Physics

Chapter 1. Spiral §1. 1 Spiral A spiral concentrator uses gravity to separate particles of different densities. It is used globally in the mineral processing industry. It is one of the most effective, low-cost devices for the gravity beneficiation of ores.

Manufactured from lightweight, corrosion and abrasion resistant materials, spirals require a minimum of maintenance and upkeep. Consists of an open trough that twists downward in helix configuration about a central axis. Particles fed to the top of the concentrator are separtated radially on the basis of density and size as the slurry gravitates downward.Spirals are made of fiberglass onto which smooth urethane surface is molded to form a trough in the shape of a spiral as the name suggests. Between individual spiral types, the profile of the trough and the pitch as well as the diameter and height and number of turns can vary according to duty. Feed slurry is introduced at the top and is subjected to a combination of gravitational and centrifugal forces imparted by its motion down the spiral. This causes high SG minerals to move towards the centre of the trough and water and low SG minerals towards the outside.

The segregated slurry discharging from the spiral at the bottom can thus be separated by cutters into high SG (concentrate) and low SG (tailings) together with intermediate SG (middling). Single Start Spiral §1. 2 Applications of Spirals Spiral concentrators have, over numerous years, found many varied applications in mineral processing, but perhaps their most extensive usage has been in the treatment of heavy mineral sand deposits, such as those carrying ilmenite, rutile, zircon, and monazite, and in recent years in the recovery of fine coal.Spirals are commonly used to separate sand sized particles with moderate SG differential in the range -2mm +75um, although varieties exist that can separate reasonably efficiently down to 38um. Below this size range efficiency falls off rapidly, and enhanced fine gravity separators are generally required. Applications for spiral separators:

• Mineral Sands

• Iron ore

• Chromite

• Silica Sands

• Coal

• Gold

• Others Spiral components ? Modular feedbox ? Splitters ? Repulpers ? Product box Modular feedbox – The feed point to a spiral separator is an area where the feed slurry is normally relatively fast flowing.The duty of the feedbox is to dissipate some of the energy in the feed slurry and present the feed to the top of the separation trough in a homogenous and quiescent slurry distributed across the width of the spiral trough.

The modular feed box system achieves this objective with replaceable componentry that can withstand a high impact abrasion. [pic] Splitters- Each spiral start has six concentrate collection splitters which direct the concentrate into the inboard collection trough. These splitter mechanisms are designed for easy adjustment and repeatable positioning (using a graduated scale).They have a positive splitter-to-spiral seal and are able to handle a high volume of concentrate. All splitter handle indicators are readily visible so that the operator s can see and adjust large numbers of handles accurately and quickly. The splitter is used to direct the particles to their respective discharge ports. At the end of the spiral there is a Splitter and discharge part Repulpers – The installation of repulpers on spiral troughs improves the separation efficiency of spiral separators.

The function of repulpers is to capture and divert a portion of water from the high velocity tailing stream and introduce it to the relatively sluggish middling stream in order fluidise the particle bed and re-initiate separation mechanisms . Repulper to Direct water from Outer Spiral Wall To concentrate collection area Product Box- The collection and laundering of the product fractions from spirals (concentrators, middlings, tailings) is facilitated by product boxes which are designed to collect common fractions from the separating troughs of multi- start spiral separators.The design of this feature is critical to ensure the effective directional change and joining of fast- moving slurries whilst minimizing splash and impact abrasion. [pic] §1. 3 Spiral Operation [pic] Cross section of a spiral concentrator divided into various regions A cross section of a spiral concentrator can be divided into various regions, with each region describing the effect it has on the slurry traveling through it :

? On the outer most region (1) (perimeter), will have mostly water, with fine particles, trapped by the high velocity of the moving water. Moving inward towards the center of the spiral, the next region

(2) would consist of a very small area where the maximum water velocity exists, and prevents any separation to occur. This region is defined since it separates the next region

(3) from the first region ? Region 3 is a very active region where the velocity begins to slow down and most of the separation occurs, as more dense particles settle to the bottom and the water velocity keeps the light density particles in the stream near the surface, where they eventually wind up in the outer regions (2 and 1).

The next region (region 4) is actually where two regions overlap (region 3 and 5), and is a very narrow region (like region 2). ? Next to the last region (region 5) is where the heavy density concentrates collect Working Principle of Spiral Concentrators: In order to have a good separation, there should be a difference in Specific Gravity’s of at least 1. 0. One main benefit of spiral concentrators is they have no moving parts. The feed range, in percent solids, to a spiral ranges from 20% solids up to 40% solids. Depending upon the material characteristics, a maximum efficiency will usually be reached somewhere in this range.All that is required are some slurry pumps, the slurry to be separated and the banks of spirals with a feed distributor.

? Slurry is pumped to the top of the spiral (typically 13′ to 15′ from the floor), and it enters a feed distributor that evenly distributes the feed to each spiral concentrator. The design and shape of the spiral make it work, when combined with gravitational acceleration. ? As the slurry travels the spiraling path down the spiral, mineral grains settle and start sorting according to size, density and to a lesser extend shape. Low density particles are carried with the bulk of the water towards the outside of the spiral (perimeter), while particles with the greatest density migrate towards the inside of the spiral Chapter 2 Types of Spirals §2. 1 Types of Spirals ? Washwaterless Spirals This type of spiral is used in most applications, particularly for concentrating low-grade ores. The only water required is added with the solids prior to introducing feed onto the spiral. Concentrates are removed either at the bottom directly into the product box or at several intermediate take-off points down the spiral.

