Preparation of montmorillonite/chitosan/γ-polyglutamic acid nanoparticles and evaluation of their adsorption and antibacterial performance

Preparation of montmorillonite/chitosan/γ-polyglutamic acid nanoparticles and evaluation of their adsorption and antibacterial performance | 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 Article Preparation of montmorillonite/chitosan/γ-polyglutamic acid nanoparticles and evaluation of their adsorption and antibacterial performance Zuolong Yu, Chen Chen, Jiatao Wang, Yunxiao Wei, Changchun Fu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6082865/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 6 You are reading this latest preprint version Abstract In this study, montmorillonite (MMT)/chitosan (CS)/γ-polyglutamic acid (γ-PGA) nanoparticles were synthesized using MMT, CS, and γ-PGA as the matrix materials, with amino silane as the crosslinking agent. Through single-factor and response surface methodology experiments, the optimal nanoparticle formulation was determined by measuring the particle size and zeta potential of the composite nanoparticles. The synthesized nanoparticles were characterized using Fourier transform infrared spectroscopy and scanning electron microscopy, and their heavy-metal (Co 2+ ) adsorption capacity and antibacterial ( Escherichia coli ) performance were evaluated. The results indicated that the optimal MMT/CS/γ-PGA nanoparticle formulation was obtained when the MMT concentration was 2.18 mg/mL, CS concentration was 2 mg/mL, and γ-PGA/CS mass ratio was 1:4 in a 100 mL aqueous solution. Under these conditions, the average particle size was 899.76 nm, and the zeta potential was 60.57 mV. During Co 2+ adsorption tests, adsorption equilibrium was achieved at 135 min, with the nanoparticles demonstrating an adsorption capacity of 17.4 mmol/g at an initial cobalt concentration of 80 mmol/L. However, antibacterial activity tests revealed very weak antibacterial effects of the nanoparticles against E. coli . Overall, this study provides theoretical support for the preparation and application of eco-friendly nanoparticles with potential use in heavy-metal adsorption. Biological sciences/Biological techniques/Nanobiotechnology/Nanoparticles Physical sciences/Materials science/Nanoscale materials/Nanoparticles Montmorillonite composite nanoparticle preparation application Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Montmorillonite (MMT), the primary component of bentonite, is a natural nonmetallic mineral with a monoclinic crystal system, first discovered in Wyoming, USA [ 1 , 2 ]. MMT is primarily composed of silica, aluminum oxide, and water, with its structural formula represented as E x (H 2 O) 4 {(Al 2–x Mg x )(Si 4 O 10 )(OH) 2 } [ 3 , 4 ]. It features a layered structure composed of tetrahedral (Si–O) and octahedral (Al–O) sheets arranged in a tetrahedral–octahedral–tetrahedral configuration [ 5 ]. This layered structure makes MMT susceptible to unequal cation displacement, leading to the development of persistent negative charges that contribute to its high heavy-metal adsorption capacity [ 6 – 8 ]. Chitosan (CS), the second most abundant natural linear compound, possesses an − NH 3 + functional group and can be used independently or in combination with other materials to create hydrogels, nanoparticles, and biodegradable films for food, chemical, and pharmaceutical applications [ 9 – 11 ]. Owing to its favorable properties, CS has also found applications in environmental remediation. For instance, Elumalai et al. [ 12 ] synthesized nanocomposite materials using graphene oxide and CS to remove Cr 4+ and Ni 2+ ions from wastewater. At pH 5, a reaction time of 105 min, an initial metal ion concentration of 10 mg/L, and an adsorbent dosage of 20 g/L, they achieved the highest removal rates of 90.74% for Cr 4+ and 58.56% for Ni 2+ . Additionally, an experiment conducted by Yan et al. [ 13 ] demonstrated that CS effectively transforms most heavy metals, such as cadmium and mercury, in contaminated soil into residual forms after 7 days of treatment [ 14 ]. γ-Polyglutamic acid (γ-PGA) is an extracellular polymeric amino acid produced through either natural or modified microbial fermentation [ 15 ]. A γ-PGA molecule contains numerous ‒COOH and − NH 2 functional groups, imparting strong water solubility and high adsorption capacity. γ-PGA has been increasingly explored for various applications, including the synthesis of hydrogels for drug delivery [ 16 ], films and coatings for food preservation [ 17 ], and nanoparticles or microcapsules for environmental protection [ 18 ]. As early as 1990, McLean et al. [ 19 ] examined the ability of γ-PGA to adsorb heavy metals and found that it effectively adsorbs a wide range of metal ions, including Cr 2+ , Cu 2+ , Zn 2+ , and Fe 3+ . More recently, Chang et al. [ 20 ] synthesized magnetic Fe 3 O 4 nanoparticles coated with γ-PGA using a co-precipitation method, achieving removal rates exceeding 99% for Cr 3+ , Cu 2+ , and Pb 2+ in deionized water and surpassing 77% for Ni 2+ . A previous literature review [ 21 ] indicates that γ-PGA-based materials can be synthesized through various methods, including crosslinking, grafting, co-precipitation, and immobilization, enabling the production of diverse adsorbents, resins, membranes, and flocculants. The primary mechanisms for γ-PGA-based heavy-metal removal involve ion exchange, electrostatic attraction, and chelation, with the amino and carboxyl functional groups in γ-PGA playing a crucial role in the adsorption process. MMT/CS-based composite films, hydrogels, and beads can be used to adsorb various substances in aqueous environments. For instance, Vedula et al. [ 22 ] reported that MMT/CS films exhibited a higher Cu 2+ adsorption capacity than CS films. Additionally, MMT/CS composites have been demonstrated to be effective in Co 2+ , Ag + , Pb 2+ , and Ni 2+ adsorption [ 23 – 25 ]. Beyond metal ion removal, MMT/CS-based multicomponent composites can also adsorb organic pollutants such as methylene red, methylene blue, and Congo red from printing and dyeing wastewater [ 23 , 26 ]. Given this background, in the current study, MMT/CS/γ-PGA nanoparticles with different ratios were synthesized, and their structure was characterized using particle size analysis, zeta potential measurements, Fourier transform infrared spectroscopy (FTIR), and electron microscopy. The functionality of the optimized formulation was evaluated based on its Co 2+ adsorption and antibacterial properties. 2 Results and discussion 2.1 Optimization of synthetic conditions 2.1.1 Optimization based on single-factor analysis The effect of MMT mass concentration on particle size and zeta potential is illustrated in Fig. 1 . At an MMT mass concentration of 2 mg/mL, the particle size reached its minimum (326.7 nm) but then increased rapidly with further increases in MMT concentration. This increase occurred because excess MMT prevented the formation of nanoparticles with CS and γ-PGA, leading to the aggregation of large particles between MMT units. Consequently, the particle size increased rapidly. The zeta potential trend initially increased and then decreased. The highest zeta potential was observed at an MMT concentration of 2 mg/mL. This indicates that the prepared MMT/CS/γ-PGA nanoparticles exhibited small and stable particle sizes, although the overall variation was minimal [ 27 ]. The effect of CS mass concentration on particle size and zeta potential is presented in Fig. 2 . As the CS concentration increased, particle size initially decreased before gradually increasing. At CS concentrations between 2 mg/mL and 3 mg/mL, the nanoparticle size increased by 387 nm. This increase was attributed to the presence of excess CS, which allowed free CS and γ-PGA to form larger particles through ionic bonding. The zeta potential exhibited an initial increase followed by a decrease. The highest zeta potential was observed at a CS concentration of 2 mg/mL. This was because, at lower CS concentrations, both MMT/CS/γ-PGA and MMT/γ-PGA nanoparticles formed. However, the zeta potential of MMT/γ-PGA nanoparticles was lower. As the CS concentration increased, the formation of MMT/CS/γ-PGA nanoparticles became more favorable, resulting in a higher zeta potential. However, when excess CS was present, the free CS compromised the stability of the prepared sample [ 28 ]. An analysis of the effect of the γ-PGA/CS mass ratio on particle size and zeta potential (Fig. 3 ) revealed that as the γ-PGA/CS mass ratio increased, particle size initially decreased sharply before gradually increasing. Specifically, at a γ-PGA/CS mass ratio of 1:2, the nanoparticles exhibited the smallest size (326.7 nm). This occurred because, at lower γ-PGA concentrations, MMT aggregated with itself and with CS, forming larger particles and increasing particle size. However, as the γ-PGA/CS ratio increased, the formation of MMT/CS/γ-PGA nanoparticles led to a smaller average particle size. Meanwhile, the zeta potential initially decreased, then increased, and subsequently decreased again, suggesting that both insufficient and excessive γ-PGA were detrimental to the stability of the prepared nanoparticles [ 29 ]. An assessment of the relationship between stirring time, particle size, and zeta potential (Fig. 4 ) indicated that the formation of MMT/CS/γ-PGA nanoparticles occurred at an optimal stirring time. Specifically, at a stirring time of 60 min, the nanoparticles exhibited the highest stability, with a particle size of 326.7 nm and a zeta potential of approximately 53.4 mV. Given that the formation of MMT/CS/γ-PGA nanoparticles is a dynamic process, an optimal stirring time promotes the formation of uniform nanoparticles and enhances the dispersion of components. In contrast, prolonged stirring disrupts nanoparticle interactions, leading to structural degradation and reduced stability. An analysis of the four single-factor experiments revealed that stirring time, MMT and CS mass concentration, and the γ-PGA/CS mass ratio significantly influenced nanoparticle formation. Experimental observations confirmed that nanoparticles formed at a stirring time of 60 min exhibited superior characteristics. Therefore, 60 min was selected as the stirring time for subsequent experiments, and further optimization of MMT/CS/γ-PGA nanoparticle formation conditions was performed. 