Multi Scale Modeling of Nano Enable Solar cell With ... - CiteSeerX

2009 Third International Conference on Power Systems, Kharagpur, INDIA December 27-29 321

Multi Scale Modeling of Nano Enable Solar cell With Implementation on an HPC Setup Rohit Pathak

Satyadhar Joshi

Pawan Kotak

Computer Science Dept. Acropolis Institute of Technology & Research Indore, Madhya Pradesh, India [email protected]

Electrical & Electronics Dept. Shri Vaishnav Institute of Technology & Science Indore, Madhya Pradesh, India [email protected]

Electronics & Communication Dept. Acropolis Institute of Technology & Research Indore, Madhya Pradesh, India [email protected]

Abstract— Multi Scale modeling of Nano solar cells is proposed and implemented in this paper. In the realm of current theories many phenomenon of Nano scale remains inexplicable and there is a need of high computation power to work on areas such as multi scale modeling. We have shown the distribution of computation in multi scale modeling being implemented on an HPC Setup for nano enable solar cells. We have found that the research in nano polymer, nano fabrication can be used in our multi scale model to accurately predict behavior of Nano enable solar cells. We concluded that since one abstraction layer cannot truly depict the phenomenon so we need to use the models at various abstraction levels and synchronize the result with the experimental data. The computation was performed on (Microsoft Compute Cluster Server) MCCS platform which is shown and the configuration of nano solar computations are shown. Using this setup, research on nano solar cells will accelerate thereby making their applications realist in true times. Keywords-Solar Cell, Reliability, Modeling, MEMS(Micro Electro Mechanical Systems)

M

I.

INTRODUCTION

ultiscale modeling plays a very important role in engineering today in predicting properties that remain inexplicable under current modeling scenarios [2]. Device modeling is to develop a relation between materials properties and the electrical device characteristics of a nanostructured solar cell which is the key to get to its practical implementation in smart and efficient devices. This is in contrast to materials modeling, where materials parameters (like the optical absorption α(λ), the electrical mobilities µe, µh of electrons and holes, various relevant energy parameters, etc.) are studied and theoretically modeled based on physical and chemical phenomena and interactions. But these models alone cannot accurately predict the behavior and thus we have shown multi scale modeling on solar nano cells which proposes a unique approach to such kind of problems. There are challenges that exist in the current proposals, in this situation harvesting energy from solar cells that can be predicted with the help of multi scale modeling. Some solutions for skin effect

resistance and low voltage per antenna that can prove to be useful for harvesting of solar energy have been discussed [1]. Future commercialization of such solar cell will see greater use in diversified areas. The cost of solar cells will decrease and their efficiency will increase significantly in the future via the use of these techniques. Solar cells are the most promising technology for green and clean energy. Futuristic models of solar cells will use advanced nanomaterials for increasing efficiency and reliability of solar cells [12]. It is shown in [12] that nano-tech solar cells through nanotubes, quantum dots, and hot carriers could reduce the cost of PV cells and modules for bulk power generation as well as improve the cell conversion efficiency. The best single-junction solar cells have conversion efficiencies of 20 - 25 %. Silicon multi crystalline cells are less expensive with maximum reported efficiencies between 18 - 22 %. Module efficiencies reported for thin film PV module are currently ranging from 9 % (Si) to 13 % (CIGSS) but they are potentially cheaper than crystalline solar cells. Thus the importance of nanotechnology is eminent in area of solar cells. Quantum dots based solar harvesting architecture has been proposed in recent years [13]. Photo voltaic effects of a PbSe nanocrystal quantum dots based photo diode has also been studied [14]. Quantum Well Multi junction solar cells have been discussed that describe the growth and testing of thin layers of AlGaAs-GaAs, where the low temperature formation of metallic nano-dots in an indium tin oxide (ITO) antireflection coating for solar cells after an appropriate hydrogen plasma treatment. Formation of highly conductive nanostructures at hydrogenation temperatures below 250 C is shown [15]. Thus it is clear that quantum mechanical behavior of the solar cell needs to be taken into consideration while modeling it. Hence the application of such solar cells depends upon the various quantum physical behaviors. But there are various models being proposed in this regard which needs to be synchronized for the study. Solar cell integration with autonomous sensor networks and their powering has also been discussed [16] which is an important application of such devices.

