Worldwide, the harmful ion contamination of water has become a serious problem because of unregulated industry, energy production, and mining, which greatly increase the concentration of pollutants in water. The novel membranes through adsorbent self-assembly, such as protein amyloids, were explored for wastewater treatment. Herein, we report amyloid fibril (AF)-embedded bacterial cellulose nanohybrid membrane for efficient removal of heavy metal from industrial effluent. AFs are synthesized by heat treatment using bovine serum albumin and embedded with bacterial cellulose nanomembrane (BCN). The AF-embedded BCN (AF/BCN) was characterized using microscopy and spectroscopic methods. In addition, the well-ordered multi-layered AF/BCN filtration assembly was fabricated in the commercial cartridge and validated for the removal of heavy metals (Pb2+ and Hg2+) from wastewater and treatment of industrial wastewater sample containing heavy metals. Our multi-layered filtration assembly removed Hg2+ and Pb2+ with efficiency of 95 and 78.34%, respectively. A computational study using molecular docking has also been performed for the identification of metal entrapment sites. Moreover, our AF/BCN filtration assembly showed high regeneration capacity up to four cycles. The isotherm model also revealed a strong fit and good adsorption behaviour. This makes potential filtration assembly for low-cost, high-efficiency for the removal of heavy metal from wastewater.

  • Computational approaches methods for the study of bovine serum albumin.

  • Synthesis of amyloid fibril, amyloid-embedded bacterial cellulose nanomembrane (BCN), and fabrication of amyloid-embedded BCN filtration assembly.

  • Heavy metal removal analysis from industrial wastewater.

Millions of people worldwide are impacted by water pollution, which is also one of the main causes of many diseases and deaths. Various anthropogenic activities create many pollutants and deteriorate the water quality (Du Plessis 2019). Rapid industrialization and urbanization are the major issues in this regard. The industries regularly discharge organic waste, micropollutants, and many heavy metals into the water bodies (Haque 2022). Among them, the presence of heavy metals in water is one of the major concerns due to their negative impact on human health (Engwa et al. 2019). In addition, many industries are working on heavy metals, minerals, surfactants, and dyes, which significantly contribute to heavy metals in the aquatic ecosystem (Ajiboye et al. 2021; Velusamy et al. 2021). Similarly, industrial and mining activities also contaminate the groundwater with heavy metals. Once accumulated in bodies of water, heavy metals may enter the food chain, accumulate in the biological system, and affect human health (Baby et al. 2010; Sankhla et al. 2016). The effects range from simple acute diseases like skin irritation or allergic reactions to chronic diseases like cancer, kidney failure, and mental retardation (Fernandez-Luqueno et al. 2013). Heavy metals like mercury and lead are known as highly toxic metals in terms of their effects on human health. As per the WHO, the permissible limit of lead and mercury in drinking water is 0.01 and 0.001 mg/L (WHO 2004). Mercury causes many diseases, and once deposited, certain microbes can change it into methyl mercury, a highly toxic form that builds up in fish, shellfish, and animals that eat fish (Scheuhammer et al. 2015; Mondal et al. 2018). Lead and mercury can affect sensitive body parts like the brain, lungs, kidneys, and some immune and cardiovascular organs (Vassallo et al. 2011; Wilk et al. 2017; Massányi et al. 2020).

Hence, there is an urgent need to treat the water containing harmful ions before using it for various domestic activities. Various types of adsorbents and materials have already been reported in this regard, including activated carbons, silica gel, activated alumina, hydrogels (Crini et al. 2019), and natural zeolites (Wang & Peng 2010). Most of the literature is available on synthesizing and characterizing different novel adsorbents for removing heavy metals, but very little has been focused on developing fabrication systems for the adsorbent materials. More recently, research has been reported on protein amyloids which can bind with metal ions and be further used for heavy metal adsorption (Bolisetty et al. 2019; Peydayesh & Mezzenga 2021). Proteins with metal ions form a co-factor that is necessary for proteins to function. Protein misfolding gives rise to aggregates of β-sheet structures that eventually form long insoluble fibres (Breydo & Uversky 2013). Amyloids have been recently explored for binding with metals such as aluminium (Peydayesh et al. 2019b), cesium (Jonnalagadda et al. 2018), cobalt (Yu et al. 2019), lead (Bolisetty & Mezzenga 2016), arsenic (Bolisetty et al. 2017), and chromium, platinum, and silver (Peydayesh et al. 2019a) and their removal from the water. In particular, the aggregates made from bovine serum albumin (BSA) have been investigated for their ability to bind metals, facilitated by computational techniques like molecular docking for metal-binding characterization (Chowdhury et al. 2020; Feizi-Dehnayebi et al. 2021). Nevertheless, there is still a gap in the design, implementation, and production of the amyloid with appropriate membrane support in the current water purification system.