2.1.2 Response-surface-methodology-based optimization of synthetic conditions Based on the results of the single-factor experiments, a response surface analysis was conducted using the mass concentration of MMT ( A ), mass concentration of CS ( B ), and mass ratio of γ-PGA/CS ( C ) as independent variables. Particle size ( W 1 ) and zeta potential ( W 2 ) were selected as dependent variables, with the three coded levels set at − 1, 0, and 1. The corresponding factors and levels are presented in Table 1 . The design and results of the Box–Behnken experiment are summarized in Table 2 , while the variance analysis outcomes are listed in Table 3 . Table 1 Factors and levels used in response surface tests Level Factor A (mg/mL) B (mg/mL) C (g/g) −1 1.8 1.8 1:4 0 2.0 2.0 1:2 1 2.2 2.2 3:4 Table 2 Response surface design and results No. A (mg/mL) B (mg/mL) C (g/g) Particle size (nm) Zeta potential (mV) 1 2.2 2.2 1:2 856.3 56.8 2 2.2 1.6 1:2 776.3 58.6 3 2.0 2.2 3:4 587.5 49.2 4 2.0 2.0 1:2 362.6 53.0 5 2.0 2.0 1:2 626.0 55.6 6 2.0 1.8 1:4 1126.0 62.0 7 1.8 2.0 1:4 937.2 59.5 8 1.8 1.8 1:2 879.8 53.4 9 2.0 2.2 1:4 808.1 58.0 10 2.0 2.0 1:2 514.2 52.3 11 2.0 2.0 1:2 733.8 52.9 12 2.0 2.0 1:2 488.2 51.6 13 2.0 2.0 1:2 506.0 52.6 14 1.8 2.0 3:4 591.0 51.0 15 2.2 2.0 3:4 628.8 51.2 16 2.2 2.0 1:4 565.9 56.0 17 2.0 1.8 3:4 441.0 48.6 Table 3 Analysis of variance for the response surface model Factor Particle Zeta potential Sum of squares df Mean squares F-value P-value Sum of squares df Mean squares F-value P-value Model 5.478 × 10 5 9 60868.2 4.18 0.0363 187.23 9 20.80 3.78 0.0468 A-mass concentration of MMT 10841.28 1 10841.3 0.74 0.4167 4.18 1 4.18 0.76 0.4127 B-mass concentration of CS 7044.85 1 7044.9 0.48 0.5091 4.10 1 4.10 0.75 0.4166 C-mass ratio of γ-PGA/CS 1.71 × 10 5 1 1.7 × 10 5 11.7 0.0111 156.38 1 156.38 28.40 0.0011 A 2 69647.1 1 69647.1 4.78 0.0649 6.10 1 6.10 1.11 0.3276 B 2 1.42 × 10 5 1 1.42 × 10 5 9.74 0.0168 6.02 1 6.02 1.09 0.3304 C 2 14024.1 1 14024.1 0.96 0.3590 0.18 1 0.18 0.03 0.8622 AB 12769.0 1 12769.0 0.88 0.3802 0.46 0.46 0.08 0.7819 AC 46031.7 1 46031.7 3.16 0.1186 3.63 3.63 0.66 0.4436 BC 53916.9 1 53916.9 3.70 0.0957 5.20 5.20 0.94 0.3636 Pure error 35129.8 4 8782.5 9.57 4 2.39 Lack of fit 66776.8 3 22259.0 2.53 0.1953 28.97 3 9.66 4.04 0.1056 Residual 1.019 × 10 5 7 14558.1 38.55 7 5.51 Correlation total 6.497 × 10 5 16 225.77 16 Multiple regression fitting was performed using Design Expert 8.0.6 software based on the test data in Table 2 , yielding the following quadratic polynomial regression model equations for the relationships between A , B , and C and the response variables W 1 and W 2 . W 1 = 499.4 − 36.81 A − 29.67 B − 146.11 C + 56.5 AB + 107.27 AC + 116.1 BC + 128.61 A 2 + 183.54 B 2 + 57.71 C 2 (1) W 2 = 53.03 + 0.72 A − 0.72 B − 4.42 C − 0.34 AB + 0.95 AC + 1.14 BC + 1.2 A 2 + 1.2 B 2 + 0.21 C 2 (2) As indicated in Table 3 , all regression models were significant ( P 0.05), indicating that the model was effective and exhibited good predictive value. The primary term C exerted a significant effect on the particle size of MMT/CS/γ-PGA nanoparticles ( P < 0.05) and an extremely significant effect on zeta potential ( P 0.05). Among the quadratic terms, B 2 had a significant effect on particle size ( P 0.05). In contrast, A 2 and C 2 had no significant effects on either particle size or zeta potential ( P > 0.05). Among the interaction terms, AB , AC , and BC had no significant effects on zeta potential ( P > 0.05). A comprehensive analysis indicated that some factors had minimal influence on particle size and zeta potential, while B 2 had a notable impact on particle size. Therefore, the experimental conditions were optimized to produce smaller and more stable nanoparticles. Additionally, the relative influence of the independent variables on particle size and zeta potential followed the order: C > B > A . According to the model, as any factor approached the midpoint of the response curve, the particle size decreased from 1,126 nm to 362.6 nm. However, once the factor exceeded the midpoint, the particle size gradually increased due to negative influences (Fig. 5 a–c), indicating that lower γ-PGA concentrations contribute to the formation of smaller nanoparticles [ 30 ]. This occurs because CS remains in a continuous gel state before nanoparticle formation. With the addition of γ-PGA, CS and γ-PGA gradually cross-link to form nanoparticles. However, at higher γ-PGA concentrations, its branched chains interact laterally with CS, leading to the formation of larger nanoparticles [ 31 ]. The zeta potential of MMT/CS/γ-PGA nanoparticles decreased as the three selected factors approached the midpoint of the response curve, ranging between 48.62 and 61.96 mV (Fig. 5 d–f). However, when the factors surpassed the midpoint of the response curve, the zeta potential began to increase, following a trend consistent with changes in particle size. When the γ-PGA/CS mass ratio was 1:4 and the CS concentration was 2 mg/mL, the nanoparticles exhibited a high zeta potential. However, as the γ-PGA/CS mass ratio increased, the zeta potential decreased. In contrast, no positive correlation was observed between the CS concentration and zeta potential. The selected parameters also influenced the relationship between particle size and zeta potential. For instance, the γ-PGA/CS mass ratio determined particle size while also affecting zeta potential. The CS mass concentration had a significant impact on zeta potential and could also alter particle size. Specifically, increasing the CS concentration led to a corresponding increase in the size of MMT/CS/γ-PGA nanoparticles, accompanied by a decrease in zeta potential. However, an excessively high CS concentration was detrimental to nanoparticle stability and had an adverse effect on zeta potential. Therefore, a smaller particle size and higher zeta potential were chosen as the target optimal conditions. To achieve these (Fig. 6 ), the optimal parameters were set as follows: an MMT mass concentration of 2.18 mg/mL, a CS mass concentration of 2 mg/mL, and a γ-PGA/CS mass ratio of 1:4. Under these conditions, the predicted particle size was 679.93 nm, and the zeta potential was 58.47 mV. 2.1.3 Response surface model validation To verify the effectiveness of the response surface model, an experimental validation was conducted under the optimized conditions. The resulting nanoparticles had an average particle size of 899.76 nm and a zeta potential of 60.57 mV, which closely matched the predicted values. The discrepancy between the predicted and experimental values was primarily attributed to variations in nanoparticle size, likely caused by the low γ-PGA content. Thus, the response surface regression model effectively predicted the particle size and zeta potential of MMT/CS/γ-PGA nanoparticles. 2.2 Characterization of nanoparticle properties 2.2.1 FTIR analysis Figure 7 presents the FTIR spectra of CS, MMT, γ-PGA, and the synthesized nanoparticles. In the FTIR spectrum of γ-PGA, the characteristic absorption peaks of ‒OH and − NH appear at 3,350 and 2,980 cm − 1 , respectively. Furthermore, the absorption peak of − C = O in the carboxyl group appears at 1,630 cm − 1 , whereas that of C‒N in the amide group appears at 1,530 cm − 1 [ 32 ]. In the FTIR spectrum of CS, peaks at 910 and 1,145 cm − 1 correspond to ‒C‒O‒ vibrations. The strong absorption peak at 1,120 cm − 1 is attributed to the stretching vibration of ‒OH groups. Further, peaks at 1,356 cm − 1 are associated with the deformation vibration of ‒CH 3 and the bending vibration of ‒CH, while the peak at 2,784 cm − 1 corresponds to the stretching vibration of ‒CH. Broad peaks appearing at 3,540 and 3,458 cm − 1 are characteristic of hydrogen bonding, attributed to the − OH and − NH groups [ 33 ]. In the spectrum of MMT, stretching vibration absorption peaks appear at 3,645, 1,670, 1,048, 720, 525, and 468 cm − 1 [ 34 ]. Meanwhile, in the nanoparticle spectrum, the Si‒O vibration characteristic peak at 1045 cm − 1 disappears, confirming that the interlayer Si‒O structure of MMT is altered during the synthesis process. The characteristic carboxyl ion peak of γ-PGA shifts from 1,630 cm − 1 to 1,670 cm − 1 , reflecting changes in the electron density within the carboxyl environment. Peaks appearing at 534 cm − 1 and 670 cm − 1 correspond to the vibrations of metal oxides from the MMT mineral composition, suggesting interactions between the inorganic and organic components. Further, the peaks at 1,537 cm − 1 and 1428 cm − 1 correspond to the amide II band, directly indicating interactions between the amide group of CS and the carboxyl functional group of γ-PGA, possibly through hydrogen or covalent bonding. The amido group peak at 1,655 cm − 1 redshifts slightly to 1,670 cm − 1 after polymerization, likely due to ionic bonding between protonated CS and MMT in the form of a cationic ammonium salt (− NH 3 + ). Collectively, the above results indicate that during the polymerization reaction, MMT binds to CS and γ-PGA through ionic bonds. After polymerization, the peak corresponding to the primary amine group of CS diminishes or disappears, indicating its interaction with the carboxyl group of γ-PGA. Meanwhile, the − OH and − NH groups, which serve as adsorption sites, remain intact, confirming the successful integration of MMT, CS, and γ-PGA. Additionally, the presence of these adsorption groups suggests that the nanoparticles retain their capacity to capture target molecules. 2.2.2 Scanning electron microscopy (SEM) analysis The microstructure of MMT/CS/γ-PGA nanoparticles was characterized using SEM, as depicted in Fig. 8 . The particles exhibited variations in size and shape and were not perfectly spherical. This irregular morphology resulted from the inherent microstructure of MMT, which influenced the overall particle shape. Larger particles indicated aggregation and accumulation that occurred during sample preparation. 2.3 Applications 2.3.1 Heavy-metal adsorption The number of cobalt ions adsorbed onto the MMT/CS/γ-PGA nanoparticle adsorbent increased over time; however, the rate of increase gradually slowed (Fig. 9 ). Before adsorption, the initial concentration of cobalt ions in the solution ( C 0 ) was 80 mmol/L. Adsorption equilibrium was reached at 135 min, with a cobalt ion concentration ( C t ) of 59.6 mmol/L and an adsorption capacity ( Q ) of 17.4 mmol/g. MMT, CS, and γ-PGA individually exhibited some adsorption capacity for heavy metals. For example, a stable complex is formed through electrostatic interactions between groups containing numerous carboxylic acid (− COOH) groups on a molecular chain of γ-PGA and positively charged heavy metal ions, such as La³ + , Ce³ + , Pb² + , and Hg² + [ 21 ]. CS mainly adsorbed heavy metals through coordination and functional groups [ 35 ]. However, nanoparticle formation led to active site depletion, impacting their ability to adsorb cobalt ions [ 36 – 38 ]. The surface area and pore structure of the nanoparticles also influenced adsorption performance. 