Nanotechnology has had a significant impact on several high-tech businesses in recent years, and it has been demonstrated that it has an impact on many microbial species, as well1.
There are several advantages of nanoparticles over the bulk form. Biologically, owing to the tiny size, nanoparticles straightforwardly penetrate and are easily taken up by the cell, which permits proficient accretion at the target site in the organism. Moreover, the retention of the nanoparticles at the target site has a longer clearance time, leading to an increase in therapeutic stability, bioavailability, and efficiency compared to the same dosage of the non-nanoparticulate form7,18.
Nanoparticles can be generated utilizing an assortment of strategies, including physical (ball milling, ultrathin films, spray pyrolysis, thermal evaporation, plasma arcing, lithographic procedures, pulsed laser desorption, layer-by-layer growth, sputter deposition, molecular beam epitasis, and diffusion flame synthesis of nanoparticles) and chemical (chemical solution deposition, electrodeposition, chemical vapor deposition, sol–gel process, soft chemical method, catalytic route, wet chemical procedure, hydrolysis coprecipitation, and Langmuir–Blodgett) methods, as well as hybrid techniques. These methods use high radiation and highly concentrated reductants and stabilizing agents that are destructive to human health and the environment as well7,18,19,20.
Alternatively, the biological-based procedure or green manufacture of nanoparticles is a bioreduction process with lower energy requirements. The technique is environmentally friendly and nontoxic, with greater stability, and nanoparticles are biosynthesized by applying a single-step process18. Moreover, to the best of the authors’ knowledge, all phytofabrication of nanoparticles is mostly restricted to metal ions, and no previous reports on CNPs phytofabrication.Many examinations have demonstrated that plant extracts act as potential precursors for the biosynthesis of nanoparticles in nondangerous manners. Therefore, plants are utilized effectively and economically in the biosynthesis of several metal-nanoparticles7,10.
The universal procedure of metallic nanoparticle biosynthesis employs the plant as a bioreducing agent and metallic salt as a precursor, resulting in biocompatible and stable nanoparticles. This promising route of nanoparticle production using a biological system utilizes three main approaches, i.e., 1) the selection of solvent intermediate, 2) the choice of an ecological, benign reducing agent, and the choice of a nontoxic material as a capping agent to stabilize the biosynthesized nanomaterials21,22. Additionally, twelve well-known green chemistry principles have now become a reference guide for developing less hazardous chemical products23.
All previous approaches were found to be achieved in the selected plant (Pelargonium graveolens) and the transformed polymer (chitosan), additionally, the 12-green chemistry principles were strongly applied in the current work. To the best of the authors’ knowledge, no previous work has reported on the synthesis of chitosan polymers into nanoforms using Pelargonium graveolens plants. Accordingly, the current paper describes a novel protocol for the green biosynthesis of CNPs, employing the leaf extract of Pelargonium graveolens. This procedure offers several merits over ordinary fabrication procedures.
Before proceeding to maximize the biosynthesis of CNPs, a primary characterization test based on UV/visible spectra was applied to ensure the development of the nanoparticles. The current absorption peak wavelength was detected at 295 nm; this result is in harmony with that previously reported at 285 nm24 and 320 nm in the UV region2. Compared with CNPs, the UV/visible spectrum of chitosan showed a wider absorption band intensity; therefore, the sharp intensity level of the CNP biopolymer indicates the success of the phytofabrication of CNPs2,24.
Exploring the experimental data of CNPs of the design matrix shows the highest level of CNPs located at the middle levels of the three tested variables, indicating the accuracy of both selected independent variables and their tested levels. It was obvious that the predicted values of CNPs were very adjacent to those of the trial CNPs; consequently, the residuals or error values were at their minimum, signifying another proven accuracy of the investigated parameters and their levels.
For the selection of the most appropriate model, the effect of each model term was screened at the P level of 0.05. The tested terms (factors) that had lower P values were considered significant and reliable for the modeling process. Regarding the model selection, R2 is considered a very important selection criterion; if R2 is higher than 0.9, the regression model is defined as very significant, and the model is adequate; however, the R2 value should not be lower than 0.7525. The current quadratic model had high R2, adjusted R2, and predicted R2 values, which were very close to one. Consequently, the quadratic model was the best-fitted model.
