Optimization and Mechanism of Ca²⁺ Biosorption by Virgibacillus pantothenticus Isolated from Gelatine Wastewater

After the strain was cultured on beef paste peptone medium plates at 37°C for 48 h, the N3-4 colonies were round with smooth surfaces, neat edges, greyish-white, opaque, and metallic luster. Light microscopy showed that N3-4 was Gram-positive and flagellated.

As shown in Table I, strain N3-4 can utilize gelatine, glucose, fructose, xylose, and rhamnose, but not starch or raffinose.

Traditional methods only identify microorganisms at the morphological level and, therefore, have certain limitations and uncertainties. With molecular biology’s rapid advancement and PCR technology’s maturation, many researchers have applied molecular identification (Sedlakova-Kadukova et al. 2019). Comparison of the 16S rRNA sequence of this strain with the NCBI database and using a phylogenetic tree showed that N3-4 had the highest homology and the closest affinity with Virgibacillus pantothenticus (Fig. 1). N3-4 was therefore identified as V. pantothenticus, a soil bacterium formerly known as Bacillus pantothenticus. This is a Gram-positive, spore-forming, aerobic, mesophilic, and halotolerant bacterium (Kuhlmann et al. 2011).

Fig 1.

Phylogenetic tree of strain N3-4 based on 16S rRNA sequence.

The effect of contact time on adsorption efficacy was examined over a time range of 20–120 min. Contact time is a pivotal factor during mass transfer processes, such as biosorption (Rouibah et al. 2023). As shown in Fig. 2a, the Ca²⁺ adsorption capacity of N3-4 increased with time until 60 min; beyond this, the capacity plateaued, indicating the limited impact of extending contact time. This plateau results from the saturation of available surface sites as Ca²⁺ penetrates further into the biosorbent and reduces biosorbent activity due to solute molecule repulsion from the bulk phase (Nasrullah et al. 2015). Hence, in subsequent studies, 60 min was preliminarily used as the optimal contact time for Ca²⁺ removal.

Fig. 2.

Effect of different parameters on biosorption capacity a) contact time; b) initial calcium concentration; c) biomass dosage; d) temperature, e) pH.

Different lowercase letters indicate significant differences (p < 0.05).

Initial concentration is pivotal for addressing the metal mass-transfer resistance between aquatic and solid phases (Long et al. 2019). As illustrated in Fig. 2b, biosorption capacity increased with initial concentration, peaking at 148.20 μg/g with a calcium content of up to 100 mg/l. Elevated initial concentrations drive metal ions to engage with binding sites, enhancing their adsorption (Kazy et al. 2009). However, the adsorption capacity of the biosorbent approaches saturation with increasing initial calcium concentration, given the finite availability of binding sites (Xu et al. 2015).

Assessing the dose effect is crucial for gauging biosorption capabilities (Tangestani et al. 2021). The correlation between biomass dosage and Ca²⁺ adsorption is shown in Fig. 2c. Adsorption capacity increased with increasing biomass dosage up to 1.5 g/l, peaking at 195.33 μg/g. Beyond 1.5 g/l, adsorption efficacy decreased. The reduction in adsorption capacity with higher biosorbent dosage is primarily due to increased unsaturated adsorption sites during the biosorption process. Another plausible factor is particle interaction and aggregation, which can reduce the total active surface area of the biosorbent (Long et al. 2019).

As shown in Fig. 2d, as the temperature increased from 20 to 30°C, the biosorption capacity of N3-4 continued to increase from 149 to 173 μg/g. However, a precipitous drop was observed at 35°C, followed by a subtle increment post-35°C. Elevated temperatures induce alterations in the microbial cell surface architecture, functional group distribution, and membrane fluidity, leading to diminished adsorptive capacity (Yang et al. 2017).

pH significantly impacts biosorption efficacy, given the presence of key binding sites on the adsorbent surface and metal ion hydrolysis (Afraz et al. 2020). pH alterations affect the surface charge of cells, the protonation or deprotonation states of functional groups, and membrane permeability (Yuan et al. 2019). Ca²⁺ adsorption by the bacteria was evaluated across a pH range of 4–9. As illustrated in Fig. 2e, pH significantly affected calcium ion uptake. Adsorption capacity was positively correlated with pH values < 6, with an apex of 186.76 μg/g observed at pH 6. Adsorption efficacy declined sharply at pH values > 6 and moderately increased beyond 7.

