Fluorescence spectroscopy analysis is a great tool for investigational research and analytical science applications. It is used often in biochemical, chemical, pharmaceutical, and medical applications, in addition to mineralogy, fluorescent labeling, sensors and forensics applications. It is also used to aid in the identification of proteins, organic compounds, oils and dyes and is used for environmental monitoring and laser induced chlorophyll fluorescence for crop yield assessments.

This type of spectroscopy focuses on the vibrational states of a sample. Certain substances can be excited to a higher electronic state by using a specific frequency. An particular excitation may deliver an emission or fluorescence peak.

Ocean Optics offers many options of ready to use fluorometry systems with different resolutions, off-the-shelf configurations and time-gating options. Cuvette holders, LVF low pass and high pass filters, a fiber optic scanning monochromator and variety of excitation sources are available. Our fluorescence spectrometers can detect fluorophores in liquids and powders, as well as from surfaces.

Our USB2000-FLG in our legacy EDS2000 System has been used to detect anthrax. Similar setups have been used to detect fluorescence in coral, fruit, and other flora and fauna.

Experimental:

Standard stock solutions of quinine sulfate solution in methanol and sulfuric acid were prepared with approximate concentrations: 1, 20, 40, 60, 80 and 100 ug/mL. Smaller concentrations of quinine sulfate stock solutions of 0.50, 0.25, 0.06, 0.03, 0.01 and 0.00 were also created. These varying concentrations of quinine sulfate standard stock solutions were measured for fluorescence using CVFL-Q-10 quartz cuvettes in a CUV-ALL 4-way cuvette holder. These measurements were performed using a QE65000 spectrometer (grating #HC1, 200 µm slit, range 349.2 nm – 1143.5 nm, optical resolution 6.4 nm (FWHM), PX-2 light source, HR4-BREAKOUT Breakout Box, MonoScan2000 scanning monochromator and SpectraSuite software. Three QP1000-2-UV-VIS fibers were used to connect the PX-2 light source to the MonoScan2000, the MonoScan2000 to the CUV-ALL cuvette holder and the CUV-ALL cuvette holder to the QE65000 spectrometer. Fibers were attached to the cuvette holder at 90 degrees. See Figure 1.

Figure 1. Equipment setup for Fluorescence

Fibers were taped down to reduce attenuation from movement. The PX-2 light source was warmed up for 15 minutes prior to measurements. A new dark measurement was stored (and subtracted) between every change in sample and/or every 15 minutes to minimize error and drift. Spectra were recorded between 375-600 nm. Measurements were recorded using both Scope Mode and Relative Irradiance Mode.

Scope mode data is unprocessed with the instrument response function not factored out. This may result in emission peaks not at the exact wavelength as reported in the literature, in variable intensity shifts and curves having different shapes.

Relative irradiance measurements were performed using the same equipment, but with the addition of a LS-1 tungsten halogen light source that was used as a black body reference with known color temperature.

Results:

Emission (or fluorescence) peak spectra were collected from various concentrations of quinine sulfate stock solutions in both Scope and Relative Irradiance Modes. Replicates measurements were taken to create calibration curves. Peak locations varied slightly (more so in Scope Mode) from the reported 450 nm maximum fluorescence peak for quinine sulfate. In Scope Mode concentrations from 20-100% solutions peaked at 457.84 nm. The 1% solution replicates were the most variable in the measured peak location. The peak height locations for these replicate measurements were averaged and rounded to the nearest nanometer for reporting on the calibration curve. The measured peak for fluorescence in relative irradiance was found to be 449.11 nm for all concentrations. Calibration curves were created for two different concentration ranges. See Figures 2, 3.

Figure 2. Calibration curve of quinine sulfate from fluorescence peak points in scope data at concentrations from 1- 100 ug/mL.

Figure 3. Calibration curve of quinine sulfate from fluorescence peak points at 449.11 nm from relative irradiance data. This graph illustrates the linear range at much smaller concentrations.

Figure 4. Emission spectra of quinine sulfate solutions.

Conclusions:

The most linear part of the calibration curve is in the small concentrations range under 1 ug/mL (or 1 ppm or 1000 ppb). As expected, at greater concentrations of fluorophore, due to the inner filter effect, the excitation drops off after reaching a maximum intensity.

There was good linearity on the calibration curve for the smaller concentrations of quinine sulfate. The R2 value for linearity of the curve was 0.998 using the relative irradiance data.

Relative irradiance data proved to be more consistent overall and was much closer to the reported fluorescence peak locations of quinine sulfate in published literature.

A quinine calibration curve is suitable for checking quinine concentrations in liquids for quantitative analysis.

The QE65000 Scientific-grade Spectrometer is a sensitive system ideal for low-light level applications such as fluorescence.  Since the QE65000 can achieve up to 90% quantum efficiency with high signal-to-noise and rapid signal processing speed, this would be the preferred spectrometer for fluorescence applications.

