Introduction

Incorporating natural materials into the development of cutting-edge bioscience and bioengineering methodologies has experienced significant growth in the last century, leading to the emergence of the new field of enhanced biomaterials. The intricate interplay involving variation of morphological, chemical, physical, and biological properties of these nanostructures further broadens their application across diverse domains [1,2,3,4,5,6]. At the core of this expanding field are biopolymer mix-coated nanoformulations, which reflect the rapidly evolving science towards sustainable and environmentally friendly materials [7,8,9]. Here, green polymeric nanocomposites (PNCs), distinguished by their biodegradability, biocompatibility, and consistent particle size distribution, are widely recognized in diverse fields, including wound healing, environmental purification, drug delivery, biosensors, and nanobiotechnology [10,11,12].

In particular, the potential application of PNCs as antibacterial agents is a promising avenue to address several healthcare issues, especially when antibiotic resistance becoming a growing concern. Here, the use of PEG-based biopolymer blends in the functionalization of structural surfaces like konjac gum (KG), extracted from the Kojanc plant, is a viable route to construct viscous polymer with excellent hydrophilicity [5, 13,14,15,16,17]. Despite its distinct characteristics, such as solubility in water, high molecular weight of viscosity, and composition of glucose, mannose, and β-1,4 glycosides, KG has not been extensively studied [18,19,20,21,22,23,24,25,26,27,28,29]. Previous studies have predominantly focused on the use of rosin pentaerythritol ester (RE), derived from the esterification of rosin with pentaerythritol as a potential antibacterial agent with applications in food, inks, paper, coatings, and adhesives. Thus, coupling KG with RE within a nanocomposite configuration can be anticipated to exhibit robust antibacterial activity owing to their complementary traits. KG’s ability to form biocompatible matrices and its natural wound-healing ability could enable its usage as a platform for the controlled release of RE. This synergic combination will ensure sustained antibacterial activity by gradually releasing RE while increasing the surface contact with microbial cells, enhancing the nanocomposites’ ability to inhibit bacterial growth.

In recent years, the use of machine learning (ML) approaches (linear regression (LR), correlation matrix (CM), polynomial regression (PR), artificial neural network (ANN), and principal component analysis (PCA)) in nanomaterial design from a multidisciplinary perspective has gained momentum. By identifying complex patterns and correlations within experimental data, ML can elucidate the complex interplay between synthesis parameters and nanomaterial characteristics. For example, Ebrahimibagha et al. [30] demonstrated the utility of ML as a valuable tool for predicting the thermoelectric performance of carbon nanotube-polyaniline NCs. Similarly, Doh et al. [31] reported an ML-based reliability assessment framework for optimizing the electrical properties of carbon nanotube-filled PNCs, focusing on the role of ML-based modeling in predicting the wear and corrosion behavior of bio-degradable Mg/CeO2 NCs [32]. Champa-Bujaico et al. [33] employed ML models to precisely estimate and forecast the specific mechanical properties of poly(3-hydroxybutyrate) (P3HB)-based nanocomposites (NCs) modified with multiwalled carbon nanotubes (MWCNTs), WS2 nanosheets, and sepiolite nano clay.

In this study, PEGylated konjac gum-supported rosin pentaerythritol nanocomposites (KG/PEG/RE PNCs) were synthesized using a green and straightforward sonochemical method, aiming to explore its inherent antibacterial activity against pathogens such as Candida albicans (C. albicans), Escherichia coli (E. coli), Pseudomonas aeruginosa (P. aeruginosa), Aspergillus brasiliensis (A. brasiliensis), and Staphylococcus aureus (S. aureus). The impact of synthesis parameters, including concentration, sonication time, and KG/RE blend ratio on the PNCs viscosity was assessed. Among these parameters, the concentration of RE was determined to have a strong correlation with the obtained viscosity, with the Higiro model proving to be a suitable model for accurate correlation of theoretical and experimental viscosity. Additionally, polynomial regression modeled the complex dynamics that governing PNC viscosity as a function of synthesis conditions. The ML-enabled generation of a mathematical formula accurately predicted viscosity with high predictive accuracy, validated by a MSE of 3.81 and R2 of 0.993. When tested as antibacterial and antifungal formulations, PNCs reached their inhibition plateau within the initial weeks, whereas the varied sensitivity to different organisms further confirmed their complex interaction with pathogens, highlighting their broad-spectrum and prolonged activity.