R2 is defined as the amount of change in the observed (experimental) response (CNPs) that is described by the three tested factors. Generally, R2 can help choose the best-fitted model. All types of R2 range from zero to 1. The closer to 1, the better the modeling of the experimental data. Interestingly, increasing the number of factors (predictors) leads to a continuous increase in the R2 value, irrespective of the significance of the factors. Therefore, the adjusted R2 is an improved R2 that considers the number of factors (variables) in the model. Contrary to R2, the adjusted R2 may be reduced with the addition of extra terms (factors) to the model. Therefore, the adjusted R2 is a better indicator than R2 to judge how well the model fits the data. The predicted R2 is used to determine the degree of the predictive capability of the model, e.g., to predict the value of the CNP response at new levels of the tested factors. Moreover, it is more beneficial than the adjusted R2 for comparing and selecting the models. Therefore, the quadratic model was selected as a modeling base in CNP bioprocessing.
The BBD data were subjected to ANOVA. The model exhibited a high F-value and low P-value; additionally, the lack-of-fit was not nonsignificant, indicating the significance of the proposed overall model. Moreover, the ratio of adequate precision is higher than 4, which is a suitable indicator that this model can be successfully employed to work within the tested range of the various tested factors along with the design space to maximize CNP biosynthesis. Another precision and trustiness of the experimental design can be noted by the lower value of the C.V. and greater value of adequate precision, which are desirable for the reliability of the model.
The weight of every individual factor was diagnosed, and the P-value was again utilized. However, the values of P indicate that the model terms are significant (< 0.05), indicating that they are important phytofabrication parameters of CNPs. This also suggests that the variables and their established levels, as well as the investigational design, are well defined and attain the peak performance of CNP phytofabrication. Therefore, the projection model was created based on such proven terms.
Data of ANOVA displays that the predicted R2 and adjusted R2 are close to each other. The values of both kinds of R2 should be less than 20% of each other to be in decent agreement25. In the current investigation, the predicted R2 was in harmony with the adjusted R2 value, indicating high compatibility between the predicted and experimental values of CNP biosynthesis and indicating the satisfactory predictive ability of the model within the examined range.
Some of the model terms showed a negative coefficient estimate value, which indicates that such a variable has an antagonistic effect on CNP biosynthesis by P. graveolens at higher concentrations. The positive coefficient value, on the other hand, indicates a cooperative effect, and the variable(s) increase CNP biosynthesis with the continuous increment of the level of the investigated factor within the region of the experiment.
Model adequacy was checked. The normal plot of residuals was plotted to check the externally studentized residuals versus normal probability (%). Values show an equal distribution of the residual data, indicating that the variance of CNP biosynthesis was independent of the biosynthesis process, thus supporting the adequacy of the model. Moreover, residuals were found to be very low at all tested points. This implies that the model can fit the actual experimental data faithfully. Additionally, the model prediction points vis the actual points lie much nearer to the line of perfect prediction. Thus, the model has a significant generalization capacity for CNP biosynthesis. Moreover, the Box-Cox plot of the model transformation of chitosan nanoparticle biosynthesis using P. graveolens leaf extract confirms the suitability of the design and data. The value of l concluded no recommendation for data transformation in this model. Consequently, these two adequacy tests authorize the aptness of the design and obtained data. Model adequacy was checked by plotting the normal probability of the externally studentized residuals. Most of the data points aggregated thoroughly around the straight line and were, thus, considered normally distributed without linearity. No value is located away from the general mean since the extreme residual values on both sides are not wanted. The plot of predicted vis actual values. The pattern also displays a normal distribution, supporting the adequacy of the model.
Analysis of the 3D plots demonstrates that all the pair tested factors generated a peak of CNP biosynthesis around the center point of the design space, meaning that the tested ranges of the three factors were carefully selected, and the model best fit the design.
To find out the best-predicted combinations that maximize CNPs, the desirability function was used, whose value ranges from undesirable (zero) to desirable (one). As the response approaches the goal, the desirability value becomes closer to 1. The desirability value is generally estimated as a mathematical evaluation of the optimization process before experimental validation26. Accordingly, the optimum levels of the incubation period, temperature, and initial chitosan concentration that maximize CNP biosynthesis by P. graveolens were estimated by solving the prediction equation. Among several options, the best solution was selected based on the desirability value. The current desirability value was sufficiently high since it reached the peak that validate the optimization process. Then, the predicted amount of CNPs was estimated, which was found to have a high intensity of agreement between the experimental values, suggesting that the desirability function effectively ascertains the best-predicted situations for the green synthesis of CNPs by using P. graveolens leaf extract.
The theoretical estimation of the polynomial model is an estimate based on a reasonably studied area of the tested independent variables; therefore, the guarantee of the real prediction effectiveness of the equation under real conditions is critical. However, the theoretical value of CNP biosynthesis was valid, since it showed close similarity to that of the experimental one. That is, in turn, produces strong evidence for the fitness of the design and the modeling process, utilizing the tested ranges of the studied variables.