Related References:

“Development of an In-Fiber Nanocavity Towards Detection of Volatile Organic Gases,” C. Elosua, I. R. Matias, et al., Sensors., 6, 578-592 (2006)  Retrieved from

http://www.mdpi.org/sensors/papers/s6060578.pdf

“Magnetic and fluorescence properties of cobalt implanted hydrogels,” H. Sozeri, A. Gelir, et al., Journal of Physics: Conference Series 153 (012067): 1-6 (2009)  Retrieved from http://iopscience.iop.org/1742-6596/153/1/012067/pdf/jpconf9_153_012067.pdf

“Rapid characterization of biomass using fluorescence spectroscopy coupled with multivariate data analysis. I. Yellow poplar (Liriodendron tulipifera L.),” K. Nkansah and B. Dawson-Andoh, AIP Journal of Renewable and Sustainable Energy (2010) Retrieved from http://jrse.aip.org/jrsebh/v2/i2/p023103_s1?view=fulltext

“Towards microalbuminuria determination on a disposable diagnostic microchip with integrated fluorescence detection based on thin-film organic light emitting diodes,” O.. Hofmann, X. Wang, et al., The Royal Society of Chemistry, Lab Chip. 5, 863–868 (2005) Retrieved from   http://www.molecularvision.co.uk/publications/LabChipMicroalbuminuria2005.pdf

The customer supplied us with a special wheel used for chlorine content measurements.  The provided sample has nine windows of varying shades of yellow, with a clear center window for reference, as shown in Figure 1.  The window numbering sequence can be seen on the perimeter of the sample in permanent marker; the sequence is from 1 to 9 in order of yellow intensity (9 = most yellow).  Transmission and reflection measurements were performed for each window in the sample in order to provide accurate spectrometric data for different film characteristics.

Figure 1. Chlorine Content Color Wheel

Experimental Procedure

We analyzed the wheel’s films using a USB4000 UV/VIS Spectrometer, an LS-1 tungsten-halogen light source with BG34 lens installed, and SpectraSuite Spectrometer Operating Software.  For the reflection measurements, a reflection probe (R400-7-VIS/NIR), reflection stage (STAGE), and the PTFE diffuse reflectance standard (WS-1) were utilized.  The overall distance from the tip of the reflection probe ferrule to the sample surface was around 3-mm. Figure 2 shows a photograph of the reflection experimental setup. Given the transparency of the windows, we used the WS-1 reflection standard as background for all reflection measurements.  Figure 3 shows the sample mounted on the WS-1 in the reflection stage.  The reference spectrum for the reflection measurements was the clear center window with the WS-1 background.

Figure 2. Reflection Experimental Setup

The transmission setup included an adjustable collimation lens holder (74-ACH), two collimating lenses (74-UV) and two premium grade 300-micron fibers (QP400-1-UV/VIS) as shown in Figure 4, utilizing the same light source and spectrometer. The transmission measurements were conducted using two different references.  In the first experiment, the clear center window was mounted in the transmission stage and a reference spectrum was captured.  All of the subsequent window spectra are referenced to the central window spectrum.  In the second experiment we removed the sample from the optical path and obtained the reference spectrum.  We captured spectra from all nine windows and the clear central window with respect to the free optical path reference.

Figure 3. Sample shown with WS-1 background

Results

The reflection and transmission results for each window in the chlorine content color wheel are shown in Figure 5 – Figure 7. A summary of the measurement  parameters used in SpectraSuite software is presented in Table 1.

The following results are color-coded as labeled in the figure legends.  All figures employ the same color-coding (e.g., window one is blue in all diagrams).  The measurement parameters from Table 1 are also visible in the left frame of the figures.

There is good correlation between the reflection data and transmission data (using the center window as reference).  Based on the setup of the reflection experiment, we would expect similar spectral shape to the transmission data because of the PTFE diffuse reflection standard background.  Essentially, the light is transmitted through each window, reflects off the 99% diffuse reflection standard, the reflection is transmitted back through the window and is read by the spectrometer.  Therefore, the spectral shapes should be essentially the same.

Figure 5. Reflection Experiment Results

From the comparison of the two sets of transmission data, it is clear that using the results obtained with the clear center window spectrum as a reference would be most advantageous for classifying the subtle differences in the sample windows.  This is primarily due to the nature of the clear center  window in the region from 350 – 500 nm.  The center window spectrum is actually concave down (negative second derivative) over that region, which actually enhances the light source signal over that region.  This region best classifies the sample windows.

Figure 6. Transmission data with center window as reference

Figure 7. Transmission data with no sample in optical path as reference

More on the web

- USB4000 UV-VIS Spectrometer
- LS-1 Light Source
- WS-1 Reflection Standard

]]> http://www.spectroscopytips.com/apps/reflection-and-transmission-of-chlorine-content-color-wheel/feed/ 0 http://www.spectroscopytips.com/apps/using-the-hr2000cg-for-reflection-testing/ http://www.spectroscopytips.com/apps/using-the-hr2000cg-for-reflection-testing/#comments Mon, 24 Jan 2011 18:58:59 +0000 Admin http://www.spectroscopytips.com/apps/?p=318 The Ocean Optics HR2000+CG Spectrometer is a versatile, high-performance option for a range of applications. In this experiment, we conducted a feasibility study for a customer measuring reflectance of ceramic discs.