2.3.2 Antibacterial properties The bacterial colonies of Escherichia coli selected for this experiment appeared round, milky white, with smooth surfaces, uniform coloration, and well-defined edges (Fig. 10 ). Notably, the size of the inhibition zone reflects the effectiveness of the tested antibacterial agent against indicator bacteria [ 39 ]. According to our findings, the MMT/CS/γ-PGA nanoparticles exhibited minimal antibacterial activity. The diameter of the antibacterial zone increased from 0 mm to 2 mm between day 1 and day 2, indicating weak antibacterial effects. However, the inhibition zone gradually disappeared between day 2 and day 4. While CS exhibits favorable antibacterial properties and high-molecular-weight γ-PGA also contributes to antibacterial activity, these effects were likely diminished when the materials were combined with MMT, primarily acting as a barrier to substance transport, to form nanoparticles [ 17 , 40 ]. This reduction in antibacterial activity was likely due to interactions between key functional groups, namely the − NH 2 group of CS and ‒COOH group of γ-PGA, as well as the electronegativity of MMT. These interactions likely weakened or deactivated these functional groups, reducing the antibacterial effectiveness of MMT/CS/γ-PGA nanoparticles. 3 Conclusion In this study, MMT/CS/γ-PGA nanoparticles were synthesized using MMT, CS, and γ-PGA as the primary raw materials. Single-factor experiments demonstrated that stirring time, MMT concentration, CS concentration, and the γ-PGA/CS mass ratio significantly influenced nanoparticle formation. Through response surface optimization, small particle size and high zeta potential were identified as the target optimal conditions. The smallest particle size was achieved when the MMT concentration was 2.18 mg/mL, CS concentration was 2 mg/mL, and γ-PGA/CS mass ratio was 1:4. Under these conditions, the predicted particle size was 679.92 nm, with a zeta potential of 58.47 mV. Experimentally synthesized nanoparticles had an average particle size of 899.76 nm and a zeta potential of 60.57 mV, which closely aligned with the predicted values. Structural analysis revealed that the three components formed nanoparticles of varying shapes, with MMT binding to CS and γ-PGA through ionic bonds. The nanoparticles demonstrated heavy-metal adsorption capability, as evidenced by the adsorption of Co 2+ ions. However, antibacterial testing revealed that the nanoparticles had weak antibacterial activity overall. 4 Methods 4.1 Synthesis and characterization of MMT/CS/γ-PGA nanoparticles 4.1.1 Preparation A measured amount of CS (average molecular weight: 50 kDa, degree of deacetylation: 95%, Nanjing Oddfoni Biological Technology Co., Ltd., Nanjing, China) was dissolved in a 5% acetic acid solution and stirred at room temperature for 2 h until the solution became clear and transparent. Next, 20 mL of the prepared CS solution was transferred into a beaker, and a specified mass of nano-MMT (Zhejiang Hongyu New Materials Co., Ltd., Huzhou, China) was added. The mixture was stirred at 300 rpm for 1 h until it became clear and transparent. Subsequently, 20 mL of a γ-PGA aqueous solution (average number-average molar mass: 2000 kDa, Nanjing Shineking Biotech Co., Ltd., Nanjing, China) at a predetermined concentration was slowly added. Once γ-PGA addition was complete, the mixture was magnetically stirred at 300 rpm for 10-min, yielding MMT/CS/γ-PGA nanoparticles. 4.1.2 Single-factor experiments To evaluate the effect of individual factors on MMT/CS/γ-PGA nanoparticles, particle size and zeta potential were used as evaluation indices. The single-factor experiments were designed by varying the CS mass concentration (3, 2, 1, 0.5, and 0.1 mg/mL), MMT mass concentration (4, 3, 2, 1, and 0.5 mg/mL), γ-PGA/CS mass ratio (1:1, 3:4, 1:2, 1:4, and 1:10), and stirring time (90, 60, 30, and 0 min) while maintaining all other conditions constant. 4.1.3 Response surface methodology experiment The results of the single-factor experiments were analyzed using SPSS V29 software, identifying three factors—CS concentration, MMT concentration, and γ-PGA/CS mass ratio—exerting a significant impact on the particle size and zeta potential of MMT/CS/γ-PGA nanoparticles. The selected factors were encoded at three levels (− 1, 0, and 1), and regression analysis was conducted on the experimental data using Design Expert 8.0.6. Factors influencing nanoparticle size and zeta potential were optimized and analyzed. By examining the response surface diagrams, the response values affected by multiple variables were modeled and predicted. The software-generated quadratic polynomial equation was as follows: $$\:{W}_{i}={\beta\:}_{0}+\sum\:_{i=1}^{k}{\beta\:}_{i}{X}_{i}+\sum\:_{i=1}^{k}{\beta\:}_{ii}{X}_{ii}^{2}+\sum\:_{i}^{k-1}\sum\:_{j}^{k}{\beta\:}_{ij}{X}_{i}{X}_{j}$$ 3 , where W i denotes the response value (zeta potential or particle size), β 0 is a constant, β i denotes the linear coefficient, β ii represents the quadratic coefficient, β ij signifies the interaction coefficient between two factors, and X i and X j represent variables (MMT mass concentration, CS concentration, and γ-PGA/CS mass ratio). 4.1.4 Measurement of particle size and zeta potential The particle size and zeta potential of the composite nanoparticles were measured using a laser particle size analyzer (Malvern Zetasizer Lab, Malvern Panalytical Ltd., Shanghai, China). During this analysis, the composite nanoparticle solution was carefully injected into a clean collapsible capillary sample tank (DTS1070) to prevent bubble formation. The measurement conditions were set as follows: a temperature of 25°C, an equilibrium time of 60 s, and three repeated tests per sample. The particle size and zeta potential values were recorded after the tests were completed. 4.1.5 FTIR analysis The FTIR spectra of the raw materials and the freeze-dried MMT/CS/γ-PGA nanoparticles were obtained based on the KBr pellet method using an FTIR spectrometer (Nicolet iS50, Waltham, USA). The measurement conditions were as follows: a temperature of 25°C, a resolution of 4 cm − 1 , and 32 scans across the 500–4000 cm − 1 wavenumber range. The acquired infrared spectra were imported into Origin software for graphical analysis. 4.1.6 SEM analysis To examine the morphology of the composite nanoparticles, an SE microscope (Hitachi S-570, Tokyo, Japan) operating at 10 kV was used to observe the sample surfaces. Prior to imaging, the sample surfaces were coated with a thin layer of gold. Observations were focused on the central regions of the coated surfaces. 4.2 Applications 4.2.1 Heavy-metal ion absorption A 20 mL solution of cobalt nitrate (0.16 mol/L) was mixed with 20 mL of the MMT/CS/γ-PGA nanoparticle adsorbent solution (predetermined concentration), and the adsorption experiment was conducted at room temperature under magnetic stirring at 200 r/min for a predetermined duration. The supernatant was then filtered using a 0.22-µm filter membrane [ 41 ]. The absorbance levels of Co 2+ solutions with concentrations of 0.04, 0.06, 0.08, 0.1, 0.12, 0.14, and 0.16 mol/L were measured at a wavelength of 512 nm (λ max = 512 nm). A linear relationship was established by fitting the experimental data. A 5.00 mL sample was used for analysis, following the procedure outlined in the standard curve experiment. The adsorption capacity was calculated using Eq. ( 4 ): $$\:\text{C}=\frac{\text{A}-{\text{A}}_{0}-\text{a}}{\text{b}\bullet\:\text{V}},$$ 4 where C denotes the concentration of Co 2+ in the sample (µg/mL), A indicates the absorbance of the sample, A 0 represents the absorbance of the blank sample, a corresponds to the intercept of the regression curve, b signifies the slope of regression curve, and V denotes sample volume (mL). The supernatant was collected, and the Co 2+ content in the solution after nanoparticle adsorption was determined using an ultraviolet spectrophotometer (UV L6S, INESA Analytical Instrument Co., Ltd., Shanghai, China). The adsorption performance of the nanoparticles for Co 2+ was evaluated based on the adsorption capacity Q , defined as in Eq. ( 5 ). $$\:\text{Q}=({\text{C}}_{0}-{\text{C}}_{\text{t}})\bullet\:\frac{\text{V}}{\text{m}}$$ 5 , where Q denotes the Co 2+ adsorption capacity of the nanoparticles (mmol/g), C 0 represents the initial concentration of Co 2+ in the solution (mol/L), C t indicates the concentration of Co 2+ at adsorption equilibrium (mol/L), V represents the volume of the Co 2+ solution (mL), and m denotes the mass of the adsorbent (g). 4.2.2 Antibacterial experiment Antibacterial tests quantify the antibacterial or bactericidal activity of a sample in vitro . When bacterial growth is inhibited, a clear antibacterial zone forms. By measuring the size of this zone, the degree of bacterial inhibition can be assessed. In general, a larger antibacterial zone diameter indicates a stronger antibacterial effect against a given bacterial strain [ 42 ]. E. coli (obtained from the microbiology laboratory of our university) is a bacterium commonly found in the intestines of humans and various animals, exhibiting significant diversity and abundance. In this study, E. coli was selected as the intestinal indicator bacterium, and its inhibition by MMT/CS/γ-PGA nanoparticles was evaluated by measuring the size of the inhibition zone. Preparation of the medium: A total of 10 g of nutrient agar medium was added to a beaker, followed by 1,000 mL of distilled water. After complete dissolution, the medium was transferred to an Erlenmeyer flask and sealed with a cotton plug. It was then sterilized in an autoclave at 0.1 MPa and 121°C for 30 min, poured into plates, and cooled to approximately 37°C before use. Culture process: After reviving, frozen E. coli was inoculated onto a plate medium using the streak inoculation method in an ultraclean workbench. After 24 h of incubation in a constant-temperature incubator at 37°C, a well-grown single bacterial colony was selected using an inoculation loop to prepare an E. coli suspension at a concentration of 10 6 cfu/mL. A total of 0.1 mL of the suspension was then transferred onto the plate culture medium and evenly spread using a sterile applicator. The medium was left undisturbed for 20 min to allow full bacterial penetration. A sterilized Oxford cup was then placed onto the culture dish using tweezers. Determination of inhibition zone: MMT/CS/γ-PGA nanoparticles (200 µL) were added to three Oxford cups containing the bacterial cultures. After the nanoparticles were fully mixed with the medium, the Oxford cups were removed, and the cultures were sealed with a sealing film and incubated at 37°C for 4 days in a constant-temperature incubator. The inhibition zone size was observed, photographed, and measured. 4.3 Data analysis GraphPad Prism 10.1 was used for mapping analysis, while Design Expert 8.0.6 and SPSS V29 were used for data analysis. A t-test was performed for statistical evaluation. P > 0.05 indicated no significant difference, whereas P < 0.05 denoted a significant difference. A P value below 0.01 indicated a highly significant difference. Declarations Acknowledgments The authors would like to express their gratitude for the financial support provided by the Horizontal Subject of Zhejiang Shuren University (2024KJ162). Author contributions Conceptualization, Z.Y., C.H., and Y.S.; methodology, C.C., J.W., Z.Y., and Y.W.; investigation, C.F. and D.L.; writing—original draft preparation, Z.Y. and Y.H.; writing—review and editing, Z.Y., Y.S., Y.W., J.W., and S.S; supervision, Z.Y.; funding acquisition, Z.Y. and C.H. All authors have read and accepted the published version of the manuscript. 