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starch nanoparticle on nitrate removal using MD simulation
Accepted Manuscript Effect of zero-valent iron/starch nanoparticle on nitrate removal using MD simulation
Soheil Rezazadeh Mofradnia, Reihaneh Ashouri, Zahra Tavakoli, Fereshteh Shahmoradi, Hamid Rashedi, Fatemeh Yazdian PII: DOI: Reference:
S0141-8130(18)34209-0 doi:10.1016/j.ijbiomac.2018.09.183 BIOMAC 10618
To appear in:
International Journal of Biological Macromolecules
Received date: Revised date: Accepted date:
12 August 2018 28 August 2018 28 September 2018
Please cite this article as: Soheil Rezazadeh Mofradnia, Reihaneh Ashouri, Zahra Tavakoli, Fereshteh Shahmoradi, Hamid Rashedi, Fatemeh Yazdian , Effect of zerovalent iron/starch nanoparticle on nitrate removal using MD simulation. Biomac (2018), doi:10.1016/j.ijbiomac.2018.09.183
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ACCEPTED MANUSCRIPT Effect of Zero-valent iron/starch Nanoparticle on Nitrate Removal Using MD Simulation Soheil Rezazadeh Mofradnia1, Reihaneh Ashouri2, Zahra Tavakoli3, Fereshteh Shahmoradi1, Hamid Rashedi*4, Fatemeh Yazdian** 3
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1. Department of Chemical Engineering, Faculty of Engineering, Islamic Azad University North Tehran Branch, Tehran, Iran
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University, Tehran, Iran
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2. Department of Environment, Faculty of Environment and Energy, Science and Research Branch, Islamic Azad
3. Department of Life Science Engineering, Faculty of New Science & Technology, University of Tehran,
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Tehran, Iran
4. School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran
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*Corresponding Author: [email protected]
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**Cocorresponding Author: [email protected]
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ACCEPTED MANUSCRIPT Abstract In this study, the efficacy of zero-valent iron nanostructure modified by starch for removal of nitrate was investigated. Effect of zero-valent iron /starch nanoparticle in the presence of Thiobacillus dinitrificans for removal of nitrate was simulated via material studio software.
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Thermodynamic principles and proper equations were used via molecular dynamic (MD) simulation. The results of software predictions were demonstrated by radial distribution
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function (RDF), density, potential energy and temperature graphs. According to the graphs,
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the simultaneous in the presence of zero-valent iron/starch nanoparticle and Thiobacillus dinitrificans increase the removal efficiency of nitrate reached 91% and in the absence of
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nanoparticle was 44.44%.
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Keywords: Nitrate; MD; Thiobacillus dinitrificans; Zero-valent iron/starch nanoparticle;
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Thermodynamic.
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ACCEPTED MANUSCRIPT 1. Introduction Nowadays, pollution of water resources is serious issues in the natural cycle of our lives. Increasing of water consumption and water pollution restrict human access to clean water [1,2]. The harmful effects of nitrate are detected at least 15 years after the use of this
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hazardous contaminant and will have irreparable complications especially in newborns and pregnant women [3].
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Several physicals, chemical, and biological methods are used for elimination of nitrate [4].