Background
Our customer supplied us with five composite ceramic disc samples. Each sample comprised both grey and white portions and was labeled accordingly. Special care was taken to isolate similar disc samples from each other.  The purpose of this study was to measure the percent reflection of the grey portion of
the discs.

Experimental Procedure
The samples were analyzed using the HR2000+CG Spectrometer (200-1050 nm); an HL-2000-HP-FHSA, which has 20 W output power; an R400-7-VIS-NIR Reflection Probe with the RPH-1 Probe Holder; and SpectraSuite spectroscopy software. Multiple measurements of each sample (on both sides) were acquired to demonstrate the repeatability of the prescribed setup (see photo above). This setup occupies a small footprint.

Results
The multiple measurements of the grey samples demonstrated superior reflection spectrum repeatability over the region of 400-1000 nm (Figure 1).

Another grey sample exhibited good repeatability over the same spectral range with deviation from the mean reflectivity within ±13%.  However, the next set of samples displayed a wide variation in reflection spectra over 400-1000 nm with a deviation from the mean on the order of ±30% (Figure 2).

Despite the superior repeatability of the first set of samples, differentiation between the two samples would be difficult given the flat spectral nature and roughly the same mean reflectivity. The second set of samples can be differentiated from the previous two in spite of a higher deviation from its mean reflectivity.

Unfortunately, the ±30% spread in reflection spectra for the initial samples renders it difficult to differentiate one from the other and from the other grey disc samples.

This conclusion reinforced the customer’s hypothesis that specular reflection alone would not be sufficient for discriminating the variations in the ceramic disc samples.

Introduction
Raman spectroscopy offers a number of benefits for testing and characterization. It is rapid and non-destructive, requires only limited sample preparation and allows for sample volumes in the microliter range. In addition, Raman can be used to measure aqueous samples or samples with high moisture content, and allows researchers to capture data from a sample contained in plastic or other materials that are optically transparent to the wavelengths of interest.

Raman is particularly useful for pharmaceutical applications. For example, Raman techniques are used to discern characteristics of pharmaceutical raw materials, including active ingredients, binders, fillers, lubricants and other excipients. Raman is also useful for through-container measurements of pharmaceutical blister packs, pill bottles and vials.

Experimental Conditions
To illustrate the capabilities of our Raman systems we analyzed Paracetamol (acetaminophen) and Carbamazepine, which are pharmaceutical active ingredients, and the excipients alpha and beta lactose. The samples studied consisted of simple organic compounds contained in standard, clear borosilicate scintillation vials. No additional preparation was necessary.

Samples were analyzed using a modular Raman setup comprising our QE65000 Spectrometer, a 785 nm laser with 500 mW output and a fiber optic probe. The spectrometer was set from ~780-940 nm and configured with a 50 µm slit for good optical resolution. High reflectivity optical bench mirrors increased spectrometer sensitivity.

To collect signal, we placed the tip of the probe at the bottom of three glass vials containing the samples. We measured the samples at an integration time of 8 seconds and averaged three spectra.

Results
Our measurements confirmed that this Raman configuration can differentiate various pharmaceutical raw materials based on their spectral fingerprints. Also, the experiment helped demonstrate that, with proper method development and application of chemometric analysis, our Raman setups can be used to obtain semi-quantitative data of active ingredients in a pharmaceutical mixture.

Our experiment also showed that fluorescence occurs in the lactose samples. Fluorescence is a common phenomenon in Raman measurements of some organic compounds and depends on the wavelength of the laser utilized.

Conclusions
The availability of both turnkey and modular Raman systems, complemented by sophisticated chemometric analysis packages and spectral libraries, makes Raman spectroscopy a versatile choice for a host of applications.

Components for Oxygen:
NeoFox Phase Fluorometer
RedEye® Oxygen Patches (Headspace and In-Solution measurement)
Bifurcated Fiber Optic probe

Components for pH:
Jaz Spectrometer
LS-1 Tungsten Light Source
Bifurcated Fiber Optic Probe
Reflective pH Patches

Experimental Procedure:
•    Oxygen Patches were placed inside the Bio-flask to monitor the oxygen in headspace and in solution.
•    pH patch was placed in solution to monitor pH changes during the fermentation process.
•    Fresh red grapes were mashed and the must was left to sit for 2 days.
•    The liquid was added in the bio-flask. Initial measurements were recorded and yeast cells and nutrients were added to start the fermentation process.
•    This process was observed over a period of 60 hours.

