combination with the oxidative stress generated within the chloroplast results in the overall decline in the cell viability, which also supports the production of antioxidant enzyme. At lower concentrations, ATZ in the presence of GFNs induces elevated oxidative stress and reduced photosynthetic efficiency in \u003cem\u003eChlorella\u003c/em\u003e sp., owing to greater bioavailability of the contaminants. Conversely, higher ATZ concentrations in combination with GFNs lead to decreased oxidative stress and improved photosynthetic parameters. This is likely due to the aggregation of ATZ with GFNs, limiting their interaction with algal cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Risk assessment\u003c/h2\u003e \u003cp\u003eThe RQ analysis is an effective method for the analysis of the environmental risk of herbicide, ATZ, in aquatic environment. As per the previous investigations, ATZ was typically exposed at a level of ng/L to \u0026micro;g/L (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the current study, to predict the environmental risk of ATZ in the marine, RQ was calculated as per the acute toxicity assessment towards marine \u003cem\u003eChlorella\u003c/em\u003e sp. The noted concentrations of ATZ in the marine were also taken from the previous literature [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. The finding (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) demonstrates that at the given experimental conditions, such as maintenance in ASW at pH 7, room temperature (23\u0026thinsp;\u0026plusmn;\u0026thinsp;2 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:℃\\)\u003c/span\u003e\u003c/span\u003e) for the interaction time of 72 h, the interaction volume of 10 mL, kept under visible light (3000 lux), shows that in the absence of GFNs (250 \u0026micro;g/L), ATZ poses a higher risk. In the presence of GFNs, ATZ is becoming less harmful to the marine environment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluated RQ of ATZ in the marine \u003cem\u003eChlorella\u003c/em\u003e sp. in the presence and absence of GFNs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSl No.\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSource\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAvailable Concentration of Atrazine (ng/L)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eRQ of Atrazine\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cem\u003eRQ of GO\u0026thinsp;+\u0026thinsp;Atrazine\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cem\u003eRQ of rGO\u0026thinsp;+\u0026thinsp;Atrazine\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e\u003cem\u003eRQ of Graphene\u0026thinsp;+\u0026thinsp;Atrazine\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLower range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigher range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigher range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigher range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLower range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHigher range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eLower range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHigher range\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth Sea, German Bight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003end\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003end\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003end\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003end\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003end\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern Baltic Sea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003end\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth Sea, Irish Sea, English Channel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003end\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003end\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003end\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003end\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003end\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth Sea, German Bight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.0009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaltic Sea, Poland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003end\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoastal lagoon, Northern Adriatic, Italy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eATZ concentrations were taken from (N\u0026ouml;dler et al., 2013)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions and Environmental Implications","content":"\u003cp\u003eThis study highlights the role of GFNs in modulating the toxicity of ATZ in \u003cem\u003eChlorella\u003c/em\u003e sp., emphasizing concentration-dependent effects. At lower ATZ concentrations, GFNs exacerbated toxicity by enhancing growth inhibition, reactive oxygen species (ROS) production, and malondialdehyde (MDA) levels (~\u0026thinsp;10% increase), while reducing photosynthetic efficiency (~\u0026thinsp;25% decline). These effects stem from the synergistic interaction between GFNs' nano-blade properties and ATZ-induced oxidative stress. In contrast, higher ATZ concentrations promoted GFN aggregation and sedimentation, reducing cellular interactions and mitigating toxicity (~\u0026thinsp;12% decline). Risk quotient analysis suggests minimal environmental risk at high ATZ but low GFN concentrations. This study underscores the complex environmental dynamics governing contaminant toxicity in aquatic systems and provides a foundation for future research on ecological risk assessments and trophic-level impacts.\u003c/p\u003e"},{"header":"6. Future perspectives","content":"\u003cp\u003eFurther research is needed to evaluate the environmental fate and ecotoxicological implications of ATZ and GFNs under realistic environmental conditions. Studies should focus on how natural variables such as organic matter, microbial activity, and photodegradation influence their interactions and toxicity. Long-term exposure assessments and multi-trophic level studies will provide a more comprehensive understanding of ecosystem risks. Exploring potential mitigation strategies, such as functionalized nanomaterials or bioremediation approaches, could help minimize adverse impacts on primary producers and aquatic biodiversity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAF - Assessment factor\u003c/p\u003e\n\u003cp\u003eASW – Artificial sea water\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eATZ – Atrazine\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCAT – Catalase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eETR – Electron transport rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFTIR - Fourier Transform Infrared\u003c/p\u003e\n\u003cp\u003eGFNs – Graphene family nanomaterials\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO – Graphene oxide\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMDA - Malondialdehyde\u003c/p\u003e\n\u003cp\u003eMEC – Measured environmental concentration\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNSW – Natural sea water\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePBS - Phosphate buffer saline\u003c/p\u003e\n\u003cp\u003ePCA - Principal Component Analysis\u003c/p\u003e\n\u003cp\u003ePNEC – Predicted no-effect concentration\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePSMPs – Polystyrene microplastics\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePSNPs – Polystyrene nanoplastics\u003c/p\u003e\n\u003cp\u003erGO – reduced graphene oxide\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROS – Reactive oxygen species\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRQ – Risk quotient\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSOD - Superoxide dismutase\u003c/p\u003e\n\u003cp\u003eUPLC - Ultra Performance Liquid Chromatography\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAbhrajit Debroy:\u0026nbsp;\u003c/strong\u003eConceptualization, Investigation, Methodology, Visualization, Formal analysis, Writing-Original Draft; \u003cstrong\u003eMrudula Pulimi:\u0026nbsp;\u003c/strong\u003eConceptualization, Formal analysis; \u003cstrong\u003eAmitava Mukherjee:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Formal analysis, Supervision, Project administration, Writing- Review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge Vellore