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Chemical methods for eliminating nitrate from the water were included ion exchange, reverse osmosis, catalytic [5] and electrolysis [6]. In biological methods for removing pollutants,
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microorganisms, especially bacteria, play as a significant role in nitrate removal from water. Bio denitrification using only an electron donor, such as n-ZVI [7–9], ferrous ions[10],
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elemental sulfur or reduced sulfur compounds is very acknowledged. Thus, the electron donor was applied to increase nitrate removal from aqueous solution[11]. These techniques
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are cost-effective for denitrification, which the lower by-products, cheaper and more comfortable than another method such as physical and chemical methods have made [12,13]. Nitrate respiration has been seen as part of the biological denitrification process in some anaerobe
bacteria,
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facultative
including
Thiobacillus,
Thiomicrospora,
Paradoxes
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denitrificants, Paracoccus, Pseudomonas aeruginosa, and Redobacter spheroids [8,14]. The reductive nitrate enzyme, a key enzyme for the regeneration of nitrate, is located in the periplasmic space [15]. Therefore, in this field, if the nanoscience and biological denitrificans were used simultaneously which are more effective. The application of nanostructures to remove biological pollutants with microorganisms has been commonly considered [16–19]. The addition of iron-based nanoparticles accelerates the process of biological decomposition of nitrate, and during the oxidation of iron, the electron is produced, which can be used by microorganisms to regenerate nitrate [20–23]. Also, for stabilizing and efficient dispersant of 3
ACCEPTED MANUSCRIPT iron nanoparticles, starch is an appropriate material that is biodegradable, economical and nontoxic substance [24]. The molecular dynamics method is one of the most accurate simulation methods in physics that is used to simulate complex multi-particle systems. The results of simulation that show the change in positions and velocities of the system particles versus time [25,26]. Molecular
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dynamics simulations are widely used in almost all critical sciences, including medicine, and
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in mainly targeted drug delivery, the effect of material size on reactivity and its effect on
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thermodynamic parameters [27–29]and many of structural studies [30]. Rajab Beigy et al. [31] The researchers investigated the effects of zero-valent iron/starch
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nanoparticle and Thiobacillus dinitrificans for bioremoval of nitrate. The results showed that in the presence of nanoparticle, the concentration of nitrate was significantly decreased in
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comparison to the absence of zero-valent iron/starch nanoparticle so, in this paper, we studied the interactions between the zero-valent iron/starch nanoparticles and Thiobacillus
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dinitrificans, for investigation of nitrate removal. Activity and structure of Thiobacillus
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dinitrificans were analyzed in the presence of nanoparticles. Molecular dynamics simulation considered the mechanism of interactions between the nanoparticles and Thiobacillus Analysis of achieving diagrams such as density, temperature, Radial
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dinitrificans.
Distribution Function (RDF) and energy was confirmed that the efficiency of nanoparticle for
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removal of nitrate.
2. Materials and methods 2.1. Molecular dynamics In this study, material studio software (version 8) was used to perform molecular simulations. In Fig. 1, structure of Thiobacillus dinitrificans extracted from RCSB database with DOI code: 10.2210 / pdb3sb1 / pdb. As shown in Fig. 2a and b, the nitrate structure and starch molecules extracted from PubChem molecular bank to achieve the optimum state, as well. 4
ACCEPTED MANUSCRIPT Molecules of starch were added to zero-valent iron nanoparticles for increasing the stability of adsorbent and preventing oxidation of nanoparticles. Furthermore, starch not only plays as a role of membrane for iron nanoparticle surface but also protects the nanoparticle structure for nearest placement between microorganism and zero-valent iron nanostructure. Therefore, reactivity of nanoparticle for removal of nitrate were increased by adding starch molecules.
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Also, molecules of water shifted into the software and optimized by MM algorithm in
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Comformer module. An atomic molecule of zero-valent iron was extracted from a databank
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of software that converts to the zero-valent iron nanoparticle, as shown in Fig3. Optimization of the nanoparticles from the geometry section of the forcite module was performed by
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compass force field that has the remarkable computational ability for structural simulation.
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Fig. 1. a. Schematic of microorganism at RCSB Databank. b. microorganism in the software
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Fig. 2. a. Optimization of nitrate molecule in the software. b. optimization of starch molecule structure.
Fig. 3. a. Zero-valent iron available in Data Bank software. b. zero-valent iron/starch nanoparticles in the
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software.
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2.2. Simulation box
Selection of components is an essential step of molecular dynamics simulation. The structures were designed based on microorganisms in an aqueous medium. Thus, the accumulation of components was performed by an amorphous cell tools module and compass force field in the constant pressure of 101.325 kPa and at 298 K. Moreover, the percentage amount of nitrate and water were achieved 45.3 and 54.7, respectively. Periodic Boundary Conditions (PBC) was used for proper placement of components in the box. Finally, unwanted parameters were optimized to 10000 units of energy. The final equilibrium for 6
ACCEPTED MANUSCRIPT implementation of molecular dynamics steps was shown in Fig. 4 that is the optimal structure
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of the simulation box.