Bacterial cellulose nanomembrane (BCN) is one of the most promising materials having a wide range of potential uses in composites and water purification (Cui et al. 2020; Sjahro et al. 2021). Nanocellulose was apparently employed as a base material to show an attraction to pollutants with ionic structures or dyes because of the high surface area and accessibility of -OH groups (Kumari et al. 2021; Pandya et al. 2021). BCN material exhibits exceptional characteristics including high mechanical strength, elasticity, a high oxygen barrier, and a low coefficient of thermal expansion with remarkable transparency. However, there has been no study into combining amyloids and BCN for heavy metal removal for wastewater treatment.

In our study, we utilized amyloid fibrils (AFs) embedded within a BCN membrane for the efficient removal of heavy metals from industrial effluents. The AFs were synthesized through heat treatment using BSA and then embedded within the BCN. A computer analysis using molecular docking has also been carried out to determine the mercury and lead binding on the amyloid protein. Also, a cartridge made of multi-layered AF-embedded bacterial cellulose nanohybrid membrane (AF/BCN) was used to develop industrial wastewater filtering system. In addition, the effectiveness of heavy metal removal utilizing a multi-layered AF/BCN filtration assembly cartridge (lead (Pb) and mercury (Hg)) was examined.

Materials

BSA of analytical grade was purchased from Hi-Media Pvt Ltd, India. ICP grade standard (inorganic chemical standard) solutions of hydrochloric acid (HCl), mercury (Hg), and lead (Pb) were purchased from Loba Chemical in India. Bacterial cellulose nanopaper (BCN) was purchased from the nanonovin polymer company, Iran. A central perforated tube was printed in-house using a Voyager 3D printer (Stardust Technologies, India). A millipore cellulose acetate membrane filter, brine seal, nylon mesh (1- and 120-μm size), and an anti-bacterial wrap of standard quality were procured from a local vendor.

Computational approaches methods

The structure BSA was retrieved from Protein Data Bank (PDB) PDB ID: 4F5S and further used for molecular docking against mercury (Hg) and lead (Pb2+) ions. Using the metal ion-binding site prediction and modelling server (MIB2 server), the metal ion-binding site prediction and modelling with the BSA were carried out (Lu et al. 2022). To identify the aggregate-prone segments in the BSA, zipperDB (zipperDB, no date) was utilized. For molecular docking, AutoDock was used as described in our previous study (Pandya et al. 2021). The cellulose coordinates were downloaded from PubChem (Kim et al. 2021) and further utilized for molecular docking with BSA amyloids after minimization.

Synthesis of amyloid fibril

For the synthesis of AFs, 1 g of BSA was dissolved in 10% HCl at a pH 2.1. Furthermore, it was filtered through a 0.45 μm millipore cellulose acetate membrane filter, and then the solution was incubated at a temperature of 80 °C at 200 rpm for 10 h on a magnetic hot plate. During this treatment, the BSA protein monomer was unfolded, hydrolyzed, and then self-assembled into AFs. The solution was then quickly cooled down in an ice bath to quench the aggregation process, and it was then stored at 4 °C till further use.

Synthesis of amyloid-embedded BCN

The A4 sized (21 × 29 cm2) BCN sheet was taken and washed thoroughly with distilled water, and the sheets were pressed to remove the extra water. Next, 100 mL of the synthetic amyloid solution was added to the BCN sheets in a gel rocker, which was then shaken at 20 rpm for 3 h. These sheets were then washed with distilled water and were stored at 4 °C.