? Coal/Mica SpiralsLarger in diameter than mineral-type spirals, these spirals are designed to take advantage of the particle shape differences as well as specific gravity differences. Take-off splitters at different points down the helix give this spiral a high capacity to remove refuse ore and siliceous contaminants from the coal or the mica. ? Washwater Spirals Washwater spirals require the addition of water at various points down the spiral providing more efficient washing of the concentrate, i. e. , transporting away light gangue (silica) from the concentrate band Standard Spiral Separators – Typically treat 2tph per start Have 3 starts per assembly (attached to one column) – Tonnage per assembly is 6tph ? High Capacity Spirals – Treat up to 5tph per start – Have 4 starts per assembly – Tonnage per Assembly is 20tph High Capacity Spiral (Rougher and Scavenger) High Capacity Spiral Fine Mineral Spiral Standard Spiral Standard High Grade Spiral New High Grade Spiral §2. 2 Benefits of the use of Spirals ? One main benefit of spiral concentrators is that they have no moving parts; ? To treat more material in less floor area; ? Low maintenance and simplicity; ? Higher separation efficiency than pinched sluice devices Chapter 3 Geometric and Operational ParametersGeometric Parameters Geometrical variables: ? Diameter ? Pitch ? Down trough slope Some of these factors are interdependent. In general, recent design spiral separator have complex geometrical trough profiles that change throughout the length of the spiral trough.

Diameter The trough diameter impacts on the trough profiles and downtrough slope and thence scale-up for higher capacity is not a trivial geometrical exercise Pitch The pitch (vertical distance between successive turns) determines the down trough slope and slurry velocity. This factor also influences the residence time of the feed slurry as does the number of turns.Down slope The slope can effect the velocity of the slurry moving through the spiral. The steeper the slope the higher the slurry velocity.

The amount of water improves the amount of slurry and the Slurry velocity. The slurry velocity also depends on the density, mass and the viscosity of the materialThe amount of water effects the slurry viscosity . If the amount of water increases the flow- rate of the slurry increases. Conclusion The wide variety of spiral separator models now available provides a selection of models that ensures most applications where gravity separation of fine minerals can be utilized. The use of spiral concentrators benefits low-cost process plant .Further benefits to operators of mineral processing plants incorporating spiral separators include:

• Efficient feed distribution and products laundering systems

• Ease of control

• Low maintenance and long service life

• The availability of circuit modeling systems

• Simplified plant operation The ongoing development effort to improve the metallurgical performance and capacity of spiral separators for specific duties has effectively extended the product life cycle of this gravity separation device.

The term thermal expansion refers to the increase in size of an object as that object is heated. With relatively few exceptions, all objects expand when they are heated and contract when they are cooled. Perhaps the most important exception to this rule is water. Water contracts as it cools from its boiling point to about 39. 2°F (4°C). At that point, it begins to expand as it cools further to its freezing point.

This unusual effect explains the fact that ice is less dense than water.Different materials expand or contract at different rates. In general, gases expand more than liquids, and liquids expand more than solids. When an object is heated or cooled, it expands or contracts in all dimensions. However, for practical reasons, scientists and engineers often focus on two different kinds of expansion, or expansivity: linear expansivity (expansion in one direction only) and volume expansivity (expansion in all three dimensions). The amount by which any given material Joints such as this one are used in bridges to accommodate thermal expansion.Objective To determinate the average coefficient of linear Expansion of the copper rod.

Theory: Solids Expand as they are heated and contract if they are cooled. Thus their length is function of temperature. Thus , when the temperature of an object is increased by ? T, its Length L initial increases by ? L. Apparatus:

• Dial Gauge
• Thermometer
• Cu rod
• Rubber tube
• Boiler
• Hot plate Method
• Measure the initial length of Copper rod, with a meter stick at room temperature.
• Adjust the micrometer dial at zero.
• Insert the thermometer in the top of the copper rod and record its reading at room temperature.

Start heating the water by switching in the electric hot plate to maximum and wait until the steam is generated from the water boiler.

When the rod reaches a constant uniform temperature, measure the corresponding change in the length ? l. Switch off the hot plate and immediately record ? l (on the dial micrometer) and the temperature measure T ( on the thermometer ) upon cooling.

A schematic of Direct Torque Control is shown. As it can be seen, there are two different loops corresponding to the magnitudes of the stator flux and torque. The reference values for the flux stator modulus and the torque are compared with the actual values, and the resulting error values are supplied into the two level and three-level hysteresis blocks respectively.

The outputs of the stator flux error and torque error hysteresis blocks, together with the position of the stator flux are used as inputs of the look up table. The inputs to the look up table are given in terms of 1,0,-1 depend on whether torque and flux errors within or beyond hysteresis bands and the sector number in which the flux sector presents at that particular moment. In accordance with the figure 1.2, the stator flux modulus and torque errors tend to be restricted within its respective hysteresis bands.