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Cite Share Download PDF Status: Published Journal Publication published 10 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 04 Jun, 2025 Reviews received at journal 04 May, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers invited by journal 14 Apr, 2025 Submission checks completed at journal 09 Apr, 2025 First submitted to journal 29 Mar, 2025 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. 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3","display":"","copyAsset":false,"role":"figure","size":293836,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of γ-PGA/CS Mass Ratio on Particle Size and Zeta Potential:\u003c/strong\u003e Particle size (gray bars) and zeta potential (blue line) as a function of the γ-PGA/CS mass ratio.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6082865/v1/4acdf0e27030a3cb2b390acc.png"},{"id":80702803,"identity":"609ca499-73c6-4a6d-b846-5240a7cc11f3","added_by":"auto","created_at":"2025-04-16 07:55:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":273651,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of Stirring Time on Particle Size and Zeta Potential:\u003c/strong\u003e Particle size (gray bars) and zeta potential (blue line) as a function of stirring time.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6082865/v1/ea6179af22143f55dfa68244.png"},{"id":80702811,"identity":"3b972696-a0e6-4494-b214-6d66a855eda2","added_by":"auto","created_at":"2025-04-16 07:55:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1677239,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInteraction Effects on Particle Size and Zeta Potential: \u003c/strong\u003e(a) Effect of \u003cem\u003eA\u003c/em\u003e and \u003cem\u003eB\u003c/em\u003eon particle size. (b) Effect of \u003cem\u003eA\u003c/em\u003e and \u003cem\u003eC\u003c/em\u003e on particle size. (c) Effect of \u003cem\u003eB\u003c/em\u003e and \u003cem\u003eC\u003c/em\u003e on particle size. (d) Effect of \u003cem\u003eA\u003c/em\u003e and \u003cem\u003eB\u003c/em\u003e on zeta potential. (e) Effect of \u003cem\u003eA\u003c/em\u003e and \u003cem\u003eC\u003c/em\u003e on zeta potential. (f) Effect of \u003cem\u003eB\u003c/em\u003e and \u003cem\u003eC\u003c/em\u003e on zeta potential.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6082865/v1/a63553b03e69d1ddeaf7d2ce.png"},{"id":80702809,"identity":"ba6bbbbb-b4e3-4459-8cef-10e6dc8283fc","added_by":"auto","created_at":"2025-04-16 07:55:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":141795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResponse Surface Model Prediction of Particle Size and Zeta Potential\u003c/strong\u003e: Optimized conditions for \u003cem\u003eA\u003c/em\u003e (MMT mass concentration), \u003cem\u003eB\u003c/em\u003e (CS mass concentration), and \u003cem\u003eC\u003c/em\u003e (γ-PGA/CS mass ratio) displayed alongside the predicted particle size, zeta potential, and overall desirability value\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6082865/v1/34fbde7533295ebbf279f3a7.png"},{"id":80703159,"identity":"112be9ea-bb9e-48bd-b69f-63d5bc0eb217","added_by":"auto","created_at":"2025-04-16 08:03:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":249061,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInfrared Spectra of CS, MMT, γ-PGA, and Their Nanoparticles\u003c/strong\u003e: FTIR spectra of individual components (\u003cem\u003eγ\u003c/em\u003e-PGA, CS, and MMT) and the synthesized nanoparticles, showing characteristic absorption peaks\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6082865/v1/5a9e6fd2664e7a9e74f278b7.png"},{"id":80704343,"identity":"67df3263-a641-4b4d-b912-0f0cd8715303","added_by":"auto","created_at":"2025-04-16 08:11:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":474001,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSEM Image of MMT/CS/γ-PGA Nanoparticles\u003c/strong\u003e: SEM image showing the surface morphology and distribution of the synthesized nanoparticles at a magnification of 20,000×.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6082865/v1/6a30e67127d87734039916b3.png"},{"id":80704344,"identity":"44fc0afb-f6c8-44cf-a58f-dedbf9c66440","added_by":"auto","created_at":"2025-04-16 08:11:59","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":93385,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdsorption Kinetics of Co²⁺ Over Time\u003c/strong\u003e: Adsorption capacity of the nanoparticles as a function of adsorption time.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-6082865/v1/83a4a99cd7f533fec3978213.png"},{"id":80704834,"identity":"99114683-2e7a-4a93-a779-0951976acc3a","added_by":"auto","created_at":"2025-04-16 08:19:59","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1076847,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAntibacterial Activity of MMT/CS/γ-PGA Nanoparticles Against \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eE. coli\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e: \u003c/strong\u003eInhibition zones observed after incubation at 37°C for (a) 1 day, (b) 2 days, (c) 3 days, and (d) 4 days.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-6082865/v1/42a1c8f35d90845ae440df85.png"},{"id":96105183,"identity":"c93cc007-235f-4a65-a2a5-82d65efa2e6c","added_by":"auto","created_at":"2025-11-17 16:09:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6251875,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6082865/v1/554b20ac-787b-4216-97ac-c1c9e798caa2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preparation of montmorillonite/chitosan/γ-polyglutamic acid nanoparticles and evaluation of their adsorption and antibacterial performance","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMontmorillonite (MMT), the primary component of bentonite, is a natural nonmetallic mineral with a monoclinic crystal system, first discovered in Wyoming, USA [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. MMT is primarily composed of silica, aluminum oxide, and water, with its structural formula represented as E\u003csub\u003ex\u003c/sub\u003e(H\u003csub\u003e2\u003c/sub\u003eO)\u003csub\u003e4\u003c/sub\u003e{(Al\u003csub\u003e2\u0026ndash;x\u003c/sub\u003eMg\u003csub\u003ex\u003c/sub\u003e)(Si\u003csub\u003e4\u003c/sub\u003eO\u003csub\u003e10\u003c/sub\u003e)(OH)\u003csub\u003e2\u003c/sub\u003e} [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It features a layered structure composed of tetrahedral (Si\u0026ndash;O) and octahedral (Al\u0026ndash;O) sheets arranged in a tetrahedral\u0026ndash;octahedral\u0026ndash;tetrahedral configuration [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This layered structure makes MMT susceptible to unequal cation displacement, leading to the development of persistent negative charges that contribute to its high heavy-metal adsorption capacity [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChitosan (CS), the second most abundant natural linear compound, possesses an \u0026minus;\u0026thinsp;NH\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e functional group and can be used independently or in combination with other materials to create hydrogels, nanoparticles, and biodegradable films for food, chemical, and pharmaceutical applications [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Owing to its favorable properties, CS has also found applications in environmental remediation. For instance, Elumalai et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] synthesized nanocomposite materials using graphene oxide and CS to remove Cr\u003csup\u003e4+\u003c/sup\u003e and Ni\u003csup\u003e2+\u003c/sup\u003e ions from wastewater. At pH 5, a reaction time of 105 min, an initial metal ion concentration of 10 mg/L, and an adsorbent dosage of 20 g/L, they achieved the highest removal rates of 90.74% for Cr\u003csup\u003e4+\u003c/sup\u003e and 58.56% for Ni\u003csup\u003e2+\u003c/sup\u003e. Additionally, an experiment conducted by Yan et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] demonstrated that CS effectively transforms most heavy metals, such as cadmium and mercury, in contaminated soil into residual forms after 7 days of treatment [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eγ-Polyglutamic acid (γ-PGA) is an extracellular polymeric amino acid produced through either natural or modified microbial fermentation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A γ-PGA molecule contains numerous ‒COOH and \u0026minus;\u0026thinsp;NH\u003csub\u003e2\u003c/sub\u003e functional groups, imparting strong water solubility and high adsorption capacity. γ-PGA has been increasingly explored for various applications, including the synthesis of hydrogels for drug delivery [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], films and coatings for food preservation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and nanoparticles or microcapsules for environmental protection [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. As early as 1990, McLean et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] examined the ability of γ-PGA to adsorb heavy metals and found that it effectively adsorbs a wide range of metal ions, including Cr\u003csup\u003e2+\u003c/sup\u003e, Cu\u003csup\u003e2+\u003c/sup\u003e, Zn\u003csup\u003e2+\u003c/sup\u003e, and Fe\u003csup\u003e3+\u003c/sup\u003e. More recently, Chang et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] synthesized magnetic Fe\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e nanoparticles coated with γ-PGA using a co-precipitation method, achieving removal rates exceeding 99% for Cr\u003csup\u003e3+\u003c/sup\u003e, Cu\u003csup\u003e2+\u003c/sup\u003e, and Pb\u003csup\u003e2+\u003c/sup\u003e in deionized water and surpassing 77% for Ni\u003csup\u003e2+\u003c/sup\u003e. A previous literature review [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] indicates that γ-PGA-based materials can be synthesized through various methods, including crosslinking, grafting, co-precipitation, and immobilization, enabling the production of diverse adsorbents, resins, membranes, and flocculants. The primary mechanisms for γ-PGA-based heavy-metal removal involve ion exchange, electrostatic attraction, and chelation, with the amino and carboxyl functional groups in γ-PGA playing a crucial role in the adsorption process.