Materials and Methods

Materials

Konjac gum (KG), a natural konjac gum glucomannan with a purity of 99% and an average molecular weight of 2.5 × 105 g mol−1, and polyethylene glycol (PEG 400) were purchased from Fluka (Switzerland). Rosin pentaerythritol ester was obtained from Pina Chemistry Company (Turkey). Ethyl acetate (99.8% anhydrous) was purchased from Sigma Aldrich, while dimethyl sulfoxide (DMSO) was purchased from Merck Company (Germany).

Synthesis of Environmentally Friendly Polymeric Nanostructures

The green KG/PEG/RE PNCs were synthesized using a one-pot sonication method at room temperature. The synthesis involved forming dispersed and continuous phases, followed by sonication for 20 min at 25 °C to produce the PNCs. The continuous phase comprised 0.05 g of KG in either 38.8 mL of distilled water or 3.7 mL of ethyl acetate solution at 25 °C for 15 min. Subsequently, 0.15 g of PEG400 was added to the solution and dissolved, forming the solvated continuous phase. The dispersed phase comprised 0.005 g of RE in either 0.5 mL of DMSO or 7 mL of ethyl acetate solution. To this phase, 7.5 mL of the continuous phase was added slowly to ensure proper mixing of the two phases. Finally, the solution was subjected to sonication at 20 kHz for 20 min at 25 ºC, forming the desired PNCs with improved properties and characteristics.

Characterization of Environmentally Friendly Polymeric Nanostructures

The morphological assessment of PNCs was carried out using a scanning electron microscope (SEM) (FEI QUANTA 450 Model) after gold coating and cooling to 2 °C under vacuum conditions and transmission electron microscopy (HRTEM) (High-tech HT7700). The crystal structure of the nanocomposite was studied using X-ray diffraction (XRD) (Rigaku Miniflex 600) in the 2-Theta range of 20–80° with Cukα λ = 1.5406 Å. Fourier transform infrared spectroscopy (FTIR) (Jasco 6800) was utilized to examine the chemical composition and identify functional groups in the KG/PEG/RE PNCs. Dynamic light scattering (DLS) (Nano Plus 3 HD zeta/nanoparticle analyzer) was employed to determine the size distribution and zeta potential of the KG/PEG/RE PNCs. Thermogravimetric analysis (TGA) (TA Instruments Discovery SDT 650) and differential scanning calorimetry (DSC) (TA Instruments Discovery SDT 650) were used to investigate the thermal stability and behavior of the nanocomposite. The surface area and porosity of the KG/PEG/RE PNCs were analyzed using Brunauer–Emmett–Teller (BET) (Micromeritics ASAP 2020) measurements. Image J processed SEM micrographs of the KG/PEG/RE PNCs were used to visualize the surface topography of the PNCs.

Optimization and Calculations

The prepared PNCs were investigated for changes in their rheological properties with various synthesis parameters, including the concentration of PNCs, sonication time, KG/RE blend ratio, temperature, and pH of the system. The relationship between the obtained and theoretical viscosity of PNCs was further evaluated using different mathematical models such as Huggins, Kraemer, Tanglertpaibul-Rao, and Higiro (Table 1).