RSM is a compilation of statistical and mathematical techniques built on fitting polynomial equations to experimental data (Luong et al. 2023). When multiple factors are involved, their interactions cannot be measured with single-factor experiments, even if they are dominant and many experiments are needed. However, using a reduced number of runs, RSM can evaluate independent variables and their interactions with dependent variables (Bhateria and Dhaka 2019). Multiple factors influence biosorption efficiency; therefore, RSM was employed.

In previous experiments, the initial concentration of calcium ions, biomass dosage, and contact time of bacteria significantly affected calcium ion biosorption. These adsorption experiments were carried out at room temperature and in alkaline wastewater. The one-way experiments clearly show that a temperature of 25–30°C has little effect on adsorption, and the maximum metal ion adsorbed was at pH 6. Therefore, the adsorption of metal ions with this strain was carried out at pH 6 and 28°C using the Box-Behnken model. The initial calcium concentration, contact time, and biomass dosage were selected for a three-factor, three-level response surface test using biosorption capacity as an indicator. Seventeen experiments were performed for three independent process parameters, and their interactions were studied. The experimental designs, as well as observed and predicted values are presented in Table II.

The interactions between the variables and response were evaluated using a second-order polynomial equation. The observed relationship between the biosorption capacity and input test variables in coded terms can be expressed as follows:

Biosorption capacity 3(μg/g)=515.13+70.16A+58.45B−47.52C−6.16AB+4.35AC+5.62BC−38.43A2−40.25B2−51.55C2({\rm{\mu g}}/{\rm{g}}) = 515.13 + 70.16{\rm{A}} + 58.45{\rm{B}} - 47.52{\rm{C}} - 6.16AB + 4.35AC + 5.62BC - 38.43{{\rm{A}}^2} - 40.25{{\rm{B}}^2} - 51.55{{\rm{C}}^2} where A, B, and C are independent singular factors, AB, AC, and BC are interaction factors, and A², B², and C² are the quadratic terms. The theoretical optimum value calculated from Eq. 3 was 572.43 μg/g and the actual optimum adsorption rate under optimum conditions was 561.95 μg/g.

A good model for calcium biosorption was established via ANOVA using V. pantothenticus biomass as an adsorbent. Statistical testing of the regression equations was performed using F-tests, and ANOVA for the fitted quadratic polynomial model of removal efficiency is shown in Table III. ANOVA was performed using Fisher’s F-test. p-Values < 0.05 indicated that the model terms were significant. In this case, A, B, C, A², B², and C² were significant model terms. Values > 0.1 indicated that the model terms were not significant. An F-value of 236.49 implies the model was significant (p < 0.01). There was only a 0.01% chance that this large F-value could have been caused by noise. R² was used to determine the quality of the proposed model (Ni’mah et al. 2022). When the correlation coefficient approached 1, the prediction precision of the model improved. The R² and adjusted R² were close to 1 (0.9968 and 0.9928, respectively), indicating a strong correlation between the observed and predicted values (Garg et al. 2014). The R² and adjusted R² values indicate that the structure of the model reflects the variation in the calcium biosorption capacity relative to changes in the initial calcium concentration, biomass dosage, and contact time.

A lack-of-fit was applied to measure the adequacy of the model. A lack-of-fit test will not be significant if the model matches the data well (Shabanizadeh and Taghavijeloudar 2023). An F-value of 0.14 implies that the lack-of-fit was not significant relative to pure error; there was a 93.40% chance that the lack-of-fit F-value occurred due to noise. The accuracy of the model (Adeq) measures the model noise (ratio of information to error) (Ding et al. 2023). A ratio > 4 is desirable. The adequate precision ratio was 46.205, which indicated an adequate signal. This model can be used to navigate design space.