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Fig. 4. Optimal structure of simulation box
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2.3. Simulation method
The forcite module is used for final investigation of molecular dynamics with the dynamic
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operating tool and the compass force field. At first, the system neutralized for avoiding the
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computational interference. Calculations were performed at constant temperature by NVT computational equation and Nosé-Hoover (NHL) equation. Therefore, proper temperature
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control selected based on the presence of nanoparticles in the system. All parts of the system are at ambient pressure that exactly according to the experimental conditions. For achieving
intervals.
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the desired results, all steps were performed in 5 ps, and calculations continue with 1fs
3. Result and Discussion 3.1. Equilibrium in the system Finally, molecular dynamics simulation carried out under optimum balanced conditions. Verification of the system equilibrium was shown by energy graphs which presented the final step of molecular dynamics. As shown in diagrams 1 and 2, the conditions are perfectly 7
ACCEPTED MANUSCRIPT balanced that will continue until 5ps. Equilibrium of systems showed the complete response of system and stability of structures that can be related to the potential and non-bonding energy. This difference amount of bonding energy indicates the capability and reactions of the system. Moreover, table 1 presented the significant difference between initial nonbonding and potential energy and final value of them, due to the enhancement of reactivity by adding
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zero-valent iron/starch nanoparticles for nitrate removal. The performance of gemcitabine-
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loaded into chitosan polymer was investigated as a drug delivery system by MD simulation.In
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mentioned research, the energy of system could present quietly stable and balanced in all
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steps of simulation [26].
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Diagram 1. Energy variations versus time in the absence of zero-valent iron /starch nanoparticle.
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Diagram 2. Energy variations versus time in the presence of Fe/starch nanoparticle.
Total Energy (Kcal/mol)
Non-Bonded
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Simulation Box
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Table 1. Results of energy changes
Energy
Van der-Waals
Electrostatic
Potential
Kinetic
(Kcal/mol)
(Kcal/mol)
energy
energy
(Kcal/mol)
(Kcal/mol)
(Kcal/mol)
Box in presence of nanoparticle
1265.058
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of nanoparticle
58343.561
-24938.911
-83291.753
986.300
278.758
54918.956
3424.606
-82454.948
-836.805
-29046.195
4107.284
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Box in absence
3.2. Temperature analysis In this study, temperature control is essential for energy variations of simulation system. Microorganism and zero-valent iron/starch nanoparticle were sensitive to temperature 9
ACCEPTED MANUSCRIPT variations. As presented in diagram 3, in each part of simulation, temperature values were controlled by Nose-Hoover thermostat [equation (1)] [32]:
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The independent momentum is g that shows the degrees of system freedom, R and P represent all coordinates and Q is an imaginary mass, which was selected carefully along
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with systems. The coordinates R, P, and t in Hamiltonian equation are virtual. They relate to
(2)
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the real coordinates as follow [equation (2)]:
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Where the coordinates with an accent are the real coordinates. The ensemble average of the is equal to the canonical ensemble average were considered.
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above Hamiltonian at
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Diagram 3. Temperature variations a. in absence of zero-valent iron/starch nanoparticle b. in presence of zerovalent iron/starch nanoparticle
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Sarkar et al., (2018) investigated the various size of copper-silver core-shell nanowire in different temperatures. Their results showed that during the imitation of the simulation
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processes, all the core-shell nanowires were equilibrated to room temperature (298 K) using
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the Nosé–Hoover thermostat[33]. Moreover, the relationship between temperature and
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reactions of energy are direct. 3.3. Density data
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Density charts show the removal of nitrate in simulation system. Increasing of density were shown in both states, in diagram 4. The basic amount of density was 0.8 g/cm3. The changes
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of density in the presence of nanoparticle indicate that the better reactions between structure
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and aqueous media of nitrate were reached to 1.65 g/cm3 within 0.5 ps. However, in the absence of zero-valent iron /starch nanoparticle, the conditions completely different and the
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maximum value of density was reached to 0.8g/cm3 within 0.6 ps, as well. Furthermore, as presented in diagram4, the value of density was significantly increased by adding
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nanoparticles in the initial time of reaction that due to the inversely relationship between nitrate concentration and density. In the presence of nanoparticles and in the absence of them, enhancement of density can be realized until approximately 1ps and 0.5ps, respectively. The inability of microorganisms and reduced active cites for decreasing the concentration of nitrate were demonstrated. Rezazadeh et al. (2018) considered the rate of biosurfactant production in the presence of Fe/starch nanoparticle and Pseudomonas aeruginosa. Density
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ACCEPTED MANUSCRIPT diagram demonstrated that in the presence of nanoparticle, the production of biosurfactant
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was significantly increased in comparison to in the absence of it.