Optical Oxygen Sensors:
•    Prominent method for luminescence sensing in which the lifetime of the indicator compound changes in response to the analyte sensed (phase measurement).
•    A fluorescence method measures the partial pressure of dissolved or gaseous oxygen. The fluorescence is generated at the tip of the optical electrode by a light source.
•    When oxygen in the gas or liquid samples diffuses into the thin film coating, it quenches the fluorescence.
•    The degree of quenching correlates to the level of oxygen pressure.

RedEye Oxygen Sensor Patch:
•    RedEye is a revolutionary oxygen sensing product designed specifically to measure oxygen.
•    RedEye consists of a sensor coating formulation trapped in a sol gel matrix, immobilized and protected from the package contents
•    The RedEye patch has no minimum and maximum diameter  sizes, but typically sizes will vary from a few millimeters to several centimeters
•    RedEye can be easily integrated into any system because of its self-adhesive acrylic  patch having the sensor coating formulation.

O2 Sensing Electronics – NeoFox:
NeoFox is an instrument platform for measurement of fluorescence lifetime and phase for oxygen sensing
•    The frequency domain electronics uses a blue LED excitation and an  avalanche photodiode for detection.
•    A bifurcated optical fiber carries excitation light produced by the blue LED to the thin-film coating of the RedEye Oxygen Sensor
•    Fluorescence generated at the surface of the RedEye Patch is collected by the probe and carried by the optical fiber to the detector.

Lifetime Measurement Technique:
The phase shift between the blue LED excitation and emission signal of fluorescence is used to calculate lifetime.

Advantages of Optical Oxygen Sensors:
•    Uses fiber optic cable for non-intrusive measurements
•    Not contaminated by water or other solutions
•    Immune to EMI
•    Non-electrical, non-conductive
•    Operable in temperature and concentration range of various applications
•    Simple calibration
•    Works with colored samples without any color or light interference

Smart pH Technology:
•    pH buffers used for calibration, references taken by software
•    Absorbance curves seen as pH increases
•    Absorbance peak at 620nm, baseline correction at 512 nm

Temperature Compensation:
Compensation for temperature effects is achieved through the van’t Hoff equation:

•    Isothermal correction corrects pH value based on user-input temperature
•    Dynamic temperature correction available for non-isothermal systems with temperature monitoring

Result Analysis and Conclusion:
•    The graph shows the complete measurement of both oxygen and pH over a period of 60 hours.
•    The next graph shows the measurements over the first two hours. The oxygen sensor in solution quickly dropped from air saturation as soon as the yeast cells and nutrients were added. The pH sensor in solution dropped slightly as the oxygen decreased and CO2 is released. Hence the drop in pH value.
•    The following graph indicates the oxygen sensor in the headspace stays at air saturation approximately the first 2.5 hours. Once the oxygen in solution is completely quenched, the yeast cells and nutrients start consuming oxygen from the headspace.
•    Conclusion: Ocean Optics sensor patches are able to monitor both oxygen and pH non-intrusively which can be used in the fermentation industries.

Introduction:
It may be necessary in certain applications to determine the elemental composition of chip components, including the core metals and coating plastics. In this experiment, LIBS was used to assess whether gold and silver were present in the core of chips, and whether the core could be distinguished from the plastic coating in resulting spectra. It was found that silver was present in the core, while gold was present only in protruding wires from certain chips. It was also found that the coating could be distinguished based on elements present in the core but not in the coating.

Hardware Used:
LIBS 2000+ broadband, high resolution spectrometer
200 mJ Nd:YAG Big Sky laser
LIBS-SC sampling chamber with imaging module

Acquisition Parameters:
3 scans/average
Laser level 8
Q switch delay: -2.5 microseconds

Experimental Conditions:
All samples were placed individually on double sided tape within the sampling chamber to prevent movement from laser ablation. The plastic sheath was assessed on all samples, while two samples with protruding filaments were assessed for metal content by creating a hole in the plastic sheath with the laser through which the metal core could be ablated.

Measurement Mode:
LIBS

Results:
Using the elemental analysis tool in OOILIBS, All samples of coating were shown to contain silicon to varying degree by the presence of corresponding peaks at 251.61, 288.15, and 252.81nm. Elemental analysis also revealed that the coating of sample 2 contained high counts of magnesium as well as silicon. High peak values and strong correlation to elemental peaks in sample 4 suggested a strong presence of hydrogen and carbon, while sample 5 also suggested carbon in high presence. However, none of the coatings displayed any detectable amounts of silver. When the coating on sample 3 was degraded down to the core via repetitive laser strikes, a strong presence of silver was detected, both in the number of peak correlations and high signal counts. Once the core had been reached, sodium peaks no longer appeared. In addition, lasing of wires protruding from sample 4 showed noticeable presence of gold via close correlation to peaks. In all coating samples, sodium and potassium were also detected, however this is likely attributable to contamination from handling.

