Article / Research Article

"Molecular Dynamics Simulation of Temperature-Dependent Distribution of Gold Nano Particles on Ferroelectrics Substrate"

Yue Liu1*, Jiuyang Li1, Jiahe Wang2, Mohan Tang2 Yuhong Qin2, Yuejun Sun1*

1College of Materials Science and Engineering, Liaoning Technical University, Fuxin, China

2Lab of Mathematical Model, Beijing National Day School, Beijing, China

*Corresponding authors: Yue Liu, College of Materials Science and Engineering, Liaoning Technical University, Fuxin, China. Email:yueliu000@163.com

Yuejun Sun, Lab of Mathematical Model, Beijing National Day School, Beijing, China. Email: sunyuejun0001@163.com

Received Date: 09 November, 2017; Accepted Date: 15 November, 2017; Published Date: 24 November, 2017

  1. Abstract

Gold Nano Particles (GNPs) have been considered the widely used catalyst for chemical industry. However, the efficiency of the GNPs has been limited by the aggregation of the GNPs, as the active sites are only located on the surface of the catalyst particles. In this paper we report the surface aggregation dynamics of GNPs on ferroelectrics substrate. The ferroelectrics substrate and temperature could regulate the coverage of the GNPs. The method of reducing the particle size to obtain the dispersed morphology of GNPs was discussed by the dissipative particle dynamics method and density functional theory. The positive surface could offer more electrons on the GNPs and are responsible for the dispersed morphology of the GNPs.

  1. Introduction

Gold Nano Particles (GNPs) are used as the photo catalyst and sensors for more than six decades [1]. GNPs own particular optical and catalytic properties especially compared with the bulk Au materials, since the active sites are only located on the surface of the catalyst particles [2-4]. Downsizing the GNPs to small clusters is awfully desirable to maximize their efficiency by utilizing gold particles up to the hilt [5-8]. The GNPs properties are strongly connected with the shape, size and morphology of the nano particles. The experimental results have already obtained the conclusion that the morphology of GNPs could be a key point to the catalyst properties. Varies experimental results about the morphology have been obtained, but the fabrication of GNPs was not easily controllable, the relationship between the fabrication detail and the morphology of GNPs have not been built sufficiently, especially in detail [9-12]. Controlled and large-scale synthesis of stable small clusters of GNPs remains a considerable challenge due to the metal atoms agglomerate at nano scale, resulting in a natural tendency for metal atoms to form larger clusters [13,14]. The temperature is one of the most important factor of fabrication, which will influence the morphology of Au particles visibly.

The fabrication of metal on the ferroelectric surface is also precursor to the development of devices that can harness the unique characteristics associated with polarizable materials, including both perovskite and related crystals [15-22]. Growth of nano-clusters and nano-crystals of noble metals has been observed at the surface of traditional ferroelectric such as strontium titanate (SrTiO3) [23-25]. However, it is not clear where the nucleation of the metal atoms occurs.

In this paper, the relationship between the fabrication temperature and morphology of Au particles on nontraditional perovskite crystals like LiNbO3 have been discussed, the Au thin films coverage rate versus fabrication temperature has been researched. LiNbO3 is currently widely used in a wide variety of existing device and its high surface charge density makes it attractive for use in other applications similar to those explored involving other metallic oxides and ferroelectrics. The growth dynamics of the Au thin films morphology on LiNbO3 surface with opposite polarization have been uncovered by the Dissipative Particle Dynamics method, to illustrate the Au morphology dynamics at different temperatures.

  1. Simulation method

In the Au thin film growth process among the experiment, the relative positions and velocities of the particles in the LiNbO3 substrate and Au thin films satisfy the proper conservation laws and symmetries, and the momentum transferred from the particles also satisfies the linear and angular conservations, which meet the demands of the DPD method.

In this work, the DPD method was applied to the solid molecular, which has agreed with the experiment results at our previous work [26]. As a result, we find the optimal deposition temperature and substrate orientation.

The Dissipative Particle Dynamics (DPD) method was optimized by Koelman and Hoogerbrugge, [27,28] which allows longer-time scale calculation and larger system simulation. Espanol R, and Warren P [29] The DPD method has been used in studying a surfactant among water molecular [30,31]. Ryjkina et al. [30] and Kuo et al. [31] reported that the DPD method can also be used in liquid molecular aggregation.