\u003c/p\u003e \u003cp\u003eMMT/CS-based composite films, hydrogels, and beads can be used to adsorb various substances in aqueous environments. For instance, Vedula et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] reported that MMT/CS films exhibited a higher Cu\u003csup\u003e2+\u003c/sup\u003e adsorption capacity than CS films. Additionally, MMT/CS composites have been demonstrated to be effective in Co\u003csup\u003e2+\u003c/sup\u003e, Ag\u003csup\u003e+\u003c/sup\u003e, Pb\u003csup\u003e2+\u003c/sup\u003e, and Ni\u003csup\u003e2+\u003c/sup\u003e adsorption [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Beyond metal ion removal, MMT/CS-based multicomponent composites can also adsorb organic pollutants such as methylene red, methylene blue, and Congo red from printing and dyeing wastewater [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Given this background, in the current study, MMT/CS/γ-PGA nanoparticles with different ratios were synthesized, and their structure was characterized using particle size analysis, zeta potential measurements, Fourier transform infrared spectroscopy (FTIR), and electron microscopy. The functionality of the optimized formulation was evaluated based on its Co\u003csup\u003e2+\u003c/sup\u003e adsorption and antibacterial properties.\u003c/p\u003e"},{"header":"2 Results and discussion","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Optimization of synthetic conditions\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Optimization based on single-factor analysis\u003c/h2\u003e \u003cp\u003eThe effect of MMT mass concentration on particle size and zeta potential is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. At an MMT mass concentration of 2 mg/mL, the particle size reached its minimum (326.7 nm) but then increased rapidly with further increases in MMT concentration. This increase occurred because excess MMT prevented the formation of nanoparticles with CS and γ-PGA, leading to the aggregation of large particles between MMT units. Consequently, the particle size increased rapidly. The zeta potential trend initially increased and then decreased. The highest zeta potential was observed at an MMT concentration of 2 mg/mL. This indicates that the prepared MMT/CS/γ-PGA nanoparticles exhibited small and stable particle sizes, although the overall variation was minimal [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe effect of CS mass concentration on particle size and zeta potential is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As the CS concentration increased, particle size initially decreased before gradually increasing. At CS concentrations between 2 mg/mL and 3 mg/mL, the nanoparticle size increased by 387 nm. This increase was attributed to the presence of excess CS, which allowed free CS and γ-PGA to form larger particles through ionic bonding. The zeta potential exhibited an initial increase followed by a decrease. The highest zeta potential was observed at a CS concentration of 2 mg/mL. This was because, at lower CS concentrations, both MMT/CS/γ-PGA and MMT/γ-PGA nanoparticles formed. However, the zeta potential of MMT/γ-PGA nanoparticles was lower. As the CS concentration increased, the formation of MMT/CS/γ-PGA nanoparticles became more favorable, resulting in a higher zeta potential. However, when excess CS was present, the free CS compromised the stability of the prepared sample [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn analysis of the effect of the γ-PGA/CS mass ratio on particle size and zeta potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed that as the γ-PGA/CS mass ratio increased, particle size initially decreased sharply before gradually increasing. Specifically, at a γ-PGA/CS mass ratio of 1:2, the nanoparticles exhibited the smallest size (326.7 nm). This occurred because, at lower γ-PGA concentrations, MMT aggregated with itself and with CS, forming larger particles and increasing particle size. However, as the γ-PGA/CS ratio increased, the formation of MMT/CS/γ-PGA nanoparticles led to a smaller average particle size. Meanwhile, the zeta potential initially decreased, then increased, and subsequently decreased again, suggesting that both insufficient and excessive γ-PGA were detrimental to the stability of the prepared nanoparticles [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn assessment of the relationship between stirring time, particle size, and zeta potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) indicated that the formation of MMT/CS/γ-PGA nanoparticles occurred at an optimal stirring time. Specifically, at a stirring time of 60 min, the nanoparticles exhibited the highest stability, with a particle size of 326.7 nm and a zeta potential of approximately 53.4 mV. Given that the formation of MMT/CS/γ-PGA nanoparticles is a dynamic process, an optimal stirring time promotes the formation of uniform nanoparticles and enhances the dispersion of components. In contrast, prolonged stirring disrupts nanoparticle interactions, leading to structural degradation and reduced stability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAn analysis of the four single-factor experiments revealed that stirring time, MMT and CS mass concentration, and the γ-PGA/CS mass ratio significantly influenced nanoparticle formation. Experimental observations confirmed that nanoparticles formed at a stirring time of 60 min exhibited superior characteristics. Therefore, 60 min was selected as the stirring time for subsequent experiments, and further optimization of MMT/CS/γ-PGA nanoparticle formation conditions was performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Response-surface-methodology-based optimization of synthetic conditions\u003c/h2\u003e \u003cp\u003eBased on the results of the single-factor experiments, a response surface analysis was conducted using the mass concentration of MMT (\u003cem\u003eA\u003c/em\u003e), mass concentration of CS (\u003cem\u003eB\u003c/em\u003e), and mass ratio of γ-PGA/CS (\u003cem\u003eC\u003c/em\u003e) as independent variables. Particle size (\u003cem\u003eW\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e) and zeta potential (\u003cem\u003eW\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e) were selected as dependent variables, with the three coded levels set at \u0026minus;\u0026thinsp;1, 0, and 1. The corresponding factors and levels are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The design and results of the Box\u0026ndash;Behnken experiment are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, while the variance analysis outcomes are listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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\u003eFactors and levels used in response surface tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA (mg/mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB (mg/mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC (g/g)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3:4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResponse surface design and results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA (mg/mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB (mg/mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC (g/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eParticle size (nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eZeta potential (mV)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e856.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.8\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e776.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.6\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e587.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.2\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e362.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53.0\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e626.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55.6\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1126.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e937.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e879.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e53.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e808.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e58.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e514.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e733.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e488.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e506.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e591.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e628.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e565.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3:4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e441.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e48.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of variance for the response surface model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eParticle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eZeta potential\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSum of squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean squares\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.478 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60868.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e187.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA-mass concentration of MMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10841.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10841.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.4167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.4127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-mass concentration of CS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7044.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7044.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.4166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-mass ratio of γ-PGA/CS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.71 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.7 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e156.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e156.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e28.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69647.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69647.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.3276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.42 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.42 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.3304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14024.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14024.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.8622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12769.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12769.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.7819\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46031.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46031.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.4436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53916.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53916.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.3636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePure error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35129.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8782.