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Indian Institute of Technology Kharagpur, INDIA

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2009 Third International Conference on Power Systems, Kharagpur, INDIA December 27-29 321 Blending and interpenetrating nanorod array of p-type polymer and n-type semiconductor offer promising pathways to efficient polymer photo voltaics are proposed in this area [17]. As proposed in [17] blending and interpenetrating nanorod array of p-type polymer and n-type semiconductor offer promising pathways to efficient polymer photovoltaics. The overall power conversion efficiency has been limited to around 5% or less in non-stacked cells. The improvement of the polymer and nanostructure should lead to a new generation of polymer solar cells with PCE of 10%, sufficient for large-scale commercial installation. Thus nano polymer is clearly the main researched area for nano enabled solar cells. The improvement of the polymer and nanostructure should lead to a new generation of polymer solar cells with PCE of 10%, sufficient for large-scale commercial installation which is proposed. Thus nano enabled solar cells are bound to make an impact on the commercialization scale. A “sweet spot” for low concentration, we can achieve 50% efficiency with a realistic number of junctions, have closer match between ideal and practical efficiency and allow new optical/electrical integrated designs has been proposed in [23]. Poly[3– 3’(vinylcarbazole)](PVK) was synthesized by oxidative polymerization with ferric chloride of N-vinylcarbazole,. the resulting polymer was then deposited on solid support by using the Langmuir–Schaefer (LS) technique has proved beneficial as given in [21]. Annealing process of laboratory cells and commercially available triple junction solar modules was performed at temperatures 70 C – 110 C, the time of exposure to heating is a very important factor in the process of regeneration of cells and modules which is given by Luczak et.al. in [22]. II.

MODELING OF NANO SOLAR CELLS

Multi scale modeling of Nano materials based solar cells can be done as shown in figure 1 considering the distribution of various domains from classical semi conductor behavior of solar cell to quantum dots. We have used C# MPI [3, 4] on a MCCS environment [5]. MPI is essentially a message-passing application programming interface (API) interface offering us a language-independent communication protocol used for pointto-point and mutual communication for performing parallel computation [6]. Though there are various methods available for multi scale modeling as shown in [10] but our work has been done on MCCS for computations nanotechnology [9]. This has been an expansion of the previous work [11], in which we have introduced the application of Nano enabled solutions in solar and fuel cells. TABLE I DIFFERENT SCALES OF MODELING

Abstraction Levels Upper most

Scale Above 1000 nm

Macro Levels Micro Levels

500-1000 nm 100-500 nm

Nano levels

Below 100 nm

Phenomenon Macro Electronics theories Intermediate Thin Films and technologies Quantum

Phenomenon In [18] Lee et. al. propose that the optical loss can reduce by anti reflection coatings (ARC). The porous silicon (PSi) formed by anodization is well known as using good ARC because P Si can be obtained easily and economically. The nano-PSi was obtained by electrochemical etching and has reflectance less than 20% in wavelength for 450- 100nm which is being given in [18]. The optical loss can reduce by anti reflection coatings (ARC). The porous silicon (PSi) formed by anodization is well known as using good ARC because PSi can be obtained easily and economically. Thus nanofabrication techniques can prove to be very useful in nano enabled solar cells where new techniques are proposed in the recent years.

Figure 1. Multi-scale modeling of Nanotech enabled solar cells.