Fabrication of amyloid-embedded BCN filtration assembly

A customized membrane wrapping tool was created to help in the wrapping of the membranes during the fabrication process. A centrally perforated tube was initially covered in a layer of thin nylon mesh for the manufacturing procedure. The nylon mesh sheet (1 μm-mesh size, length 65 cm) was first placed, followed by placing the amyloid-embedded BCN sheet (size ∼ 21 × 29 cm2) (Figure 1(a)) over the base layer of the nylon mesh, and now using the proposed membrane wrapping tool (Figure 1(b)), the sheet was wrapped. Further, another nylon mesh (120 μm-mesh size, length 30 cm) was added as additional support material to withstand 125-140 PSI water pressure and to provide wrapping strength to the second sheet. By regulating the wrapping tool, the second BCN sheet was also added to the first one. Thus, by following the same procedure, a membrane element was formed comprising three amyloid-embedded BCN sheets. Finally, it was wrapped with an anti-bacterial cover and sealed with a brine seal. The brine seal was wrapped with an adhesive tape to fix it. This BCN-based multi-layered membrane element can be installed in standard commercial cartridges. The membrane element wrapped by a wrapping tool is shown in Figure 1(c).
Figure 1

(a) Photograph of amyloid-embedded BCN sheet with a surface area of 21 × 29 cm2, (b) BCN sheet wrapping tool, and (c) schematic diagram of the process of membrane wrapping.

Figure 1

(a) Photograph of amyloid-embedded BCN sheet with a surface area of 21 × 29 cm2, (b) BCN sheet wrapping tool, and (c) schematic diagram of the process of membrane wrapping.

Close modal
Further, Pb2+ and Hg2+ heavy metals were passed through the fabricated amyloid-embedded BCN filtration assembly having concentrations of 1 ppm to check the removal efficacy of amyloid-embedded BCN sheet. The percentage removal of heavy metals was calculated by measuring the concentration through inductively coupled plasma mass spectrometry (ICP-MS):
formula
(1)

Heavy metal removal analysis

Using ICP-MS Thermo Scientific Model (iCAPQc), the heavy metal removal analysis was carried out. For performing the ICP-MS analysis, Hg2+ (1 ppm) and Pb2+ (1 ppm) solutions were passed through fabricated amyloid-embedded BCN filtration assembly. The ICP multi-element standard, Loba Chemical UN NO3264, was successively diluted in 2% (v/v) 1 N nitric acid to create the calibration curve for heavy metals. During sample analysis in ICP-MS, the appropriate conditions were maintained, including the spray chamber temperature (2.6 °C), interface temperature (29.28 °C), nebulizer supply pressure (2.47 bar), nebulizer plasma flow rate (1.07 mL/min), radiofrequency power (1,548.6 W), focus lens voltage (28.7 V), detector voltage (1,341 V), argon plasma rate (14 l/min), and focus lens voltage (28.7 V). The ICP-MS study used yttrium as an internal standard (recovery 90–105%). By calculating the relative percentage difference between duplicate samples (about 20% of samples) and the ICP multi-element standard, the quality control of analytical data was achieved.

Membrane regeneration

The procedure used by Almasian et al. (2018) for the regeneration studies was followed with a little modification. A solution of Pb2+ with an initial concentration of 1 ppm was passed through the AF/BCN sheet, and the permeated water was collected for 2 min in each cycle. The membrane was first carefully washed for 5 min using 50 mL distilled water while being sonicated. The pollutant layer from the membrane surface was then removed by immersing the device for 10 min in a 50 mL solution of HCl (0.05 M) before being washed. To test the AF/BCN membranes' capacity to regenerate, the previously mentioned procedures were carried out four times.

Isotherm study (adsorption test)

For the isotherm study, the stock solutions of Pb2+, having various concentrations as 125, 100, 75, and 50 ppm, were formulated by dissolving lead nitrate Pb(NO3)2 in distilled water. Then adsorption tests were conducted to investigate any variations in lead adsorption on amyloid under operational parameters, i.e. acidic condition (pH < 4) and the adsorption time (5–6 min) at room temperature. The adsorption capacity (qe) and removal efficiency of the amyloid at equilibrium were determined using Equations (2) and (3), respectively:
formula
(2)
formula
(3)
where qe stands for the adsorption capacity (mg/g), Ci and Ce represent the initial and equilibrium concentrations (mg/L), respectively, of the adsorbate, and V and W represent the solution volume (L) and mass (g), respectively, of the adsorbent. The equilibrium adsorption properties were explained using the Langmuir and Freundlich adsorption isotherms. Equation (4) represents Langmuir's isotherm:
formula
(4)
The Langmuir's isotherm was transformed into its linear form, as represented in Equation (5), to determine the adsorption parameters:
formula
(5)
where qmax represents the maximum adsorption capacity (mg/g) and KL (L/mg) is Langmuir's isotherm constant which shows the binding affinity.
Equation (6) represents Freundlich's isotherm:
formula
(6)
The linear form of Freundlich's isotherm is shown in Equation (7):
formula
(7)
where Kf is Freundlich's constant and is used to measure the adsorption capacity and the adsorption intensity. The value of Kf demonstrates the adsorption process either favourable (0.1 < <0.5) or unfavourable (>2).