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of fit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66776.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22259.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.1056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.019 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14558.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrelation total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.497 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e225.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultiple regression fitting was performed using Design Expert 8.0.6 software based on the test data in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, yielding the following quadratic polynomial regression model equations for the relationships between \u003cem\u003eA\u003c/em\u003e, \u003cem\u003eB\u003c/em\u003e, and \u003cem\u003eC\u003c/em\u003e and the response variables \u003cem\u003eW\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e and \u003cem\u003eW\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eW\u003csub\u003e1\u0026nbsp;\u003c/sub\u003e= 499.4 \u0026minus; 36.81\u003cem\u003eA\u003c/em\u003e \u0026minus; 29.67\u003cem\u003eB\u003c/em\u003e \u0026minus; 146.11\u003cem\u003eC\u003c/em\u003e + 56.5\u003cem\u003eAB\u003c/em\u003e + 107.27\u003cem\u003eAC\u003c/em\u003e + 116.1\u003cem\u003eBC\u003c/em\u003e + 128.61\u003cem\u003eA\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e+ 183.54\u003cem\u003eB\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e+ 57.71\u003cem\u003eC\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(1)\u003c/p\u003e\n\u003cp\u003eW\u003csub\u003e2\u003c/sub\u003e = 53.03 + 0.72\u003cem\u003eA\u003c/em\u003e \u0026minus; 0.72\u003cem\u003eB\u003c/em\u003e \u0026minus; 4.42\u003cem\u003eC\u003c/em\u003e \u0026minus; 0.34\u003cem\u003eAB\u003c/em\u003e + 0.95\u003cem\u003eAC\u003c/em\u003e+ 1.14\u003cem\u003eBC\u003c/em\u003e + 1.2\u003cem\u003eA\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e + 1.2\u003cem\u003eB\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e+ 0.21\u003cem\u003eC\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; (2)\u003c/p\u003e \u003cp\u003eAs indicated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all regression models were significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the lack-of-fit terms were not significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating that the model was effective and exhibited good predictive value. The primary term \u003cem\u003eC\u003c/em\u003e exerted a significant effect on the particle size of MMT/CS/γ-PGA nanoparticles (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and an extremely significant effect on zeta potential (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas \u003cem\u003eA\u003c/em\u003e and \u003cem\u003eB\u003c/em\u003e had no significant effects on either particle size or zeta potential (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Among the quadratic terms, \u003cem\u003eB\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e had a significant effect on particle size (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but no significant effect on zeta potential (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, \u003cem\u003eA\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e and \u003cem\u003eC\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e had no significant effects on either particle size or zeta potential (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Among the interaction terms, \u003cem\u003eAB\u003c/em\u003e, \u003cem\u003eAC\u003c/em\u003e, and \u003cem\u003eBC\u003c/em\u003e had no significant effects on zeta potential (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). A comprehensive analysis indicated that some factors had minimal influence on particle size and zeta potential, while \u003cem\u003eB\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e had a notable impact on particle size. Therefore, the experimental conditions were optimized to produce smaller and more stable nanoparticles. Additionally, the relative influence of the independent variables on particle size and zeta potential followed the order: \u003cem\u003eC\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eB\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003eA\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eAccording to the model, as any factor approached the midpoint of the response curve, the particle size decreased from 1,126 nm to 362.6 nm. However, once the factor exceeded the midpoint, the particle size gradually increased due to negative influences (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea\u0026ndash;c), indicating that lower γ-PGA concentrations contribute to the formation of smaller nanoparticles [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This occurs because CS remains in a continuous gel state before nanoparticle formation. With the addition of γ-PGA, CS and γ-PGA gradually cross-link to form nanoparticles. However, at higher γ-PGA concentrations, its branched chains interact laterally with CS, leading to the formation of larger nanoparticles [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe zeta potential of MMT/CS/γ-PGA nanoparticles decreased as the three selected factors approached the midpoint of the response curve, ranging between 48.62 and 61.96 mV (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed\u0026ndash;f). However, when the factors surpassed the midpoint of the response curve, the zeta potential began to increase, following a trend consistent with changes in particle size. When the γ-PGA/CS mass ratio was 1:4 and the CS concentration was 2 mg/mL, the nanoparticles exhibited a high zeta potential. However, as the γ-PGA/CS mass ratio increased, the zeta potential decreased. In contrast, no positive correlation was observed between the CS concentration and zeta potential.\u003c/p\u003e \u003cp\u003eThe selected parameters also influenced the relationship between particle size and zeta potential. For instance, the γ-PGA/CS mass ratio determined particle size while also affecting zeta potential. The CS mass concentration had a significant impact on zeta potential and could also alter particle size. Specifically, increasing the CS concentration led to a corresponding increase in the size of MMT/CS/γ-PGA nanoparticles, accompanied by a decrease in zeta potential. However, an excessively high CS concentration was detrimental to nanoparticle stability and had an adverse effect on zeta potential. Therefore, a smaller particle size and higher zeta potential were chosen as the target optimal conditions. To achieve these (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), the optimal parameters were set as follows: an MMT mass concentration of 2.18 mg/mL, a CS mass concentration of 2 mg/mL, and a γ-PGA/CS mass ratio of 1:4. Under these conditions, the predicted particle size was 679.93 nm, and the zeta potential was 58.47 mV.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.1.3 Response surface model validation\u003c/h2\u003e \u003cp\u003eTo verify the effectiveness of the response surface model, an experimental validation was conducted under the optimized conditions. The resulting nanoparticles had an average particle size of 899.76 nm and a zeta potential of 60.57 mV, which closely matched the predicted values. The discrepancy between the predicted and experimental values was primarily attributed to variations in nanoparticle size, likely caused by the low γ-PGA content. Thus, the response surface regression model effectively predicted the particle size and zeta potential of MMT/CS/γ-PGA nanoparticles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Characterization of nanoparticle properties\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 FTIR analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e presents the FTIR spectra of CS, MMT, γ-PGA, and the synthesized nanoparticles. In the FTIR spectrum of γ-PGA, the characteristic absorption peaks of ‒OH and \u0026minus;\u0026thinsp;NH appear at 3,350 and 2,980 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively. Furthermore, the absorption peak of \u0026minus;\u0026thinsp;C\u0026thinsp;=\u0026thinsp;O in the carboxyl group appears at 1,630 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, whereas that of C‒N in the amide group appears at 1,530 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In the FTIR spectrum of CS, peaks at 910 and 1,145 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e correspond to ‒C‒O‒ vibrations. The strong absorption peak at 1,120 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e is attributed to the stretching vibration of ‒OH groups. Further, peaks at 1,356 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e are associated with the deformation vibration of ‒CH\u003csub\u003e3\u003c/sub\u003e and the bending vibration of ‒CH, while the peak at 2,784 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e corresponds to the stretching vibration of ‒CH. Broad peaks appearing at 3,540 and 3,458 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e are characteristic of hydrogen bonding, attributed to the \u0026minus;\u0026thinsp;OH and \u0026minus;\u0026thinsp;NH groups [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In the spectrum of MMT, stretching vibration absorption peaks appear at 3,645, 1,670, 1,048, 720, 525, and 468 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Meanwhile, in the nanoparticle spectrum, the Si‒O vibration characteristic peak at 1045 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e disappears, confirming that the interlayer Si‒O structure of MMT is altered during the synthesis process. The characteristic carboxyl ion peak of γ-PGA shifts from 1,630 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e to 1,670 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, reflecting changes in the electron density within the carboxyl environment. Peaks appearing at 534 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 670 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e correspond to the vibrations of metal oxides from the MMT mineral composition, suggesting interactions between the inorganic and organic components. Further, the peaks at 1,537 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 1428 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e correspond to the amide II band, directly indicating interactions between the amide group of CS and the carboxyl functional group of γ-PGA, possibly through hydrogen or covalent bonding. The amido group peak at 1,655 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e redshifts slightly to 1,670 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e after polymerization, likely due to ionic bonding between protonated CS and MMT in the form of a cationic ammonium salt (\u0026minus;\u0026thinsp;NH\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCollectively, the above results indicate that during the polymerization reaction, MMT binds to CS and γ-PGA through ionic bonds. After polymerization, the peak corresponding to the primary amine group of CS diminishes or disappears, indicating its interaction with the carboxyl group of γ-PGA. Meanwhile, the \u0026minus;\u0026thinsp;OH and \u0026minus;\u0026thinsp;NH groups, which serve as adsorption sites, remain intact, confirming the successful integration of MMT, CS, and γ-PGA. Additionally, the presence of these adsorption groups suggests that the nanoparticles retain their capacity to capture target molecules.