Diagram 4. Density variations in the presence and absence of nanoparticle.
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3.4. Radial Distribution Function
RDF is an appropriate choice for description of molecular dynamic system. The radial
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distribution function, gives the probability of finding a particle at a defined distance from
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another particle. This function is useful to describe the structure of a molecular system and calculated the RDF for characterizing the molecular interactions of simulated systems
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[34,35].
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In this simulation, based on the radial distribution function (RDF) and density graph, the effectiveness of zero-valent iron/starch nanoparticle for decreasing the pollutant was demonstrated. RDF charts including successive peaks which have different fluctuations. Each of these peaks was shown the reactivity of nitrate removal. The initial peaks were shown the ability of MD simulation for showing the reactivity of structures. The capabilities of RDF chart is to find the closest binding pair of electrons that can provide the suitable reactivity and stability of the system. As presented in the Diagram.6, the initial RDF peak is the highest peak but in the absence of zero-valent iron /starch nanoparticle initial 12
ACCEPTED MANUSCRIPT peak lower than in the presence of zero-valent iron/starch nanoparticle, which reflects the higher reactivity and the higher surface tension. The greater surface tension results from the application of electrostatic energy due to the presence of zero-valent iron/starch nanoparticles. However, in the absence of nanoparticle, by lack of high electrostatics, the stability of reaction was decreased. Moreover, the initial peak shows the ability of
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nanoparticles and microorganism for removal of nitrate, which confirms our hypothesis,
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because this peak is a symbol of bounds in MD systems.
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In Diagram6, the fluctuations continue at distances, and tiny fluctuations can be seen in the system, but in Diagram5, the fluctuation is almost stopped after the 6-angstrom gap, and no
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reaction occurs after this distance. The RDF chart significantly demonstrated the effect of the nanoparticle for increasing the removal of nitrate. Thus, the graphs show that the increase in
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density is due to decreasing the concentration of nitrate. Samanta et al. (2012) employed a molecular dynamics simulation for investigation of the interaction between curcumin with
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block copolymer based on polypropylene oxide (PPO) and polyethylene oxide (PEO). The radial distribution of oxygen atoms of solvent molecules in the presence of curcumin was
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analyzed for the high interaction with curcumin by RDF chart [36].
Diagram 5. RDF, g(r) variations versus distance of cutoff (r) for Thiobacillus denitrificans in the absence of nanoparticle
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Diagram 6. RDF, g(r) variations versus distance of cutoff (r) for Thiobacillus denitrificans in the presence of
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zero-valent iron/starch nanoparticle.
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ACCEPTED MANUSCRIPT 5. Conclusion In this study, zero-valent iron/starch nanoparticle and Thiobacillus dinitrificens were used for removal of nitrate as a contaminant. The ability and the effect of nanostructures for increasing nitrate removal were investigated via MD simulation software. All structures were
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fabricated in a simulation software or was extracted from bioinformatics banks, and each of them was optimized for investigation of removal rate. The effectiveness of zero-valent
Energy graphs were demonstrated that in the presence of
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potential energy diagram.
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iron/starch nanoparticle was evaluated by various diagrams such as RDF, density and
nanoparticle all parameters of energy such as potential, non-bonding, wan-der walls were
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lower amount of energy, in comparison to in the absence of nanoparticle. Therefore, by adding nanoparticle the reaction between nanostructure and microorganism and stability of
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system were significantly increased for removal of nitrate. The molecular simulation presented that in the presence of nanoparticles and microorganisms, nitrate elimination is
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50% more than the control phase (in the absence of nanoparticle). Density amount in the
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presence of the nanoparticle reached 1.65 g/cm3 while in the absence of nanoparticle was 0.82 g/cm3. Furthermore, the simulation shows that the direct relationship between the
Reactivity in simulation system was increased via applying force
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was achieved.
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amount of zero-valent iron nanoparticles and the value of the reaction in simulation system
electromagnetism by adding zero-valent iron/starch nanoparticle in aqueous media.
