Two common methods for corrosion monitoring are electrochemical impedance spectroscopy (EIS) and polarization methods. While both methods are used to measure the corrosion rate, they differ in their approach and the information they provide.

EIS measures the impedance of a material as a function of frequency. By analyzing the impedance spectrum, it is possible to determine the electrical properties of the material, such as its resistance, capacitance, and conductivity. EIS can be used to monitor corrosion by measuring changes in the impedance spectrum over time. Corrosion can cause changes in the electrical properties of a material, which can be detected by EIS.

Polarization methods, on the other hand, measure the potential and current of a material under an applied voltage or current. There are two main types of polarization methods: potentiodynamic and potentiostatic. Potentiodynamic polarization measures the current as the potential is swept over a range of values, while potentiostatic polarization measures the potential as the current is held constant.

One of the main differences between EIS and polarization methods is their sensitivity to different types of corrosion. EIS is more sensitive to localized corrosion, such as pitting and crevice corrosion, while polarization methods are more sensitive to uniform corrosion. This is because localized corrosion can cause changes in the electrical properties of a material, which can be detected by EIS, while uniform corrosion does not typically cause such changes.

Another difference between EIS and polarization methods is their ability to provide information about the corrosion mechanism. EIS can provide information about the electrochemical reactions that occur during corrosion, such as the formation of passive films and the dissolution of metal ions. Polarization methods, on the other hand, provide information about the kinetics of the corrosion reaction, such as the activation energy and the rate constant.

EIS and polarization methods also differ in their ease of use and cost. EIS requires specialized equipment and expertise to perform, while polarization methods can be performed with simpler equipment and require less expertise. However, EIS provides more detailed information about the corrosion mechanism and is more sensitive to localized corrosion, which can be important in certain applications.

In summary, both EIS and polarization methods are useful for corrosion monitoring, but they differ in their sensitivity to different types of corrosion, their ability to provide information about the corrosion mechanism, and their ease of use and cost. Choosing the appropriate method for a particular application depends on the specific corrosion concerns and the desired level of detail in the corrosion monitoring.

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Adsorption is a process where a solid or liquid substance is attracted and held onto the surface of another material. It is an essential process in many industries, including water treatment, food processing, and pharmaceuticals. The effectiveness of adsorption depends on the properties of the adsorbent material, such as its surface area, pore size distribution, and chemical composition. Therefore, finding an optimum adsorbent material is crucial to achieving efficient and cost-effective adsorption processes.

One of the most powerful tools for determining the properties of an adsorbent material is the BET analysis. The BET (Brunauer-Emmett-Teller) analysis is a technique used to measure the specific surface area of a material by measuring the amount of gas adsorbed onto its surface at different pressures. The data obtained from BET analysis can be used to calculate the pore size distribution and other critical parameters that determine the adsorption capacity and efficiency of the material.

BET analysis can be used to evaluate various types of materials, including activated carbon, zeolites, silica gels, and metal-organic frameworks. By using BET analysis, researchers can determine the optimum conditions for preparing and using these materials as adsorbents. Here are some ways BET analysis can help in finding an optimum material for using as an adsorbate:

1. Determining the Specific Surface Area

The specific surface area of an adsorbent material is one of the most critical parameters that affect its adsorption capacity. BET analysis can accurately measure the specific surface area of a material by analyzing the amount of gas adsorbed onto its surface at different pressures. The higher the specific surface area, the more adsorption sites are available for attracting and holding onto target molecules.

2. Calculating Pore Size Distribution

The pore size distribution of an adsorbent material is another crucial factor that affects its adsorption capacity. BET analysis can provide information about the pore size distribution of a material by analyzing the adsorption isotherm data. The pore size distribution can be used to determine the optimum pore size range for the target molecules to be adsorbed.

3. Evaluating Adsorption Capacity

BET analysis can also be used to evaluate the adsorption capacity of an adsorbent material. By measuring the amount of gas adsorbed onto the material at different pressures, researchers can determine the maximum amount of target molecules that can be adsorbed onto the material. This information can be used to optimize the adsorption process and determine the most effective operating conditions.

4. Comparing Different Materials

BET analysis can also be used to compare the properties of different adsorbent materials. By analyzing the specific surface area, pore size distribution, and other parameters, researchers can determine which material is most suitable for a specific application. This information can help in selecting the best material for a particular adsorption process and improve its efficiency and cost-effectiveness.

In conclusion, BET analysis is a powerful tool for evaluating the properties of adsorbent materials and finding an optimum material for using as an adsorbate. By analyzing the specific surface area, pore size distribution, and other parameters, researchers can determine the most effective operating conditions and select the best material for a specific application. BET analysis can help in improving the efficiency and cost-effectiveness of adsorption processes in various industries, making it an essential technique for researchers and engineers working in this field.

Raman and Fourier Transform Infrared (FTIR) spectroscopy are two of the most widely used analytical techniques in the field of chemistry. Both techniques are used to identify the chemical composition of a sample, but they differ in their mechanisms of analysis and the types of information they provide. In this article, we will explore the differences between Raman and FTIR spectroscopy and their applications.

Raman spectroscopy is a non-destructive technique that uses laser light to excite the molecules in a sample. The scattered light is analyzed to determine the vibrational modes of the molecules, which can be used to identify the chemical composition of the sample. The Raman effect was first discovered by C.V. Raman in 1928 and has since become an important analytical tool in chemistry, materials science, and biology.

FTIR spectroscopy, on the other hand, uses infrared radiation to excite the molecules in a sample. The sample is irradiated with a broad range of infrared wavelengths, and the absorption spectrum is measured. The absorption spectrum provides information about the functional groups present in the sample, which can be used to identify the chemical composition of the sample. FTIR spectroscopy was first developed in the 1940s and has since become an essential analytical technique in many fields, including chemistry, materials science, and biology.

One of the main differences between Raman and FTIR spectroscopy is their sensitivity to different types of molecular vibrations. Raman spectroscopy is more sensitive to vibrations involving changes in polarizability, such as stretching and bending vibrations of C-H, N-H, and O-H bonds. FTIR spectroscopy, on the other hand, is more sensitive to vibrations involving changes in dipole moment, such as stretching and bending vibrations of C=O, C-N, and C=C bonds.

Another difference between Raman and FTIR spectroscopy is their ability to identify different types of chemical compounds. Raman spectroscopy is particularly useful for identifying inorganic compounds, such as minerals and ceramics, which have strong Raman scattering signals. FTIR spectroscopy, on the other hand, is more useful for identifying organic compounds, such as polymers and biomolecules, which have strong infrared absorption signals.

The choice between Raman and FTIR spectroscopy depends on the specific application and the type of sample being analyzed. Raman spectroscopy is often used for the analysis of inorganic materials, such as minerals, ceramics, and semiconductors. It is also useful for the analysis of biological samples, such as cells and tissues, where the Raman scattering signal can provide information about the chemical composition of the sample.

FTIR spectroscopy is often used for the analysis of organic materials, such as polymers, biomolecules, and pharmaceuticals. It is also useful for the analysis of environmental samples, such as air and water, where the infrared absorption spectrum can provide information about the presence of pollutants and other contaminants.

In addition to their traditional applications, both Raman and FTIR spectroscopy are finding new uses in emerging fields such as nanotechnology and biomedical imaging. Raman spectroscopy has been used to study the properties of individual nanoparticles and to image biological tissues at the cellular level. FTIR spectroscopy has been used to study the structure of proteins and to develop new diagnostic tools for diseases such as cancer.