Under pH 6 and a temperature of 28°C, the optimal calcium adsorption parameters for strain N3-4 were determined to be a contact time of 72.70 minutes, an initial Ca2+ concentration of 141.98 mg/l, and a biomass dosage of 1.30 g/l. The theoretical maximum calcium adsorption was found to be 572.43 μg/g. Five parallel experiments were conducted under the same conditions to validate these findings, resulting in an error margin of only 1.83% compared to the theoretical value.

3D surface plots are graphical diagrams of regression equations showing two factors; all other factors were maintained at fixed levels (Hadiani et al. 2018). The impacts of the interactions between factors were examined using 3D surface plots (Kamani et al. 2018), which represented both the main and interaction effects of the variables well.

The response value of the response surface plot is composed of two interactions of each test factor, A, B, and C, as well as the interaction between the optimal parameters and each other parameter; the more complex and steep the surface, the more significant the influence of each factor on the response value. Each point on the same contour represents the same value, and the contour shape reflects the significance of the interaction between the factors. In the case of an elliptical contour, the corresponding contour line will also exhibit an elliptical shape, indicating that the interactions among the factors are significantly different. Conversely, if the contour is circular, this suggests that the differences in interactions among the factors are not significant.

The combined effects of the adsorbent and initial calcium ion concentrations are illustrated in Fig. 3a and b, with the median biomass dosage. The lowest observed biosorption capacity was 300.87 μg/g with an initial calcium concentration of 50 mg/l and a contact time of 40 min. This capacity reached a high of 559.73 μg/g at an initial calcium concentration of 150 mg/l and a contact time of 80 min. Adsorption capacity increased with initial calcium ion concentration at a fixed contact time. Conversely, at a constant initial calcium ion concentration, adsorption capacity increased with increasing contact time. Consequently, both initial calcium ion concentration and contact duration enhanced Ca²⁺ biosorption efficacy (Ding et al. 2023). Ca²⁺ ion adsorption capacity consistently increased with increased initial calcium concentration and contact time until equilibrium was reached.

Fig. 3.

The binary interactions of factors of calcium biosorption capacity by response surface and contour a) and b) contact time and initial calcium ion concentration interact to affect the contour of bacterial adsorption capacity; c) and d) Biomass dosage and initial calcium ion concentration affects contours of bacterial adsorption capacity; e) and f) Biomass dosage and contact time affects contours of bacterial adsorption capacity.

This echoes Samuel et al. (2015), who presented analogous 3D response plots based on the interplay between initial concentration and contact duration. Absorption spiked within the first 40 min and reached equilibrium at approximately 60 min, with a noticeable increase in biosorption capacity with elongated contact periods.

A holistic view of the impact of the adsorbent and initial calcium ion concentration can be drawn from Fig. 3c and 3d, while median contact time was maintained. Ca²⁺ extraction rate increased with increasing initial calcium ion concentrations. Peak Ca²⁺ removal (540.47 μg/g) was observed at a biomass dosage of 2.5 g/l and an initial calcium ion concentration of approximately 100 mg/l. For a specific biomass dosage, adsorption capacity spiked dramatically with increasing initial calcium ion concentrations, which is a key determinant of Ca²⁺ removal. The biosorption capacity increases at low initial biomass dosages as the initial Ca²⁺ concentration rises. The specific sites crucial for low-concentration biosorption become saturated when Ca²⁺ concentrations are elevated. Consequently, at high Ca²⁺ concentrations, further ion uptake did not occur with increased calcium ion concentrations because of saturation (Bhateria and Dhaka 2019).