Institute of Technology (VIT), Vellore for High-resolution transmission electron microscopy (HR-TEM), and Ultra-performance liquid chromatography (UPLC) facilities used in this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eHu N, Xu Y, Sun C, et al (2021) Removal of atrazine in catalytic degradation solutions by microalgae Chlorella sp. and evaluation of toxicity of degradation products via algal growth and photosynthetic activity. Ecotoxicol Environ Saf 207:111546. https://doi.org/10.1016/j.ecoenv.2020.111546\u003c/li\u003e\n \u003cli\u003eMatejczyk M, Kondzior P, Ofman P, et al (2023) Atrazine toxicity in marine algae Chlorella vulgaris, in E. coli lux and gfp biosensor tests. Arch Environ Prot 49:87\u0026ndash;99. https://doi.org/10.24425/aep.2023.147331\u003c/li\u003e\n \u003cli\u003eTaverna ME, Busatto CA, Lescano MR, et al (2018) Microparticles based on ionic and organosolv lignins for the controlled release of atrazine. J Hazard Mater 359:139\u0026ndash;147, https://doi.org/10.1016/j.jhazmat.2018.07.010\u003c/li\u003e\n \u003cli\u003eKabra AN, Ji MK, Choi J, et al (2014) Toxicity of atrazine and its bioaccumulation and biodegradation in a green microalga, Chlamydomonas mexicana. Environ Sci Pollut Res 12270\u0026ndash;12278. https://doi.org/10.1007/s11356-014-3157-4\u003c/li\u003e\n \u003cli\u003eRodr\u0026iacute;guez-Robledo V, Vendrell A, Garc\u0026iacute;a-Cifuentes A, et al (2022) Determination of atrazine and propazine metabolites deemed endocrine disruptors in human seminal plasma by LC\u0026ndash;ESI-MS/MS. Chem Biol Technol Agric 9:22, https://doi.org/10.1186/s40538-022-00285-4\u003c/li\u003e\n \u003cli\u003eBlahova J, Cocilovo C, Plhalova L, et al (2020) Embryotoxicity of atrazine and its degradation products to early life stages of zebrafish (Danio rerio). Environ Toxicol Pharmacol 77:103370, https://doi.org/10.1016/j.etap.2020.103370\u003c/li\u003e\n \u003cli\u003eWang S, Bryan C, Xie J, et al (2022) Atrazine exposure in zebrafish induces aberrant genome-wide methylation. Neurotoxicol Teratol 92:107091, doi: 10.1016/j.ntt.2022.107091\u003c/li\u003e\n \u003cli\u003eOwolabi OD, Omotosho JS (2017) Atrazine-mediated oxidative stress responses and lipid peroxidation in the tissues of Clarias gariepinus. Iran J Toxicol 11:29\u0026ndash;38, Doi: 10.29252/arakmu.11.2.29\u003c/li\u003e\n \u003cli\u003eSantos TG, Martinez CBR (2012) Atrazine promotes biochemical changes and DNA damage in a Neotropical fish species. Chemosphere 89:1118\u0026ndash;1125, https://doi.org/10.1016/j.chemosphere.2012.05.096\u003c/li\u003e\n \u003cli\u003eSchmidt AM, Sengupta N, Saski CA, et al (2017) RNA sequencing indicates that atrazine induces multiple detoxification genes in Daphnia magna and this is a potential source of its mixture interactions with other chemicals. Chemosphere 189:699\u0026ndash;708, https://doi.org/10.1016/j.chemosphere.2017.09.107\u003c/li\u003e\n \u003cli\u003eStara A, Kouba A, Velisek J (2018) Biochemical and histological effects of sub-chronic exposure to atrazine in crayfish Cherax destructor. Chem Biol Interact 291:95\u0026ndash;102, https://doi.org/10.1016/j.cbi.2018.06.012\u003c/li\u003e\n \u003cli\u003eZhu X, Sun Y, Zhang X, et al (2016) Herbicides interfere with antigrazer defenses in Scenedesmus obliquus. Chemosphere 162:243\u0026ndash;251, https://doi.org/10.1016/j.chemosphere.2016.07.087\u003c/li\u003e\n \u003cli\u003eReligia P, Kato Y, Fukushima EO, et al (2019) Atrazine exposed phytoplankton causes the production of non-viable offspring on Daphnia magna. Mar Environ Res 145:177\u0026ndash;183, https://doi.org/10.1016/j.marenvres.2019.02.007\u003c/li\u003e\n \u003cli\u003eZhao F, Li Y, Huang L, et al (2018) Individual and combined toxicity of atrazine, butachlor, halosulfuron-methyl and mesotrione on the microalga Selenastrum capricornutum. Ecotoxicol Environ Saf 148:969\u0026ndash;975. https://doi.org/10.1016/j.ecoenv.2017.11.069\u003c/li\u003e\n \u003cli\u003eCamuel A, Guieysse B, Alc\u0026aacute;ntara C, B\u0026eacute;chet Q (2017) Fast algal eco-toxicity assessment: Influence of light intensity and exposure time on Chlorella vulgaris inhibition by atrazine and DCMU. Ecotoxicol Environ Saf 140:141\u0026ndash;147. https://doi.org/10.1016/j.ecoenv.2017.02.013\u003c/li\u003e\n \u003cli\u003eBianco A, Cheng H-M, Enoki T, et al (2013) All in the graphene family\u0026ndash;A recommended nomenclature for two-dimensional carbon materials. Carbon 65:1\u0026ndash;6, https://doi.org/10.1016/j.carbon.2013.08.038\u003c/li\u003e\n \u003cli\u003eDing X, Pu Y, Tang M, Zhang T (2022) Environmental and health effects of graphene-family nanomaterials: Potential release pathways, transformation, environmental fate and health risks. Nano Today 42:101379. https://doi.org/10.1016/j.nantod.2022.101379\u003c/li\u003e\n \u003cli\u003eZhang Y, Duan X, Bai L, Quan X (2020) Effects of nanomaterials on metal toxicity: Case study of graphene family on Cd. Ecotoxicol Environ Saf 194:110448. https://doi.org/10.1016/j.ecoenv.2020.110448\u003c/li\u003e\n \u003cli\u003eAli J, Li Y, Shang E, et al (2023) Aggregation of graphene oxide and its environmental implications in the aquatic environment. Chinese Chem Lett 34:107327. https://doi.org/10.1016/j.cclet.2022.03.050\u003c/li\u003e\n \u003cli\u003ePires A, Figueira E, Silva MSS, et al (2022) Effects of graphene oxide nanosheets in the polychaete Hediste diversicolor: Behavioural, physiological and biochemical responses. Environ Pollut 299:118869\u003c/li\u003e\n \u003cli\u003eZhao J, Cao X, Wang Z, et al (2017) Mechanistic understanding toward the toxicity of graphene-family materials to freshwater algae. Water Res 111:18\u0026ndash;27. https://doi.org/10.1016/j.watres.2016.12.037\u003c/li\u003e\n \u003cli\u003eWu S, Ji X, Li X, et al (2022) Mutual impacts and interactions of antibiotic resistance genes, microcystin synthetase genes, graphene oxide, and Microcystis aeruginosa in synthetic wastewater. Environ Sci Pollut Res 29:3994\u0026ndash;4007, doi: 10.1007/s11356-021-15627-2.\u003c/li\u003e\n \u003cli\u003eYuan X, Zhang F, Wang Z (2024) Impacts of Micro/Nanoplastics Combined with Graphene Oxide on Lactuca sativa Seeds: Insights into Seedling Growth, Oxidative Stress, and Antioxidant Gene Expression. Plants 13:. https://doi.org/10.3390/plants13243466\u003c/li\u003e\n \u003cli\u003ePretti C, Oliva M, Pietro R Di, et al (2014) Ecotoxicity of pristine graphene to marine organisms. Ecotoxicol Environ Saf 101:138\u0026ndash;145. https://doi.org/10.1016/j.ecoenv.2013.11.008\u003c/li\u003e\n \u003cli\u003eBarari F, Eydi M, Bonyadi Z (2024) Heliyon Recent progress on the toxic effects of microplastics on Chlorella sp . in aquatic environments. Heliyon 10:e32881. https://doi.org/10.1016/j.heliyon.2024.e32881\u003c/li\u003e\n \u003cli\u003eKhalil S, Mahnashi MH, Hussain M, et al (2021) Saudi Journal of Biological Sciences Exploration and determination of algal role as Bioindicator to evaluate water quality \u0026ndash; Probing fresh water algae. Saudi J Biol Sci 28:5728\u0026ndash;5737. https://doi.org/10.1016/j.sjbs.2021.06.004\u003c/li\u003e\n \u003cli\u003eBi C, Junaid M, Liu Y, et al (2022) Graphene oxide chronic exposure enhanced perfluorooctane sulfonate mediated toxicity through oxidative stress generation in freshwater clam Corbicula fluminea. Chemosphere 297:134242. https://doi.org/10.1016/j.chemosphere.2022.134242\u003c/li\u003e\n \u003cli\u003eThomas MC, Flores F, Kaserzon S, et al (2020) Toxicity of ten herbicides to the tropical marine microalgae Rhodomonas salina. Sci Rep 10:1\u0026ndash;16. https://doi.org/10.1038/s41598-020-64116-y\u003c/li\u003e\n \u003cli\u003eBhatt P, Sethi K, Gangola S, et al (2022) Modeling and simulation of atrazine biodegradation in bacteria and its effect in other living systems. J Biomol Struct Dyn 40:3285\u0026ndash;3295, https://doi.org/10.1080/07391102.2020.1846623\u003c/li\u003e\n \u003cli\u003eNatarajan L, Annie Jenifer M, Peijnenburg WJGM, Mukherjee A (2023) Algal extracellular polymeric substances (algal-EPS) for mitigating the combined toxic effects of polystyrene nanoplastics and nano-TiO2 in Chlorella sp. Nanotoxicology 0:1\u0026ndash;14. https://doi.org/10.1080/17435390.2023.2179438\u003c/li\u003e\n \u003cli\u003eDebroy A, Roy N, Giri S, et al (2024) EPS-corona formation on graphene family nanomaterials (GO, rGO and graphene) and its role in mitigating their toxic effects in the marine alga Chlorella sp. Environ Pollut 