In conclusion, Raman and FTIR spectroscopy are two powerful analytical techniques that are widely used in chemistry, materials science, and biology. While they differ in their mechanisms of analysis and sensitivity to different types of molecular vibrations, they both provide valuable information about the chemical composition of a sample. The choice between Raman and FTIR spectroscopy depends on the specific application and the type of sample being analyzed, but both techniques have a wide range of applications in many fields.

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It is well known that determination of concentrations of materials in different solutions is an important step for investigation of the under-studied solution. Usually, photometric techniques are used due to this fact that they are accessible and cost-effective options.

Generally, absorption of irradiated light to a solution by the presence molecules is the base of (spectro)-photometric techniques. In UV/Vis spectroscopy visible and ultraviolet light uses for detection of concentration of a solution.

Spectroscopy is a science that studies the interaction of electromagnetic radiation with matter. In such interactions, electromagnetic radiation can be thought of as a set of separate energy packets called photons. The dual property of electromagnetic radiation as a particle and a wave is not only non-existent but also complementary. According to the theory, electromagnetic radiation is made up of two components, electric fields and magnetic field. These fields are propagating the wave in the environment, on the environment and also perpendicular to the wave propagation (Figure 1).

The electric field of electromagnetic radiation causes phenomena such as transmission, reflection, refraction, and absorption when it interacts with matter. The magnetic field of electromagnetic radiation is also effective in the process of absorbing waves related to radio frequencies in nuclear magnetic resonance. Therefore, here only the electric field of electromagnetic radiation is examined due to its effectiveness in the above phenomena.

As was mentioned previously, determination of concentrations of materials in different solutions is an important step for investigation of the under-studied solution. Usually, photometric techniques are used due to this fact that they are accessible and cost-effective options.

For the calculation of an analytic concentration, the Lambert-Beer law form the basis can be used as follow:

1. Transmission or transmittance (T) = I/I₀
   
2. Absorbance (A) = log (I₀/I)
   
3. Absorbance (A) = C x L x Ɛ => Concentration (C) = A/(L x Ɛ)
   

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Fourier transform infrared spectroscopy (FTIR) is an experimental test to identify organic/inorganic materials. But for understanding and interpretation of FTIR results, the principles of the analysis should be studied.

Principles of FTIR

To better interpretation of FTIR results, it’s useful to know about the principles of the test.

Generally, by introducing infrared radiation (IR) to a material, FTIR’s detectors detect a sample’s absorbance of infrared light at various wavenumbers to determine the material’s chemical structure. FTIR spectrometer converts the experimental data from the broadband source to the absorbance level at each wavenumber.

This method can be employed for solids, liquids, and gaseous samples. Some times, the weight of samples required for a viable analysis is very low and most analyses can be conducted with little sampling process.

How to interpret FTIR spectra

The X-Axis

The horizontal axis shows the IR spectrum, which represents the intensity of the IR spectra. The absorbance peaks, can be attributed to the various vibrations of the sample’s bonds exposed to the IR energy of the electromagnetic spectrum. Usually, the wavenumber on the IR spectrum is drew between 4000 to 400 cm-1.

The Y-Axis

The vertical axis shows the amount of IR absorbance/transmittance by the under-studied material.

The Absorbance Bands

Generally, absorbance bands can be derived into two groups: Group frequencies and fingerprint frequencies.

Group frequencies are assigned to small groups of bonds such as C-H, O-H, and C=O. These types of bonds are usually observable at 1500-400 cm-1 in the IR spectrum and they are typically unique to a specific bond or functional group in a structure.

For fingerprint frequencies, these are assigned to the chemical structure as a whole. These types of absorbances are usually observable at 400-1500cm-1 in the IR spectrum. Therefore, this region is less reliable for identification, however the absence of a peak is often more indicative than the presence of a peak in this region.

How to analyze FT-IR Spectra

Generally, interpretation of FT-IR spectra starts at the high frequencies end to identify the presence functional groups. The fingerprint regions are then investigated to identify the chemical bonds.

Still Curious About FTIR Analysis?

FTIR is a useful technique for manufacturers and researchers of various industries. If you have more questions about the analysis or are wondering if it may be a fit for your testing needs, contact us for a quote.

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Most metallic corrosion occurs via electrochemical reactions at the interface between the metal and an electrolyte solution. For example, a thin film of moisture on a metal surface forms the electrolyte for atmospheric corrosion. A second example is when wet concrete is the electrolyte for reinforcing rod corrosion in bridges. Although most corrosion takes place in water, corrosion in non-aqueous systems is not unknown.

Corrosion normally occurs at a rate determined by an equilibrium between opposing electrochemical reactions. One reaction is the anodic reaction, in which a metal is oxidized, releasing electrons into the metal. The other is the cathodic reaction, in which a solution species (often O₂ or H⁺) is reduced, removing electrons from the metal. When these two reactions are in equilibrium, the flow of electrons from each reaction is balanced, and no net electron flow (electrical current) occurs. The two reactions can take place on one metal or on two dissimilar metals (or metal sites) that are electrically connected.

Figure 1 sketches this process. The vertical axis is electrical potential and the horizontal axis is the logarithm of absolute current. The theoretical current for the anodic and cathodic reactions is represented as straight lines. The curved line is the total current: the sum of the anodic and cathodic currents. This is the current that you measure when you sweep the potential of the metal with your potentiostat. The sharp point in the curve is actually the point where the current reverses polarity as the reaction changes from anodic to cathodic, or vice versa. The sharp point is caused by plotting along a logarithmic axis. The use of a logarithmic axis is necessary because of the wide range of current values that must be recorded during a corrosion experiment. Because of the phenomenon of passivity, the current often change by six orders of magnitude during a corrosion experiment.

Figure 1. Corrosion process showing anodic and cathodic components of current.

The potential of the metal is the means by which the anodic and cathodic reactions are kept in balance. Refer to Figure 1. Notice that the current from each half reaction depends on the electrochemical potential of the metal. Suppose that the anodic reaction releases too many electrons into the metal. Excess electrons thus shift the potential of the metal more negative, which slows the anodic reaction and speeds up the cathodic reaction. This counteracts the initial perturbation of the system. 

The equilibrium potential assumed by the metal in the absence of electrical connections to the metal is called the open-circuit potential, E_oc. In most electrochemical corrosion experiments, the first step is the measurement of E_oc. 

The value of either the anodic or cathodic current at E_oc is called the corrosion current, I_corr. If we could measure I_corr, we could use it to calculate the corrosion rate of the metal. Unfortunately, I_corr cannot be measured directly. However, it can be estimated using electrochemical techniques. In any real system, I_corr and corrosion rate are a function of many system parameters, including type of metal, composition of the solution, temperature, movement of the solution, metal history, and many others.

The above description of the corrosion process does not say anything about the state of the metal surface. In practice, many metals form an oxide layer on their surface as they corrode. If the oxide layer inhibits further corrosion, the metal is said to passivate. In some cases, local areas of the passive film break down, allowing significant metal corrosion to occur in a small area. This phenomenon is called pitting corrosion or simply pitting.

Because corrosion occurs via electrochemical reactions, electrochemical techniques are ideal for the study of the corrosion processes. In electrochemical studies, a metal sample with a surface area of a few square centimeters is used to model the metal in a corroding system. The metal sample is immersed in a solution typical of the metal’s environment in the system being studied. Additional electrodes are immersed in the solution, and all the electrodes are connected to a device called a potentiostat. A potentiostat allows you to change the potential of the metal sample in a controlled manner and measure the current that flows as a function of applied potential.