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Soheil Rezazadeh Mofradnia, Reihaneh Ashouri, Zahra Tavakoli, Fereshteh Shahmoradi, Hamid Rashedi, Fatemeh Yazdian PII: DOI: Reference:
S0141-8130(18)34209-0 doi:10.1016/j.ijbiomac.2018.09.183 BIOMAC 10618
To appear in:
International Journal of Biological Macromolecules
Received date: Revised date: Accepted date:
12 August 2018 28 August 2018 28 September 2018
Please cite this article as: Soheil Rezazadeh Mofradnia, Reihaneh Ashouri, Zahra Tavakoli, Fereshteh Shahmoradi, Hamid Rashedi, Fatemeh Yazdian , Effect of zerovalent iron/starch nanoparticle on nitrate removal using MD simulation. Biomac (2018), doi:10.1016/j.ijbiomac.2018.09.183
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ACCEPTED MANUSCRIPT Effect of Zero-valent iron/starch Nanoparticle on Nitrate Removal Using MD Simulation Soheil Rezazadeh Mofradnia1, Reihaneh Ashouri2, Zahra Tavakoli3, Fereshteh Shahmoradi1, Hamid Rashedi*4, Fatemeh Yazdian** 3
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1. Department of Chemical Engineering, Faculty of Engineering, Islamic Azad University North Tehran Branch, Tehran, Iran
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University, Tehran, Iran
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2. Department of Environment, Faculty of Environment and Energy, Science and Research Branch, Islamic Azad
3. Department of Life Science Engineering, Faculty of New Science & Technology, University of Tehran,
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Tehran, Iran
4. School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, Iran
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*Corresponding Author: [email protected]
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**Cocorresponding Author: [email protected]
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ACCEPTED MANUSCRIPT Abstract In this study, the efficacy of zero-valent iron nanostructure modified by starch for removal of nitrate was investigated. Effect of zero-valent iron /starch nanoparticle in the presence of Thiobacillus dinitrificans for removal of nitrate was simulated via material studio software.
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Thermodynamic principles and proper equations were used via molecular dynamic (MD) simulation. The results of software predictions were demonstrated by radial distribution
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function (RDF), density, potential energy and temperature graphs. According to the graphs,
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the simultaneous in the presence of zero-valent iron/starch nanoparticle and Thiobacillus dinitrificans increase the removal efficiency of nitrate reached 91% and in the absence of
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nanoparticle was 44.44%.
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Keywords: Nitrate; MD; Thiobacillus dinitrificans; Zero-valent iron/starch nanoparticle;
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Thermodynamic.
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ACCEPTED MANUSCRIPT 1. Introduction Nowadays, pollution of water resources is serious issues in the natural cycle of our lives. Increasing of water consumption and water pollution restrict human access to clean water [1,2]. The harmful effects of nitrate are detected at least 15 years after the use of this
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hazardous contaminant and will have irreparable complications especially in newborns and pregnant women [3].
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Several physicals, chemical, and biological methods are used for elimination of nitrate [4].