From the schematic of DTC it is cleared that, for the proper selection of voltage sector from lookup table, the DTC scheme require the flux and torque estimations.

Techniques for Quantifications of Stator Flux in DTC:

Accurate flux quantifications in Direct Torque controlled induction motor drives is necessary to ensure proper drive operation and stability. Most of the flux estimation methods proposed was based on voltage model, current model, or the combination of both. The estimation based on current model normally applied at low frequency, and stator current and rotor mechanical speed or position.

In some industrial applications, the use of incremental encoder to get the speed or position of the rotor is undesirable since it reduces the robustness and reliability of the drive. It has been generally known that even though the current model has managed to remove the sensitivity to the stator resistance variation. The use of rotor parameters in the estimation introduced error at high rotor speed due to the rotor parameter variations. So in this present DTC control scheme the flux and torque are quantified by using voltage model which does not need a position sensor and the only motor parameter used is the stator resistance. (Oghanna, 2011)

Introduction of FLC

Fuzzy logic has become one of the most successful of today’s technology for developing sophisticated control system. With it aid, complex requirement may be implemented in simply, easily and inexpensive controlling method. The application ranges from consumer products such as cameras, camcorder, washing machines and microwave ovens to industrial process control, medical instrumentation and decision support system .many decision-making and problem solving tasks are too complex to be understand quantitatively however, people succeed by using knowledge that is imprecise rather than precise.

Fuzzy logic is all about the relative importance of precision. It has two different meanings. In a narrow sense, fuzzy logic is a logical system which is an extension of multi valued logic, but in wider sense fuzzy logic is synonymous with the theory of fuzzy sets. Fuzzy set theory is originally introduced by LotfiZadeh in the 1960s, resembles approximate reasoning in it use of approximate information and uncertainty to generate decisions.

Several studies shows, both in simulations and experimental results, that Fuzzy Logic control yields superior results with respect to those obtained by conventional control algorithms thus, in industrial electronics the FLC control has become an attractive solution in controlling the electrical motor drives with large parameter variations like machine tools and robots.

However, the FL Controllers design and tuning process was often complex because several quantities, such as membership functions, control rules, input and output gains, etc. must be adjusted. The design process of a FLC can be simplified if some of the mentioned quantities are obtained from the parameters of a given Proportional-Integral controller (PIC)for the same application. (Lotfizabeh, 2011).

Why fuzzy logic controller (FLC)

• Fuzzy logic controller was used to design nonlinear systems in control applications. The design of conventional control system is normally based on the mathematical model. If an accurate mathematical model is available with known parameters it can be analyzed and controller can be designed for specific performances, such procedure is time consuming.
• Fuzzy logic controller has adaptive characteristics. The adaptive characteristics can achieve robust performance to system with uncertainty parameters variation and load disturbances.

The Main Principles of Fuzzy Logic Controller

The fuzzy logic system involves three steps fuzzification application of fuzzy rules and decision making and defuzzification. Fuzzification involves mapping input crisp values and decision is made based on these fuzzy rules. These fuzzy rules are applied to the fuzzified input values and fuzzy outputs are calculated in the last step, a defuzzifier coverts the fuzzy output back to the crisp values.

The fuzzy controller in this thesis is designed to have three fuzzy input variables and one output variable for applying the fuzzy control to direct torque control of induction motor. There are three variable input fuzzy logic variables. The stator flux error, electromagnetic torque error, and angle of the flux in the stator.

Block Diagram of Fuzzy Logic Controller

The membership functions of these Fuzzy sets are triangular with two membership function N and P for the flux-error, three membership functions N, Z, P for the torque-error, six membership variables for the stator flux position sector and eight membership functions for the output commanding the inverter. The inference system contains thirty six Fuzzy rules which is framed in order to reduce the torque and flux ripples.

Each rule takes three inputs, and produces one output, which is a voltage vector. Each voltage vector corresponds to a switching state of the inverter. The switching state decides the pulse to be applied to the inverter. The Fuzzy inference uses MAMDANI’s procedure for applying Fuzzy rules which is based on minimum to maximum decision.

Depending on the values of flux error, torque error and stator flux position, the output voltage vector is chosen based on the Fuzzy rules. Using Fuzzy Logic controller the voltage vector is selected such that the amplitude and flux linkage angle is controlled. Since the torque depends on the flux linkage angle the torque can be controlled and hence the torque error is very much reduced.

Fuzzy logic controller (FLC)

Fuzzy logic expressed operational laws in linguistics terms instead of mathematical equations. Many systems are too complex to model accurately, even with complex mathematical equations, therefore traditional methods become impracticable in these systems.
However fuzzy logics linguistic terms provide a possible method for defining the operational characteristics of such system.
Fuzzy logic controller can be considered as a special class of symbolic controller. The configuration of fuzzy logic controller block diagram is shown in Fig.2.6

Block diagram for Mamdani type Fuzzy Logic Controller

The fuzzy logic controller has three main components

1. Fuzzification.
2. Fuzzy inference.
3. Defuzzification.
4. Fuzzification

The following functions:

1. Multiple measured crisp inputs first must be mapped into fuzzy membership function this process is called fuzzification.
2. Performs a scale mapping that transfers the range of values of input variables into corresponding universes of discourse.
3. Performs the function of fuzzification that converts input data into suitable linguistic values which may be viewed as labels of fuzzy sets.