Both controlled-potential (potentiostatic) and controlled-current (galvanostatic) polarization are useful. When the polarization is done potentiostatically, current is measured, and when it is done galvanostatically, potential is measured. This discussion will concentrate on controlled-potential methods, which are much more common than galvanostatic methods. With the exception of open-circuit potential versus time, electrochemical noise, galvanic corrosion, and a few others, potentiostatic mode is used to perturb the equilibrium corrosion process. When the potential of a metal sample in solution is forced away from E_oc, it is referred to as polarizing the sample. The response (that is, resulting current) of the metal sample is measured as it is polarized. The response is used to develop a model of the sample’s corrosion behavior. 

Quantitative Corrosion Theory

In the previous section we pointed out that I_corr cannot be measured directly. In many cases, you can estimate it from current-versus-voltage data. You can measure a logarithmic current versus potential curve over a range of about one half volt. The voltage scan is centered on E_oc. You then fit the measured data to a theoretical model of the corrosion process.

The model we use for the corrosion process assumes that the rates of both the anodic and cathodic processes are controlled by the kinetics of the electron-transfer reaction at the metal surface. This is generally the case for corrosion reactions. An electrochemical reaction under kinetic control obeys Eq. 1, the Tafel equation.

                                 In this equation,

The Tafel equation describes the behavior of one isolated reaction. In a corrosion system, we have two opposing reactions: anodic and cathodic.

The Tafel equations for the anodic and cathodic reactions in a corrosion system can be combined to generate the Butler-Volmer equation (Eq. 2).

where

What does Eq. 2 predict about the current-versus-voltage curve? At E_corr, each exponential term equals one. The cell current is therefore zero, as you would expect. Near E_corr, both exponential terms contribute to the overall current. Finally, as the potential is driven far from E_corrby the potentiostat, one exponential term predominates and the other term can be ignored. When this occurs, a plot of logarithmic current versus potential becomes a straight line. 

A plot of log I versus E is called a Tafel plot. The Tafel plot in Figure 1 was generated directly from the Butler-Volmer equation. Notice the linear sections of the cell current curve.

In practice, many corrosion systems are kinetically controlled and thus obey Eq. 2. A curve of logarithmic current versus potential that is linear on both sides of E_corr is indicative of kinetic control for the system being studied. However, there can be complications, such as:

• Concentration polarization, where the rate of a reaction is controlled by the rate at which reactants arrive at the metal surface. Often cathodic reactions show concentration polarization at higher currents, when diffusion of oxygen or hydrogen ion is not fast enough to sustain the kinetically controlled rate. 
• Oxide formation, which may or may not lead to passivation. This process can alter the surface of the sample being tested. The original surface and the altered surface may have different values for the constants in Eq. 2.
• Other effects that alter the surface, such as preferential dissolution of one component of an alloy, can also cause problems.
• A mixed control process where more than one cathodic, or anodic, reaction occurs simultaneously may complicate the model. An example of mixed control is the simultaneous reduction of oxygen and hydrogen ion.
• Finally, potential drop as a result of cell current flowing through the resistance of your cell solution causes errors in the kinetic model. This last effect, if it is not too severe, may be correctable via IR-compensation in the potentiostat. 

In most cases, complications like those listed above cause non-linearities in the Tafel plot. Use with caution the results derived from a Tafel plot without a well-defined linear region.

Classic Tafel analysis is performed by extrapolating the linear portions of a logarithmic current versus potential plot back to their intersection. See Figure 2 (which is Figure 1 reprinted with annotations that demonstrate the analysis). The value of either the anodic or the cathodic current at the intersection is I_corr. Unfortunately, many real-world corrosion systems do not provide a sufficient linear region to permit accurate extrapolation. Most modern corrosion test software., performs a more sophisticated numerical fit to the Butler-Volmer equation. The measured data are fit to Eq. 2 by adjusting the values of E_corr, I_corr, β_a, and β_c. The curve-fitting method has the advantage that it does not require a fully developed linear portion of the curve.

Figure 2. Classic Tafel analysis.

Polarization Resistance

Eq. 2 can be further simplified by restricting the potential to be very near to E_corr. Close to E_corr, the current-versus-voltage curve approximates a straight line. The slope of this line has the units of resistance (Ω).  The slope is, therefore, called the polarization resistance, R_p. An R_p value can be combined with an estimate of the β coefficients to yield an estimate of the corrosion current.

If we approximate the exponential terms in Eq. 2 with the first two terms of a power-series expansion ( ) and simplify, we get one form of the Stern-Geary equation:

In a polarization resistance experiment, you record a curve of current versus voltage as the cell voltage is swept over a small range of potential that is very near to E_oc (generally ±10 mV). A numerical fit of the curve yields a value for the polarization resistance, R_p. Polarization resistance data do not provide any information about the values for the β coefficients. Therefore, to use Eq. 3, you must provide β values. These can be obtained from a Tafel plot, or estimated from your experience with the system you are testing.

Calculation of Corrosion Rate from Corrosion Current

The numerical result obtained by fitting corrosion data to a model is generally the corrosion current. We are interested in corrosion rates in the more useful units of rate of penetration, such as millimeters per year. How is corrosion current used to generate a corrosion rate? Assume an electrolytic dissolution reaction involving a chemical species, S:

            S   ®  Sⁿ⁺ +   n^e–

You can relate current flow to mass via Faraday’s Law.

Q = nFM           Eq. 4

where

A more useful form of Eq. 4 requires the concept of equivalent weight. The equivalent weight (EW) is the mass of species S that will react with one Faraday of charge. For an atomic species, EW = AW/n (where AW is the atomic weight of the species). 

Recalling that M = m/AW and substituting into Eq. 4 we get:

where m is the mass of species S that has reacted.

In cases where the corrosion occurs uniformly across a metal surface, the corrosion rate can be calculated in units of distance per year. Be careful: this calculation is only valid for uniform corrosion; it dramatically underestimates the problem when localized corrosion occurs! 

For a complex alloy that undergoes uniform dissolution, the equivalent weight is a weighted average of the equivalent weights of the alloy components. Mole fraction, not mass fraction, is used as the weighting factor. If the dissolution is not uniform, you may have to measure the corrosion products to calculate EW.

Conversion from a weight loss to a corrosion rate (CR) is straightforward. We need to know the density, d, and the sample area, A. Charge is given by Q = It, where t is the time in seconds and I is a current. We can substitute in the value of Faraday’s constant. Modifying Eq. 5,

where

Table 1. Corrosion Rate Constants

IR Compensation

When you pass current between two electrodes in a conductive solution, there are always regions of different potentials in the solution. Much of the overall change in potential occurs very close to the surface of the electrodes. Here the potential gradients are largely caused by ionic concentration gradients set up near the metal surfaces. Also, there is always a potential difference (a potential drop) caused by current flow through the resistance in the bulk of the solution.

In an electrochemical experiment, the potential that you wish to control or measure is the potential of a metal specimen (called the Working Electrode) versus a Reference Electrode.  We are normally not interested in the potential drops caused by solution resistances because they are negligible in typical electrolyte solutions such as 1 M H₂SO₄ or 5% NaCl. 

Gamry Instruments potentiostats, like all modern electrochemical instruments, are three-electrode potentiostats. They measure and control the potential difference between a non-current-carrying Reference Electrode and one of the two current-carrying electrodes (the Working Electrode). The potential drop near the other current-carrying electrode (the Counter Electrode) does not matter when a three-electrode potentiostat is used. 

Careful placement of the Reference Electrode can compensate for some of the IR-drop resulting from the cell current, I, flowing through the solution resistance, R.  You can think of the Reference Electrode as sampling the potential somewhere along the solution resistance. The closer it is to the Working Electrode, the closer you are to measuring a potential free from IR errors.  However, complete IR compensation cannot be achieved in practice through placement of the reference electrode, because of the finite physical size of the electrode. The portion of the cell resistance that remains after placing the Reference Electrode is called the uncompensated resistance, R_u. 