Identification of metal-binding site in BSA and molecular docking of metal ions to amyloid fibrils

A total of five Hg metal ion-binding sites on the BSA protein were predicted using the MIB2 server as shown in Figure 2(a). The zipperDB identified the following segments in the BSA protein, which are highly prone to AF formation (VTFISL, GLVLIA, LRCASI, CASIQK, AEFVEV, QDTISS, and EKSHCI) with a potential to bind metal ions. The Hg2+ ion was prominently binding to one of the aggregate-prone segment EKSHCI through the cysteine and histidine residue. The Pb2+ ion was prominently binding to one of the aggregate-prone segment AEFVEV and QDTISS through the cysteine and histidine residues. The segments were further utilized for molecular docking with cellulose as shown in Figure 2(b). Briefly, the ions were docked with the AFs which were stocked in between the two fibril chains. According to our fabrication strategy, we docked the fibrils with cellulose polymer. Most of the interactions in the EKSHCI with cellulose was mediated by the H-bond to His and Lys residues as shown in Figure 3(a), with a dock score of −4.2 kcal/mol. Most of the interactions in the AEFVEV with cellulose was mediated by the H-bond to Glu and Ala residues as shown in Figure 3(b), with a dock score of −4.3 kcal/mol. This demonstrated that the BSA AFs possess the effective Hg2+-binding sites, suggesting the underlying mechanism of the ion binding.
Figure 2

BSA docked with mercury (Hg2+) ion (A) and lead (Pb2+) ion. The regions in blue (a) and red (b) are the aggregation-prone segments interacting with the Hg2+ and Pb2+ ions, respectively. The metal-binding sites in the aggregation-prone segment are only shown in atoms in both the panels.

Figure 2

BSA docked with mercury (Hg2+) ion (A) and lead (Pb2+) ion. The regions in blue (a) and red (b) are the aggregation-prone segments interacting with the Hg2+ and Pb2+ ions, respectively. The metal-binding sites in the aggregation-prone segment are only shown in atoms in both the panels.

Close modal
Figure 3

Molecular modelling and docking of fibrils: (a) EKSHCI fibrils entrapping mercury (Hg2+) ions and in complex with cellulose. (b) AEFVEV fibrils entrapping lead (Pb2+) ions and in complex with cellulose.

Figure 3

Molecular modelling and docking of fibrils: (a) EKSHCI fibrils entrapping mercury (Hg2+) ions and in complex with cellulose. (b) AEFVEV fibrils entrapping lead (Pb2+) ions and in complex with cellulose.

Close modal

Mechanism of amyloid synthesis

The dynamics and growth of amyloid are influenced by a variety of variables. The focus of the study is on amyloidogenesis mediated by pH and temperature. Conditions in solutions, such as pH, temperature, and the presence of salts or denaturing agents, can affect the formation of AFs. Usually, lowering pH and raising temperature are used to stimulate the development of AFs by proteins. Short peptides can aggregate depending on their pI or the pKa of the residues that make them up, as well as their relationship to the pH of the solution. At a lower pH, the peptides own aggregation was examined. Lys28 was uncharged at pH levels greater than 9.5, and hence, aggregation was not seen. According to some research studies, fibril production is encouraged when the peptide's net charge is between −1Q and +1Q. The investigation proved experimentally that the aggregation of the amyloid is a function of pH, and the analysis was facilitated by molecular mechanics modelling of the fragmented peptide amyloid-β (Aβ) proteins which favourable electrostatic interactions between Asp23 and above the pI of the peptide. Proteins undergo conformational denaturation when subjected to thermal stress. Proteins that were denatured revealed hydrophobic regions. It has also been shown that hydrophobic interactions play an essential role in aggregate formation. Incubation of protein at a higher temperature for longer duration results in the formation of irreversible aggregate. At high temperatures and low pH (80 °C and 2.1 pH), tiny spherical micelles are observed (Figure 4).
Figure 4

Mechanism of synthesis of amyloid fibrils.