Fuzzy logic’s linguistic terms are often expressed in the form of logical implication, such as IF-THENrules. These rules define a range of values known as fuzzy membership functions.
Fuzzy membership function may be in the form of a triangle, a trapezoidal, and a bell as shown in Fig. 2.7

Triangle Trapezoid

Bell

Figure 2.7. (a) Triangle, (b) Trapezoid, and (c) BELL membership functions.
The inputs of the fuzzy controller are expressed in several linguist levels. As shown in Fig.2.8 these levels can be described as positive big (PB), positive medium (PM), positive small (PS), negative small (NS), negative medium (NM), and negative big (NB). Each level is described by fuzzy set below.

Figure.2.8.Seven levels of fuzzy membership function

Fuzzy inference

Fuzzy inference is the process of draw up the mapping from a given input to an output using fuzzy logic. The mapping then provides a basis from which decisions can be made. There are two types of fuzzy inference systems that can be implemented in the Fuzzy Logic Toolbox: Mamdani-type and Sugeno-type. These two types of inference systems vary to some extent in the way outputs are determined.

Fuzzy inference systems have been successfully applied in fields such as automatic control, data classification, decision analysis, expert systems, and computer vision. Because of its multi-disciplinary nature, fuzzy inference systems are associated with a number of names, such as fuzzy-rule-based systems, fuzzy expert systems, fuzzy modeling, fuzzy associative memory, fuzzy logic controllers, and simply, fuzzy Mamdani’s fuzzy inference method is the most commonly seen fuzzy methodology.

Mamdani’s method was among the first control systems built using fuzzy set theory. It was proposed in 1975 by Ebrahim Mamdani as an attempt to control a steam engine and boiler combination by arranging a set of linguistic control rules obtained from experienced human operators. Mamdani’s effort was based on LotfiZadeh’s 2011on fuzzy algorithms for complex systems and decision processes.

The second phase of the fuzzy logic controller is its fuzzy inference where the knowledge base and decision making logic reside .The rule base and data base from the knowledge base. The data base contains the description of the input and output variables. The decision making logic evaluates the control rules .the control-rule base can be developed to tolerate the output action of the controller to the inputs.

Defuzzification

The output of the inference mechanism is fuzzy output variables. The fuzzy logic controller must convert its internal fuzzy output variables into crisp values so that the actual system can use these variables. This conversion is called defuzzification.

Fuzzy Direct Torque Controller

The fuzzy direct torque control technique consists of inverter, induction motor, torque controller, flux controller, flux estimator, torque estimator and clarke’s transform. The fuzzy logic technique which is based on the language rules, is used to solve this nonlinear issue. In a three phase voltage source inverter, the switching commands of each inverter leg are matched. For each leg a logic state Ci (I = a,b,c) is defined, thatCi is 1 IF the upper switch turned ON and zero IF the lower switch turned OFF. IFCi is 0 THEN it means that the lower switch is ON and upper switch is turned OFF. Since three are independent there will be eight different states, so eight different voltages.

To study the performance of the developed DTC model, a closed loop torque control of the drive is simulated using MATLAB/Simulink simulation package. The torque error and flux errors were compared in their respective hysteresis band to generate their respective logic state as (ST) and (S?). The sector logic state (S?) is used as the third controlling signal for referring the DTC switching table. These three controlling signals are used to determine the instantaneous inverter switching voltage vector from three dimensional DTC switching lookup table. The simulation results are implemented for conventional DTC scheme and proposed fuzzy based DTC scheme. There are three non-zero voltage vectors and two voltage vectors.

Block Diagram of fuzzy logic DTC

The DTFC on induction motor drives is designed to have three fuzzy input variables and one output control variable to achieve fuzzy logic based DTC of the induction machine. Its functional block diagram is as shown in fig. 2.9 the three input variables are the stator flux error, electromagnetic torque error and angle of stator flux. The output was the voltage space vector. The DTF Cconsist of fuzzification, rule base, data base, decision making and defuzzification.

The input variable (?T) and (?) are fuzzified using fuzzy functions over the respective domains. The output of DTFC was also fuzzified, the all possible fuzzy rules are stored in fuzzy rule base.

DTFC takes the decision for the given input crisp variables by firing this rule base.

DTC functional Block Diagram

SUMMARY

With the principle of direct torque control (DTC)of induction motor, the high ripple torque in the motor have being reduced to above 65% in the reviewed work.
These controls have being one of the best controls for driving induction motor because of its principles. Though DTC strategy is popular and simpler to implement than the flux vector control method because voltage modulators and coordination transformations are not required.

Although, it introduces some drawbacks as follows:

1. High magnitude of torque ripple
2. Torque and small errors in flux and torque are not distinguished. In other word, the same vectors are used during start up and step changes and during steady state.
3. Sluggish response in both start up and step changes in either flux or torque.