Gamry potentiostats can use current-interrupt or positive-feedback IR compensation to dynamically correct uncompensated resistance errors. In the current-interrupt technique, the cell current is periodically turned off for a very short time. With no current flowing through the solution resistance, its IR drop disappears instantly. The potential drop at the electrode surface remains constant on a rapid time scale.  The difference in potential with the current flowing and without is a measure of the uncompensated IR drop.

The potentiostat makes a current-interrupt measurement immediately after it acquires each data point. The potentiostat actually takes three potential readings: E₁ before the current is turned off, and E₂ and E₃ while it is off (see Figure 3). Normally, the latter two are used to extrapolate the potential difference, ∆E, back to the exact moment when the current was interrupted. The timing of the interrupt depends on the cell current. The interrupt time is 40 µs on the higher current ranges. On lower current ranges, the interrupt lasts longer.

Figure 3. Current-interrupt potential versus time.

In controlled potential modes, the applied potential can be dynamically corrected for the measured IR error in one of several ways. In the simplest of these, the IR error from the previous point is applied as a correction to the applied potential. For example, if an IR free potential of 1 V is desired, and the measured IR error is 0.2 V, the potentiostat applies 1.2 V. The correction is always one point behind, for the IR error from one point is applied to correct the applied potential for the next point. In addition to this normal mode, a Gamry Instruments potentiostat offers more-complex feedback modes in which the two points on the decay curve are averaged.

By default in the controlled potential modes, the potential error measured via current-interrupt is used to correct the applied potential. In the controlled current modes, no correction is required. If IR compensation is selected, the measured IR error is subtracted from the measured potential. All reported potentials are therefore free from IR error.

For a detailed theoretical discussion of uncompensated resistance, see Keith B. Oldham, et al., Analytical Chemistry, 72 (2000), 3972 and 3981.

Current and Voltage Conventions

Current polarities in electrochemical measurements can be inconsistent. A current value of –1.2 mA can mean different things to workers in different branches of electrochemistry or in different countries or even to different potentiostats. To an analytical electrochemist it represents 1.2 mA of anodic current. To a corrosion scientist it represents 1.2 mA of cathodic current. A Gamry Instruments potentiostat in default mode follows the corrosion convention for current in which positive currents are anodic and negative currents are cathodic. For the convenience of our users around the world, Gamry Instruments potentiostats can provide the current polarity as per your preference with a simple software command.

The polarity of the potential can also be a source of confusion. In electrochemical corrosion measurement, the equilibrium potential assumed by the metal in the absence of electrical connections to the metal is called the open-circuit potential, E_oc. We use the term corrosion potential, E_corr, for the potential in an electrochemical experiment at which no current flows, as determined by a numerical fit of current-versus-potential data. In an ideal case, the values for E_oc and E_corr are identical. One reason the two voltages may differ is that changes have occurred to the electrode surface during the scan.

With most modern potentiostats, all potentials are specified or reported as the potential of the working electrode with respect to either the reference electrode or the open-circuit potential. The former is always labeled as “vs. E_ref” and the latter as “vs. E_oc”. The equations to convert from one form of potential to the other are:

            E vs. E_oc = (E vs. E_ref) – E_oc

            E vs. E_ref = (E vs. E_oc) + E_oc

Regardless of whether potentials are versus E_ref or versus E_oc, one sign convention is used. The more positive a potential, the more anodic it is. More anodic potentials accelerate oxidation at the Working Electrode. Conversely, a negative potential accelerates reduction at the Working Electrode.

Some References on Corrosion Theory and Electrochemical Corrosion Tests

DC Electrochemical Test Methods, N.G. Thompson and J.H. Payer, National Association of Corrosion Engineers. ISBN: 1-877914-63-0.

Principles and Prevention of Corrosion, Denny A. Jones, Prentice-Hall, 1996. ISBN 0-13-359993-0.

Polarization Resistance Method for Determination of Instantaneous Corrosion Rates, J.R. Scully, Corrosion, 56 (2000), 199.

Several electrochemical corrosion techniques are approved by the ASTM (American Society for Testing and Materials, 100 Barr Harbor Drive, West Conshohocken, PA 19428. They may be found in Volume 3.02 of the ASTM Standards:

G 5: Potentiostatic and Potentiodynamic Anodic Polarization Measurements

G 59: Polarization Resistance Measurements

G 61: Cyclic Polarization Measurements for Localized Corrosion Susceptibility of Iron-, Nickel-, and Cobalt-Based Alloys

G 100: Cyclic Galvanostaircase Polarization

G 106: Verification of Algorithm and Equipment for Electrochemical Impedance Measurements

G 108: Electrochemical Potentiokinetic Reactivation (EPR) for Detecting Sensitization

G 150: Electrochemical Critical Pitting Temperature Testing of Stainless Steels

Electrochemical Techniques in Corrosion Engineering, National Association of Corrosion Engineers, 1986.

Corrosion Testing and Evaluation, STP 1000, Ed. R. Baboian and S.W. Dean, American Society for Testing and Materials, West Conshohocken, PA, 1991. ISBN 0-8031-1406-0.

Electrochemical Corrosion Testing, STP 727, Ed. F. Mansfeld and U. Bertocci, American Society for Testing and Materials, West Conshohocken, PA, 1979.

Corrosion and Corrosion Control, 3rd ed., Herbert H. Uhlig, John Wiley and Sons, New York, 1985.

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1. Introduction

Today several instruments for fast spectra recording are available. In most cases the difficulty
is to process and analyze the data quickly in a reliable way. The Maud program, in one of its
many undocumented features, can be used to process a list of analyses in batch mode from the
console without requiring the interface. This is useful to process quickly similar spectra or launch
a slow/time consuming refinement in a remote computer without recurring to the interface that
would need to open a session involving the remote display setting.

The overall procedure is to prepare the analysis locally using the interface or to prepare a starting point for a series of spectra
(one common starting point) also using the interface, then to prepare an instruction file in CIF like
format to specify the analyses, the spectra and the kind of refinement to conduct and finally to run
Maud in batch mode providing the instruction file previously prepared. The program will run and
process one analysis at time and prepare an output file extracting some key information (either the
default or some to be specified) in a format suitable to be imported in spreadsheet or graphical
programs to analyze the results.
As an example we will show the procedure to analyze a series of ball milled Cu-Fe mixed powders
in which two different phases may form with a different composition. By an automatic Rietveld
analysis performed in batch mode we will extract information about phase content [2, 1], crystallite
and microstrain for each sample/spectrum. The analysis is further complicated from the fact that
the powders milled at higher energy show the presence of planar defects [5] and texture arising
from sample preparation and the platelet like shape of the grains [3].

2 Analysis and procedure
In this section we will present the procedure to analyze 25 spectra of Cu-Fe different samples. The
spectra has been collected by a Philips X-pert system in Le Mans at the LPEC laboratory of the
1
University du Maine, thanks to A. Gibaud.
2.1 Analysis preparation through the interface
We start the Maud program and load all the datafiles together to check their integrity and to prepare
a common starting analysis file. A plot of all spectra and their differences is available in Figure 1.