Figure 4

Mechanism of synthesis of amyloid fibrils.

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Fluorescence spectroscopy

Thioflavin T (ThT) binding assay fluorescence spectroscopy was carried out using a Cary Eclipse Fluorescence spectrophotometer to confirm the synthesis of AFs. ThT is a modest fluorescent dye in an aqueous environment giving an excitation at 340 nm and emission at 440 nm. However, as a result of a bathochromic shift upon interaction with the amyloid, both excitation and emission rise at 450 and 490 nm, respectively (Jameson et al. 2012). A stock solution of (10 mM) of ThT was prepared in double-distilled water, filtered through a 0.22 μM filter, and stored at 4 °C for further use. Next, ThT was added at a fivefold molar excess (25 M) to the AF solution that was produced at pH 2.1 and maintained at room temperature in the dark for 45 min. ThT fluorescence was then measured in a 1 cm path-length quartz. Following the collection of the sample's fluorescence emission spectra, emission at 475 nm was observed, as shown in Figure 5.
Figure 5

Fluorescence spectra of amyloid formation.

Figure 5

Fluorescence spectra of amyloid formation.

Close modal

Mechanism and characterization of amyloid-embedded BCN

By taking advantage of the hydroxyl-containing groups in BCN and the surface hydrophilic groups in amyloids, a nano network of amyloid-embedded BCN was engineered. Van der Waals interactions and hydrogen bonds are involved in the formation of amyloid-embedded BCN. The amide, carboxyl, hydroxyl, epoxy, and the C-Cl groups present on the amyloid-embedded BCN facilitate the adsorption of heavy metal ions by providing active binding sites. Further, the amyloid-embedded BCN has been characterized by spectroscopic (dynamic light scattering (DLS), Fourier-transform infrared spectroscopy (FT-IR)) and microscopic analysis (scanning electron microscopy (SEM)).

DLS analysis

DLS was performed to determine the particle size distribution of BSA, AF, and amyloid-embedded BCN. According to the findings, BSA protein uniform average particle size distribution was 11.7 nm, whereas the uniform average particle size distribution of AFs was 273.9 nm, as shown in Figure 6(a) and 6(b). It indicates that the BSA protein is transformed to large-sized fibrils during amyloidogenesis. Similarly, the particle size distribution of amyloid-embedded BCN was found to be∼ 747.9 nm, as shown in Figure 6(c), thereby indicating that the AF is embedded with BCN.
Figure 6

DLS results of (a) BSA protein, (b) amyloid fibrils, and (c) amyloid-embedded BCN.

Figure 6

DLS results of (a) BSA protein, (b) amyloid fibrils, and (c) amyloid-embedded BCN.

Close modal

FT-IR study

The FT-IR spectrum was captured to describe the production of amyloid. The peak of alkenes was visible in both the BSA and the amyloid at –C = C– (1,620 cm−1), stretching vibrations of N–O (1,525 cm−1), and N-H (3,264 cm−1). It shows a broad peak near 3,240 cm−1, confirming the presence of O-H and N-H. The FT-IR spectra were recorded to characterize the conjunction of amyloid-embedded BCN as shown in Figure 7(a). Compared to BCN, amyloid-embedded BCN displayed a broad peak at 3,350 cm−1 (alcohol -OH, stretching) and 1,048 cm−1 (–C–O– bending), indicating the formation of H-bonding between amyloid and BCN. Moreover, the broad peak on the fingerprint region also indicated the electrostatic and π–π stacking interaction. The appearance of a peak at 1,154 cm−1 (–C–O– bending) also indicated the epoxy conjugation between amyloid and BCN, which suggested the formation of amyloid-embedded BCN (Figure 7(b)).
Figure 7

FT-IR spectra of (a) BSA and amyloid and (b) BCN and amyloid-embedded BCN.

Figure 7

FT-IR spectra of (a) BSA and amyloid and (b) BCN and amyloid-embedded BCN.