The low temperature formation of metallic nano-dots in an indium tin oxide (ITO) antireflection coating for solar cells after an appropriate hydrogen plasma treatment which has been discussed in [19]. Formation of highly conductive nanostructures at hydrogenation temperatures below 250 ̊C was observed. Thermo dynamical formulas for nano enabled cells and their synchronization with other computations are the key to these type of analysis. The properties of the zinc oxide films, especially the structural and electrical ones, can be formed and improved by proper post-deposition rapid photo thermal processing under suitable conditions - temperature and ambient room temperature resistivity of 105cm for as-deposited ZnO, decreased to 103 cm after rapid photothermal processing as proposed in [20]. Thus the properties of the zinc oxide films, especially the structural and electrical ones, can be formed and improved by proper post-deposition rapid photo thermal processing under suitable conditions - temperature and ambient room temperature resistivity of 105cm for as-deposited ZnO, decreased to 103 cm after rapid photothermal processing. Thus looking at the above requirements we have modeled and implemented some aspects as shown below which a part of the complete library.

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2009 Third International Conference on Power Systems, Kharagpur, INDIA December 27-29 321 The requirement here is to calculate various parameters of different solar cells at various levels from Macro to Nano as well as hybrid models of solar cells arranged in any array. As stated in [7] the Fermi level of an intrinsic semiconductor is calculated to be

EF

=

EC + EV kT NV + ln 2 2 NC

(1)

Here EF is Fermi energy, k the bolltzmann constant, T is absolute temperature, N is the effective density of holes in the valence band and Nc is the effective density of states of electrons in the conduction band. The basic principles of solar cell using nano crystalline materials have been discussed and an analytical approximation for the lowest excited 1s state is given by the expression [7]

h2 1 1 1.8q 2 E* ≈ Eg + 2 ( + ) − 8r me mh r

(2)

where r is the crystal radius, me an effective mass of electrons and mh is the effective mass of holes for the crystalline. As we know that the electrical property of a dye sensitized solar cell

J = JSC[1 − exp

q (V − Voc ) ] nKBT

(3)

where J is the output current density, Jsc the short-circuit current density, q the elementary charge, V the external applied voltage, kB the Boltzmann constant, and T the absolute temperature. Here, n is called “ideality factor”. It is an extension for the diode based model. Fullerenes based photo voltaic material follow the expression for current as

⎡ ⎛ q(V + IRS ) ⎞ ⎤ V + IRS − Ip I = Io ⎢exp ⎜ ⎟ − 1⎥ + RSh ⎝ nKT ⎠ ⎦ ⎣

(4)

Figure 2. Fill Factor vs. Voltage which an important way of analysis. CODE I GRAPH GENERATION CODE

syms v; q=1.6e-19; t=300; k=1.3e-23; FF = (((q*v)/(k*t))log(0.72+((q*v)/k*t)))/(1+((q*v)/(k*t))); x=0:10; y=subs(FF, v, x); plot(x,y);

The fill factor increases with the increase of the opencircuit voltage. The upper limit of the conversion efficiency is a strong function of the band gap energy. The optimum efficiency of 30% occurs when the bandgap is between 1.4 eV and 1.6 eV, as shown in Fig. 20 at AM 1.5 and 1 sun. The band gap energy between 1 eV and 2 eV is suitable for solar cell to achieve relatively high efficiency. The core phenomena of the solar cell are taking place on a microscopic (even ‘nano-scopic’) scale at the boundary between the two constituent phases. A photon is absorbed, giving rise to a free electron–hole (eh) pair and or a bound excitation pair. The eh pair is then separated into an electron in the electron conductor, and a hole in the hole conductor. Both the n- and the p-phases of a nano-structured solar cell form an inter connected 3-D network and both networks are supposed to interpenetrate perfectly.

where Ip is the photocurrent, Io and n respectively denote the reverse saturation current and diode quality factor of the p-n junction, Rs and Rsh are lumped series and shunt resistances of the cell, respectively. Thus these are further being modeled as shown in Code 2 for multi scale optimizations.

Light with wavelength λ is absorbed in a solar-cell material with a total absorption constant α(λ), giving rise to a total absorption G(λ,y). In a simple case as given in,

Thus the distribution of modeling in four different domains has been achieved to form multi scale modeling of a nanotechnology enabled solar cell.

Photo excitations in conjugated polymers show diffusion lengths of only around 5–20 nm, the structure of the polymeric nano phase within the photoactive layer has a large influence on the device properties and the solar power conversion efficiency.