In other to overcome the mentioned drawbacks, there are difference solution like fuzzy logic duty ratio control method. In this work fuzzy logic with duty ratio control is proposed to use with direct torque control to reduce this high ripple torque and realized the best DTC improvement.

Backlash can be defined as the maximum distance or angle through which any part of a mechanical machine may be moved in one direction without applying appreciable force or motion to the next part in mechanical sequence and is a mechanical form of dead band. More so, it is any non-movement that occurs during axis reversals.

For instance, when x-axis is commanded to move one inch in the positive direction, immediately, after this x – axis movement, these x-axis is also commanded to move one inch in the negative direction if any backlash exists in the x-axis, then it will not immediately start moving in the negative direction and the motion departure will not be precisely one inch.

So, it can cause positioning error on holes location, if the motion required to drill the holes causes a reversal in axis direction, it also causes loses ofmotion between reducer input and output shafts, making it difficult to achieve accurate positioning in equipment such as machines tools etc.

The main cause of this problem electrically is vibrations from electric motor as a result of high ripple torque in the induction motor.

• Benefits of solving the problem

1. High quality products will be produce.
2. Productivity will increase because adjustment and readjustment of machine feeding handle or feeding screw to eliminate backlash have been reduced.
3. Operational cost will reduced.
4. Greater efficiency will be guaranteed.
5. Greater accuracy and precision of product will be guaranteed.
6. Wasting of materials will be highly reduced.

RESEARCH OBJECTIVES

1. To develop a model that will control the error to achieve stability using DTC and fuzzy logic with duty ratio.
2. To determine the error in the torque of the machine that causes vibration which lead to backlash that result in production of less standard products.
3. To determine the position of the stator flux linkage space vector in the poles of the induction motor.
4. To determine the stator linkage flux error in the induction motor that also causes vibration.
5. To simulate the model above in the Simulink environment and validate the result.

SCOPE AND LIMITATION OF THE WORK

This project work is limited to the use of fuzzy logic controller with duty ratio to replace the torque and stator flux hysteresis controllers in the conventional DTC techniques. The controllers have three variable inputs, the stator flux error, electromagnetic torque error and position of stator flux linkage vector. The inference method used was the Mamdam fuzzy logic inference system. The deffuzzification method adopted in this work is the maximum criteria method.

SIGNIFICANCE OF THE WORK

The importance of this work in industry where induction motor drives are mainly in application cannot be over emphasis.

As earlier noted, induction motors because of their ruggedness simple mechanical structure and easy maintenance; electrical drives in industries are mostly based on them.

Also, a wide range of induction motor applications require variable speed, therefore induction motor speed, if not accurately estimated will affect the efficiency of the overall industrial processes. Equally, the harmonic losses if not put in check will shorten the life p and efficiency of the motor inverter.
Based on the above, it is aimed at reducing the principle causes of the inefficiency in the DTC induction motor and improves the performance of the system.

ORGANIZATION OF THE WORK

The work is organized into five chapters. Various control techniques were discussed in chapter two, in chapter three, we discusses the methodology, design and implementation of the direct torque control of induction motor using fuzzy logic with duty ratio controller.

Chapter four discusses data collection, analysis and the simulated results showing the system using conventional method of control and the proposed fuzzy logic with duty ratio method of control under applied load torque conditions.

Conclusion, recommendations and suggestion for further work are mentioned in chapter five.

High ripple torque is the main causes of vibration in induction motor that lead to backlash. It is the distance through which any part of a machine system may move in one direction without applying enough force to the next part in mechanical sequence and is a mechanical form of dead band. It can cause positioning error on a hole location, if the motion required to create a holes lead to a reversal in axis direction, it also causes loss of motion between reducer front as well as back shafts, making it difficult to achieve accurate part in machine equipment. It further lead to the production of less standard products of machine equipment etc.

The experiment was carried out to determine the error that causes the backlash using torque ripple test apparatus. The motor with ripple torque of 0.9Nm was linked to the shaft of the motor and with a load torque sensor that can measure the vibration and equally give the vibrational result of the motor. The DC voltage was supplied to the motor and observed a peak to peak torque equal to 0.9Nm, and 0.15Nm. The flux leakage error was also determined using flux meter to measure the coils in the slots of the stator of the induction motor and also the different poles of the motor was measured to determine the position the stator flux linkage of the space vector.

The errors were simulated in Simulink environment using director torque control and fuzzy logic with duty ratio control. The torque error of 0.15Nm were reduced to 0.05 and fuzzy logic duty ratio reduce it further to 0.0055Nm The use of the duty ratio control resulted in improved steady state torque response with less torque ripple than the conventional DTC. Fuzzy logic control was used to implement the duty ratio controller. The effectiveness of the duty ratio method was verified by simulation using MATLAB/SIMULINK.

After the implementation, we observed that the ripple was reduced drastically and we are able to achieve 95% of improvement.