We load the two possible phases, bcc iron and fcc copper, from the Maud database. By computing
the spectra once and comparing them visually with the experimental spectra we may notice that
for some samples, milled at longer time, an alloyed fcc phase form (out of equilibrium) and the
bcc iron disappears. Unluckily we could not use the copper rich phase cell parameter to monitor
the Fe content in it as the cell parameter tends to growth as a result probably of oxygen entrapping.
In a first attempt we discovered the spectra were affected by texture, anisotropic crystallite sizes
and microstrain as well as planar defects (especially on the Cu like phase). So we decide here
to include also texture and anisotropic/planar defects effects in the analysis. For both the bcc
and fcc phases we select in the proper panel the Popa model for anisotropic broadening [4], the
Warren model for planar defects and the harmonic model for texture (specifying cylindrical sample
symmetry and Lmax = 6 in the options; it is required by the experiment geometry).
Next step was to adjust the cell parameters for both bcc and fcc phases in order to get a mean
starting value good for all spectra (especially for the fcc); and to adjust the crystallite value to a
good starting point (around 200 angstrom) obtaining peak shapes a little sharper than in the less
broadened spectrum. The background constant parameter was also adjusted to the value of the
spectrum with the lower background. Actually only the cell parameter adjustment is critical, the
background one is even not necessary.
Finally we remove all the spectra (we will specify which datafile to use for each analysis later in an
instruction file) and save the analysis containing everything except the spectrum/a. For the purpose
of this article we save the analysis with the name: FeCustart.par.
2.2 Preparation of the instruction file and batch processing
To run Maud in batch we need to write an instruction file containing the list of analyses to execute
one at time. The file is in CIF format but containing some terms not available in the official CIF
dictionary, but that Maud recognize. All the analyses to be performed are specified through the
loop CIF instruction. The first term of the loop must be the one specifying the starting analysis
file to be loaded (full path in unix convention) and then the others to instruct Maud for the kind
of analysis to perform, iterations and eventually datafile to load and name of the file were to save
the analysis. Additional keywords can be used to append specific results to a file for spreadsheet
analysis. The simplest instruction file is something containing the following:
First example (paths for windows):
loop
riet analysis file
riet analysis iteration number
2
´//C:/mypathfortheanalysis/analysis1.par´ 5
´//C:/mypathfortheanalysis/analysis2.par´ 3
´//C:/mypathfortheanalysis/analysis3.par´ 7
The analysis1.par (or 2 or 3) are some analyses files prepared with Maud, containing also
the datafile/spectrum, already set for the parameters to be refined and saved just ready for the refinement step. Maud will load each analysis, starts the refinement with the number of iterations
specified and save the analysis with the refined parameters under the same name. The analyses can
be loaded at end in Maud (with the interface) to see the result of the refinement.
In the case of the Cu-Fe we need to perform some more steps: first we start from one common analysis point (the FeCustart.par analysis file) but we want to specify different datafiles; second
we want to perform a full automatic analysis in which Maud performs different cycles deciding
which parameters to refine at each step and third we will specify the name of each analysis for the
saving process and a file name were to append some selected results in a tab/column format for
subsequent easy loading in a spreadsheet program.
Cu-Fe example:
loop
riet analysis file
riet analysis iteration number
riet analysis wizard index
riet analysis fileToSave
riet meas datafile name
riet append simple result to
´//mypath/FeCustart.par´ 7 13 ´//mypath/FECU1010.par´ ´//mypath/FECU1010.UDF´
´//mypath/FECUresults.txt´
´//mypath/FeCustart.par´ 7 13 ´//mypath/FECU1011.par´ ´//mypath/FECU1011.UDF´
´//mypath/FECUresults.txt´
…………(lines with all the other 23 datafiles omitted for brevity)
´//mypath/FeCustart.par´ 7 13 ´//mypath/FECU1038.par´ ´//mypath/FECU1038.UDF´
´//mypath/FECUresults.txt´
With this instruction file (that we save under the name: fecu.ins) we specify for example that
as a first analysis, Maud has to load the FeCustart.par file, then to load in the analysis the
FECU1010.UDF datafile, to perform the automatic analysis number 13 (in the wizard panel of
Maud the automatic analysis number 13 is the texture analysis; we need to refine also the texture
parameters along with phase analysis and microstructure) and to use 7 iterations for each cycle (the
texture automatic analysis is composed by 4 cycles) to ensure sufficient convergence. At the end
the analysis is saved with the name FECU1010.par and simple selected results will be appended
in the file FECUresults.txt. The simple results saved in the spreadsheet like file are some of
the most used parameters and results. It is possible to specify the parameters we want in output
using the CIF word riet append result to (in addition or as an alternative), but in the
preparation of the starting analysis file in the Maud interface, the parameters to be added to the
results must be specified by turning to true the switch in the output column of the parameter list
window or panel.
Now to run Maud in batch in the console (
where the Maud.jar is located the following:
DOS (everything in the same line): java -mx512M -cp
“Maud.jar;lib\miscLib.jar;lib\JSgInfo.jar;lib\jgaec.jar;lib\ij.jar”
it.unitn.ing.rista.MaudText -f fecu.ins
Unix (everything in the same line): java -mx512M -cp
Maud.jar:lib/miscLib.jar:lib/JSgInfo.jar:lib/jgaec.jar:lib/ij.jar
it.unitn.ing.rista.MaudText -f fecu.ins
For Mac OS X, it is advised to use the generic Unix Maud installation (or to change the path to
the jar files). Before to run Maud in batch mode it is important to run Maud interactive (with the
interface) at least once to create and extract the databases, examples and preferences folder.

2.3 Analysis of results
After running Maud in batch mode, we can check quickly the results by loading the results file
FECUresults.txt in a spreadsheet program. The results are arranged in rows and separated
by tabs. The first row contains the column titles, each subsequent row a different analysis. The
Rwp value for each analysis is reported in the second column and the biggest value found was
5.6% as an indication of the success of the analysis. As an example we report in Figure 2 the
graphical correlation of the copper-rich phase percentage and its mean crystallite value as found
in the analysis versus the sample number. The files and examples used in this articles will be
uploaded in a tutorial in the Maud web page along with some additional files with the batch mode
commands for an easier use.

3 How to get Maud 2.0 and further informations
For this analysis we need Maud version 2.037 or later and it can be freely downloaded from the
Maud web page at http://www.ing.unitn.it/ maud for the preferred platform. There are two archives
for Windows and Mac OS X plus a generic unix version that can be used for Linux, Solaris or
every unix based system with a Java 2 virtual machine installed. The new version 2.0 has a new
interface focused on reducing the effort of a new user and simplifying the most common tasks.
Some particularity of the new version respect to the previous one are (most of them to provide
some useful routines for ab-initio structure solution):
• Different minimization/search algorithms selectable: Marquardt least squares, Evolutionary
algorithm, Simulated annealing, Metadynamic search algorithm. As an example the evolutionary algorithm can be used in the early steps of the refinement to select the proper starting
solution and the Marquardt to drive it to convergence.
4
• Possibility to use crystallites and microstrain distributions for peak shape description instead
of analytical fixed shape functions.
• Maximum Entropy Electron Map full pattern fitting. An electron map can be used for fitting
instead of atoms.
• Full pattern fitting by a list of peaks. Either an arbitrary list of peaks (each one with its own
position, intensity and shape), or simply a list of structure factors to be imported, instead of
a list of atoms.
• Indexing directly on the pattern, selecting the Le Bail fit and the evolutionary algorithm for
the cell search. This may be used to improve a difficult indexing or a partly done one.
• Introduction of fragments. So fragment search can be done directly on the pattern or on a
list of extracted structure factors.
• Energy minimization. At the moment only the simple repulsion energy is completed. Other
energy principles are under completition.
• Spectra integration from image plate or CCD transmission/reflection 2D images. Center,
tilting errors and distance from sample can be refined in the spectra fitting.
Bugs and errors should be reported to the author through the bug reporter web page; questions in
the Maud forum accessible from the Maud web page.
In a future article we will report the instructions on how to modify/extend the program by little Java programming or provide a new alternative model/plugin for the instrument or the structure/microstructure or datafiles importing.
References
[1] D. L. Bish and S. A. Howard. J. Appl. Cryst., 21, 86–91, 1988.
[2] R. J. Hill and C. J. Howard. J. Appl. Cryst., 20, 467–474, 1987.
[3] L. Lutterotti and S. Gialanella. Acta Mater., 46(1), 101–110, 1998.
[4] N. C. Popa. J. Appl. Cryst., 31, 176–180, 1998.
[5] B. E. Warren. X-ray Diffraction. Addison-Wesley, Reading, MA, 1969