The fill factor is a function of the open-circuit voltage and is approximately expressed by [7].

qV∞ qV ⎞ ⎛ − ln ⎜ 0.72 + ∞ ⎟ kT kT ⎠ ⎝ FF = qV 1+ ∞ kT

G ( λ , y ) = Φ 0α ( λ ) exp [ − yα (λ )]

III. (5)

(6)

PROPOSED CHANGE IN METHODOLOGY AND IMPLEMENTATION

Suppose we have a phenomenon which is described by proposed F1, F2, and F3 at various scales and suppose we have a set of reading X1, X2, and X3 on various scales from 3

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2009 Third International Conference on Power Systems, Kharagpur, INDIA December 27-29 321 experimental results. Now the challenge for us is to synchronize the results for getting the real results, here we have shown the use of HPC setup. Step 1. Feel the formulas to the HPC setup Step 2. Feed the results from experimental data Step 3. Execute the synchronization on HPC setup as shown Step 4. Get the results and check again TABLE II DISTRIBUTION OF VARIOUS MULTI-SCALE PARAMETERS

Scale 20-100 100-500 500-above

Formulation F1 F2 F3

Experimental X1 X2 X3

Current in this case can be function of various parameters:I=F1(a,b,c,d,e) I=F2(a,b,c,d) I=F3(a,d,c) Here a,b,c,d,e are various dependencies, here we can use the same to synchronize the reliability calculations. We can see that the we have to make various factors for computation as we go to small scale for the same current (I). MPI program code has been executed in code II and the respective output is shown in figure 2. And finally programming code for an extreme optimization numerical demo on a four node cluster setup has been achieved as exemplified by figure 2. The outputs are thus shown in figure 3. The distribution of computation programmed can be seen from the code which is the part of larger code used for complete computations and optimization. The code is part of formulas written for analysis of solar cells. The code smartly distributes all the formulas and do intensive calculations on various parameters as pre-programmed to find the matching phenomenon. CODE II EXTREME OPTIMIZATION NUMERICAL DEMO CODE FOR COMPUTATION ON FOUR NODE CLUSTER SETUP

using System; using System.Collections.Generic; using System.Linq; using System.Text; namespace hydroExtreme1{ using MPI; // The DoubleComplex structure resides in the Extreme namespace. using Extreme; // The delegate classes reside in the Extreme.Mathematics // namespace. using Extreme.Mathematics;

// We also use the Extreme.Mathematics.SpecialFunctions namespace. using Extreme.Mathematics.SpecialFunctions; // calculas part using Extreme.Mathematics.Calculus; class Program { static void Main(string[] args) { using (new MPI.Environment(ref args)) { Communicator comm = Communicator.world; Console.WriteLine("Check from process " + comm.Rank); if (comm.Rank == 0) { double Ec = 1, Ev = 1, T = 1, Nv = 1, Nc = 1; Console.WriteLine("Process " + comm.Rank + " status: calculating Ef"); Console.WriteLine("Process " + comm.Rank + " status: " + "Ef as calculated is: " + Ef(Ec, Ev, T, Nv, Nc)); } else if (comm.Rank==1){ double Eg = 1, me = 1, mn = 1, r = 1; Console.WriteLine("Process " + comm.Rank + " status: calculating Excited Energy"); Console.WriteLine("Process " + comm.Rank + " status:" + "Excited Energy as calculated is: " + energyExcited(Eg, me, mn, r)); } else if (comm.Rank == 2) { double Jsc = 1, T = 1, V = 1, Voc = 1, n = 1; Console.WriteLine("Process " + comm.Rank + " status: calculating J"); Console.WriteLine("Process " + comm.Rank + " status: " + "J as calculated is: " + J(Jsc, T, V, Voc, n)); } else if (comm.Rank == 3) { double Io = 1, V = 1, I1 = 1, Rs = 1, Rsh = 1, Ip = 1, n = 1, T = 1; Console.WriteLine("Process " + comm.Rank + " status: calculating I"); Console.WriteLine("Process " + comm.Rank + " status: " + "I as calculated is: " + I(Io, V, I1, Rs, Rsh, Ip, n, T)); } else { Console.WriteLine("Process " + comm.Rank + " status: Idle"); } /*All processes join here*/ comm.Barrier(); /*All processes completed*/ if (comm.Rank == 0) { Console.WriteLine("All processes finished"); } }/* End of MPI Environment namespace */ }