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Chemical methods for eliminating nitrate from the water were included ion exchange, reverse osmosis, catalytic [5] and electrolysis [6]. In biological methods for removing pollutants,
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microorganisms, especially bacteria, play as a significant role in nitrate removal from water. Bio denitrification using only an electron donor, such as n-ZVI [7–9], ferrous ions[10],
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elemental sulfur or reduced sulfur compounds is very acknowledged. Thus, the electron donor was applied to increase nitrate removal from aqueous solution[11]. These techniques
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are cost-effective for denitrification, which the lower by-products, cheaper and more comfortable than another method such as physical and chemical methods have made [12,13]. Nitrate respiration has been seen as part of the biological denitrification process in some anaerobe
bacteria,
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facultative
including
Thiobacillus,
Thiomicrospora,
Paradoxes
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denitrificants, Paracoccus, Pseudomonas aeruginosa, and Redobacter spheroids [8,14]. The reductive nitrate enzyme, a key enzyme for the regeneration of nitrate, is located in the periplasmic space [15]. Therefore, in this field, if the nanoscience and biological denitrificans were used simultaneously which are more effective. The application of nanostructures to remove biological pollutants with microorganisms has been commonly considered [16–19]. The addition of iron-based nanoparticles accelerates the process of biological decomposition of nitrate, and during the oxidation of iron, the electron is produced, which can be used by microorganisms to regenerate nitrate [20–23]. Also, for stabilizing and efficient dispersant of 3
ACCEPTED MANUSCRIPT iron nanoparticles, starch is an appropriate material that is biodegradable, economical and nontoxic substance [24]. The molecular dynamics method is one of the most accurate simulation methods in physics that is used to simulate complex multi-particle systems. The results of simulation that show the change in positions and velocities of the system particles versus time [25,26]. Molecular
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dynamics simulations are widely used in almost all critical sciences, including medicine, and
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in mainly targeted drug delivery, the effect of material size on reactivity and its effect on
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thermodynamic parameters [27–29]and many of structural studies [30]. Rajab Beigy et al. [31] The researchers investigated the effects of zero-valent iron/starch
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nanoparticle and Thiobacillus dinitrificans for bioremoval of nitrate. The results showed that in the presence of nanoparticle, the concentration of nitrate was significantly decreased in
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comparison to the absence of zero-valent iron/starch nanoparticle so, in this paper, we studied the interactions between the zero-valent iron/starch nanoparticles and Thiobacillus
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dinitrificans, for investigation of nitrate removal. Activity and structure of Thiobacillus
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dinitrificans were analyzed in the presence of nanoparticles. Molecular dynamics simulation considered the mechanism of interactions between the nanoparticles and Thiobacillus Analysis of achieving diagrams such as density, temperature, Radial
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dinitrificans.
Distribution Function (RDF) and energy was confirmed that the efficiency of nanoparticle for
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removal of nitrate.
2. Materials and methods 2.1. Molecular dynamics In this study, material studio software (version 8) was used to perform molecular simulations. In Fig. 1, structure of Thiobacillus dinitrificans extracted from RCSB database with DOI code: 10.2210 / pdb3sb1 / pdb. As shown in Fig. 2a and b, the nitrate structure and starch molecules extracted from PubChem molecular bank to achieve the optimum state, as well. 4
ACCEPTED MANUSCRIPT Molecules of starch were added to zero-valent iron nanoparticles for increasing the stability of adsorbent and preventing oxidation of nanoparticles. Furthermore, starch not only plays as a role of membrane for iron nanoparticle surface but also protects the nanoparticle structure for nearest placement between microorganism and zero-valent iron nanostructure. Therefore, reactivity of nanoparticle for removal of nitrate were increased by adding starch molecules.
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Also, molecules of water shifted into the software and optimized by MM algorithm in
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Comformer module. An atomic molecule of zero-valent iron was extracted from a databank
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of software that converts to the zero-valent iron nanoparticle, as shown in Fig3. Optimization of the nanoparticles from the geometry section of the forcite module was performed by
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compass force field that has the remarkable computational ability for structural simulation.
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Fig. 1. a. Schematic of microorganism at RCSB Databank. b. microorganism in the software
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Fig. 2. a. Optimization of nitrate molecule in the software. b. optimization of starch molecule structure.
Fig. 3. a. Zero-valent iron available in Data Bank software. b. zero-valent iron/starch nanoparticles in the
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software.
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2.2. Simulation box
Selection of components is an essential step of molecular dynamics simulation. The structures were designed based on microorganisms in an aqueous medium. Thus, the accumulation of components was performed by an amorphous cell tools module and compass force field in the constant pressure of 101.325 kPa and at 298 K. Moreover, the percentage amount of nitrate and water were achieved 45.3 and 54.7, respectively. Periodic Boundary Conditions (PBC) was used for proper placement of components in the box. Finally, unwanted parameters were optimized to 10000 units of energy. The final equilibrium for 6
ACCEPTED MANUSCRIPT implementation of molecular dynamics steps was shown in Fig. 4 that is the optimal structure
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of the simulation box.
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Fig. 4. Optimal structure of simulation box
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2.3. Simulation method
The forcite module is used for final investigation of molecular dynamics with the dynamic
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operating tool and the compass force field. At first, the system neutralized for avoiding the
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computational interference. Calculations were performed at constant temperature by NVT computational equation and Nosé-Hoover (NHL) equation. Therefore, proper temperature
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control selected based on the presence of nanoparticles in the system. All parts of the system are at ambient pressure that exactly according to the experimental conditions. For achieving
intervals.