Following the optimization conditions of the biofabrication process of CNPs from chitosan by P. graveolens leaf extract, the surface morphological structure of the obtained CNPs was monitored by SEM, which are widely considered the main accepted procedures for the characterization of nanoparticles. These techniques are erroneously used interchangeably, but in reality, they vary substantially. However, both techniques provide some similarities but a distinct analysis, which is why the accurate interpretation of their images is essential. Collectively, SEM and TEM offer powerful tools for the investigation of size, shape, surface area, crystal structure, and morphological structure. Although TEM systems can bring much greater 2D resolution for size analysis, SEM provides accurate information about the 3D surface and shape features27,28.
The 3D SEM image of CNPs exhibits a good dispersion of the nanoparticles, which are entangled to form a larger exposed surface area, making the CNPs very appropriate for adsorption29. Like the current phytofabricated CNPs, most of the CNPs prepared from chitosan were spherical in shape30, and few had oval pleated31 or rod-shaped structure29,32.
The 2D TEM image undoubtedly indicates that the CNPs show a highly porous surface owing to low agglomeration attributes. These porous and agglomerated CNPs have been considered key phenomena for the synthesis of novel CNPs, hence maximizing their usefulness as antibiological phytosynthesized nanomaterials in biomedical and agricultural applications, where the porous nature can effectively adsorb harmful chemicals and antagonize the pathogens2,33. In contrast to bulk materials, which have lower porosity, nanoparticles with high porosity have a greater specific exterior area and high reaction activity1.
The terms agglomeration and aggregation are repeatedly used interchangeably, but they definitely differ, where agglomeration indicates more weakly bonded particles and aggregation indicates strongly bonded or fused particles. In our case, the CNPs showed low agglomeration without aggregation, and the low agglomeration phenomenon is accepted since many nanoparticle types have high ionic strength and agglomerate in aqueous matrices, such as in phosphate-buffered saline and cell culture medium34,35.
EDXS study was used together with electron microscopy investigation to analyze the component elements of CNPs. When the electron beam of SEM hits the inner shell of an element atom, its inner-shell electron is relocated by another electron from an outer shell to fill the vacancy, and the process is accompanied by the release of an energy difference in the form of an X-ray that is unique to the specific element. Moreover, the intensity of the specific X-ray is directly related to the concentration of the element in the particles36. However, the test confirms the presence of the various elemental compositions of native chitosan, confirming the uniformity and stability of CNPs during the biotransformation process.
The ζ-potential is an indicator of the stability of colloidal dispersions. The weight of the ζ-potential specifies the degree of electrostatic repulsion among similarly charged adjacent particles37. For tiny particles and molecules, a high ζ-potential confers stability and resists aggregation of nanoparticles in the solution or dispersion. In contrast, at small ζ-potentials, attractive forces may exceed, leading to flocculation owing to the breakdown of the dispersion. Therefore, colloids with high negative or positive ζ-potentials are more electrically stabilized than those with low ζ-potentials, which tend to coagulate or flocculate38. The current ζ-potential value of CNPs suggests nanoparticles with good stability. The CNPs were positively charged. From an antimicrobial point of view, when ζ-potential is positive, particles can easily interact with the negatively charged cell membrane and/or DNA of a biological system and can then be released simply into the cytoplasm of the cell39.
Regarding FTIR, the presence of various intense bands indicates the presence of a capping agent, which acts as a stabilizer that inhibits the overgrowth of nanoparticles and prevents their aggregation and/or coagulation in colloidal synthesis. Therefore, the observed intense bands were matched with standard values to classify the functional groups. The first range of bands that appeared in the spectra was due to stretching vibrations of OH groups at wavenumbers ranging from 3736 to 3442 cm−1, indicating the presence of alcohols and phenols. The stretching vibration of methylene (C=H) was at 2350 cm −1. It is also already known that the band at 2350 cm−1 generally arises from the background CO240 and has no corresponding group associated with the chitosan structure. Absorption at wavenumber 1572 was correlated to the vibrations of carbonyl bonds (C=O stretching) of the amide group CONHR or protonated amine (NH2. Bending vibrations of the methyl group (C-H bending, alkane) of CNPs were visible at 1413 cm−1. Absorption in the wavenumber range 1072 and 914 cm−1 is generated from the stretch vibration of CO groups (COH and COC) in the oxygen bridge, emerging from the deacetylation of chitosan. At the end of the FTIR spectra, the small peak at 645 cm−1 corresponds to the wagging of the saccharide structure of chitosan41. FTIR analysis strongly emphasizes the structural stability of chitosan during phytoconversion into CNPs.
