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In the second article of our series outlining some of the basic tests and procedures used in commercial oil analysis laboratories, our attention is focused on Fourier Transform Infrared (FTIR) spectroscopy, a versatile tool used to detect common contaminants, lube degradation by-products and additives.
FTIR is one of the most widely used tools in the oil analysis laboratory. Its value lies principally in the fact that it is a purely instrument-based test (meaning it does not need extensive sample preparation or wet chemistry), it is relatively quick to perform and is capable of simultaneously detecting multiple parameters, including water, fuel, glycol, oil oxidation, soot and certain additives. In fact, at face value, it would appear to be the ideal solution to providing quick, inexpensive oil analysis.
FTIR is based on the fundamental principles of molecular spectroscopy. This broad-ranging area of physics and chemistry covers a multitude of experimental techniques, some of which are found in other oil analysis tests, and others that are so sophisticated that they are of importance only in research laboratories.
The basic principle behind molecular spectroscopy is that specific molecules absorb light energy at specific wavelengths, known as their resonance frequencies. For example, the water molecule resonates around the 3450 wavenumber (given the symbol cm-1), in the infrared region of the electromagnetic spectrum.
An FTIR spectrometer works by taking a small quantity of sample and introducing it to the infrared cell, where it is subjected to an infrared light source, which is scanned from 4000 cm-1 to around 600 cm-1.
The intensity of light transmitted through the sample is measured at each wavenumber allowing the amount of light absorbed by the sample to be determined as the difference between the intensity of light before and after the sample cell. This is known is the infrared spectrum of the sample.
A wavenumber, given the symbol cm-1, is simply the inverse of the wavelength of the light. For example, 3450 cm-1, the typical resonance frequency of water corresponds to a light wavelength of 0.00000290 2.9 x 10-6)m,
in the infrared region of the electromagnetic spectrum. Rather than using the cumbersome unit of 10-6 m, spectroscopists simply take the inverse to give a number that is easier and more convenient to use.
In the infrared region of the spectrum, the resonance frequencies of a molecule are due to the presence of molecular functional groups specific to the molecule.
A functional group is simply a group of two or more atoms, bonded together in a specific way. In the water molecule (H2O), it is the O-H functional group that contributes to the resonance frequency around 3450 cm-1.
To the oil analyst, functional groups are both a benefit and a hindrance to FTIR analysis. The benefit is that different types of molecules, such as the additive ZDDP (zinc dialkyl dithiophosphate) and water, or fuel and glycol that have different functional groups absorb infrared light at different wavelengths.
Therefore, it is possible to determine the presence of different molecules in the sample with FTIR, simply by measuring the absorption at different wavelengths, or wavenumber. A list of typical resonance frequencies for common molecules measured using FTIR is given in Table 1.
Table 1. Common Molecular Species Measured by FTIR
However, some molecules have very similar functional groups. For example, three common molecules often found in used oil samples: water, glycol and the hindered phenol antioxidant additive BHT. These molecules are illustrated in Figure 1.
Figure 1. Water, Ethylene Glycol and BHT, a
Commonly Used Hindered Phenol Antioxidant Additive,
all Absorb Light in the 3600 to 3400 cm-1 Region Due to
the O-H Functional Group Present in Each Molecule.
As seen in Figure 1, it is clear that all three molecules possess the same O-H functional group as the water molecule, meaning that they will absorb light in the 3600 to 3400 cm-1 region, although the actual wavenumber will vary slightly due to the effects of the rest of the molecule.
The similarity of functional groups creates a problem with FTIR. For example, if a sample is analyzed and the infrared absorption recorded in the 3600 to 3400 cm-1 region, one may not be able to differentiate between absorption due to water, glycol contamination or antioxidant additives, because their absorptions peaks are usually fairly broad and may overlap.
Fortunately, the glycol molecule also absorbs light in other regions of the infrared spectrum, specifically 880, 1040 and 1080 cm-1, which provides confirmation. However, in this region of the spectrum, absorption due to the oil molecules themselves can, and does, occur.