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Scanning electron microscopy (SEM) analysis\u003c/h2\u003e \u003cp\u003eThe microstructure of MMT/CS/γ-PGA nanoparticles was characterized using SEM, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The particles exhibited variations in size and shape and were not perfectly spherical. This irregular morphology resulted from the inherent microstructure of MMT, which influenced the overall particle shape. Larger particles indicated aggregation and accumulation that occurred during sample preparation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Applications\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Heavy-metal adsorption\u003c/h2\u003e \u003cp\u003eThe number of cobalt ions adsorbed onto the MMT/CS/γ-PGA nanoparticle adsorbent increased over time; however, the rate of increase gradually slowed (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Before adsorption, the initial concentration of cobalt ions in the solution (\u003cem\u003eC\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e) was 80 mmol/L. Adsorption equilibrium was reached at 135 min, with a cobalt ion concentration (\u003cem\u003eC\u003c/em\u003e\u003csub\u003et\u003c/sub\u003e) of 59.6 mmol/L and an adsorption capacity (\u003cem\u003eQ\u003c/em\u003e) of 17.4 mmol/g. MMT, CS, and γ-PGA individually exhibited some adsorption capacity for heavy metals. For example, a stable complex is formed through electrostatic interactions between groups containing numerous carboxylic acid (\u0026minus;\u0026thinsp;COOH) groups on a molecular chain of γ-PGA and positively charged heavy metal ions, such as La\u0026sup3;\u003csup\u003e+\u003c/sup\u003e, Ce\u0026sup3;\u003csup\u003e+\u003c/sup\u003e, Pb\u0026sup2;\u003csup\u003e+\u003c/sup\u003e, and Hg\u0026sup2;\u003csup\u003e+\u003c/sup\u003e [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. CS mainly adsorbed heavy metals through coordination and functional groups [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, nanoparticle formation led to active site depletion, impacting their ability to adsorb cobalt ions [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The surface area and pore structure of the nanoparticles also influenced adsorption performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Antibacterial properties\u003c/h2\u003e \u003cp\u003eThe bacterial colonies of \u003cem\u003eEscherichia coli\u003c/em\u003e selected for this experiment appeared round, milky white, with smooth surfaces, uniform coloration, and well-defined edges (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Notably, the size of the inhibition zone reflects the effectiveness of the tested antibacterial agent against indicator bacteria [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. According to our findings, the MMT/CS/γ-PGA nanoparticles exhibited minimal antibacterial activity. The diameter of the antibacterial zone increased from 0 mm to 2 mm between day 1 and day 2, indicating weak antibacterial effects. However, the inhibition zone gradually disappeared between day 2 and day 4. While CS exhibits favorable antibacterial properties and high-molecular-weight γ-PGA also contributes to antibacterial activity, these effects were likely diminished when the materials were combined with MMT, primarily acting as a barrier to substance transport, to form nanoparticles [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This reduction in antibacterial activity was likely due to interactions between key functional groups, namely the \u0026minus;\u0026thinsp;NH\u003csub\u003e2\u003c/sub\u003e group of CS and ‒COOH group of γ-PGA, as well as the electronegativity of MMT. These interactions likely weakened or deactivated these functional groups, reducing the antibacterial effectiveness of MMT/CS/γ-PGA nanoparticles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Conclusion","content":"\u003cp\u003eIn this study, MMT/CS/γ-PGA nanoparticles were synthesized using MMT, CS, and γ-PGA as the primary raw materials. Single-factor experiments demonstrated that stirring time, MMT concentration, CS concentration, and the γ-PGA/CS mass ratio significantly influenced nanoparticle formation. Through response surface optimization, small particle size and high zeta potential were identified as the target optimal conditions. The smallest particle size was achieved when the MMT concentration was 2.18 mg/mL, CS concentration was 2 mg/mL, and γ-PGA/CS mass ratio was 1:4. Under these conditions, the predicted particle size was 679.92 nm, with a zeta potential of 58.47 mV. Experimentally synthesized nanoparticles had an average particle size of 899.76 nm and a zeta potential of 60.57 mV, which closely aligned with the predicted values. Structural analysis revealed that the three components formed nanoparticles of varying shapes, with MMT binding to CS and γ-PGA through ionic bonds. The nanoparticles demonstrated heavy-metal adsorption capability, as evidenced by the adsorption of Co\u003csup\u003e2+\u003c/sup\u003e ions. However, antibacterial testing revealed that the nanoparticles had weak antibacterial activity overall.\u003c/p\u003e"},{"header":"4 Methods","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Synthesis and characterization of MMT/CS/γ-PGA nanoparticles\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Preparation\u003c/h2\u003e \u003cp\u003eA measured amount of CS (average molecular weight: 50 kDa, degree of deacetylation: 95%, Nanjing Oddfoni Biological Technology Co., Ltd., Nanjing, China) was dissolved in a 5% acetic acid solution and stirred at room temperature for 2 h until the solution became clear and transparent. Next, 20 mL of the prepared CS solution was transferred into a beaker, and a specified mass of nano-MMT (Zhejiang Hongyu New Materials Co., Ltd., Huzhou, China) was added. The mixture was stirred at 300 rpm for 1 h until it became clear and transparent. Subsequently, 20 mL of a γ-PGA aqueous solution (average number-average molar mass: 2000 kDa, Nanjing Shineking Biotech Co., Ltd., Nanjing, China) at a predetermined concentration was slowly added. Once γ-PGA addition was complete, the mixture was magnetically stirred at 300 rpm for 10-min, yielding MMT/CS/γ-PGA nanoparticles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Single-factor experiments\u003c/h2\u003e \u003cp\u003eTo evaluate the effect of individual factors on MMT/CS/γ-PGA nanoparticles, particle size and zeta potential were used as evaluation indices. The single-factor experiments were designed by varying the CS mass concentration (3, 2, 1, 0.5, and 0.1 mg/mL), MMT mass concentration (4, 3, 2, 1, and 0.5 mg/mL), γ-PGA/CS mass ratio (1:1, 3:4, 1:2, 1:4, and 1:10), and stirring time (90, 60, 30, and 0 min) while maintaining all other conditions constant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Response surface methodology experiment\u003c/h2\u003e \u003cp\u003eThe results of the single-factor experiments were analyzed using SPSS V29 software, identifying three factors\u0026mdash;CS concentration, MMT concentration, and γ-PGA/CS mass ratio\u0026mdash;exerting a significant impact on the particle size and zeta potential of MMT/CS/γ-PGA nanoparticles. The selected factors were encoded at three levels (\u0026minus;\u0026thinsp;1, 0, and 1), and regression analysis was conducted on the experimental data using Design Expert 8.0.6. Factors influencing nanoparticle size and zeta potential were optimized and analyzed. By examining the response surface diagrams, the response values affected by multiple variables were modeled and predicted. The software-generated quadratic polynomial equation was as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{W}_{i}={\\beta\\:}_{0}+\\sum\\:_{i=1}^{k}{\\beta\\:}_{i}{X}_{i}+\\sum\\:_{i=1}^{k}{\\beta\\:}_{ii}{X}_{ii}^{2}+\\sum\\:_{i}^{k-1}\\sum\\:_{j}^{k}{\\beta\\:}_{ij}{X}_{i}{X}_{j}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eW\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e denotes the response value (zeta potential or particle size), β\u003csub\u003e0\u003c/sub\u003e is a constant, β\u003csub\u003ei\u003c/sub\u003e denotes the linear coefficient, β\u003csub\u003eii\u003c/sub\u003e represents the quadratic coefficient, β\u003csub\u003eij\u003c/sub\u003e signifies the interaction coefficient between two factors, and \u003cem\u003eX\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e and \u003cem\u003eX\u003c/em\u003e\u003csub\u003ej\u003c/sub\u003e represent variables (MMT mass concentration, CS concentration, and γ-PGA/CS mass ratio).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.1.4 Measurement of particle size and zeta potential\u003c/h2\u003e \u003cp\u003eThe particle size and zeta potential of the composite nanoparticles were measured using a laser particle size analyzer (Malvern Zetasizer Lab, Malvern Panalytical Ltd., Shanghai, China). During this analysis, the composite nanoparticle solution was carefully injected into a clean collapsible capillary sample tank (DTS1070) to prevent bubble formation. The measurement conditions were set as follows: a temperature of 25\u0026deg;C, an equilibrium time of 60 s, and three repeated tests per sample. The particle size and zeta potential values were recorded after the tests were completed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.1.5 FTIR analysis\u003c/h2\u003e \u003cp\u003eThe FTIR spectra of the raw materials and the freeze-dried MMT/CS/γ-PGA nanoparticles were obtained based on the KBr pellet method using an FTIR spectrometer (Nicolet iS50, Waltham, USA). The measurement conditions were as follows: a temperature of 25\u0026deg;C, a resolution of 4 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 32 scans across the 500\u0026ndash;4000 cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e wavenumber range. The acquired infrared spectra were imported into Origin software for graphical analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.1.6 SEM analysis\u003c/h2\u003e \u003cp\u003eTo examine the morphology of the composite nanoparticles, an SE microscope (Hitachi S-570, Tokyo, Japan) operating at 10 kV was used to observe the sample surfaces. Prior to imaging, the sample surfaces were coated with a thin layer of gold. Observations were focused on the central regions of the coated surfaces.