Conclusions:
It was shown that LIBS compounded with the elemental analysis function in OOILIBS is a capable tool for distinguishing between elemental components of the core and coating of computer chips. By correlating peak intensity counts and the number of elemental peaks present in each spectrum, it was possible to distinguish the major elements within each chip coating of samples 1-7, and the core of sample 3. Further ablation of sample 3 showed that the presence of silver distinguishes core material from coating, and ablation of sample 4 on protruding wires showed that metal components present on the chip or in the core of the chip can be differentiated using LIBS.

To measure oxygen concentration is measured in the headspace of organic solvents such as Acetonitrile) using a phase fluorometer systemMethod:
O2 Sensing

Hardware Used:
Phase Fluorometer Electronics (MFPF)
Bifurcated Fiber Optic Cable
Hioxy-R Sensor

Feasibility Test Procedure
The MFPF electronics is an instrument platform for measurement of fluorescence lifetime and phase. This frequency domain electronics uses a blue LED excitation and a photodiode for detection. A fluorescence method is used to measure the partial pressure of dissolved or gaseous oxygen. A bifurcated optical fiber carries excitation light produced by the blue LED to the thin-film coating of the Hioxy-R sensor.. Fluorescence generated at the tip of the probe is collected by the probe and carried by the optical fiber to the detector of the MFPF. The phase shift between the blue LED excitation and emission signal of fluorescence is used to calculate the lifetime.  The Lifetime is an essential parameter for the calculation of oxygen.

To test the feasibility of the Hioxy sensor in the Acetonitrile headspace, the Hioxy Sensor is evaluated for its performance before exposure to the acetonitrile vapor.
The Hioxy sensor is exposed to Nitrogen and then to Air as show in Figure 1. The ratio of the lifetime from Nitrogen to Air is noted to be approximately 4.01. Now the sensor is exposed to the vapors of acetonitrile for about 2 hours. After the exposure the sensor is pulled out from the vapors and left in ambient conditions. The ratio of Nitrogen to air is measured again in terms of lifetime. The ratio after this exposure has changed to 4.27. The sensor is again exposed to acetonitrile vapor and the ratio is measured after exposure to be the same at 4.27.

Results
As shown in Figure 1, the Hioxy Sensor looks very stable in the headspace of the Acetonitrile container. The ratio of Nitrogen to Air has changed after the first exposure to Acetonitrile vapor. After the first exposure the ratio stays the same. This means there is a one time effect on the sensor when exposed to acetonitrile vapor.

Conclusion
The Hioxy sensor can be used to measure oxygen in the headspace of Acetonitrile. When a sensor is sold for this application, the sensor needs to be cured with acetonitrile vapor before calibration.

Note:
The sensor needs to be placed in acetonitrile vapor for at least 2 hours before use.

Method:
LIBS

Hardware Used:
LIBS2000+ spectrometer
Big Sky Nd:YAG 50 mJ 1064 nm laser
LIBS Sample chamber with 125 mm focal length lens
OOILIBS software

Acquisition Parameters:
Data was acquired for different levels of surface cleaning (0 to 10 laser shots before acquiring data) and for samples where 10 laser shots were averaged. All of the spectra were collected with a 1.5 microsecond delay between when the laser fired and when data acquisition began.

Measurement Mode:
LIBS

Experimental Conditions:
The copper sample was placed in the LIBS sample chamber and the probe was focused until the maximum intensity was observed from the plasma. Three replicates at different locations on the sample were acquired using the following data acquisition parameters:

No surface cleaning (OOILIBS has a cleaning function that allows the user to fire several cleaning shots to clean contaminants from the surface of the sample before acquiring spectral data.)

10 cleaning shots

3 cleaning shots

10 laser shots averaged with no cleaning shots

Single laser shot resulted in surface pitting of the sample.

Results:
The average spectral data for the three replicates acquired using varying data acquisition parameters are shown in the figure below. The spectra were offset to facilitate comparison of spectral features.

For the bottom spectrum where no cleaning shots were fired, it appears that there is a significant amount of material on the surface of the sample as evidenced by the region between 500 and 600 nm. This region of the spectrum is filled with broad bands and elemental lines unique to the uncleaned surface. In the remaining spectra, when the samples are cleaned 3 or 10 times or 10 laser shots are averaged at the same location, the spectral data look similar. Even though the top spectrum where 10 laser shots at the same spot were averaged is similar to the condition where the surface is pre-cleaned with 10 shots before acquiring data, the top spectrum represents the average of 10 individual laser shots at the same location so it includes a contribution from the uncleaned spectrum shown at the bottom of the plot.

The elements identified in one of the replicate measurements for each set of data acquisition parameters are shown in the table. As anticipated based on the spectral data, the number of elements identified decreases with increasing cleaning shots. For example, numerous magnesium (Mg) and calcium (Ca) lines are observed when no cleaning shots are used to remove contaminants from the sample surface (first two columns in the table). With surface cleaning, the number of lines observed for these elements decreases. The data shown in the final two columns of the table for 10 shots averaged together without any surface cleaning still shows the Ca and Mg lines since the surface was not cleaned before the measurements.