Crystal structure, optical and electrical characteristics of rutile

TiO2- based photoanodes doped with GeO2

The effect of germanium dioxide GeO2 doping on dye-sensitized solar cells (DSSCs) TiO2 nanocrystallites photoanodes with different concentrations with composition

(TiO2-(GeO2)x: 0 * x  0.3 wt%)

has been studied. The samples have been prepared by a solid-state reaction method and examined by means of the X-ray diffraction (XRD) and UV-visible spectroscopy techniques. The photovoltaic characteristics for the prepared samples have been studied by employing J–V measurements. XRD spectrum depicted that the main phase in the sample with x = 0 is a tetragonal one of the rutile – type with P42/mnm space group and increasing in GeO2 ratio leads to appearance and rise of another orthorhombic phase Ge4Ti5 with Pnma space group.

Data obtained from the UV-visible spectroscopy measurements reflect that the optical energy gap (Optical) increases with increasing GeO2 content while the optical refractive index decreases. J–V photovoltaic characteristics confirms that the DSSCs co-doped with low concentration doping x = 0.05 and 0.1 of GeO2 have high power conversion efficiency, fill factor (FF), and short circuit current density (Jsc) in contrast samples with high concentration doping x = 0.2 and 0.3 of GeO2. The present results showed that the (TiO2-(GeO2)x with x =0.05 and 0.1 wt%) films are potential candidates for optical filter materials, optoelectrical, and photo-conversion energy devices.

Keywords: TiO2 nanocrystallites; rutile – type; optical energy gap; refractive index; efficiency.

Introduction

Recently, the enhancement of the efficiency of dye-sensitized solar cells (DSSCs) has been devoted by many researchers [1]. The most effectively utilized wide bandgap oxide material is still titanium dioxide TiO2 among other wideband semiconductor oxides such as ZnO, SnO2, Nb2O5, and SrTiO3, this is for its stability, non-toxicity and exceedingly refractive nature [2]. The improvement of progressed photovoltaic anode materials that successfully use solar energy from the visible region is an appealing challenge, especially for DSSC applications, as the light absorption and charge collection take place at the dye-sensitized mesoporous photoanode [3, 4].

One of the compelling methods to improve the power conversion efficiency of TiO2 DSSCs photoanodes is by doping foreign transition or alkaline earth ions into the TiO2 lattice [5–10]. The transition ions lead to expanding the absorption edge of TiO2 to the visible-light region, by embedding a new brand into the unique bandgap. On the other hand, alkaline earth ions change the crystal lattice, providing TiO2 with characteristic physical and electronic structures. Previously, TiO2 films have been doped by Ag and La. These films were sensitive to visible light and have high photocatalytic activity [11]. From the base knowledge, in the visible light region, TiO2 nanoparticles had higher catalytic activity when it doped by silver and strontium. This is due to the intensive effect caused by narrow bandgap and improvement in charge separation [12].

When TiO2 doped with chromium and antimony, absorption bands in the visible-light region of light and possessed 2.4 and 2.2 eV of energy gaps for chromium and antimony, respectively [13]. Films of TiO2 doped with Ta and Ni were synthesized; it found that these films were sensitive for the visible-light and the charges able to transfer from the electron donor levels to the conduction bands of the host materials [14]. Co and Nb co-doped TiO2 films were performed via pulsed laser deposition on (001) single crystal LaAlO3, dilute Nb doping significantly enhances the conductivity and microstructure of the TiO2 anatase film [15]. By the hydrothermal method, Zn and Mg co-doped TiO2 have been synthesized and these films claimed that the DSSCs depend on the co-doped TiO2 electrode performed high power conversion efficiency [16]. The main aim of the present work is to study the effect of germanium dioxide (GeO2) doping on the TiO2 photoanodes for DSSCs applications in order to improve the properties of TiO2 nanostructured and to enhance the photovoltaic efficiency. In addition, this study illustrates the effect of GeO2 doping in the range 0-0.3 mass fraction on the structure, optical, and electrical properties of rutile TiO2.

Experimental Technique

Titanium oxide TiO2 paste was prepared by grinding 2gm of nanocrystallites TiO2 (Aldrich 99.995%) with acetylacetone (0.25 ml) and triton X-100 (0.25ml) using a mortar for 60 minutes. Doctor – blade method was employed to coat the transparent conducting glass FTO with the paste and then annealed at 450°C for 30 min. Subsequently, the samples were soaked in an ethanol solution of N-719 dye for 24 h at room temperature and then gently splashed by ethanol. The DSSCs samples were accumulated and the electrolyte KI2 was added into the aperture by repeated addition of electrolyte drops see ref. [17]. The crystal structure and phase purity of powder samples were examined by powder X-ray diffraction at room temperature using Bruker D8 Advance diffractometer with CuK radiation  = 1.54056 Å. UV–vis measurements have been carried out by using Jasco V-576 (Japan) model double-beam spectrophotometer in the range of = 190 to 1100 nm.

The J-V curves of DSSCs were carried out using the current amplifier (Keithley 427), multimeter (Aplab 1087), and data acquisition (DataQ: DI-158U). The DSSCs devices were irradiated with a homemade solar simulator with a xenon lamp (35 W), halogen lamp (55 W), and equipped with IR and UV filters to irradiate DSSCs effective area of 0.35 cm2. The power of the simulator was measured by using Lutron (SPM-1116SD) solar power meter.