This illustrates one of the biggest drawbacks with FTIR for used oil analysis. Because most used oil samples are complex mixtures of thousands of different molecules including base oil molecules, additives, oil degradation by-products, wear debris and contaminants, the infrared spectrum of a used oil sample is typically complex, and can be difficult to interpret with any degree of certainty (Figure 2). Despite these drawbacks, FTIR still has value in used oil analysis and is employed by the majority of oil analysis labs as a screening tool.
In an FTIR experiment, whenever a resonance with one of the constituent molecules present in the sample is met, the amount of light absorbed increases, or conversely the amount of light transmitted through the sample decreases.
This is called an attenuation cell FTIR instrument, the most common type of instrument used by oil analysis labs. By recording the amount of absorbed light as a function of the scanning wavenumber, an FTIR absorption spectrum can be recorded as illustrated in Figure 2.
For some parameters such as soot, water and glycol, their concentrations can be determined using a simple calibration procedure, which relates the amount of absorbed light to a known concentration of each contaminant, using a calibration curve supplied with the FTIR spectrometer.
For soot specifically, FTIR data is sometimes also reported as the amount of light transmitted rather than absorbed, typically around 2000 cm-1. In this instance, a higher degree of soot loading results in a reduction in the amount of light transmitted, which is reflected in the FTIR data reported.
To try to minimize the effects of the base oil and additive molecular resonances, FTIR analysis of used oil samples is a three-stage process. The first stage is to record the FTIR spectrum of a new oil sample to obtain a baseline FTIR trace.
The second stage is to record the same FTIR spectrum of the used oil sample. The third and final stage is to subtract the new oil baseline, often referred to as the new oil reference from the used oil spectrum to obtain the difference spectrum, as illustrated in Figure 3.
In theory, the difference spectrum allows the changes in both the chemical composition of the oil, such as oil oxidation (represented by an increase in a peak centered around 1740 cm-1), and any contaminants to be measured, without interference from the new oil molecular resonances.
The one major limitation of this difference spectrum procedure is that it is often not practical to send a sample of new oil with the used oil sample each time analysis is required. In order for the procedure to be accurate, the new oil reference used for this purpose should not only be the same type, brand and grade as the used oil, but also from the same manufactured batch of oil.
Manufacturers of FTIR instruments are aware of this limitation and many have introduced the concept of autoreferencing to attempt to resolve this issue. Autoreferencing allows the lab to run a single new oil reference for each type of oil in use, and store the FTIR spectrum as a new oil reference for future use, whenever used oil samples are submitted to the lab for FTIR analysis.
While autoreferencing helps to minimize problems associated with not having a new oil sample for each used oil sample, variations between oil batches, changes in oil formulation and the fact that multiple resonance absorption features can occur at the same wavenumber serve to limit the sensitivity and accuracy of FTIR data as indicated in Table 1.
For this reason, most oil analysis labs treat FTIR as a screening tool and back-up this method with confirming tests such as Karl Fischer if water is indicated, or the flash point test if fuel is suspected.
One parameter for which FTIR is of great value is the determination of soot content in diesel engine oil samples. Soot is typically measured as an increase in baseline absorption at 2000 cm-1, due to the sample becoming opaque to infrared light at this wavelength, in much the same way as soot makes an oil sample opaque to visible wavelengths of light.
2000 cm-1 is chosen because in this region of the spectrum, there are typically no other resonances associated with the base oil, additives or common contaminants. Because this measurement is essentially baseline-free the accuracy to which soot can be determined using FTIR is less sensitive to having an accurate new oil reference, allowing for reliable accurate data determination.
One other area where FTIR can prove extremely valuable is in determining significant changes in new oil chemistry, such as what might be expected when two oils with different chemical compositions are added. Figure 4 shows the FTIR spectrum of a blend of a PAO (polyalphaolefin) based synthetic oil and a phosphate ester EHC fluid.
By recording the FTIR spectrum or the suspected blend, along with the known new oil reference spectra of the pure PAO and pure EHC fluids, confirmation of this accidental mixing can be determined. In fact, whenever an unknown contamination issue is suspected, it often advisable to immediately run an FTIR spectrum, in conjunction with a fresh new oil reference whenever possible.
Despite its limitations, FTIR is a valuable addition to any oil analysis program. By understanding how the technique works and its strengths and limitations, oil analysts and end-users can obtain a vast amount of infor-mation using FTIR, all at the press of a button!