Note that the library used to identify the spectral lines in the OOILIBS software is a partial library containing the persistent lines of the elements. With a more complete library, like the MIT Wavelength Tables containing thousands of spectral lines, additional elemental lines would be identified. As expected, several copper lines were observed for each measurement. In the case of the uncleaned sample, a larger number of lines was detected. It is important to note that these samples were measured in the presence of air. The sample chamber was not purged to remove the air. For this reason, the oxygen and nitrogen lines observed result from ambient air in the plasma in addition to any oxygen and nitrogen present in the sample.

Method:
Absorbance

Introduction:
Feasibility testing was done to assess the detection of bleach with OOI hardware and to determine the optimal hardware configuration for detecting 10, 20 and 30 ppm sodium hypochlorite.

Attempts to measure chlorine with another system (USB2000 (USB2E3017) with grating #2, 25 um slit, UV2 detector upgrade, OFLV order sorting filter and L2 detector collection lens, DT1000-S source, CUV-UV cuvette holder, 400 um SR fiber on one leg, 200 um UV/VIS fiber on the other leg, CVD-UV1S cuvette were not successful at the 30 ppm sodium hypochlorite level.

Hardware Used:
USB2000 (USB2E214) with grating #1, 50 um slit, L2 detector collection lens and OFLV order sorting filter
CUV-UV cuvette holder
P400-025-UV-SR optical fibers
DT1000 deuterium tungsten halogen

Acquisition Parameters:
Integration Time (msec): 7
Spectra Averaged: 100
Boxcar Smoothing: 3

Measurement Mode:
Absorbance

Experimental Conditions:
Generic ultra bleach for household use was diluted with water to provide sodium hypochlorite samples. According to information on the internet, ultra bleach is 6% sodium hypochlorite or 60,000 ppm. Water was used to dilute the 6% solution down to 10, 20 and 30 ppm.

Results:
The plot below contains spectral data for 10, 20 and 30 ppm sodium hypochlorite. A peak at 295 nm is observed. This peak intensity decreases with dilution of the sodium hypochlorite.

Conclusions:
The use of a larger slit and UV optimized grating in the spectrometer (50 um slit versus 25 um and grating 1 versus grating 2) resulted in excellent detection of 10 to 30 ppm sodium hypochlorite. These concentrations are easily detectable with OOI hardware.

Method:
LIBS

Introduction:
Elemental analysis of alloy beads composed primarily of aluminum (80% Al) and iron (20% Fe) was carried out with LIBS to provide an elemental profile for the sample. Detection of contaminants present in the samples in addition to the major components was desired.

Hardware Used:
LIBS2000+ broadband, high-resolution spectrometer
50 mJ Big Sky Nd:YAG laser
LIBS sampling chamber with 125 mm focal length lens

Measurement Mode:
LIBS

Experimental Conditions:
The alloy beads were pressed onto a piece of double-sided, photo-mounting tape attached to a microscope slide. A total of 10 single shot spectra were acquired at different locations on the slide. An average spectrum was generated from the single shot spectra. The elements present in the average spectrum were identified with the OOILIBS Elemental Identification module.

Results:
The average spectrum (n=10) for the aluminum alloy beads is shown in Figure 1. The elements identified for the average spectrum are shown in Table 1 with the number of elemental lines identified per lines in the Elemental Identification library shown in Table 2. As shown in Table 2, 3 out of 9 Al lines were detected with all 15 of the Fe lines included in the LIBS Elemental Identification library detected with LIBS.

Other elements detected included potassium (P), gold (Au), iodine (I), lead (Pb), tin (Sn), calcium (Ca), magnesium (Mg), cadmium (Cd), phosphorus (P), chromium (Cr) and barium (Ba). In addition to the lines detected (see Table 1 for a complete list), numerous other emission lines were not identified by the library. The identity of these lines could be determined with a resource like the MIT Wavelength Tables. The identity and concentration of the elements present in the alloy beads (as determined by an alternative analytical method) was not provided with the samples for comparison.

Method:
LIBS

Introduction:
The target analyte for the samples is the element Lanthanum (La). There are two spectral regions of interest. In the region centered at 324 nm, there is a La doublet at 317 nm and an emission line at 332 nm. The second spectral region contains La emission lines at 427 nm and 433 nm.

Hardware Used:
LIBS2000+ broadband high-resolution spectrometer
50 mJ Big Sky Nd:YAG laser
LIBS sampling chamber with 125 mm focal length lens

Acquisition Parameters:
2 microsecond Q-switch delay between laser event and data acquisition

Measurement Mode:
LIBS

Experiment Conditions:
Samples were prepared by evaporation of a series of 3 microliter aliquots of La at three different concentrations. Concentrations were 10,000, 1,000 and 50 ppm, labeled 10k, 1k, and 0.05, respectively. Two different substrates were used, 6061 aluminum (0.125″ thick), and 304 stainless steel (0.025″ thick). The discrete spots where La was deposited on the metal surfaces were easily visible (with the naked eye) on the aluminum substrate at all concentrations, and on the stainless steel substrate at the 10k concentration. The La spots were not visible (with the naked eye) on the stainless steel substrate for the 1k and 0.05 La concentrations.