Results and discussion

XRD patterns and lattice parameters

Figure 1 displays the powder X-ray diffraction patterns for the samples Tio2-(GeO2)x with 0 x 0.3 wt%. From Fig.1, one can notice that the main phase in the sample with x = 0 is a tetragonal one of the rutile – type with the P42/mnm space group. Doping of GeO2 into the material leads to the appearance and rise of an orthorhombic phase Ge4Ti5 with the Pnma space group [18] (marked with asterisks in Fig. 1. The volume fraction of the orthorhombic phase Ge4Ti5 increases with the increase of the GeO2 content. As seen from the insertions in Fig. 1, there are no shifts of peaks (101) and (110) belonging to the first rutile tetragonal phase with increasing GeO2. This indicates that there is no change in the lattice parameters a, c of rutile tetragonal phase with increasing GeO2.

Fig.2 shows that Rietveld refinement of diffraction pattern for the samples with x = 0, 0.05 and 0.3. As seen from Fig.2, the sample with x = 0 contains only the peaks of the main rutilte tetragonal phase (space group P42/mnm). In addition, with increasing GeO2 content (x = 0.05 and 0.3), the samples are observed to contain two phases; the main rutile tetragonal phase (space group P42/mnm) and another orthorhombic phase Ge4Ti5 (space group Pnma) [18]. The concentration dependences of lattice parameters a and c of rutile TiO2 tetragonal phase of TiO2-(GeO2)x samples are shown in Table 1. It is clearly seen that there is no change in the lattice parameters a and c with increasing GeO2 content. In order to show the effect of GeO2 on the crystalline structure of TiO2

Optical Measurements

The spectra distributions of absorbance for the studied films in the wavelength range 200–1100 nm are illustrated in Fig.3. UV-vis spectra depict that the basic absorption of Ti–O bond in UV light range around 400 nm, which is consistent with a bandgap of rutile TiO2 (3.0 eV) [21], while the absorption spectra of TiO2-(GeO2)x films are successfully extended to a visible wavelength range around 650 nm. The spectral distribution of transmittance T and reflectance R for the studied films in the wavelength range 200–1100 nm are depicted in Fig.4. The spectra show that high transmittance values of the films in the visible region, this reveals that the films have good homogeneity. The transmission edge appears not depending on GeO2 concentration in the films.

The presence of such an edge in the TiO2-GeO2 films suggested that it can be considered as a good optical filter material. The transmittance T and reflectance R) spectra were characterized by homogeneity and uniform thickness, this is due to the absence of ripples in the obtained spectra. In fact, the absorption coefficient spectrum near the fundamental edge is very useful to investigate the type of optical transition [22]. Therefore, the optical bandgap can be obtained from the dependence of the absorption coefficient on the photon energy (h). Band-to band transitions theory used to analyses the absorption coefficient at the fundamental edge for the organic materials [22,23].

The absorption data follow a power-law behavior which is described by Tauc’s equation [24] and modified by Mott and Davis as follows [25]. By Beer–Lambert’s law, the absorption coefficient  can be calculated. G is a constant, (HV) represents to the energy of the incident photons, (Topical.) is the optical band gap energy, and the power (m) characterizes electronic transition, where, m = 2 and 1/2 for indirect and direct allowed transition, respectively [24]. In the present work, the optical energy bandgap for the studied films has been calculated using the absorption spectrum fitting (ASF) method. In this method, the optical absorption coefficient expressed as a function of the wavelength of incident photon and Eq. Can be rewritten as [26,27].

Where is the cut-off wavelength corresponding to the optical band gap, c is the light velocity, and h is Planck’s constant. Equation  can be simplified and rewrite as:

D = [C(hc)m-1 d/2.303].

The equation helps to calculate optical bandgap only by using the absorbance data avoiding the thin film thickness. Thus, the energy band gap can be calculated directly from using the following relation: The value of can be deduced from extrapolating the linear region of (A/)1/m against (-1) curve at (A/ )1/m = 0. The variation of (A/)1/2 with (1/) for direct allowed transition shown in Fig.5. The values for the studied TiO2-(GeO)x films are listed in Table 2. Energy gap values depend on the concentration of GeO2 in the formed film and it is varying from 2.80 to 3.34 eV.
The Refractive index of TiO2-(GeO2)x films was calculated by the following Eq. [28].

The values of the calculated refractive index, n are tabulated in Table 2. The values reveal that the refractive index of all studied films is considerably high. Fig.6 shows the variation of the energy bandgap and refractive index with GeO2 content in the studied films. As seen from Fig.6, the energy bandgap decreased with increasing GeO2, the lower energy gap is responsible for the enhancement of electron injection and transport properties as reported [29, 30]. This enhancement of electron injection and transport in dye-sensitized solar cells leads to the improvement of current density. As a result of the improvement of current density, we expect the fill factor, and the efficiency of the DSSCs increases with increasing GeO2.