The lithium niobate molecule and the gold atom were chosen to be the DPD particles to understand the gold nano particles morphology on the LiNbO3 substrate. Assume that the lithium niobate molecules are in stoichiometry (Li-Nb-O3) and electric neutrality under ideal target and oxygen atmosphere, and the total molecular number is 100 as a substrate.

The GNPs is set as 100-1000 particles in each simulation work. The particle numbers of GNP are related to the laser energy in the PLD system. The LiNbO3 molecular lattice parameters are a = b = 0.515 nm, c = 1.3863 nm, and the average distance between the molecular is 1 nm in the substrate. In the two-dimensional simulation of a 10 nm×10 nm film. The simulation result is probably one shot of the laser pulse, which is important in the first growth stage of thin films [26].

Considering the ideal condition which does not include the defects and supposes that the particles at the boundaries are in balance. Approximately, the whole progress in PLD is designed to deposit on the LiNbO3 surface of 100 nm_100 nm at different temperatures.

The unreconstructed LiNbO3 (001) positive and negative surfaces are selected as the deposition surface. The final position of a DPD particle is determined by the force exerted on it. The time evolution of the process follows Newton’s law. A DPD particle is affected by the neighbor DPD particles via three non-bonded forces: a conservative force FijC, a dissipative force FijD, and a random force FijR. Therefore, the effective force Fi acting on particle i is given by [28].

                  (1)


where i, j represents the DPD particles of the Au atom and lithium niobate molecule, respectively. The FijC is a repulsive central force with a maximum value aij as shown in Ref [30].

                              (2)

where aij, eij, rij, rc are the maximum repulsive parameter, the unit vector, the distance between particles i and j, and the cutoff radius, respectively.

To calculate FijC, aij must be calculated. Here the interaction parameter is obtained by considering the mean pair contact interaction energy EijMIX(T) between lithium niobate molecules and silicon atoms. First, the mixing energy Eij MIX(T) was calculated between two DPD particles i and j according to [31]


   (3)


Where the Zs are the coordination number of each DPD particle pair. The averaged pair interaction energy was optimized. The electron state was set as the ground state, default spin and B3LYP based on the Density Function Theory (DFT) by using the Gaussian software. And the basis set is 6-31G and no charge, since detail information is a key factor to the accuracy, the electron spin was considered and the energy between particle i and j was optimized. These calculated averaged mixing energies are used to obtain the Flory-Huggins interactions parameter χij(T) as shown in [32]


  (4)


And the χ parameter is used to determine the maximum repulsive force aij between particle i and j given as [32]


(5)


Where the terms are offered by interaction between the pure DPD particles i (aii=15kBT) [33], and end to this the calculation of aij(T) can be defined. The force FDij and FRij are responsible for the conservation of total momentum in the system. To calculate FDij and FRij, the parameters are derived from Groot and Warren [33]. The whole programming is realized in Mathematica, and the detail was offered in our former work [26].

  1. Results and discussion

It has been demonstrated that a strong interaction between the deposited metal and supported material plays a vital role in the stabilization of supported catalysts [34]. Despite the controllable deposition process during PLD experiment, atomic diffusion and agglomeration are still possible and probable, if a weak interaction exists between the atoms and the support, thus resulting in a large particle. It has been reported in the literature that doping the graphene lattice with N can enhance the Pt-C support interaction energy, resulting in highly stable Pt catalysts [35-37].

The comparison of energy between the positive and negative surface is presented in (Table 1), The energies were obtained through DFT method. The strongest interaction was at the distance of 1 nm; therefore, the electrons will form a strong bond. The electrons combination energy there is stronger on positive surface than negative. As the reference mentioned above, the positive surface will form smaller particles since the energy is stronger than the negative surface at all the distance. This verified that Au-positive surface bond is stronger and more difficult to break, and the positive surface will prevent the movement of Au by the high bond energy and increase the dispersion of the particles.

Applying the DPD method, the Au and LiNbO3 particles are modeled by the Gaussian software, in (Figure 1) The arrow indicates the polarization direction. This figure shows the interaction between the Au particles and the LiNbO3 polarized surface. In this result, the polarization is alternated with different orientation.

To verify the polarized surface effect on the Au dispersion, the DPD method is applied to uncover the surface morphology. The 2-D surface morphology of the Au nano particles are displayed in (Figure 2).