The La deposition pattern for the stainless steel samples were all the same, a 5 x 5 matrix, with one spot missing at the label. The 10k aluminum sample had a 4 x 5 spot pattern with one spot missing at the label, while both the 1k and 0.05 concentrations had a full 5 x 5 spot pattern. Due to differences in spot diameter these samples were not suitable for development of a traditional calibration curve.

Results:
Single and multi-shot LIBS spectra were acquired for several La spots on each sample. For the 10k La samples, extra data was acquired to profile the effect of subsequent shots at the same spot. The 10k samples were analyzed the most extensively.

Results are shown in the plots below. The LIBS analysis of La deposited on 6061 aluminum is shown in Figure 1. Each spectrum shown is the average of multiple single shot spectra (see legend for number of single shot spectra averaged). Two spectral regions that contain the La lines of interest are shown.

LIBS analysis of La deposited on 304 stainless steel is shown in Figure 2. Each spectrum shown is the average of multiple 3-shot spectra. As shown for the aluminum substrate, the spectral regions containing the La lines of interest are shown. In Figure 3, the effect of taking multiple shots at the same location is shown. Note that the richest spectrum is achieved with the second shot.

In Figures 4 and 5, the LIBS spectra for the 6061 aluminum and 304 stainless steel substrates are shown, respectively. Each spectrum represents the average of three 3-shot spectra. Note that the spectrum for stainless steel has more elemental lines indicative of differences in the plasmas obtained for the different materials.

Conclusions:
For La deposited on 6061 aluminum, 3 of 6 La peaks included in the Elemental Identification library were detected at the 50 ppm La concentration. At the 1000 ppm La concentration, 4 out of 6 peaks were detected with 5 of 6 La peaks detected in the 10,000 ppm concentration. With a 304 stainless steel substrate, 5 of the 6 La lines were detected at all La concentrations. The data show that La deposited on aluminum and stainless steel substrates is detectable down to the 50 ppm level with more peaks detected at higher La concentrations. Improved detection on the stainless steel substrate illustrates the impact of the matrix on detection. Differences in the plasma characteristics for the two substrates may have resulted in the detection of additional elemental lines (example of matrix effects on detection).

For the elemental lines of interest, the La doublet around 317 nm was detected on both the aluminum and stainless steal substrates at the 10,000 and 1000 ppm La concentrations. The elemental line around 332 nm was not detected at any La concentration on either substrate. The peaks around 427 and 433 nm were detected on the stainless steel substrate at all three La levels. For the aluminum substrate, the peak around 433 nm was detected at all three La levels with the peak around 427 nm detected at the 1000 and 10,000 ppm levels.

Method:
LIBS

Introduction:
Three features of interest can be distinguished visually in the glued wood composite. The very thin dark lines are a phenol formaldehyde resin bondline (sodium catalyst). The light tan (earlywood) and darker brown (latewood) lines represent different growth rates for the wood.

Experimental Conditions:
For the initial analysis to assess the discriminating capabilities of LIBS, a series of LIBS spectra were acquired at random locations on the sample. The goal was to obtain data that could be subjected to chemometrics analysis to determine if the different layers could be distinguished using LIBS and PCA and PLS analysis. Five different spots were measured at random locations on the sample with 7 spectra acquired at each spot to provide a depth profile. Each spectrum was single laser shot. To provide more specific data for chemometrics analysis, additional analysis was carried out on the specific features of the composite.

Hardware Used:
LIBS2000+ broadband, high-resolution spectrometer
50 and 200 mJ Nd:YAG Big Sky lasers
LIBS-SC sampling chamber with a 38 mm focal length lens (to provide a small spot size since spatial resolution is important)

Experimental Parameters:
-2 microsecond Q-switch setting

Measurement Mode:
LIBS

Results:
Based on the chemometrics analysis of the original set of data acquired with the 200 mJ laser at random locations on the glued wood composite (PLS and PCA analysis carried out by the customer – proprietary data), PCA and PLS analysis of the LIBS spectra were not sufficient for discrimination of the three layers of interest. PCA analysis showed greater differences between spectra acquired at the same spot than for the different areas sampled. PLS analysis showed a limited correlation between the depth and spectra suggesting that the actual laser pulse may have created a limited “memory” effect as it changed the wood (i.e. crater formation).