Electrical measurements

Figure 7 depicts the photocurrent density-voltage curves (J-V) of DSSCs for the studied films with different GeO2 doping concentrations of TiO2 nanocrystallites photoanodes. We determined the conversion efficiency of the DSSCs by short-circuit current density (Jsc), open circuit potential (Voc), and the intensity of the incident light per unit area (Pin). We calculated the overall conversion efficiency according to Eq, where Jmax and Vmax are the current density and potential at the point of the maximum power respectively. The calculated photovoltaic parameters listed in Table 3. We found that doping with GeO2, short-circuit current density (Jsc) was increased in comparison with undoped sample x = 0 as shown in Fig.8, this is can be attributed to the decreasing of the energy gap of the samples.

On the other hand, at low concentration of doping with GeO2 doping at x = 0.05 and 0.1 open-circuit voltage (Voc) was increased in comparison with undoped sample x = 0 as shown in Fig.8 and at high concentration of doping at x = 0.2 and 0.3 open-circuit voltage (Voc) was decreased. As a result, the efficiency of doped DSSCs devices at low concentrations is higher than that of undoped cells. Due to the decrease of crystal size for doped films, their surface area will be increased. Thus reveals the increasing number of absorbed dyes which increases the number of injected electrons in metal oxide electrodes, leading to an increase of Jsc [31, 32]. As GeO2 content is; 0.1 wt, it could assist as a crystal growth inhibiter that can cause a higher surface area. This is expected to increase the surface area of TiO2 and consequently increases dye loading and photo-generated electrons [20]. In contrast, if the amount of added GeO2 is; 0.1 wt a distinctive decrease in of the binary oxides electrodes was observed. This corresponds to the property of the pure GeO2 electrode [33].

Conclusion

In this study, the effect of (GeO2)x co-doped on dye-sensitized solar cells (DSSCs) TiO2 nanocrystallites photoanodes at very low concentrations : x = 0, 0.05, 0.1, 0.2, and 0.3 wt%) has been studied. Synthesized samples were examined via the X-ray diffraction (XRD) and UV-visible spectroscopy techniques. The photovoltaic characteristics for the prepared samples have been studied by employing J–V measurements. The obtained results have been revealed: XRD pattern showed that the main phase in the undoped sample (x = 0) is a tetragonal one of the rutile – type with P42/mnm space group, while in doped samples the increasing in GeO2 ratio leads to appearance and rise of another orthorhombic phase Ge4Te5 with Pnma space group.

The optical energy gap (Optical) for the studied films increases with increasing GeO2 content and takes values in the range from 2.80 to 3.34 eV, while the optical refractive index decreases. The crystal size of doped films has been decreased as compared with that of pure TiO2. Thus will increase the surface area TiO2 and consequently increases dye loading and photo-generated electrons. J–V photovoltaic characteristics confirms that the DSSCs co-doped with low concentration doping x = 0.05 and 0.1 of GeO2 have high power conversion efficiency , fill factor (FF), and short circuit current density (Jsc) in contrast samples with high concentration doping x = 0.2 and 0.3 of GeO2. Thus it is reasonable to infer that TiO2-(GeO2)x with x =0.05 and 0.1 wt%) films are potential candidates for optoelectrical devices and dye-sensitized solar cells (DSSCs).

Figures captions

• Fig.1. X-ray diffraction patterns for the Tio2-(GeO2)x samples. The peaks associated with another orthorhombic phase Ge4Ti5 are marked with asterisks. The insets show peaks (110) and (101) belonging to the first rutile tetragonal phase with increasing GeO2.
• Fig.2. Observed (symbols) and calculated (line) X-ray diffraction pattern for Tio2-(GeO2)x samples with x =0, 0.1 and 0.2. Vertical bars indicate positions of Bragg reflections for the phases with different structures: first row – tetragonal P42/mnm., second-row – orthorhombic Pnma.
• Fig.3. UV absorption spectra for TiO2-(GeO2)x , (x = 0.0, 0.05, 0.1, 0.2, and 0.3 wt%) thin film samples.
• Fig.4(a-e): Spectral distribution of transmittance T and reflectance R for TiO2-(GeO2)x, (x = 0.0, 0.05, 0.1, 0.2, and 0.3 mol%, respectively) thin film samples.
• Fig.5(a-e): Variation of (A/)1/2 with (-1) for TiO2-(GeO2)x, (x = 0.0, 0.05, 0.1, 0.2, and 0.3 mol%, respectively) thin film samples.
• Fig.6. Variation of energy band gap (Eoptical) and refractive index, n for TiO2-(GeO2)x, (x = 0.0, 0.05, 0.1, 0.2, and 0.3 mol%) thin film samples.
• Fig.7. The photocurrent density-voltage curves (J-V) of DSSCs with different GeO2-doping concentrations of TiO2 photoanodes.
• Fig.8. Dependence of DSSCs characteristics on GeO2 content; (a) Short-circuit current density of photoanode (Jsc), (b) open-circuit voltage (Voc) and (c) solar energy conversion efficiency.

Lots of students get this question during the Chemistry exam. The answer is simple: Photosynthesis. If it is an open question, write down about the process of Photosynthesis. It is that process which is used by different plants to penetrate light which comes from the sun and turn it into chemical energy. Oxygen is released as the waste product, allowing people to breathe in fresh air.