Author: Luca Lutterotti
Dipartimento di Ingegneria dei Materiali e delle Tecnologie Industriali
Universita di Trento, 38050 Trento, Italy `
E-mail: Luca.Lutterotti@ing.unitn.it
WWW: http://www.ing.unitn.it/ maud

Only 10$ per sample for interpreting of your UV-Vis spectrum
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STEP1: Open the absorption graph of the material, which is obtained from the UV Vis spectroscopy.

Theory Behind Calculations: UV Vis Spectroscopy absorption peak means the Electrons are absorbing the Energy at some specific wavelength. Electrons are absorbing Energy means the Electrons are going to excited state from its ground state. Electrons are going to excited state from its ground state means the material is having band gap, thus which can be determine by absorption wavelength.

Energy Equation of Quantum Mechanics:

Energy (E) = Planks Constant (h) * Speed of Light (C) / Wavelength (λ)

Where, Energy (E) = Band gap, Planks constant (h) = 6.626×10^-34 Joules sec, Velocity of Light (C) = 2.99×10^8 meter/sec and Wavelength (λ) = Absorption peak value. Also 1eV = 1.6×10^-19 Joules (Conversion factor)

By this formula band gap can be calculated easily, from UV Vis spectroscopy absorption peak.

The basis of the spectrophotometer
In general, the amount of light absorbed by a substance in a liquid state is directly related to the concentration of that substance in the liquid. If the sample is solid, it must first be dissolved in a clear solvent to be measurable. The sample solvent (known as the control) is usually considered without adsorption or in practice its partial adsorption is less than the total adsorption (sample with solvent). The sample with the solvent is usually poured into a clear glass container or a quartz container and placed in front of the light passing through the spectrophotometer. This dish is called Cell or Quvette. Of course, using add-ons on the spectrometer device, solid or gas samples can also be analyzed, which will be discussed in detail in the articles of this article.

The spectrophotometer uses a tungsten lamp to produce visible light and a deuterium lamp to produce ultraviolet or UV light. The normally measured wavelength range in this device is from 1100 nm to 190 nm. More equipped devices are usually used to measure areas outside this range. Given that a particular molecule may absorb light in a well-defined region of the wavelength range, the light produced must be separated and adjustable to the component wavelengths in a given region. Grating Mirror or prism mirror is used to uniformize the light in the spectrophotometer.

Parts of the ultraviolet and visible spectrometer
the source of light
Prism or grating mirror
Monochromator
Detector, detector or photodiode
Processor
The following figure shows an overview of how this device works.

Visible ultraviolet spectrophotometer
Spectrophotometer device diagram

In the visible and ultraviolet spectrometer, after the light passes through the solution, the remaining light sample is inside a detector of Photomultiplier or Photodiode type and after computer processing as a number of one hundred as the percentage of light transmission or its logarithm with The title of the light absorption number appears on the display. Calculations of light absorption or transmission follow Lambert Beer’s law. Mathematically, the amount of light I0 passes through an environment with length X and concentration C, the intensity of the residual light I after passing through the environment is:
I = I0e-KCX
In this relation, K will be a relative constant (absorption coefficient). Therefore, the absorption of the environment or A is obtained as follows:
A = log (I0 / I) = KCX

Spectrophotometer is available in two types of single beam single beam and double beam double beam. The single beam system compares the light absorbed after placing the sample in the device with the main light before placing the sample in the device. One of the advantages of this system is its simplicity, smallness and cheapness, and one of its disadvantages is a small error due to the instability of the measurement environment.

But the two-beam system has two beams, one of which goes to the detector at the same time and the other passes through the sample and the difference between the two is calculated. One of the advantages of this system is more accuracy compared to the single-beam system, and its disadvantages are its complexity and more expensive price. The image below is a schematic of a 2-ray spectrophotometer.

Depending on the spectral region in which the spectral region is performed and which radiation properties (absorption, emission, transmission, scattering, reflection, etc.) are examined, the type of electronic transmissions and consequently the type of spectroscopy and device will be different.
In nasal spectroscopy, absorption is a process in which a chemical species in a transparent medium selectively attenuates (reduces its intensity) certain frequencies of electromagnetic radiation. In the ultraviolet / visible region, the energy of electromagnetic radiation is such that it causes electron transitions in valence electrons. For atoms and ions in the elemental state, the energy of each level is due to the movement of electrons around the nucleus. These states of energy are called electronic states. In addition to having electron energy levels, molecules also have vibrational energy levels and rotational energy levels. These alignments result from the vibration between the atoms in the molecule and from the rotation of the molecules around their own center of mass in space, respectively. In the energy level level diagram, several rotational levels are placed between the two vibrational levels and several vibrational levels are placed between the electronic level levels. Accordingly, each electronic level has vibrating levels and each vibrating level in turn has its own rotational levels. Each of these energy states is about ten times smaller than each other

Only 10 $ per sample for interpreting of your FT-IR spectrum
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FTIR Spectroscopy is an analytical technique used to identify organic, polymeric, and, in some cases, inorganic materials. The FTIR analysis method uses infrared light to scan testsamples and observe chemical properties. When trying to identify an unknown material, FTIR (Fourier Transform Infrared Spectroscopy) analysis is a great tool to answer, “What is it?”. It works well for solids, liquids and gases, and can be applied to pure substances or mixtures. Quantitative or qualitative analysis is available. FTIR is not the best technique to measure trace contaminants, but functions extremely well identifying bulk materials. 

Are you trying to determine material composition, identify impurities, or track changes in your raw materials or finished product? FTIR can provide quality control for your manufacturing process. FTIR analysis also has regulatory compliance applications, such as Respirable silica (NIOSH 7602), for industrial hygiene at construction and petroleum fracking sites. 
Fourier Transform Infrared Spectroscopy (or FTIR for short) identifies chemical bonds in materials via their infrared absorption spectrum. Transmission and Attenuated Total Reflectance (ATR) modes permit analysis of a wide range of solids, powders, non-aqueous liquids and gases. 
The FTIR spectrum is the “infrared fingerprint” of the material. Qualitatively, unknowns can be identified by comparison with an extensive library of FTIR spectra. Our reference sample database includes tens of thousands of spectra for comparison purposes. Quantitatively, FTIR-ATR Analysis is often the first step in the materials analysis process due to its speed and simplicity. 
Samples weighing as little as 50 milligrams can be evaluated using FTIR-ATR analysis. The small sample size allows for selective identification of particles, residues, films or fibers.