Fig. 3e and 3f illustrates the interplay between biomass dosage and contact time concerning calcium biosorption capacity while maintaining median initial calcium concentration. The lowest recorded biosorption capacity was 314.55 μg/g with a biomass dosage of 2 g/l and a contact time of 40 min. This capacity surged to 520.87 μg/g when the biomass dosage was reduced to 1 g/l and contact time extended to 80 min. The gradient and curvature of the contact time surface are more pronounced than those associated with biomass dosage, indicating that the duration of the contact is the primary factor influencing adsorption capacity (Jiang et al. 2022). Each adsorbent has a finite number of adsorption sites (Soleimani et al. 2023). In addition, particle aggregation and the unsaturation of binding sites lead to a decrease in the overall accessible surface area of the adsorbent (Shukla and Pai 2005). This diminishes uptake capacity at increased biosorbent concentrations.

Using SEM to characterize biosorbents efficiently provides data on their morphology and elemental composition (Gupta et al. 2019). SEM-EDS provides valuable information regarding a sample’s physical structure and elemental makeup.. This technique enables the detection of trace elements within micron-scale specimens with a precision that can reach submicron levels (Singh et al. 2023). SEM was therefore employed to examine surface alterations during adsorption.

Bacteria can adsorb Ca²⁺ ions through nucleation on the cell wall and within extracellular polymers, facilitating Ca²⁺ clearance (Li et al. 2022). Fig. 4 shows SEM-EDS analysis of the interaction mechanisms of Ca²⁺ elimination by V. pantothenticus. Electron micrographs of untreated bacterial cells are shown in Fig. 4a, revealing a smooth terrain (Das et al. 2014), which indicates a consistent distribution of elements across bacterial cell surfaces. This indicates a uniform distribution of components on the bacterial cell surface. Ca²⁺-treated bacteria formed clusters and large particles with dense structures on the surface. This suggests that the aggregation of metal ions was facilitated by their attachment to the hydrophilic groups of peptides, leading to the formation of densely packed particles (Qi et al. 2023). As shown in Fig. 4b, the surface of Ca²⁺-treated bacterial cells was rough, with bulges and sediments, which may have been caused by extracellular polymers that contribute to heavy metal binding (Yang et al. 2017). Extracellular polymeric substances reacting with calcium form sediments. The appearance of irregular bulges in the cells may have been caused by the exchange of mechanical forces and metal ions with active functional groups on the cell surface (Kazy et al. 2009; Sharma et al. 2022).

Fig. 4.

SEM images and EDS spectra of Virgibacillus pantothenticus biomass: a) non-adsorbed biomass, b) Ca(II)-adsorbed biomass, c) EDS spectrum of non-adsorbed biomass and d) EDS spectrum of Ca(II)-adsorbed biomass.

Distinct calcium peaks were discernible in the calcium-enriched biomass but absent in its untreated counterpart, confirming the presence of calcium on the surface, as shown in Fig. 4d. Notably, pronounced carbon and oxygen peaks were observed in the EDS spectra of both Ca²⁺-adsorbed and -unadsorbed biomass, suggesting their inherent presence in the biomass and their potential involvement in metal biosorption ion exchange dynamics (Dixit et al. 2015).

FTIR spectroscopy is a powerful technique that determines the vibrational properties of molecular compounds. By probing molecular vibrations, FTIR determines the presence of- and changes in functional groups and improves our understanding of surface interactions, especially during processes such as biosorption (Gupta et al. 2019; Mekpan et al. 2025). Shifts in FTIR absorption peaks are a reliable indicator of chemical structure modifications and indicate the interactions between metal ions and organic ligands at the molecular level (Qi et al. 2023). Each molecule exhibits a specific vibrational signature due to its unique bond vibrations. Distinctive FTIR spectra emerge for these unique molecular vibrations (Deng et al. 2022).