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the desired results, all steps were performed in 5 ps, and calculations continue with 1fs
3. Result and Discussion 3.1. Equilibrium in the system Finally, molecular dynamics simulation carried out under optimum balanced conditions. Verification of the system equilibrium was shown by energy graphs which presented the final step of molecular dynamics. As shown in diagrams 1 and 2, the conditions are perfectly 7
ACCEPTED MANUSCRIPT balanced that will continue until 5ps. Equilibrium of systems showed the complete response of system and stability of structures that can be related to the potential and non-bonding energy. This difference amount of bonding energy indicates the capability and reactions of the system. Moreover, table 1 presented the significant difference between initial nonbonding and potential energy and final value of them, due to the enhancement of reactivity by adding
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zero-valent iron/starch nanoparticles for nitrate removal. The performance of gemcitabine-
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loaded into chitosan polymer was investigated as a drug delivery system by MD simulation.In
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mentioned research, the energy of system could present quietly stable and balanced in all
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steps of simulation [26].
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Diagram 1. Energy variations versus time in the absence of zero-valent iron /starch nanoparticle.
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Diagram 2. Energy variations versus time in the presence of Fe/starch nanoparticle.
Total Energy (Kcal/mol)
Non-Bonded
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Simulation Box
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Table 1. Results of energy changes
Energy
Van der-Waals
Electrostatic
Potential
Kinetic
(Kcal/mol)
(Kcal/mol)
energy
energy
(Kcal/mol)
(Kcal/mol)
(Kcal/mol)
Box in presence of nanoparticle
1265.058
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of nanoparticle
58343.561
-24938.911
-83291.753
986.300
278.758
54918.956
3424.606
-82454.948
-836.805
-29046.195
4107.284
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Box in absence
3.2. Temperature analysis In this study, temperature control is essential for energy variations of simulation system. Microorganism and zero-valent iron/starch nanoparticle were sensitive to temperature 9
ACCEPTED MANUSCRIPT variations. As presented in diagram 3, in each part of simulation, temperature values were controlled by Nose-Hoover thermostat [equation (1)] [32]:
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The independent momentum is g that shows the degrees of system freedom, R and P represent all coordinates and Q is an imaginary mass, which was selected carefully along
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with systems. The coordinates R, P, and t in Hamiltonian equation are virtual. They relate to
(2)
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the real coordinates as follow [equation (2)]:
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Where the coordinates with an accent are the real coordinates. The ensemble average of the is equal to the canonical ensemble average were considered.
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above Hamiltonian at
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Diagram 3. Temperature variations a. in absence of zero-valent iron/starch nanoparticle b. in presence of zerovalent iron/starch nanoparticle
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Sarkar et al., (2018) investigated the various size of copper-silver core-shell nanowire in different temperatures. Their results showed that during the imitation of the simulation
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processes, all the core-shell nanowires were equilibrated to room temperature (298 K) using
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the Nosé–Hoover thermostat[33]. Moreover, the relationship between temperature and
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reactions of energy are direct. 3.3. Density data
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Density charts show the removal of nitrate in simulation system. Increasing of density were shown in both states, in diagram 4. The basic amount of density was 0.8 g/cm3. The changes
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of density in the presence of nanoparticle indicate that the better reactions between structure
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and aqueous media of nitrate were reached to 1.65 g/cm3 within 0.5 ps. However, in the absence of zero-valent iron /starch nanoparticle, the conditions completely different and the
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maximum value of density was reached to 0.8g/cm3 within 0.6 ps, as well. Furthermore, as presented in diagram4, the value of density was significantly increased by adding
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nanoparticles in the initial time of reaction that due to the inversely relationship between nitrate concentration and density. In the presence of nanoparticles and in the absence of them, enhancement of density can be realized until approximately 1ps and 0.5ps, respectively. The inability of microorganisms and reduced active cites for decreasing the concentration of nitrate were demonstrated. Rezazadeh et al. (2018) considered the rate of biosurfactant production in the presence of Fe/starch nanoparticle and Pseudomonas aeruginosa. Density
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ACCEPTED MANUSCRIPT diagram demonstrated that in the presence of nanoparticle, the production of biosurfactant
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was significantly increased in comparison to in the absence of it.