Applications of FTIR Transmission & ATR Analysis:

• Quantitative Scans
• Qualitative Scans
• Solids
• Non-Aqueous Liquids
• Organic Samples
• Inorganic Samples
• Unknowns Identification
• Impurities Screening – Routine QA/QC analysis with Accept/Reject limits
• Soil Pharmaceuticals
• Paints, Coatings
• Laminates
• Assessing purity – raw materials, intermediate materials, finished product
• Polymers, plastics – Identifying:
  • Base polymer composition
  • Additives
  • Organic contaminants
  • General type of material being analyzed when there are unknows
• Common Household Items
  • Cleansers and Detergents
  • Baking Powders and Ingredients
  • Paints
  • Oils
  • Paper
  • Medications
• Fibers
  • Synthetic Fibers (acrylic, nylon, polyester, rayon)
  • Natural Fibers (cotton, silk, wood)
• Adhesives
  • Glue
  • Epoxy
  • Resin
• Biodiesel Content in Diesel Fuel
  • Trace Level (0.025%) measurement for biodiesel averse applications
  • Gross composition

Qualitative Scans 

Qualitative scans can be used to rapidly assess unknown materials for identification and for rapid checks on impurities. In terms of process QC, high quality spectral scan of your reference material(s) can be generated and stored in our spectral library database and quickly compared to new materials in your manufacturing process and flag them as acceptable or unacceptable.

Quantitative Scans 

A wide variety of materials can be quantified using the FTIR-ATR materials characterization technique. Quantification requires that a standard calibration curve of known concentrations be created. This is how FTIR is used for the analysis of respirable silica using the NIOSH 7602 method or for determining low levels of Biodiesl in diesel fuel.

ATR-FTIR can be effectively used for quantitative analysis. Non-destructive measurement of samples is possible using ATR-FTIR. Prepare known concentrations of your samples and analyze. For this you must know the prominent IR peak in your sample. Measure  peak heights/areas and prepare a calibration curve. From this you can determine the concentration in unknown sample by noting peak height. It depends on what kind of material you are analyzing. If your material varies in composition as a function of time or temperature, the thickness of your sample may vary too (e.g. due to evaporation of solvent etc). In such case, you have to select a peak that remains constant (not shifting) during the entire process. In absorption mode, find out the area (not the height) of the main peak (of your interest) and divide with the area of the constant peak.

Below is our calibration for respirable alpha silica using NIST standards:

A few of the spectra used in this calibration (from NIST Standards) are shown below:

How do I find the area under my curve using origin?

Plot your data (if you have not already) and make the graph window active, you can either use Integration gadget or Peak Analyzer.

For Integration gadget, go to Gadgets:Integrate… and click OK in the coming up dialog to bring up the yellow Region of Interest (ROI) box. Drag to position and resize the box to the area you want to calculate, then the Area and FWHM information will show up on the ROI top.

For Peak Analyzer, follow the steps below:

1. Choose Analysis: Peaks and Baseline: Peak Analyzer.
2. In the first page (the Goal page), select the Integrate Peaks radio button in the Goal group.
3. For nominal data with positive and negative peaks, step through the four steps in the dialog window: Baseline Mode, Subtract Baseline, Find Peaks and Integrate Peaks.
4. The resulting plot will label each peak with the x-coordinates.
5. The workbook containing results output shows the calculated result parameters for each peak, including peak areas, in the Integration_Resultn worksheet. The data for the integral curve can be found in the Integrated_Curve_Datan worksheet.

How To Calculate Area Under A Plotted Curve In Excel?

For example, you have created a plotted curve as below screenshot shown. This method will split the area between the curve and x axis to multiple trapezoids, calculate the area of every trapezoid individually, and then sum up these areas.

1. The first trapezoid is between x=1 and x=2 under the curve as below screenshot shown. You can calculate its area easily with this formula:  =(C3+C4)/2*(B4-B3). 

2. Then you can drag the AutoFill handle of the formula cell down to calculate areas of other trapezoids.
Note: The last trapezoid is between x=14 and x=15 under the curve. Therefore, drag the AutoFill handle to the second to last cell as below screenshot shown.   

3. Now the areas of all trapezoids are figured out. Select a blank cell, type the formula =SUM(D3:D16) to get the total area under the plotted area.

 Calculate Area Under A Plotted Curve With Chart Trendline

This method will use the chart trendline to get an equation for the plotted curve, and then calculate area under the plotted curve with the definite integral of the equation.

1. Select the plotted chart, and click Design (or Chart Design) > Add Chart Element > Trendline > More Trendline Options. See screenshot:

2. In the Format Trendline pane:
(1) In the Trendline Options section, choose one option which is most matched with your curve;
(2) Check the Display Equation on chart option. 

3. Now the equation is added into the chart. Copy the equation into your worksheet, and then get the definite integral of the equation.

In my case, the equation general by trendline is y = 0.0219x^2 + 0.7604x + 5.1736, therefore its definite integral is F(x) = (0.0219/3)x^3 + (0.7604/2)x^2 + 5.1736x + c.

4. Now we plug in the x=1 and x=15 to the definite integral, and calculate the difference between both calculations results. The difference represents the area under the plotted curve. 
 

Area = F(15)-F(1)
Area =(0.0219/3)*15^3+(0.7604/2)*15^2+5.1736*15-(0.0219/3)*1^3-(0.7604/2)*1^2-5.1736*1
Area = 182.225

1. EIS Spectrum Analyser

EIS Spectrum Analyser is a standalone program for analysis and simulation of impedance spectra. The analyser routine is based on algorithms of the PDEIS spectrometer. In the original (potentiodynamic) version the impedance data analysis is applied on a 3D spectrum and gives dependences of the ac response components on electrode potential.

This standalone program has been adapted to solve a wide range of tasks in the common (stationary) impedance spectroscopy. In addition to data fitting to equivalent circuits with resistors, capacitors, inductors, constant phase, Warburg (3 types), user-defined and Gerischer elements, the EIS Spectrum Analyser provides various tests for data consistency and quality of fit. It has also a built-in impedance spectra simulation routine, tools for impedance data processing (subtraction of circuit elements and subcircuits, normalisation for electrode surface area) and plotting in various formats. The program is free for noncommercial use.

See: http://www.abc.chemistry.bsu.by/vi/analyser/

2. ZsimpWin

ZSimpWin is a EIS Data Analysis program that does not require user-input on initial values. ZSimpWin is an Electrochemical Impedance Spectroscopy (EIS) Data Analysis Software integrated with the 
VersaStudio software to provide straightforward and versatile equivalent circuit model fitting.  Innovative concepts have been implemented to achieve the following performance:

• Minimal user input: The user specifies a job by selecting a model for an impedance data set, and simply requests execution to ZSimpWin.  
• Automatic analysis: Parameters associated with the selected model are determined automatically. ZSimpWin assigns an initial guess of these parameters (default = Auto Setup option), starts computation using the initial guess, finds results, improves these results a number of times until desired results are obtained, and then saves the final results. 
• Batch Analysis: Setup a batch by including multiple jobs and process in sequence.  
• Output results in various forms: Results consist of plots, estimated parameters, and historical records of computation process.  Each or several combinations can be printed or copied to Windows clipboard. 
  
• Requires only mouse button clicks:  The whole process requires no entry of numbers or character strings.                                                                
• Compatible with Windows 10, 8 , 7 and XP.

See: https://www.ameteksi.com/products/software/zsimpwin

3. Zview

ZView software from Scribner Associates offers best-in-class equivalent circuit modeling. Fit common circuits instantly, generate publication-quality graphs quickly. ZView integrates easily with SAI measurement softwares, and supports testing hardware from Solartron, PAR, and others. Increase your data processing efficiency quickly and easily with ZView.