341:632014. https://doi.org/10.1016/j.envpol.2023.123015\u003c/li\u003e\n \u003cli\u003eChia JSY, Tan MTT, Khiew PS, et al (2014) A novel one step synthesis of graphene via sonochemical-assisted solvent exfoliation approach for electrochemical sensing application. Chem Eng J 249:270\u0026ndash;278. https://doi.org/10.1016/j.cej.2014.03.081\u003c/li\u003e\n \u003cli\u003eYang ZZ, Zheng Q Bin, Qiu HX, et al (2015) A simple method for the reduction of graphene oxide by sodium borohydride with CaCl2 as a catalyst. Xinxing Tan Cailiao/New Carbon Mater 30:41\u0026ndash;47. https://doi.org/10.1016/S1872-5805(15)60174-3\u003c/li\u003e\n \u003cli\u003eLu J, Zhu X, Tian S, et al (2018) Graphene oxide in the marine environment: Toxicity to Artemia salina with and without the presence of Phe and Cd2+. Chemosphere 211:390\u0026ndash;396. https://doi.org/10.1016/j.chemosphere.2018.07.140\u003c/li\u003e\n \u003cli\u003eFlood S, Burkholder JA, Cope G (2018) Assessment of atrazine toxicity to the estuarine phytoplankter, Dunaliella tertiolecta (Chlorophyta), under varying nutrient conditions. Environ Sci Pollut Res 25:11409\u0026ndash;11423. https://doi.org/10.1007/s11356-018-1310-1\u003c/li\u003e\n \u003cli\u003eJacomini AE, Bonato PS, Avelar WEP (2003) HPLC method for the analysis of atrazine in freshwater bivalves. J Liq Chromatogr Relat Technol 26:1885\u0026ndash;1894. https://doi.org/10.1081/JLC-120021757\u003c/li\u003e\n \u003cli\u003eThiagarajan V, M. P, S. A, et al (2019) Diminishing bioavailability and toxicity of P25 TiO2 NPs during continuous exposure to marine algae Chlorella sp. Chemosphere 233:363\u0026ndash;372. https://doi.org/10.1016/j.chemosphere.2019.05.270\u003c/li\u003e\n \u003cli\u003eOECD (2011) Test No. 201: Freshwater Alga and Cyanobacteria, Growth Inhibition Test, OECD Guidelines for the Testing of Chemicals, Section 2, OECD Publishing, Paris,\u003c/li\u003e\n \u003cli\u003eRex M C, Debroy A, Mukherjee A (2024) The impact of nTiO2 and GO (graphene oxide), and their combinations, on freshwater Chlorella sp.: a comparative study in lake water and BG-11 media. Environ Sci Process Impacts. https://doi.org/10.1039/d4em00041b\u003c/li\u003e\n \u003cli\u003eGiri S, Debroy A, Nag A, Mukherjee A (2024) Evaluating the role of soil EPS in modifying the toxicity potential of the mixture of polystyrene nanoplastics and xenoestrogen, Bisphenol A (BPA) in Allium cepa L. J Hazard Mater 477:135252. https://doi.org/10.1016/j.jhazmat.2024.135252\u003c/li\u003e\n \u003cli\u003eDebroy A, Saravanan JS, Joyce Nirmala M, et al Algal Eps Modifies the Toxicity Potential of the Mixture of Polystyrene Nanoplastics (Psnps) and Flame Retardant, Triphenyl Phosphate in Freshwater Microalgae Chlorella Sp. Chemosphere, 143471, https://doi.org/10.1016/j.chemosphere.2024.143471\u003c/li\u003e\n \u003cli\u003eLee JW, Lee SH, Han JW, Kim GH (2020) Early Light-Inducible Protein (ELIP) Can Enhance Resistance to Cold-Induced Photooxidative Stress in Chlamydomonas reinhardtii. Front Physiol 11:. https://doi.org/10.3389/fphys.2020.01083\u003c/li\u003e\n \u003cli\u003eGiri S, Mukherjee A (2021) Ageing with algal EPS reduces the toxic effects of polystyrene nanoplastics in freshwater microalgae Scenedesmus obliquus. J Environ Chem Eng 9:105978. https://doi.org/10.1016/j.jece.2021.105978\u003c/li\u003e\n \u003cli\u003eSmith PN, Armbrust KL, Brain RA, et al (2021) Assessment of risks to listed species from the use of atrazine in the USA: a perspective. J Toxicol Environ Heal - Part B Crit Rev 24:223\u0026ndash;306. https://doi.org/10.1080/10937404.2021.1902890\u003c/li\u003e\n \u003cli\u003eDe Caroli Vizioli B, Silva da Silva G, Ferreira de Medeiros J, Montagner CC (2023) Atrazine and its degradation products in drinking water source and supply: Risk assessment for environmental and human health in Campinas, Brazil. Chemosphere 336:. https://doi.org/10.1016/j.chemosphere.2023.139289\u003c/li\u003e\n \u003cli\u003eCui H, Chen B, Jiang Y, et al (2021) Toxicity of 17 disinfection by-products to different trophic levels of aquatic organisms: ecological risks and mechanisms. Environ Sci Technol 55:10534\u0026ndash;10541\u003c/li\u003e\n \u003cli\u003eMouhat F, Coudert F-X, Bocquet M-L (2020) Structure and chemistry of graphene oxide in liquid water