Close modal

Scanning electron microscopy analysis

SEM and element energy-dispersive spectroscopy (EDS) were used to examine the morphological behaviour of bare BCN and BCN-embedded in amyloid. The SEM morphological examination revealed the existence of a nanofibrous network with an estimated length of more than 10 μm and an average BCN diameter ∼ 40–45 nm as shown in Figure 8(a) and 8(b), wherein the amyloid-embedded nanofibril network of BCN has an estimated length of more than 10 μm and an average diameter of ∼ 250–290 nm as shown in Figure 8(c) and 8(d).
Figure 8

SEM image of (a) and (b) bare BCN; (c) amyloid fibrils; and (d) amyloid-embedded BCN.

Figure 8

SEM image of (a) and (b) bare BCN; (c) amyloid fibrils; and (d) amyloid-embedded BCN.

Close modal
EDS was also used to observe the conjugation of amyloids onto the BCN. EDS was used to determine the chemical compositions of the cellulose fibres (C and O) in bare BCN. Similar to this, an apparent increase in oxygen concentration and a minor nitrogen peak were seen in the EDS graph for BCN that was embedded in amyloid (Figure 9). The conjugation of amyloid with BCN is well demonstrated by this observation.
Figure 9

EDS graph of amyloid-embedded BCN.

Figure 9

EDS graph of amyloid-embedded BCN.

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Validation of amyloid-embedded BCN for purifying standard heavy metal

Removal of Hg2+ and Pb2+ using amyloid-embedded BCN filtration assembly

The purifying efficacy of amyloid-embedded BCN filtration assembly was evaluated by removing heavy metals (Hg2+ and Pb2+) from wastewater. In this study, 1 L solution of Hg2+ (1 ppm) and Pb2+ (1 ppm) was allowed to pass through amyloid-embedded BCN filtration assembly and analysed using SEM and EDS.

The bright spots in the SEM examination showed that the heavy metal was trapped on amyloid-embedded BCN, as shown in Figure 10(b). Furthermore, EDS also showed the peaks of Hg2+ and Pb2+, ensuring the heavy metal's entrapment on amyloid-embedded BCN as shown in Figure 10(d).
Figure 10

SEM image of (a) amyloid-embedded BCN and (b) amyloid-embedded BCN after passing heavy metal, and EDS graph of (c) amyloid-embedded BCN and (d) amyloid-embedded BCN subsequent to filtration.

Figure 10

SEM image of (a) amyloid-embedded BCN and (b) amyloid-embedded BCN after passing heavy metal, and EDS graph of (c) amyloid-embedded BCN and (d) amyloid-embedded BCN subsequent to filtration.

Close modal

Moreover, the heavy metal (Hg2+ and Pb2+) removal capability of amyloid-embedded BCN filtration assembly was also investigated using ICP-MS. In this analysis, the standard aqueous solutions of Hg2+ and Pb2+ were reduced from a concentration of 1,000 ppb to 216.86 and 74.53 ppb, respectively. Therefore, this study significantly validated the efficacy of amyloid-embedded BCN filtration assembly for Hg2+ and Pb2+ removal.

Interaction study of amyloid-embedded BCN with heavy metal using FT-IR spectroscopy

To characterize the interaction of amyloid-embedded BCN with Hg2+, the FT-IR spectra were recorded. After the filtration of Hg2+ with amyloid-embedded BCN, spectra displayed a sharp peak at 1,030, 1,055, and 1,099 cm−1 (–C–O– bending), and a new peak appeared at 1,455 cm−1. New peaks are also observed at 665, 610, and 556 cm−1 (–C–Cl) in comparison to amyloid-embedded BCN spectra. It indicates the formation of a complex of Hg2+ with carboxyl and hydroxyl groups and that of Cl with the alkane group network of the amyloid-embedded BCN. Similarly, after filtration of Pb2+, spectra displayed a small sharp peak at 1,591 cm−1 (–C = O– bending), and at the fingerprint region, there was a formation of a complex of Pb2+ with the carboxyl group network of amyloid-embedded BCN as shown in Figure 11. The results of this study suggested that the functional group network of BCN embedded with amyloid leads to the development of complexes with Hg2+ and Pb2+, which enhance the effectiveness of removal.
Figure 11

FT-IR spectra of amyloid-embedded BCN, amyloid-embedded BCN after passing Hg2+ and Pb2+, and BCN after passing Hg2+.

Figure 11

FT-IR spectra of amyloid-embedded BCN, amyloid-embedded BCN after passing Hg2+ and Pb2+, and BCN after passing Hg2+.

Close modal
Figure 12

Heavy metal removal capacity from 1 to 20 L (concentration = 1 ppm).