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2009 Third International Conference on Power Systems, Kharagpur, INDIA December 27-29 321 /*Mathematical functions listed below*/ static double Ef(double Ec, double Ev, double T, double Nv, double Nc) { double Ef, t1, t2, t3, K; K = 1.3806503e-23; t1 = (Ec+Ev)/2; t2 = (K*T)/2; t3 = ElementaryFunctions.Log1PlusX((Nv - Nc) / Nc); Ef = t1 + (t2*t3); return Ef; } static double energyExcited(double Eg, double me, double mn, double r) { double Ex, h, Epsilon, q, t1, t2, t3; h = 6.62606896e-23; Epsilon = 8.8541878176e-12; q = -1.602176487e-19; t1 = ElementaryFunctions.Pow(h, 2) / (8 * ElementaryFunctions.Pow(r, 2)); t2 = (1/me)+(1/mn); t3 = (1.8 * ElementaryFunctions.Pow(q, 2))/(Epsilon*r); Ex = Eg + (t1*t2) - t3; return Ex; } static double J(double Jsc, double T, double V, double Voc, double n) { double J, K, t1, t2, t3, q; K = 1.3806503e-23; q = -1.602176487e-19; t1 = q*(V-Voc); t2 = t1 / (n * K * T); t3 = ElementaryFunctions.ExpMinus1(t2); J = -1 * Jsc * (t3); return J; } static double I(double Io, double V, double I1, double Rs, double Rsh, double Ip, double n, double T) { double K, t1, t2, t3, t4, q, I; K = 1.3806503e-23; q = -1.602176487e-19; t1 = q * (V + (I1 * Rs)); t2 = n * K * T; t3 = ElementaryFunctions.ExpMinus1(t1/t2); t4 = (V + (I1 * Rs)) / Rsh; I = (Io * t3) + t4 - Ip; return I; } }}

Above sudo code of the developed library demonstrates the use of MPI.NET and Extreme Optimization Numerical Library for .NET. The MPI.NET communicator is used for communication between different processes.The MPI.NET environment gives us access to all the functions and objects for interprocess communication. In the above part of the code we have used MPI.NET Library to distribute various computation involving calculus. The code distributes the computation as

shown to perform different calculations through different processes. Also the computation in last 5th line uses ElementaryFunctions.ExpMinus elementry exponentials. Thus in this example we have covered all aspects of numerical analysis needs for setting up an HPC Setup for Nano computation on fuel cells.

Figure 3. Results of the computations for various scales.

Node vs. Scales 1200 1000 800 600 400 200 0 0

50

100

150

Figure 4. Node vs. Scales (in nano meter) realization of multi scale in our HPC setup.

Thus in figure four we have expressed the requirements of computations power at different scales. Advantages of the multi scale study in solar cell are thus eminent and are rigorously explored in this paper thereby making areas like application and reliability benefit the most. Other benefits of multi scale modeling MEMS devices are also in reliability analysis as shown in [8]. IV.

CONCLUSION

Multi Scale modeling has been shown on an MCCS environment with the computation on various scales of Nano enable solar cells. A novel approach was introduced for solar 5

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2009 Third International Conference on Power Systems, Kharagpur, INDIA December 27-29 321 cells in which concepts of multi scale modeling is applied and nano computations being modeled. The output reveals that computation on multi scale modeling on an HPC synchronized with experimental data can be used for further advancement of nano enabled solar cells. Current research in this regard has been shown and a computation model development for modeling has been introduced. The nano solar cells needs to modeled keeping in view the recent theories and optimizing it with the multi scale model developed can provide solutions to various problems in practical implementation of nano solar cells. The result of the computation distributed on various HPC nodes is shown which was implementing in the experiment.

[13]

[14]

[15]

[16]

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