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Applications\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Heavy-metal ion absorption\u003c/h2\u003e \u003cp\u003eA 20 mL solution of cobalt nitrate (0.16 mol/L) was mixed with 20 mL of the MMT/CS/γ-PGA nanoparticle adsorbent solution (predetermined concentration), and the adsorption experiment was conducted at room temperature under magnetic stirring at 200 r/min for a predetermined duration. The supernatant was then filtered using a 0.22-\u0026micro;m filter membrane [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The absorbance levels of Co\u003csup\u003e2+\u003c/sup\u003e solutions with concentrations of 0.04, 0.06, 0.08, 0.1, 0.12, 0.14, and 0.16 mol/L were measured at a wavelength of 512 nm (λ\u003csub\u003emax\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;512 nm). A linear relationship was established by fitting the experimental data. A 5.00 mL sample was used for analysis, following the procedure outlined in the standard curve experiment. The adsorption capacity was calculated using Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e4\u003c/span\u003e):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\text{C}=\\frac{\\text{A}-{\\text{A}}_{0}-\\text{a}}{\\text{b}\\bullet\\:\\text{V}},$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eC\u003c/em\u003e denotes the concentration of Co\u003csup\u003e2+\u003c/sup\u003e in the sample (\u0026micro;g/mL), \u003cem\u003eA\u003c/em\u003e indicates the absorbance of the sample, \u003cem\u003eA\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e represents the absorbance of the blank sample, \u003cem\u003ea\u003c/em\u003e corresponds to the intercept of the regression curve, \u003cem\u003eb\u003c/em\u003e signifies the slope of regression curve, and \u003cem\u003eV\u003c/em\u003e denotes sample volume (mL).\u003c/p\u003e \u003cp\u003eThe supernatant was collected, and the Co\u003csup\u003e2+\u003c/sup\u003e content in the solution after nanoparticle adsorption was determined using an ultraviolet spectrophotometer (UV L6S, INESA Analytical Instrument Co., Ltd., Shanghai, China). The adsorption performance of the nanoparticles for Co\u003csup\u003e2+\u003c/sup\u003e was evaluated based on the adsorption capacity \u003cem\u003eQ\u003c/em\u003e, defined as in Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:\\text{Q}=({\\text{C}}_{0}-{\\text{C}}_{\\text{t}})\\bullet\\:\\frac{\\text{V}}{\\text{m}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eQ\u003c/em\u003e denotes the Co\u003csup\u003e2+\u003c/sup\u003e adsorption capacity of the nanoparticles (mmol/g), \u003cem\u003eC\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e represents the initial concentration of Co\u003csup\u003e2+\u003c/sup\u003e in the solution (mol/L), \u003cem\u003eC\u003c/em\u003e\u003csub\u003et\u003c/sub\u003e indicates the concentration of Co\u003csup\u003e2+\u003c/sup\u003e at adsorption equilibrium (mol/L), \u003cem\u003eV\u003c/em\u003e represents the volume of the Co\u003csup\u003e2+\u003c/sup\u003e solution (mL), and \u003cem\u003em\u003c/em\u003e denotes the mass of the adsorbent (g).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Antibacterial experiment\u003c/h2\u003e \u003cp\u003eAntibacterial tests quantify the antibacterial or bactericidal activity of a sample \u003cem\u003ein vitro\u003c/em\u003e. When bacterial growth is inhibited, a clear antibacterial zone forms. By measuring the size of this zone, the degree of bacterial inhibition can be assessed. In general, a larger antibacterial zone diameter indicates a stronger antibacterial effect against a given bacterial strain [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. \u003cem\u003eE. coli\u003c/em\u003e (obtained from the microbiology laboratory of our university) is a bacterium commonly found in the intestines of humans and various animals, exhibiting significant diversity and abundance. In this study, \u003cem\u003eE. coli\u003c/em\u003e was selected as the intestinal indicator bacterium, and its inhibition by MMT/CS/γ-PGA nanoparticles was evaluated by measuring the size of the inhibition zone.\u003c/p\u003e \u003cp\u003ePreparation of the medium: A total of 10 g of nutrient agar medium was added to a beaker, followed by 1,000 mL of distilled water. After complete dissolution, the medium was transferred to an Erlenmeyer flask and sealed with a cotton plug. It was then sterilized in an autoclave at 0.1 MPa and 121\u0026deg;C for 30 min, poured into plates, and cooled to approximately 37\u0026deg;C before use.\u003c/p\u003e \u003cp\u003eCulture process: After reviving, frozen \u003cem\u003eE. coli\u003c/em\u003e was inoculated onto a plate medium using the streak inoculation method in an ultraclean workbench. After 24 h of incubation in a constant-temperature incubator at 37\u0026deg;C, a well-grown single bacterial colony was selected using an inoculation loop to prepare an \u003cem\u003eE. coli\u003c/em\u003e suspension at a concentration of 10\u003csup\u003e6\u003c/sup\u003e cfu/mL. A total of 0.1 mL of the suspension was then transferred onto the plate culture medium and evenly spread using a sterile applicator. The medium was left undisturbed for 20 min to allow full bacterial penetration. A sterilized Oxford cup was then placed onto the culture dish using tweezers.\u003c/p\u003e \u003cp\u003eDetermination of inhibition zone: MMT/CS/γ-PGA nanoparticles (200 \u0026micro;L) were added to three Oxford cups containing the bacterial cultures. After the nanoparticles were fully mixed with the medium, the Oxford cups were removed, and the cultures were sealed with a sealing film and incubated at 37\u0026deg;C for 4 days in a constant-temperature incubator. The inhibition zone size was observed, photographed, and measured.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Data analysis\u003c/h2\u003e \u003cp\u003eGraphPad Prism 10.1 was used for mapping analysis, while Design Expert 8.0.6 and SPSS V29 were used for data analysis. A t-test was performed for statistical evaluation. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicated no significant difference, whereas \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 denoted a significant difference. A \u003cem\u003eP\u003c/em\u003e value below 0.01 indicated a highly significant difference.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude for the financial support provided by the Horizontal Subject of Zhejiang Shuren University (2024KJ162).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Z.Y., C.H., and Y.S.; methodology, C.C., J.W., Z.Y., and Y.W.; investigation, C.F. and D.L.; writing\u0026mdash;original draft preparation, Z.Y. and Y.H.; writing\u0026mdash;review and editing, Z.Y., Y.S., Y.W., J.W., and S.S; supervision, Z.Y.; funding acquisition, Z.Y. and C.H. All authors have read and accepted the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNassery, S. A., Agayeva, Z. R., Abdullayeva, L. A. \u0026amp; Behbudova, S. K. The use of clay minerals modified by nanoclay in the refining of oils. \u003cem\u003eProcess. Petrochem. 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Study on the Inhibition Zone Method in Antimicrobial Test. \u003cem\u003eFood Industries\u003c/em\u003e. \u003cb\u003e37\u003c/b\u003e, 122\u0026ndash;125 (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/CNKI:SUN:SPGY\u003c/span\u003e\u003cspan address=\"https://doi.org/CNKI:SUN:SPGY\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e 0.2016-11-035.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Montmorillonite, composite nanoparticle, preparation, application","lastPublishedDoi":"10.21203/rs.3.rs-6082865/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6082865/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, montmorillonite (MMT)/chitosan (CS)/γ-polyglutamic acid (γ-PGA) nanoparticles were synthesized using MMT, CS, and γ-PGA as the matrix materials, with amino silane as the crosslinking agent. Through single-factor and response surface methodology experiments, the optimal nanoparticle formulation was determined by measuring the particle size and zeta potential of the composite nanoparticles. The synthesized nanoparticles were characterized using Fourier transform infrared spectroscopy and scanning electron microscopy, and their heavy-metal (Co\u003csup\u003e2+\u003c/sup\u003e) adsorption capacity and antibacterial (\u003cem\u003eEscherichia coli\u003c/em\u003e) performance were evaluated. The results indicated that the optimal MMT/CS/γ-PGA nanoparticle formulation was obtained when the MMT concentration was 2.18 mg/mL, CS concentration was 2 mg/mL, and γ-PGA/CS mass ratio was 1:4 in a 100 mL aqueous solution. Under these conditions, the average particle size was 899.76 nm, and the zeta potential was 60.57 mV. During Co\u003csup\u003e2+\u003c/sup\u003e adsorption tests, adsorption equilibrium was achieved at 135 min, with the nanoparticles demonstrating an adsorption capacity of 17.4 mmol/g at an initial cobalt concentration of 80 mmol/L. However, antibacterial activity tests revealed very weak antibacterial effects of the nanoparticles against \u003cem\u003eE. coli\u003c/em\u003e. Overall, this study provides theoretical support for the preparation and application of eco-friendly nanoparticles with potential use in heavy-metal adsorption.\u003c/p\u003e","manuscriptTitle":"Preparation of montmorillonite/chitosan/γ-polyglutamic acid nanoparticles and evaluation of their adsorption and antibacterial performance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 07:55:54","doi":"10.21203/rs.3.rs-6082865/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-06-04T05:55:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-04T07:38:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155978280581452557465127537365810803626","date":"2025-04-17T10:46:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-15T02:55:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-09T05:24:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-30T01:36:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"157d6873-8c2d-46c1-bc39-66fb0ca3b8e8","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47160990,"name":"Biological sciences/Biological techniques/Nanobiotechnology/Nanoparticles"},{"id":47160991,"name":"Physical sciences/Materials science/Nanoscale materials/Nanoparticles"}],"tags":[],"updatedAt":"2025-11-17T16:04:08+00:00","versionOfRecord":{"articleIdentity":"rs-6082865","link":"https://doi.org/10.1038/s41598-025-05752-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-10 15:58:43","publishedOnDateReadable":"November 10th, 2025"},"versionCreatedAt":"2025-04-16 07:55:54","video":"","vorDoi":"10.1038/s41598-025-05752-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-05752-0","workflowStages":[]},"version":"v1","identity":"rs-6082865","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6082865","identity":"rs-6082865","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}