Figure 12

Heavy metal removal capacity from 1 to 20 L (concentration = 1 ppm).

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Membrane capacity of amyloid-embedded BCN filtration assembly

To determine the amyloid-embedded BCN filtration capacity, the heavy metal solution (1 ppm) was passed with varying volumes, i.e. 1, 2, 5, 10, and 20 L. As a result, 75.79% of heavy metal removal was observed in a total volume of 1 L at a flowrate of 9-LPM at 125 PSI. On running the assembly for a sample volume of 2L, a relatively less amount of heavy metal was trapped due to the gradual saturation (Table 1 and Figure 12). High pressure and water flow were also applied to the assembly. From the multi-layered amyloid-embedded BCN filtering assembly, no amyloid leakage or loss was seen. Yet, improper assembly manufacturing might cause leakage.

Table 1

Removal of Pb2+ and Hg2+

Volume (L)% Removal (lead)% Removal (mercury)
92.54 ± 0.21 75.78 ± 0.31 
91.56 ± 0.35 67.36 ± 0.52 
82.36 ± 0.56 42.10 ± 0.84 
10 69.12 ± 0.89 21.05 ± 0.56 
20 17.40 ± 0.26 5.26 ± 0.39 
Volume (L)% Removal (lead)% Removal (mercury)
92.54 ± 0.21 75.78 ± 0.31 
91.56 ± 0.35 67.36 ± 0.52 
82.36 ± 0.56 42.10 ± 0.84 
10 69.12 ± 0.89 21.05 ± 0.56 
20 17.40 ± 0.26 5.26 ± 0.39 

Membrane regeneration capacity of amyloid-embedded BCN filtration assembly

To ensure that the created membrane can be used repeatedly, an evaluation of its regeneration potential is required. Therefore, the addition of diluted HCl (1%) was used to regenerate the treated membrane. The removal percentage gradually decreased with an increase in consecutive cycles as shown in Figure 13(a) and 13(b). It is believed to be caused by heavy metal and membrane deformation that is irreversible in the spaces created by amyloid-embedded BCN assembly. However, the efficiency of the membrane was maintained as 90.73 and 83.54% even after the fourth filtration cycle for standard lead and mercury solutions, respectively.
Figure 13

Regeneration graph of lead and mercury, where concentration and volume were 1 ppm and 2.685 accordingly (a) lead removal and (b) mercury removal.

Figure 13

Regeneration graph of lead and mercury, where concentration and volume were 1 ppm and 2.685 accordingly (a) lead removal and (b) mercury removal.

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Filtration of industrial wastewater

Further, the efficacy of the membrane was also tested by passing an industrial wastewater sample. For this analysis, 5 L of the industrial wastewater was passed through the multi-layered amyloid-embedded BCN filtration assembly, and the filtrate was analysed by ICP-MS. The % removal was calculated using the previously mentioned formula no (1). The ICP-MS analysis showed a 65% removal of Fe as the concentration reduced from 4.56 to 1.60 mg/L. The concentration of As reduced from 0.55 to 0.33 mg/L, showing a 40% removal. The removal of Co was measured as 35%, as the concentration reduced from 81.55 to 52.87 mg/L. Significant removal of 15% was seen in the case of Pb from 2.22 to 1.89 mg/L (Table 2).

Table 2

ICP-MS analysis of heavy metals in industrial wastewater

Sr No.Heavy metalBefore (mg/L)After (mg/L)% Removal
Iron (Fe) 4.56 1.60 65 
Cobalt (Co) 81.55 52.87 35 
Arsenic (As) 0.55 0.33 40 
Lead (Pb) 2.22 1.89 15 
Sr No.Heavy metalBefore (mg/L)After (mg/L)% Removal
Iron (Fe) 4.56 1.60 65 
Cobalt (Co) 81.55 52.87 35 
Arsenic (As) 0.55 0.33 40 
Lead (Pb) 2.22 1.89 15 

Isotherm study for adsorption behaviour

In the current study, the isotherms provide essential details about the adsorbent's surface characteristics, binding affinity, and adsorption capacity that aid in understanding how the adsorbate binds to the adsorbent. In this study, two isotherms, Freundlich and Langmuir, were applied to understand the aforementioned information (Table 3). The results presented in Table 3 show that the linear regression coefficient R2 for the Langmuir isotherm is 0.997 for Pb and 0.931 for Hg, confirming the monolayer adsorption of lead and mercury on polymers. Further, the Qmax of the polymer for the lead and mercury ions was calculated at 17.10 and 5.917 mg/g, respectively, showing the maximum adsorption capacity. The value of KL in Langmuir isotherm below 0.5 for both metal ions represents the perfect binding affinity of the adsorbent.