The positive surface offered more electrons on the GNPs since the Li atoms could carry more electrons which are not immobilized, and could form more bonds between the Li+ deficiency vacancy and Au particles. With the strong connection, the GNPs formed smaller particles stably and reduced the aggregation of GNPs since the bond between the Au is weaker than the Au-Li deficiency vacancy on the positive surface than the negative.

We further calculated the coverage of GNPs on positive and negative surface under different temperature. The coverage is defined by the GNPs particle numbers divided by the LiNbO3 molecular numbers.

As shown in (Figure 3a), when the deposition number is 100, the variation of the 500 K-1000 K coverage of Au on LiNbO3 crystal, red dots represent the coverage of the positive surface of lithium niobate crystal; black dots represent the coverage of the negative surface of LiNbO3 crystal. when the GNPs number is 100, with increasing temperature in the range of 550 K-1000 K, the coverage of positive surface is larger than the negative one; the coverage increases both linearly, thus we could obtain the change of GNPs by alternating the spontaneous polarization of ferroelectrics substrate.

In (Figure 3b), the dispersed GNPs particles increased to 1000, and the difference between the positive and negative surface coverage was more remarkable. The effect becomes obvious in the range of more GNPs, since the polarization and GNPs interact strongly. This is due to the increase of GNPs numbers, which offered more atoms to the substrate, and the interaction between the Li+ deficiency vacancy and GNPs on the positive surface becomes even more violent.

It could be seen that the increase of temperature added the coverage linearly on both surface. This dynamic is related to the bond between the gold and LiNbO3 molecular, the Li deficiency vacancy is much easier than O atoms to connect with the Au atoms, and the positive surface always occupied the most Li deficiency vacancy, as reported by YF Kong et al. [38]. The molecular dynamics simulation of Au on LiNbO3 substrate explained the experiment results obtained by the Satyaveda et al. [1], and this will further help forecast the morphology of Au on different ferroelectrics substrate.

  1. Conclusion

In summary, the positive surface of LiNbO3 offered more electrons to the bond between Li+ deficiency vacancy and GNPs, which makes more dispersed morphology than the negative surface, and which explained the experiment and first principle calculation results. And the alternation of polarization could change the coverage of GNPs at large temperature range. The coverage of GNPs could be adjusted by the temperature linearly.


Figure 1: The molecular model of Au and LiNbO3 molecule with different polarization direction. The white arrow is the polarization direction.




Figure 2: The GNP morphology on different polarized surface: 1) Positive surface and 2) negative surface. The unit is Å on each axis.



Figure 3(a-b): the coverage rate versus the temperature on polarized surface: a) the Au atoms are 100 and b) the Au atoms are 1000.

 

Positive

Negative

Distance (nm)

Energy(eV)

Distance (nm)

Energy (eV)

0.5

 

0.5

1009.74852

1.0

979.942545

1.0

946.445557

1.5

921.236717

1.5

900.642205

2.0

882.565429

2.0

868.336042

2.5

855.312869

2.5

844.842028

3.0

835.148555

3.0

827.110048

3.5

819.651413

3.5

813.283610

4.0

807.377837

4.0

802.208625

4.5

797.419613

4.5

793.139851

5.0

789.178857

5.0

785.577389

5.5

782.246654

5.5

779.174262

6.0

776.334151

6.0

773.682377

6.5

771.231615

6.5

768.919729

7.0

766.783127

7.0

764.749799

7.5

762.870375

7.5

761.068181

8.0

759.401970

8.0

757.793666

8.5

756.306203

8.5

754.862129

9.0

753.526013

9.0

752.222263

9.5

751.015435

9.5

749.832521

10.0

748.737021

10.0

747.658903

20

726.1475172

20

725.8510816

30

718.2105163

30

718.0744224

40

714.1596913

40

714.081876

50

711.7019964

50

711.6517013

100

706.7238199

100

706.7109945

200

704.2026298

200

704.1993914

500

702.6794323

500

702.6789111

1000

702.1699339

1000

702.1698046

 

Table 1: The comparison of energy between the positive and negative surface is presented in table.

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Citation: Liu Y, Li J, Wang J, Tang M, Qin Y, et al. (2017) Molecular Dynamics Simulation of Temperature-Dependent Distribution of Gold Nano Particles on Ferroelectrics Substrate. J Mater Sci Res: JMSR-104. DOI: 10.29011/ JMSR-104. 100004