Diagram 4. Density variations in the presence and absence of nanoparticle.
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3.4. Radial Distribution Function
RDF is an appropriate choice for description of molecular dynamic system. The radial
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distribution function, gives the probability of finding a particle at a defined distance from
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another particle. This function is useful to describe the structure of a molecular system and calculated the RDF for characterizing the molecular interactions of simulated systems
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[34,35].
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In this simulation, based on the radial distribution function (RDF) and density graph, the effectiveness of zero-valent iron/starch nanoparticle for decreasing the pollutant was demonstrated. RDF charts including successive peaks which have different fluctuations. Each of these peaks was shown the reactivity of nitrate removal. The initial peaks were shown the ability of MD simulation for showing the reactivity of structures. The capabilities of RDF chart is to find the closest binding pair of electrons that can provide the suitable reactivity and stability of the system. As presented in the Diagram.6, the initial RDF peak is the highest peak but in the absence of zero-valent iron /starch nanoparticle initial 12
ACCEPTED MANUSCRIPT peak lower than in the presence of zero-valent iron/starch nanoparticle, which reflects the higher reactivity and the higher surface tension. The greater surface tension results from the application of electrostatic energy due to the presence of zero-valent iron/starch nanoparticles. However, in the absence of nanoparticle, by lack of high electrostatics, the stability of reaction was decreased. Moreover, the initial peak shows the ability of
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nanoparticles and microorganism for removal of nitrate, which confirms our hypothesis,
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because this peak is a symbol of bounds in MD systems.
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In Diagram6, the fluctuations continue at distances, and tiny fluctuations can be seen in the system, but in Diagram5, the fluctuation is almost stopped after the 6-angstrom gap, and no
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reaction occurs after this distance. The RDF chart significantly demonstrated the effect of the nanoparticle for increasing the removal of nitrate. Thus, the graphs show that the increase in
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density is due to decreasing the concentration of nitrate. Samanta et al. (2012) employed a molecular dynamics simulation for investigation of the interaction between curcumin with
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block copolymer based on polypropylene oxide (PPO) and polyethylene oxide (PEO). The radial distribution of oxygen atoms of solvent molecules in the presence of curcumin was
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analyzed for the high interaction with curcumin by RDF chart [36].
Diagram 5. RDF, g(r) variations versus distance of cutoff (r) for Thiobacillus denitrificans in the absence of nanoparticle
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Diagram 6. RDF, g(r) variations versus distance of cutoff (r) for Thiobacillus denitrificans in the presence of
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zero-valent iron/starch nanoparticle.
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ACCEPTED MANUSCRIPT 5. Conclusion In this study, zero-valent iron/starch nanoparticle and Thiobacillus dinitrificens were used for removal of nitrate as a contaminant. The ability and the effect of nanostructures for increasing nitrate removal were investigated via MD simulation software. All structures were
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fabricated in a simulation software or was extracted from bioinformatics banks, and each of them was optimized for investigation of removal rate. The effectiveness of zero-valent
Energy graphs were demonstrated that in the presence of
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potential energy diagram.
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iron/starch nanoparticle was evaluated by various diagrams such as RDF, density and
nanoparticle all parameters of energy such as potential, non-bonding, wan-der walls were
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lower amount of energy, in comparison to in the absence of nanoparticle. Therefore, by adding nanoparticle the reaction between nanostructure and microorganism and stability of
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system were significantly increased for removal of nitrate. The molecular simulation presented that in the presence of nanoparticles and microorganisms, nitrate elimination is
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50% more than the control phase (in the absence of nanoparticle). Density amount in the
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presence of the nanoparticle reached 1.65 g/cm3 while in the absence of nanoparticle was 0.82 g/cm3. Furthermore, the simulation shows that the direct relationship between the
Reactivity in simulation system was increased via applying force
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was achieved.
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amount of zero-valent iron nanoparticles and the value of the reaction in simulation system
electromagnetism by adding zero-valent iron/starch nanoparticle in aqueous media.
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