from first principles. Nat Commun 11:1566, https://doi.org/10.1038/s41467-020-15381-y\u003c/li\u003e\n \u003cli\u003eKahsay MH, Belachew N, Tadesse A, Basavaiah K (2020) Magnetite nanoparticle decorated reduced graphene oxide for adsorptive removal of crystal violet and antifungal activities. RSC Adv 10:34916\u0026ndash;34927, https://doi.org/10.1039/D0RA07061K\u003c/li\u003e\n \u003cli\u003eSinha S, Warner JH (2021) Recent progress in using graphene as an ultrathin transparent support for transmission electron microscopy. Small Struct 2:2000049, https://doi.org/10.1002/sstr.202000049\u003c/li\u003e\n \u003cli\u003eWang Q, Peng L, Wang P, et al (2024) Changes of atrazine dissipation and microbial community under coexistence of graphene oxide in river water. J Hazard Mater 462:132708, https://doi.org/10.1016/j.jhazmat.2023.132708\u003c/li\u003e\n \u003cli\u003ePereira HA, da Boit Martinello K, Vieira Y, et al (2023) Adsorptive behavior of multi-walled carbon nanotubes immobilized magnetic nanoparticles for removing selected pesticides from aqueous matrices. Chemosphere 325:. https://doi.org/10.1016/j.chemosphere.2023.138384\u003c/li\u003e\n \u003cli\u003eGupta B, Kumar N, Panda K, et al (2017) Role of oxygen functional groups in reduced graphene oxide for lubrication. Sci Rep 7:1\u0026ndash;14. https://doi.org/10.1038/srep45030\u003c/li\u003e\n \u003cli\u003eKhoshnamvand M, You D, Xie Y, et al (2024) Presence of humic acid in the environment holds promise as a potential mitigating factor for the joint toxicity of polystyrene nanoplastics and herbicide atrazine to Chlorella vulgaris: 96-H acute toxicity. Chemosphere 357:. https://doi.org/10.1016/j.chemosphere.2024.142061\u003c/li\u003e\n \u003cli\u003eSun C, Xu Y, Hu N, et al (2020) To evaluate the toxicity of atrazine on the freshwater microalgae Chlorella sp. using sensitive indices indicated by photosynthetic parameters. Chemosphere 244:. https://doi.org/10.1016/j.chemosphere.2019.125514\u003c/li\u003e\n \u003cli\u003eOjha A, Samriti, Thakur S, Prakash J (2023) Graphene family nanomaterials as emerging sole layered nanomaterials for wastewater treatment: Recent developments, potential hazards, prevention and future prospects. Environ Adv 13:100402. https://doi.org/10.1016/j.envadv.2023.100402\u003c/li\u003e\n \u003cli\u003eShi L, Chen J, Teng L, et al (2016) The Antibacterial Applications of Graphene and Its Derivatives. Small 12:4165\u0026ndash;4184. https://doi.org/10.1002/smll.201601841\u003c/li\u003e\n \u003cli\u003eHashemi E, Giesy JP, Liang Z, et al (2024) Impacts of graphene oxide contamination on a food web: Threats to somatic and reproductive health of organisms. Ecotoxicol Environ Saf 285:117032. https://doi.org/10.1016/j.ecoenv.2024.117032\u003c/li\u003e\n \u003cli\u003eTsavatopoulou VD, Manariotis ID (2022) Chlorococcum sp. and mixotrophic algal biofilm growth in horizontal and vertical\u0026ndash;oriented surfaces using wastewater and synthetic substrate. Biomass Convers Biorefinery 1\u0026ndash;16, https://doi.org/10.1007/s13399-022-02752-2\u003c/li\u003e\n \u003cli\u003eHarshkova D, Majewska M, Pokora W, et al (2021) Diclofenac and atrazine restrict the growth of a synchronous Chlamydomonas reinhardtii population via various mechanisms. Aquat Toxicol 230:. https://doi.org/10.1016/j.aquatox.2020.105698\u003c/li\u003e\n \u003cli\u003eZhao J, Wang Z, White JC, Xing B (2014) Graphene in the aquatic environment: Adsorption, dispersion, toxicity and transformation. Environ Sci Technol 48:9995\u0026ndash;10009. https://doi.org/10.1021/es5022679\u003c/li\u003e\n \u003cli\u003eCheng J, Wang F, Yu D-F, et al (2011) The cytotoxic mechanism of malondialdehyde and protective effect of carnosine via protein cross-linking/mitochondrial dysfunction/reactive oxygen species/MAPK pathway in neurons. Eur J Pharmacol 650:184\u0026ndash;194. https://doi.org/https://doi.org/10.1016/j.ejphar.2010.09.033\u003c/li\u003e\n \u003cli\u003eYang W, Gao P, Nie Y, et al (2021) Comparison of the effects of continuous and accumulative exposure to nanoplastics on microalga Chlorella pyrenoidosa during chronic toxicity. Sci Total Environ 788:147934\u003c/li\u003e\n \u003cli\u003eBhattacharyya A, Chattopadhyay R, Mitra S, Crowe SE (2014) Oxidative