Table 1 Formulas of mathematical models and parameters
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Antimicrobial Test of Polymeric Nanostructures

The antimicrobial activity of the green KG/PEG/RE PNCs was evaluated against a diverse range of pathogenic microorganisms, including S. aureus (NCTC 10788), P. aeruginosa (ATCC 9027), E. coli (ATCC 8739), C. albicans (NCPF 3179), and A. brasiliensis (NCPF 2275) using the standard NF EN ISO 11930:2012 method (Table 2). The microbial strains were sourced from the official collection of the European Union microbial strains to ensure reliability and reproducibility. The PNCs were inoculated with the respective microorganisms and incubated in a dark environment at temperatures between 20 and 25 °C for 28 days. The inhibition assay was carried out using various concentrations of the PNCs ranging from 105 to 106 CFU g−1. A viability assessment was conducted at specific time intervals to evaluate the effectiveness of PNCs against pathogenic microorganisms. The evaluation included measurements of viable microorganism counts at four time points: 0 days (immediately after collection), 7 days, 14 days, and 28 days. The antimicrobial test followed the NF EN ISO 11930 standard; detailed information can be found in Table 3. All experiments were performed in triplicate, and the mean values of each experiment were calculated. Statistical analysis was conducted using SPSS software (version 16, SPSS Inc., Chicago) through analysis of variance (ANOVA), with a significance level set at P < 0.05. This rigorous experimental design and statistical analysis ensured a reliable and accurate evaluation of the antimicrobial activity of the KG/PEG/RE PNCs against the tested microorganisms.

Table 2 Cultivation conditions for the pathogenic microorganisms
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Table 3 The criteria for the biological test
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Results and Discussion

A simple and green chemical route was adopted to prepare KG/PEG/RE PNCs to evaluate their suitability as an eco-friendly alternative to conventional antimicrobials. The application of the sonochemical route not only provides a sustainable path to future large-scale production but also ensures a homogenous dispersion of antibacterial agents (RE) within the polymer matrix, potentially leading to improved efficacy. The adopted chemical configuration, consisting of KG with the chemical resilience of rosin and the flexibility of PEGylation, offers higher stability and biofunctionality in the prepared PNCs, leading to a synergistic mechanism of action against pathogens without the drawbacks of traditional antibiotics, such as resistance development. Figure 1 shows the SEM of PNCs in reference to pristine KG. Unlike KG (Fig. 1a), which exhibited smooth surface features typical of polysaccharides, PNCs exhibited a distinct morphology characterized by clustered nanoparticles with rough and heterogeneous surfaces. The rough/heterogenous surface features of PNCs could provide a high surface area for bacterial interaction, whereas the aggregation, in this case, can be anticipated to be conducive to the slow release of antibacterial agents, enabling improved and prolonged activity. The surface heterogeneity was further visualized using a 3D SEM contour (Fig. 1c–d) image derived from the original SEM image using ImageJ processing. The colored topographical map confirmed the rich surface features with distinct elevation, a critical factor in improving surface area for microbial interaction. The corresponding 3D plot shows this heterogeneity in palpable terrain of peaks and troughs highlighted by red and blue-green valleys as the possible regions with relatively concentrated or segregated antibacterial agents, thus a visualized interpretation of multiple zones of defense against microbial colonization and proliferation.

Fig. 1
figure 1

SEM images depicting surface features of a pristine KG, b PNCs and image J processed SEM image and derived 3D contour plot showing typical surface heterogeneity in the form of peaks and troughs highlighted by red and blue-green valleys

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The particle heterogeneity was further evaluated using TEM and DLS analysis. Figure 2a–b shows representative TEM images displaying highly dispersed PNC particles, whereas DLS data (Fig. 2c) confirmed the average particle size distribution within 50 nm ± 1.5 nm, with a mean diameter of 40 nm and a low polydispersity index (PDI) value of 0.05. The compositional and surface characteristics of the PNCs were evaluated using XRD and BET analysis. Figure 3a shows the XRD pattern of the PNCs with a typical broad peak near 20° dedicated to the (016) plane of amorphous RE [41, 42]. The observed peak profile was similar to those previously reported for RE, whereas no additional peaks confirmed the compositional purity of synthesized PNCs. N2 adsorption/desorption isotherms were measured to evaluate the BET surface area of the prepared PNCs.