XRD analysis is a fast practice, primarily used in materials science for the phase identification of a crystalline nature and can deliver information on unit cell dimensions; thus, the XRD pattern is considered the fingerprint of periodic atomic arrangements in a given material32. The XRD of the current CNPs showed three peaks at 2-theta of 13, 19, and 35°. It is conventionally accepted that chitosan stretches two characteristic peaks at 2-theta of 10 and 20°, and the current shift that occurred to CNPs from the normal chitosan peaks indicates the amplified amorphous nature, thus lessening the crystal structure of chitosan, which comes in line with studies that focused on decreasing the crystallinity for improving the sorption properties of the materials32,42.
Both TGA and DSC investigations were performed, and both are measures of the thermo analytical features used to describe the analysis of nanoparticles that take part in chemical reactions over a controlled temperature range. TGA measures the differential thermal analysis in terms of the change in mass of the sample in relation to temperature changes or as a response of time with constant temperature and/or constant mass loss. DSC, on the other hand, measures the heat flow released or required against the temperature change at a particular time. The main dissimilarity between TGA and DSC is the method of measuring the changes in samples that are triggered by heat29,31,32,43.
At the beginning of TGA, common drying as a function of temperature can easily cause a quick initial drop in CNP mass due to the loss of residual water bound to the two polar groups in CNPs, which is not known to correspond to any chemical reactions32. Another reason for the drop in weight at the beginning step may be due to the dehydration of the saccharide rings, depolymerization, and decomposition of volatile products29. Next, successive weight losses in CNPs with increasing temperature may be due to evaporation and/or sublimation; however, multistage decomposition shown as a step-like pattern is due to the thermal degradation of CNPs43.
The loss of weight in the next stages of thermal analysis may be due to the decomposition of the polymer matrix; however, the CNPs did not fully decompose at the high temperature (800 °C) and conversely showed some stability in the polysaccharide structure. This result indicates that CNPs are thermally stable over a temperature up to 800 °C, which may be due to the high crosslinking of the CNPs that forms a stronger and stiffer hydrogel network29,32,43.
However, the thermal analysis steps were not blended during dynamic TGA; however, there is still a possibility of hidden interference of the decomposition steps, necessitating either far slower heating rates or stepwise TGA methods. That is why TGA itself may not be sufficient to identify the decomposition products, therefore chemical testing such as DSC is often, required alongside TGA, to ascertain the identities of suspected decomposition products31.
DSC of CNPs was performed to highlight phase transitions and clarify every single step of the thermal degradation mechanism. Usually, the temperature program for a DSC analysis is designed such that the sample holder temperature increases linearly as a function of time. That, in turn, can offer pieces of evidence about physical phenomena, such as glass transition, thermal stability, and purity31. Two definite broad endothermic peaks were generated, the first at a lower temperature that was due to the removal of absorbed water. The second endothermic peak that appeared at 242 °C is generally associated with the breakage of cross-linkage of CNPs. Additionally, the higher value of the glass transition temperature is due to the presence of a crosslinking agent and high thermal stability. Only a single glass transition in the DSC heating curves indicates the uniformity of the CNPs under high temperatures29. The transformation of chitosan into CNPs decreases the crystallinity due to changes in the solid-state structure of chitosan due to crosslinking, and thus, the decomposition of CNPs occurs above 300°C32.
Interestingly, in the present study, all proceeding investigations came in harmony with each other to provide an accurate perspective for the characterization of CNPs. Moreover, the currently proposed phytofabrication method for CNP preparation is considered ideal for generating high-quality CNPs.
The developed phytosynthesized CNPs were monitored regarding their antifungal properties. Depending on the molecular weight, concentration, degree of substitution, and the type of functional groups on chitosan, the free chitosan polymer exhibits various antifungal activities against a wide array of fungi. Derivatives of the polymer can be formed to target specific pathogens. Chitosan shows natural antifungal capability without the necessity for any chemical alterations2, which is why the generated CNPs were tested against the isolated B. cinerea SIB-1.
Koch’s postulates were applied to the isolated fungi to confirm the pathogenic ability and to select the most aggressive isolate as well. The colony characteristics of the isolated fungus and SEM investigation showed the typical features of the already known phytopathogenic B. cinerea SIB-1. These features are in line with those previously described17.