stress: An essential factor in the pathogenesis of gastrointestinal mucosal diseases. Physiol Rev 94:329\u0026ndash;354. https://doi.org/10.1152/physrev.00040.2012\u003c/li\u003e\n \u003cli\u003eMurchie EH, Lawson T (2013) Chlorophyll fluorescence analysis: A guide to good practice and understanding some new applications. J Exp Bot 64:3983\u0026ndash;3998. https://doi.org/10.1093/jxb/ert208\u003c/li\u003e\n \u003cli\u003eYang L, Zhang Y (2020) Effects of atrazine and its two major derivatives on the photosynthetic physiology and carbon sequestration potential of a marine diatom. Ecotoxicol Environ Saf 205:111359. https://doi.org/10.1016/j.ecoenv.2020.111359\u003c/li\u003e\n \u003cli\u003eJiao W-B, Zhang Y-Q, Yu K, et al (2019) Porous graphitic biomass carbons as sustainable adsorption and controlled release carriers for atrazine fixation. ACS Sustain Chem Eng 7:20180\u0026ndash;20189, https://doi.org/10.1021/acssuschemeng.9b06269\u003c/li\u003e\n \u003cli\u003eN\u0026ouml;dler K, Licha T, Voutsa D (2013) Twenty years later - Atrazine concentrations in selected coastal waters of the Mediterranean and the Baltic Sea. Mar Pollut Bull 70:112\u0026ndash;118. https://doi.org/10.1016/j.marpolbul.2013.02.018\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Scheme 1","content":"\u003cp\u003eScheme 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Graphene family nanomaterials, atrazine, mixture toxicity, oxidative stress, photosynthetic activities","lastPublishedDoi":"10.21203/rs.3.rs-5853275/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5853275/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAtrazine is a widely used chlorine herbicide, but recent studies raised concerns about its environmental and human health risks. Graphene family nanomaterials (GFNs) have various applications and are often released into aquatic environments, impacting marine microflora. However, the combined effects of atrazine and GFNs on marine organisms like \u003cem\u003eChlorella\u003c/em\u003esp. have not been thoroughly assessed. The physicochemical interactions between atrazine and GFNs were examined using Raman spectroscopy, electron microscopy, contact angle measurements, surface charge analyses, and chromatography. The contact angle analysis revealed a decline with increasing atrazine concentration, indicating enhanced hydrophilicity of the mixture. Key toxicity parameters, including growth inhibition, total reactive oxygen species (ROS) production, malondialdehyde (MDA) generation, photosynthetic efficiency, and antioxidant enzyme activity, were assessed for individual contaminants and their binary mixtures. ROS and antioxidant enzyme activity exhibited the most significant modulation in response to atrazine concentration. Low atrazine levels exacerbated toxicity by elevating oxidative stress markers (ROS and MDA) in mixtures with GFNs, whereas higher concentrations mitigated these effects by reducing ROS and MDA generation compared to individual exposures. The study also uses statistical tools to evaluate the interconnection between the biochemical parameters and the treatment groups. The results clearly show how the GFNs can reduce the harmful effects of atrazine in marine ecosystems. GFNs provide a surface for the adsorption of the atrazine molecules, thereby reducing their availability to the algal cells and reducing their toxic potential. This deepens our understanding of the environmental applications of the GFNs for mitigating the risk of emerging pollutants like atrazine.\u003c/p\u003e","manuscriptTitle":"Toxicological Impacts and Environmental Risk of Atrazine in the Presence of Graphene Family Nanomaterials (GFNs) on Chlorella sp","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-30 10:00:15","doi":"10.21203/rs.3.rs-5853275/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18787d6e-30c6-43bc-bdc1-b52b8c8aca99","owner":[],"postedDate":"April 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-04T05:24:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-30 10:00:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5853275","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5853275","identity":"rs-5853275","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}