Fig. 2
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a–b Representative TEM images of PNCs at low and high magnification depicting particle size heterogeneity, and corresponding c DLS analysis depicting particle size distribution within 100 nm

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Fig. 3
figure 3

a the XRD pattern of PNCs; b corresponding N2-based BET isotherm with inset depicting the pore volume histogram, c TGA, d DSC and e DTG curves of PNCs reflecting improved thermal stability in comparison to pristine KG, f FTIR spectra of PNCs in reference to compositional counterparts

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Figure 3b shows the typical type-IV isotherm with increasing hysteresis at high pressure and a rise at a low pressure near 0.2 P/Po, confirming the mesoporous nature of the PNCs. The surface area in this case was determined to be 358.1 m2 g−1 with a total pore volume of 74.2 m2 g−1 (Fig. 3b inset). The surface area and mesoporous nature of PNCs could be beneficial for improved inhibition of bacterial organisms, enabling abundant bacterial attachment and porous channels that could facilitate the disruption of bacterial cell walls and, thus, their ability to replicate. At the same time, the high pore volume (74.2 m2/g) reflected a spacious internal structure that can facilitate subsequent storage and controlled release of the antibacterial agent (inset Fig. 3b).

TGA and DSC thermal analyses under a nitrogen atmosphere were carried out to compare the thermal stabilities of KG and PNCs (Fig. 3c). The weight loss of pure KG followed three stages as the temperature rises from 0 to 211, 211 to 366.9 °C, and 366.9 to 480.8 °C with corresponding weight loss changes of about 11.1%, 65.3%, and 98.4%, respectively. The observed weight loss changes were similar to those previously reported for KG [23, 25]. In contrast, the TGA curve of the PNCs showed four stages in the range from 0 to 147 °C, 147 to 231 °C, 231 °C to 457 °C, to 457 to 582 °C with weight loss changes of about 6.3% (dehydration stage), 21.9% (decomposition of biopolymer components), 96.5% (the breaking down of the crosslinking and the polymer blend) and 100% (phase transformation), respectively. This confirmed that the onset of maximum thermal degradation for the PNCs occurs at a higher temperature than that of pure KG, signifying the enhanced thermal stability imparted by the nanocomposite structure. This was further corroborated by DSC analysis, which showed an increased thermal resistance with an exothermic peak at 152.8 °C for PNCs and an enthalpy change of 51.73 J/g, likely due to the desorption of crystalline water within the nanocomposite. In comparison, the pristine KG exhibited an exothermic peak at 125.38 °C with an enthalpy change of 48.73 J/g, validating the thermal stability of the synthesized PNCs at elevated temperatures. Thus, the KG/PEG/RE PNCs synthesized via an eco-friendly sonochemical method exhibited a high surface area conducive to potential antibacterial applications and realized excellent thermal and colloidal stability, positioning them as a promising material for a diverse range of applications.

FTIR analysis was carried out to evaluate the functional group changes in KG post-transformation into PNCs. Figure 3f compares the FTIR spectra of PNCs with their compositional constituents (RE and KG). In case of KG, characteristic peaks were observed at 3275.5 cm−1 (–OH stretching), 2890.5 cm−1 (C–H stretching), 1628.3 cm−1 (–OH bending), 1374.1 cm−1 (C–H bending), 1249.7 cm−1 (C–O bending vibration), and 1014.4 cm−1 (C–O–C stretching). RE exhibited characteristic peaks at 2971.8 (–OH stretching vibrations), 2902.3 cm−1 (C–H stretching), 1708.6 cm−1 (C = O), 1408.8 cm−1 (C–H asymmetric bending), 1012.5 cm−1 (C–O–C stretching), and 950.7 cm−1 (–OH stretching of carboxy groups in abietic acid). In the case of PNCs, typical peaks were observed at 3365.2 cm−1 (–OH stretching), 2868.6 cm−1 (C–H stretching), 1728.3 cm−1 (C = O), 1593.9 cm−1 (–OH bending), 1425.1 cm−1 (C–H bending), 1209.1 cm−1 (C–O bending vibration), and 1019.2 cm−1 (C–O–C stretching). In addition, new bands appeared due to the presence of PEG (2868.8 cm−1, 1728.3 cm−1, and 1593.9 cm−1, corresponding to C–H stretching, C = O, and –OH bending) [43]. The typical peaks were attributed to PEGylation nanoparticles based on hydrogen bond interactions, which confirmed the PEG’s interaction with the surface of KG/RE. Table 4 illustrates the shift and presence of new FTIR peaks within the PNCs when compared with the KG and RE spectra.