Table 3

Results of isotherm study

Langmuir (parameters)LeadMercury
Qmax (mg/g) 17.10 5.917 
KL 0.052 0.249 
r2 0.997 0.931 
Freundlich (parameters) Lead Mercury 
Kf 2.621 0.813 
1/n 0.395 0.406 
r2 0.976 0.967 
Langmuir (parameters)LeadMercury
Qmax (mg/g) 17.10 5.917 
KL 0.052 0.249 
r2 0.997 0.931 
Freundlich (parameters) Lead Mercury 
Kf 2.621 0.813 
1/n 0.395 0.406 
r2 0.976 0.967 

In the case of the Freundlich isotherm, Kf is the Freundlich constant or maximum adsorption capacity, i.e. 2.621 and 0.813 for Pb and Hg, respectively, and the value of 1/n demonstrates the adsorption process as either favourable (0.1 < 1/n < 0.5) or unfavourable (1/n > 2). The value of 1/n in the current study is 0.395 for Pb and 0.406 for Hg, indicating that the conditions for adsorption are favourable.

Operational cost analysis for filtration system

The operational cost is calculated based on the cost of the membranes and the energy expenditure of the pumps. We have used a 40–80 pressure pump and roughly 0.67–1 kWh of electricity to run the filtration cartridge per run. The membrane typically costs $15 /210 × 297 mm with 40 L for each membrane treatment capacity and five times recyclability. As a result, the treatment cost per litre is projected to be approximately 0.01$ and will be 75% lower for bulk manufacture.

For the purpose of removing heavy metals from industrial wastewater, we have created synthetic AFs, embedded them on BCN, and created a well-ordered multi-layered amyloid-embedded BCN filtration assembly. The amide, hydroxyl, and carboxyl functional groups on amyloid-embedded BCN facilitate the adsorption of heavy metal ions by providing active binding sites. Our multi-layered filtration assembly showed 95% removal of Pb2+, while for Hg2+, 78.34% removal was obtained for the respective standard solutions. We further analysed our multi-layered filtration assembly by treating industrial wastewater. ICP-MS analysis of the filtrate showed 65% removal of Fe and 40% removal of As. The removal of Co was measured as 35%, while removal of 15% in the case of Pb. The membrane regeneration capability was also demonstrated by the multi-layered filtering assembly. The effectiveness of amyloid-embedded BCN in removing lead and mercury was maintained for up to four cycles at 90.73 and 83.54%, respectively. Langmuir and Freundlich isotherm models were also applied to the experimental data which followed the adsorption mechanism. These findings suggest that multi-layered filtration assembly could be of practical use for heavy metal removal from industrial wastewater.

Not applicable. Our study does not included use of any animal or human data or tissue.

Alok Pandya, Gajendra Singh, and Dhaval Patel conceived and planned the experiments. Sachin Vaidh, Aastha Surana, Viraj Nagariya, Ravindrasinh Rahewar, and Harsh Prajapati carried out the experiments. Dhaval Patel planned and carried out the simulations. All authors contributed to experimental work. Sachin Vaidh, Aastha Surana, Viraj Nagariya, Alok Pandya, and Gajendra Singh contributed to the interpretation of the results. Ravindrasinh Rahewar and Harsh Prajapati carried out the designed filtration setup. Sachin Vaidh, Aastha Surana, and Viraj Nagariya took the lead in writing the manuscript. All authors provided critical feedback and helped shape the research, analysis, and manuscript.

We gratefully acknowledge the Department of Science and Technology, Government of India (DST/TMD(EWO)/OWUIS-2018/RS-20(G)) for financial support. Dr Gajendra Singh Vishwakarma gratefully acknowledges GSBTM, Government of Gujarat, India (GSBTM/JDRD/604-2019/307) for financial support. Dr Gajendra Singh Vishwakarma gratefully acknowledges GSBTM, Government of Gujarat, India (GSBTM/JD/662/2012-23/00292847) for financial support.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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Supplementary data