The FTIR spectra of free and calcium-loaded cells were analyzed to identify the functional groups responsible for Ca²⁺ binding. As shown in Fig. 5, the cell surface exhibited multiple characteristic peaks before adsorption, suggesting the intricate nature of the strain (Li et al. 2018). In the calcium-loaded bacterial spectrum, the broad and intense band at 3,200–3,600 cm⁻¹ represents the -OH (Nagarajan et al. 2023) and/–NH₂ symmetric stretching vibrations from hydroxyl or amino groups (Long et al. 2019). Meanwhile, the peaks at 2,923 cm⁻¹ and 1,639 cm⁻¹ signify aliphatic stretching and C≡C stretching in aromatic rings (Wang et al. 2014); these shifted approximately 10 cm⁻¹ and 5 cm⁻¹, respectively, compared to the control biomass. The peak at 1,537 cm⁻¹ was ascribed to the amide (II) stretching of aromatic rings (Isik et al. 2021) is crucial for bacterial identification using FTIR spectroscopy.

Fig. 5.

FTIR spectra of Virgibacillus pantothenticus cells before and after calcium biosorption.

Fig. 6.

Schematic diagram of the adsorption mechanism of Virgibacillus pantothenticus.

The FTIR spectrum of calcium-loaded V. pantothenticus featured a characteristic amide bond at 1,537 cm⁻¹. The 1,434 cm⁻¹ peak can be linked to the C–O bond of the carboxyl group, accompanied by S = O stretching (Nagy et al. 2014). Another significant adsorption peak at 1,427 cm⁻¹ corresponds to C = O stretching vibrations of amide I and –CH₂ bending (Banerjee et al. 2019), which shifted approximately 5 cm⁻¹ from the control biomass. The unique peaks for Ca-loaded V. pantothenticus at 1,228 cm⁻¹ and 1,070 cm⁻¹ suggest that the stretching of C = O and -C-N in amino groups plays a role in Ca²⁺ binding (Chakravarty and Banerjee 2012). These peaks also shifted approximately 10 cm⁻¹ and 15 cm⁻¹, respectively, compared with the control cells. Lastly, the –C–O– or –C–N group-associated biosorption peak transitioned from 1,051 cm⁻¹ to 1,074 cm⁻¹, denoting the potential interaction between these groups and metal ions (Choińska-Pulit et al. 2018).

Bacteria are formidable natural scavengers that eff convert heavy metals into less harmful forms or stabilize them in solid structures (Chaudhary et al. 2021; Sharma and Shukla 2021; Sharma et al. 2022). Microbial communities found near industrial dumps and waste sites demonstrate a natural resilience to heavy metals. The existing literature indicates that these microorganisms have undergone evolutionary adaptations that enable them to effectively absorb heavy metals (Mejias Carpio et al. 2018). Klebsiella sp. AW2 was isolated from the rhizosphere of Solanum nigrum and exhibited tolerance to 240 mg/l of Cd²⁺ (Chi et al. 2024). The study focused on removing chromium (Cr) from water using biosorbents from live and dead Bacillus nitratireducens cells, isolated from textile wastewater, which tolerated Cr concentrations up to 1,000 mg/l MIC (Imron et al. 2024). The study enriched rare earth elements from aqueous solutions using Bacillus spp. strain DW015 and the urease-producing strain Sporosarcina pasteurii are isolated from ion-adsorbed rare earth ores (Bian et al. 2024).

The cell wall is the initial cellular structure that encounters metal ions. Metal biosorption by non-living biomaterials occurs through the complexation between metal ions and various functional groups, including carboxyl, phosphate, and hydroxyl groups found on the cell surface (Huang et al. 2020). Biosorption of heavy metals is a multifaceted process that encompasses various mechanisms (Wang et al. 2019; Liu et al. 2023b). The adsorption of metal ions onto cell surfaces, facilitated by stoichiometric interactions with metal functional groups, is called passive biosorption. This process may involve one or several mechanisms, including ion exchange, complexation, coordination, adsorption, electrostatic interactions, chelation, and microprecipitation (Kumar et al. 2022; Liu et al. 2023a). The FT-IR pattern reveals changes in peak broadening and contraction during the binding process with calcium, indicating interactions between functional groups and metal particles. The main functional groups that bind calcium ions include –OH, –NH₂, C = O, –C–O–, and –C–N. The adsorption of calcium ions from gelatin wastewater by the strains discussed in the article occurs through interactions with surface functional groups.