Table 4 The shift and presence of new FTIR peaks in the PNCs compared to the KG and RE spectra
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Variation in Viscosity of PNCs with Synthesis Parameters

The intrinsic viscosity, critical to achieving homogenous nanostructures, is essential for realizing controllable PNC size [44,45,46]. Thus, the change in viscosity of the PNCs was evaluated against different synthesis parameters, including PNCs concentration (20–100 mg/L), polymer blend ratio, sonication duration, temperature, and pH (Fig. 4). Previous studies have proven that the increase in viscosity with concentration plays a significant role in the rheological properties of nanostructures [26, 47, 48].In this case, an increasing viscosity trend was observed with the rise in PNC concentration from 20 to 100 mg/L, indicative of enhanced molecular interactions and chain entanglement owing to the formation of homogeneous nanostructure, particularly with only 10% RE additive. The optimal viscosity and stability for PNCs were realized when the blend ratio was set to 3:4 (KG/PEG), sonication time of 10 min, and optimal pH of 5 at 20 °C, respectively (Fig. 4b–c). At optimal conditions, a balanced interaction between the components within PNCs ensures uniform distribution and effective performance of the NCs as antibacterial agents.

Fig. 4
figure 4

Variation of PNCs viscosity with synthesis parameters a concentration of PNCs, b KG/PEG blend ratio, c sonication time, d temperature, and e pH of the system, and fitting analyses for viscosity changes Relative to KG/PEG Blend Ratio using f–g Huggins, Kraemer, Tanglertpaibul-Rao, and h Higiro model

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To further explore the rheological behavior of PNCs, different mathematical models were adopted to determine their intrinsic viscosity and understand the interaction within the colloidal system, which can potentially impact PNCs’ antibacterial efficacy. Figure 4f–h shows various models fitted for viscosity-concentration relationship PNCs composed at different blend ratios. The Huggins and Kraemer models, typically linear in nature, suggested a certain degree of polymer–solvent interaction. However, the models failed at higher concentrations where non-linear behaviors emerged. The tanglertpaibul-Rao model, being non-linear, offered better fitting and thus was relatively suitable for evaluating particle interactions as concentrations rose. However, the Higiro model exhibited the closest agreement between theoretical ([η]: 75) and experimental viscosity values with the highest correlation coefficient (R2 = 0.969–0.982), suggesting its suitability for predicting the viscosity across the concentration range of 20 to 100 mg/L. Table 5 summarizes the fitting parameters where the Higiro model’s superior fitting confirmed the nanostructure’s suitability to be finely tuned to optimize antibacterial activity, thereby enhancing the PNCs’ utility in medical and hygiene products.

Table 5 Huggins, Kraemer, Tanglertpaibul-Rao, and Higiro fitting parameters for PNCs
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Data Modeling of the Colloidal Polymeric Nanostructures

Data analysis techniques were applied to the experimental data as a foundational step to evaluate the observed correlation and assess the interdependencies among the experimental parameters, including temperature, pH, concentration, rosin ester, time, and the KG/PEG ratio, and their collective influence on the viscosity. The correlation analysis was conducted using the polynomial regression correlation coefficient, which provided insight into the strength and direction of relationships between these variables. Figure 5 revealed several correlations that highlight the complex chemical and physical interactions affecting the behavior of the antimicrobial nanoagents.