Since the first set of measurements were made at random locations on the composite, it was difficult to determine whether or not each of composite layers was sampled. To provide a more specific set of data to assess the discrimination capabilities of LIBS, an additional set of data was acquired for each of the specific layers in the glued wood composite. Analysis was carried out with the 50 mJ laser to minimize the spot size and allow sampling of the individuals layers. The average of 3 single shot spectra are shown in Figure 1 for each of the glued wood composite layers. Additional data for 6 consecutive LIBS spectra acquired at the same spot on the light tan layer are shown in Figure 2 to provide a pseudo-depth profile of this layer.

Conclusions:
A quick assessment of the spectral data showed a unique spectral line around 586 nm for the thin black bondline. This line is most likely a sodium line indicative of the sodium used to cure the phenol formaldehyde resin. Compared to the early and latewood layers, the black bondline had more sodium lines (as detected by the Elemental Identification software) and more intense sodium lines around 589 nm.

Unique spectral lines were observed for the early and latewood layers around 821, 822 and 857 nm. The identity of these lines is not known but all three of them occur near reported lines for nitrogen. The early and latewood layers also had a larger hydrogen alpha line around 656 nm related to the moisture content of the layer.

All of these unique spectral features, along with others that were not observed in the quick data assessment done, provide potential discriminating features for chemometrics analysis.

Figure 1: LIBS Spectra for Different Layers of Glued Wood Composite: Average of 3 Single Shot Spectra

Figure 2: LIBS Depth Profile for Light Tan Layer of Glued Wood Composite

Method:
LIBS

Introduction:
Two steel samples were tested for the presence of magnesium (Mg), manganese (Mn), sulfur (S) and silicon (Si) to assess the ability of LIBS to detect dirt in steel. Two samples were analyzed: approximately 38 mm diameter metal cylinder (approximately 50 mm tall) and approximately 25 mm diameter metal disk (less than 12 mm tall).

Experimental Conditions:
The smaller steel sample was measured on the flat surfaces on either side of the sample. The larger sample was measured on two of the curved surfaces. Sand was observed on the outside of the larger sample. Several single shot spectra and multi-shot spectra were acquired for each of the surfaces chosen for analysis.

Hardware Used:
LIBS2000+ broadband, high-resolution spectrometer
50 mJ Nd:YAG Big Sky laser
LIBS-SC sampling chamber with a 125 mm focal length lens

Experimental Parameters:
-2 microsecond Q-switch delay setting
1 to 10 spectra averaged

Measurement Mode:
LIBS

Results:
Average spectral data for the different surfaces measured for each of the samples are shown in Figures 1 and 2. As expected for alloy samples, the spectral data are very rich in elemental emission lines.

Results for LIBS detection of the elements of interest are presented in separate tables for each sample. The number of elemental lines identified by the Elemental Identification mode of the OOILIBS software is shown in Table 1. Note that the elemental identification library contains multiple lines for each element taken from the persistent lines in the MIT wavelength tables. The values shown represent the number of lines identified out of the total number of lines contained in the elemental identification library.

Conclusions:
Based on the results shown in Table 1 for LIBS analysis carried out with a 50 mJ laser, all of the elements were detected for the smaller steel sample. Note that sulfur was only observed for the single shot spectra acquired at fresh spots on the sample. When 10 shots were averaged at the same location on the sample, sulfur was not detected. This suggests that sulfur is only found on the surface of the samples. For the larger sample, all of the elements except sulfur were detected. The use of a more powerful laser may improve detection by allowing for the detection of additional emission lines for the elements of interest.

Figure 1: Average spectral data (n=3) for larger steel piece

Figure 2: Average spectral data (n=3) for smaller steel piece

Experimental Conditions:
The coupons were sent with tape protecting the analysis surface. The tape was peeled back and 3 spectra were acquired at different locations on each sample. Additional spectra for silicone RTV were acquired to provide a reference for the silicone peaks.

Hardware Used:
LIBS2000+ broadband, high-resolution spectrometer
200 mJ Nd:YAG Big Sky laser
LIBS-SC sampling chamber with imaging module
Experimental Parameters:
Laser at full power (setting 8)
-1.5 Q-switch setting (1.5 microsecond delay between firing laser and data acquisition)
Measurement Mode:
LIBS

Results:
LIBS spectra for the coupons and silicone RTV are shown in Figure 1. In Figures 2 and 3, the regions where several silicone peaks occur are shown. The LIBS spectrum for silicone RTV is included with the coupon spectra for comparison.

All of the silicone spectral lines contained in the Element Identification software (5 lines total) were detected in samples labeled 0.2, 0.5, 1, 1.5 and 2. Four out of five spectral lines were detected in the sample labeled 0.1. For the 0 sample, 1 – 2 silicone lines were detected although they are difficult to see in the spectral data (low intensity). Note that it may be possible to obtain a standard curve for silicone by taking the ratio of each silicone peak to the carbon peak at ~247 nm. This ratio may provide a method for addressing shot-to-shot reproducibility issues.

Figure 1: LIBS spectra for coupons

Figure 2: LIBS spectra for coupons: Region where silicone is detected

Figure 3: LIBS spectra for coupons: Region where silicone is detected (cont)