See: https://sai-zview1.software.informer.com/3.4/

Introduction of EIS

Electrochemical impedance spectroscopy (EIS) is one of the most powerful methods in the study of corrosion. The EIS method can be used to measure the rate or rate of corrosion, monitor corrosion, determine coating integrity, and study the mechanism of reactions. In this article, which is a compilation, translation and purification of references [1] and [2], the applications, limitations and benefits of this method are introduced. EIS is usually performed by applying an AC current signal to a state-steady electrochemical system and then measuring the current response. Because the amount of disturbance applied, the AC signal, is a small excitation signal, EIS is essentially a non-destructive technique. To apply this method requires a geometrically corroded cell that includes a reference electrode, as well as equipment capable of measuring and recording the electrical response of an electrochemical cell over a wide range of applied AC frequencies. When a small sine voltage is applied to an electrochemical system according to Equation 9, a sine current response in the form of Equation 2 will be observed. Due to the lack of rapid response of relaxation processes or the release of dipoles, the rotation of bipolar components in response to the applied alternating electric field results in a phase change.

Typical measurements in EIS are usually made in a three-electrode system, as shown in Figure 9. The entire set includes an electrochemical cell, a frequency generator, a frequency response analyzer (FRA), and a computer that is used to control experiments and store information. A potentiostat is used to control the electrode potential. The FRA is the heart of the system that calculates the imaginary and real parts of impedance. The frequency studied is usually in the cell range. 0.01–100,000 Hz (cycles / s) Electrochemical, the test material is embedded as an electrode working. Electrode counters, which must be neutral and not involved in the electrochemical reaction, are usually made of Pt, gold, or graphite. Reference electrodes are usually conventional saturated calomel electrodes (SCE) or AgCl / Ag electrodes. However, in many applications, such as thin electrolyte layers or in high temperature environments, conventional reference electrodes do not work properly. In these cases, systems without conventional reference electrodes should be used. As an example, we can refer to the two-electrode system, which usually consists of two identical electrodes consisting of test materials (Figure 2-a) and is widely used in atmospheric corrosion monitoring [5. [Figure 2-b) Indicates that it is used to monitor high-corrosion corrosion. , Multi-electrode array (Figure 2-c) can be used for EIS monitoring.

An uncompensated electrolyte resistance (Rs), a specific capacitance value related to the coating applied to the metal surface (Cc), a hole resistance in the coating of resistance pathways (pore solution resistance) (Rcp) in the coating where ions are transported, a The specific capacitance corresponding to the double layer in the solution / metal (Cdl) and a resistance (Rp) which is the resistance of the charge transfer process (ie corrosion), and in other words, the resistance to polarization at the solution / metal interface. In Beaunier rectified circuits, usually other additional components, such as the constant phase element (CPE), the phase component of the inductance or induction coefficient (L) and the resistor (W (Warburg,) replace the resistor or capacitor. Special capacitance, accuracy and quality of experimental data fitting with these circuits are improved, but the physical interpretation of the results will be ambiguous, this is because the CPE module can not be easily obtained with capacitor capacitance, and the capacity power calculation is calculated. Capacitor from CPE parameters requires accurate knowledge of the physical reasons for CPE behavior [7.] An example of a Nyquist diagram and its equivalent wind diagram in doubt ل 4 is given. The position of the equivalent circuit components in these diagrams is given on each diagram. In addition to common, simple equivalent circuit models, more complex physical models are sometimes used to interpret EIS data obtained from more complex systems. An example is the line transmission model (TML), which was first used by Levie de in his research on porous electrodes [11] [TML model and its modified models for analyzing EIS data on atmospheric corrosion under electrolyte layers Thin [5] as well as stress corrosion [12] have been used.

Atmospheric corrosion is an electrochemical process that usually occurs beneath a thin electrolyte surface layer, in the presence or absence of salt contaminants and dissolved gases in these layers. It has been shown that the atmospheric corrosion rate of metals depends on the thickness of the electrolyte thin film. The thickness of the electrolyte layer affects the rate of oxygen transfer through the electrolyte layer and the dissolution of corrosion products. The rate of oxygen transfer determines the rate of cathodic reaction (in neutral and alkaline solutions) and the dissolution of corrosion products determines the anodic process. Monitoring or corrosion of thin electrolytic films using conventional electrochemical methods is challenging The electrolyte is thin, very high, leading to a sharp drop in ohmic potential and a non-uniform current distribution that makes it difficult to measure the corrosion rate [5. The solution resistance is estimated from the impedance measured in the high frequency range of the EIS spectrum, and the sum of the resistivity (Rp) and the solution resistance (RS) from the impedance in the low frequency range. Figure 4b: The calculated resistive palliation is then converted to the corrosion rate of the metal. To study atmospheric corrosion of the metal surface under thin electrolytic layers (100010-1000 ~) by EIS, it is possible to expose the corrosion cell to the atmosphere. weather Use outside or use laboratory-drier simulation cycles [19–13,10,9,5. Used in epoxy resin (Figure 2-A) to make cells. The study can be done in two ways: either the impedance spectrum is recorded over a wide range of applied frequencies or the impedance value is checked continuously at two constant frequencies. The study of EIS spectrum in a wide frequency range has shown that a one-dimensional equivalent circuit model called TML can be used to model the corrosion rate in these systems [5. Palrization is calculated from the impedance difference measured at the above two frequencies. The corrosion rate is then calculated using the polarization resistance [5]. Nishikata et al. [5] also used shoulder-shaped electrodes to study atmospheric corrosion to EIS. Impedance information was monitored at 10 mHz and 10 kHz. The results showed that the inverse of the mean impedance at low frequency completely corresponds to the corrosion rate obtained by gravimetry. Wetting of Time also occurs when the amount of solution conductivity or high frequency impedance image (Rs image) exceeds a threshold value. One of the disadvantages of this method in the study of atmospheric corrosion is that if the metal surface is covered with a thick layer of corrosion products, the low frequency impedance can not be equated to the polarization resistance. Finally, Ma et al. [20] used a complex multi-electrode system to study atmospheric corrosion and found the results to be more accurate than two-electrode systems. 3.2 Corrosion of reinforced concrete Rebar (in concrete is the main reason for reducing the life of reinforced concrete structures that are exposed to strong corrosive environments) such as marine environment. Therefore, reinforcement corrosion monitoring is very important to assess the health status of reinforced concrete. Various methods are used to evaluate corrosion in reinforced concrete, and electrochemical methods are among the most common. Among these, EIS is an attractive technique because, as mentioned earlier, it is almost a non-destructive method. In addition, EIS is suitable for environments with very high strength, such as concrete, because it is essentially a transient method and does not require the system to be in a stable state [21. [In steel systems (reinforcement) / Concrete, information Various parameters such as the presence of surface films, concrete properties, joint joint corrosion and mass transfer phenomena can be obtained from the EIS method [21. [22] [In addition, the high-frequency impedance of information in Moore Provides dielectric properties of concrete, and low-frequency impedance information on the properties of passive films (surface oxide layers) on steel. Studies that began three decades ago have proven the validity of EIS as a technique for studying rebar corrosion in concrete, both experimentally and theoretically. John et al. (9189) monitored corrosion of rebar in high porosity concrete using EIS. They obtained both the corrosion rate of the steel rebars and the information on the steel surface layers. Later, more fundamental work was done by McDonald et al. To establish the application of EIS in the detection of rebar corrosion in concrete [3]. In this work, the rebar was simulated as a one-dimensional electrical transmission line. Their results show that imaginary and real components of impedance and phase angle can be used to detect corrosion of rebar embedded in concrete, but this is only possible at very low frequencies (for example, 1 mHz). It was also found that monitoring the peak voltage at the concrete surface just above the rebar helps to fully detect corrosion.