Fig. 5
figure 5

Correlation matrix defining the inter-relationship of experimental parameters on the viscosity of PNCs

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A strong positive correlation between concentration and viscosity (0.62) indicates that as the concentration of the nanoagent increases, so does the viscosity. This relationship is crucial for optimizing the formulation of antimicrobial nanoagents. Conversely, the time of the experiment exhibited a moderate to strong negative correlation (−0.556) with viscosity, indicating that prolonged exposure results in decreased viscosity. This trend may be attributed to the gradual destabilization or reconfiguration of nanoagent structures over time, potentially due to physicochemical transformations or interactions with the surrounding medium. The correlation between the KG/PEG ratio and viscosity (0.5) further emphasized the significance of the compositional balance in determining the formulation’s physical characteristics. The positive correlation suggests that an increased ratio of KG to PEG contributes to the viscosity, likely through mechanisms that enhance the network structure or intermolecular interactions within the formulation.

Recognizing the impact of compositional factors such as the KG/PEG ratio on viscosity enabled the application of polynomial regression, a method adopted to explain the complex, nonlinear dynamics that govern the performance of antimicrobial nanoagents. Polynomial regression, an extension of the linear model, considers polynomial components as input variables, thus enabling the fitting of a broader range of data complexities. In this case of PNCs, the synthesis parameters such as temperature, pH, concentration, RE content, sonication period, and KG/PEG blend ratio were adopted as input parameters of second-degree based on prior preliminary analyses, confirming that second-degree polynomials would offer an optimal balance, without the risk of overfitting. The dataset was split into training (80%) and testing (20%) sets to develop and validate the model. The polynomial regression model was then trained on the transformed features, with the viscosity of the nanoagents serving as the dependent variable. In this case, the superior predictive accuracy of the model highlights its potential as a powerful tool for optimizing nanoagent formulations for diverse applications.

The following is the mathematical formula for the viscosity derived from the polynomial regression modeling of experimental data:

$$Viscosity=101.3921-0.0167+\left(-0.0074\right)*T+\left(0.0338\right)*P+\left(0.9123\right)*C+\left(0.0129\right)*R+\left(0.0691\right)*t-\left(0.0240\right)*K-\left(0.2322\right)*{T}^{2}+\left(0.0090\right)*T*P+\left(18.2439\right)*T*C-\left(0.0755\right)*T*R+\left(1.3699\right)*T*t+\left(0.6797\right)*T*K-\left(0.1282\right)*{P}^{2}+\left(4.5606\right)*P*C-\left(0.0841\right)*P*R+\left(0.3400\right)*P*t+\left(0.0073\right)*P*K+\left(0.8726\right)*{C}^{2}+\left(0.0107\right)*C*R-\left(0.2247\right)*C*t-\left(1729.8409\right)*C*K+\left(18.2468\right)*{R}^{2}+\left(0.6911\right)*R*t-\left(0.1909\right)*R*K+\left(1.3921\right)*{t}^{2}-\left(14.4331\right)*t*K$$

The derived formula incorporated the linear interaction terms between the variables and their squared terms to capture the nonlinear relationships affecting viscosity. The coefficients (e.g., −0.0167 for temperature, 0.0338 for concentration) quantify the impact of each term on the viscosity, with positive values indicating a direct relationship and negative values indicating an inverse relationship. Figure 6a shows the scatter plot comparing the actual viscosity values against those predicted by the polynomial regression model for the concentration variable. The proximity of the predicted values and the exact values for each concentration level confirmed the model’s precision. The residuals plot (Fig. 6b) further corroborates the model’s performance by depicting the residuals against predicted viscosity, where a significant residuals’ distribution near the zero line, without any apparent patterns, confirms the absence of systematic bias in the model’s predictions across the range of analyzed viscosity values. The performance of the polynomial regression model was evaluated using the MSE and the coefficient of determination (R2) metrics. In this case, the model achieved an MSE of 3.81 and an R2 of 0.993, indicative of the model’s superior predictive capability and, thus, its suitability as a valuable tool in optimizing nanocomposite formulations.