A severe phytopathogen (Botrytis cinerea SIB-1) was used in this study as a model for the evaluation of CNPs as an anti-biological agent. The main reason for selecting such phytopathogen is the wide host range since it can infect more than 200 host plants, and it can infect several parts of the plants, including the upper parts such as seeds, leaves, bulbs, and other propagation material at pre- and postharvest stages. Moreover, Botrytis spp. infect the host plant in all climate areas of the world and under great humidity in the presence or absence of water films. The fungus can generate high numbers of conidia that pose a long-lasting threat to susceptible hosts; in addition, the genotypic and phenotypic variation of the fungus is another broad-spectrum thread for the plant production sector16,17. Importantly, the fast alterations in populations and resistance in response to exposure to xenobiotics, e.g., fungicides, are quite widespread in the genus44, urging the discovery of alternative commercial approaches of considerable disease suppression to be integrated into crop management protocols15.
The selected fungus was identified as B. cinerea SIB-1 on a molecular basis, which is sensitive and specific for the rapid recognition of filamentous fungi at different systematic levels. The ITS region is usually used and can be adequate for fungal identification at the species level. The ITS region is also contemplated among the markers with the fastest and uppermost probability of precise identifications for a very broad group of fungi45. Interestingly, the culture morphology, SEM investigation, and molecular identification computably confirmed the fungus to be B. cinerea SIB-1.
Gray mold caused by the phytopathogenic Botrytis cinerea, is a serious disease that affects all strawberry growing regions and is the main cause of concern most years. The gray mold disease is a problem not only in the field, but also during storage, transportation, and marketing of strawberries as a result of severe rot as the fruits begin to ripen. Leafs, fruit caps, flower stalks, petals, and crowns are among the other parts infected by the fungus. CNPs showed strong inhibition against B. cinerea SIB-1. Possible protocols include inhibition of mycelial growth and sporogenesis3.
There are three mechanisms proposed as the inhibition mode of chitosan. In the first mechanism, the cell membrane of fungi is the main target of chitosan. The inhibitory effect of chitosan may be related to its interaction with the cell membrane of the fungal cell and alteration of membrane permeability46. The positive charge of chitosan allows it to interact with phospholipid components of the fungal cell membrane that are negatively charged. This increases membrane permeability, allowing cellular contents to leak out, ultimately resulting in cell death. The second mechanism involves chitosan acting as a chelating agent, binding to trace elements and rendering them unavailable to fungi for normal growth. Finally, the third mechanism proposed that chitosan could pass through fungi’s cell walls and bind to their DNA or proteins. This will stop the production of essential proteins and enzymes by inhibiting the synthesis of mRNA3. Chitosan inhibited mycelia growth, sporulation, and spore germination. It induces morphological changes characterized by excessive branching, mycelial swelling, agglomeration of hyphae, abnormal shapes, dissolution of protoplasm, large vesicles, cytoplasm aggregation, or empty cells devoid of cytoplasm in the mycelium47.
Ridomil Gold is a systematic fungicide that inhibits fungal development by interfering with the biosynthesis of sterols in the cell membrane. Thus, provides excellent disease control, ensures double protection to the target plants due to systemic activity of Mefenoxam fungicide and contact protective activity of mancozeb fungicide. Mefenoxam penetrates rapidly into the plant through the leaves and stems and is distributed upwards with the flow of sap. This way, new growth is protected as well. Mancozeb provides a protective film on the surface of the plant and inhibits germination of the spores and controls leaf and tuber blight as well as leaf spot disease.
The current experimental results on strawberry leaves show that treatment with CNPs reduced infection lesions. A key factor for a pathogenic fungus to be able to magnificently infect plants is to secrete a category of effector proteins into plant cells, which makes plants more susceptible to diseases48. These effector proteins decreased after treatment with chitosan5; moreover, chitosan can stimulate defense-related enzymes and augment the accumulation of antimicrobial ingredients in the infected plant, mainly diminishing the success rate of the fungal infection and inducing plant resistance49.
It is of essential importance to note that leaf treatment with a high concentration of CNPs (50 mg/mL) was found to have a reverse effect on the infected area compared with those of lower concentrations. These results suggested that increasing the concentration of nanoparticles might result in the crowding of these particles on the leaf pores that limit their penetration into the inner tissues. Furthermore, several findings concluded the potential phytotoxicity of these nanoparticles at high concentrations. In this respect, the application of CNPs at a concentration higher than the optimum causes a reduction in the mineral and nitrogen contents of the coffee leaves50. Similarly, high concentrations of chitosan nanoparticles markedly reduce the growth and development of Capsicum annuum, while lower concentrations have a growth-promoting effect51. These results suggest additional investigation on the optimum concentration of CNPs and further indicate the need for caution when using CNPs to reduce the harmful side effects of elevated concentrations.