Fig. 6
figure 6

a polynomial regression analysis comprising the actual with predicted viscosity values and corresponding plot depicting b residual value distribution against the predicted concentration

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Biological Activity of the Prepared Polymeric Nanostructures (PNCs)

The KG/PEG/RE PNCs were tested on various pathogens such as S. aureus, P. aeruginosa, E. coli, C. albicans, and A. brasiliensis to evaluate the antimicrobial and antifungal characteristics. Figure 7a shows the corresponding plot of colony-forming units of each pathogen during 30 days. The control experiment indicates a negligible microbial reduction, with a decrease of less than 0.1 CFU/mL, which is not statistically significant (P > 0.05). In contrast, the PNCs exhibited considerable antibacterial activity against S. aureus (5.60 CFU/mL) and A. brasiliensis (3.54 CFU/mL) by day 7 of exposure. The activity was gradually pronounced by day 14, confirming improving activity against P. aeruginosa (5.73 CFU/mL), E. coli (5.58 CFU/mL), and C. albicans (3.60 CFU/mL). Compared to other polymeric antibacterial agents (Table 6), the KG/PEG/RE PNCs exhibited relatively higher activity against these harmful pathogens.

Fig. 7
figure 7

Biological efficacy of the synthesized PNCs against a S. aureus, b P. aeruginosa, c E. coli, d C. albicans, and e A. brasiliensis organism with inset figures depicting the corresponding zone of the inhibition in a disc diffusion assay

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Table 6 Comparison of antimicrobial performances of PNCs with different polymeric nanostructures
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The inset figure further corresponds to the zone of inhibition in a disc diffusion assay, visually displaying a clear area around the disc, which confirms the inhibited bacterial or fungal growth. The statistical notation (P > 0.05) confirms that the bacterial counts over the course are not statistically significant, reflecting the reach of an antibacterial activity plateau within the first week for some organisms and within two weeks for others. This distinct variation in the antibacterial and fungal activity could be attributed to differences in the interactions of PNCs with these organism and varied sensitvity of these organism with PNCs. The prolonged and broad spectrum activity of PNCs, combined with their biocompatibility and polymeric versatility, recognizes their suitability for a wide range of applications. These applications span from tailored antibacterial effects to surface coatings for food preservation and public transportation, aiming to achieve long-term antibacterial effects.

Conclusion

In conclusion, a green chemical-based sonication approach was utilized to synthesize KG/PEG/RE PNCs. XRD analysis confirmed the amorphous nature of PNCs, while SEM-based image processing further visualized the morphological heterogeneity of the PNCs. Careful optimization of synthesis parameters, including concentration, sonication time, KG/RE blend ratio, and temperature, was conducted to ensure optimal viscosity characteristics for improved antibacterial and antifungal activity. The intrinsic behavior of the PNC’s viscosity was evaluated using different mathematics models such as Huggins, Kraemer, Tanglertpaibul-Rao, and Higiro Models, with the Higiro model exhibiting the closest agreement between theoretical and experimental viscosity. Furthermore, machine learning (ML)-based data modeling confirmed the suitability of polynomial regression and the Pearson correlation coefficient model in understanding the intricate relationship between operational parameters and the PNCs’ viscosity. The optimized PNCs, tested against pathogens such as S. aureus, P. aeruginosa, E. coli, C. albicans, and A. brasiliensis over 30 days, demonstrated robust antibacterial and antifungal activity based on the zone of inhibition assay. The prolonged and broad-spectrum activity of PNCs, combined with their biocompatibility and versatility, confirms their suitability for various applications. Moreover, utilizing ML approaches for complex data modeling of biological assay paves the way to streamlining the antibacterial characteristics of future materials with integrated diverse applications.