Rapid Quantification of 4,4â²-Methylenedianiline by Surface

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Rapid Quantification of 4,4’-Methylenedianiline by Surface-Enhanced Raman Spectroscopy Samuel L Kleinman, Mark C Peterman, Merwan Benhabib, Michael T. Cheng, James D Hudson, and Rachel E Mohler Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b02936 • Publication Date (Web): 17 Nov 2017 Downloaded from http://pubs.acs.org on November 20, 2017

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Analytical Chemistry

Rapid Quantification of 4,4’-Methylenedianiline by Surface-Enhanced Raman Spectroscopy

Samuel L. Kleinman1, Mark C. Peterman1, Merwan Benhabib1, Michael T. Cheng2, James D. Hudson2, Rachel E. Mohler2,* 1. OndaVia Inc., Hayward, California 94545, United States 2. Chevron Corporation, Richmond, California 94801, United States * Corresponding author email: [email protected]

Abstract Methylenedianiline (MDA) is a common industrial chemical with health and product safety concerns. Common analysis methods require many steps including extraction and derivatization ending in GC-MS or HPLC analysis, which minimize its use as an on-line or at-line technique. The procedure can take hours, prohibiting its use as a real-time decision-making tool as well as using valuable resources and laboratory space. The new method presented here has been validated for MDA quantification in industrial grease samples over the concentration range of 1-40 ppm 4,4’-MDA. We present comparative results to the currently accepted method with excellent fidelity. This analytical method using surface-enhanced Raman spectroscopy reduces sample preparation and analysis time by more than an hour while preserving method accuracy, specificity, and dynamic range.

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Introduction Accurate quantification of 4,4’-methylenedianiline (4,4’-MDA) is of importance to a variety of industries including biochemistry, plastics, food, and lubricants.1-2 Although this compound has not been classified by the United States Environmental Protection Agency for carcinogenicity, the International Agency for Research on Cancer (IARC) has classified 4,4’-MDA as a Group 2B chemical, a possible human carcinogen.3 The analysis methods typically rely on extraction from the matrix followed by separation on sophisticated instrumentation such as liquid chromatography (LC) or gas chromatography (GC) and detection on a mass spectrometer (MS or MS/MS).4-5 In the GC-MS method, after extraction of the analyte from the matrix, a derivatization step is necessary, adding another step in the analysis process with additional room for error. In the process environment, there is a need for a rapid, near-real-time, simple analysis method. Chromatography-based methods have been optimized so that 4,4’-MDA elutes in under two minutes, but large and sophisticated analytical instruments can prohibit use in a production setting. Spectroscopy methods are ideal for use as process analyzers because of their rapid analysis time, nondestructive sampling, and robustness. Raman scattering has been observed for nearly 100 years, and its application to industrial process control is well-documented.6 The Raman scattering interaction is a lowprobability event, therefore requiring either high laser power or high analyte concentrations. Since many industrial processes are sensitive to trace molecular concentrations, competing analytical technologies have been better suited for low concentration analysis. Herein, we challenge this notion and present a trace-level analytical method using a portable Raman spectrometer. Raman spectroscopy can be amplified by the use of noble metal nanoparticles. These nanoparticles act as tiny antennae, increasing the efficiency of Raman scattering by three to nine orders of magnitude.7 This signal enhancement allows detection of a wide variety of molecules down to trace concentrations. Surface-enhanced Raman Spectroscopy (SERS) was first discovered in the 1970’s, and within thirty


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years, it was conclusively demonstrated to be sensitive enough to observe the vibrations of a single molecule.8-10 Recently, SERS has been used to detect and quantify amines such as ethanolamine and methylamine in aqueous solutions.11 Reported herein is a relatively rapid method for the quantification of 4,4’-MDA in aqueous grease extracts, furthermore, we will describe differentiation of 4,4’-, 3,3’-, and 2,4’-MDA molecules in an extracted sample. The 4,4’-MDA and 2,4’-MDA are impurities in the manufacturing of grease, derived from the corresponding 4,4’- and 2,4’-methylene diphenyl diisocyanate (MDI) starting materials. The conversion of MDI to the corresponding MDA is a common undesirable side reaction.12 Herein we demonstrate a tool for MDA monitoring and therefore grease quality analysis in an industrial application. To the best of our knowledge, this work is the first report of SERS for the detection and quantification of 4,4’-MDA. Experimental Gold Nanoparticle Concentrate Gold colloids were created using the citrate reduction method13 starting with HAuCl4 (CAS 06903-35-8) from Salt Lake Metals (Salt Lake City, UT) and sodium citrate dihydrate from Fisher Scientific (Pittsburgh, PA). Briefly, 250 mL Milli-Q water and 1.257 g 1% HAuCl4 solution were brought to a rolling boil with stirring on a hotplate. A 1.055-mL volume of citrate solution (10mg/mL) was injected and boiled for an additional ten minutes. During this process, the solution transitioned from the clear yellow of dilute chloroauric acid to the purple-gray of colloidal gold nanoparticles. The nanoparticles are approximately 80-nm in diameter as determined from their UV/vis spectrum. Once the solution cooled to room temperature, the nanoparticles were concentrated using centrifugation to a final volume of 3 mL, providing enough material for sixty tests. This gold nanoparticle concentrate is shelf-stable and remains active in high-salinity samples, and is strongly SERS enhancing. MDA Standards Preparation 4,4’-MDA (CAS 101-77-9) and 3,3’-MDA (CAS 19471-12-6) were purchased from Sigma-Aldrich (St. Louis, MO) and used as received. 1 M NaOH (aq.) was prepared from



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50% NaOH (Sigma-Aldrich, St. Louis, MO). All solutions were created using Milli-Q deionized water (18.2 MΩ-cm) from a Milli-Q academic system. Lab standards between 1 and 20 parts per million (ppm) of 4,4’-MDA were prepared from a 1000-ppm aqueous stock solution. The 1000-ppm stock solution for both regioisomers was created by weighing one flake of the respective chemical and adding the appropriate weight of deionized water to a 50-mL centrifuge tube. The tubes were rotated overnight using a Labquake shaker/rotator. Overnight agitation was necessary to dissolve the MDA flakes, resulting in a transparent and slightly brown-hued solution. We employed 3,3’-MDA as an internal standard, present at identical concentrations in sample solutions while the concentration of 4,4’-MDA varied. The predictive model was created by analyzing a set of solutions with 3,3’-MDA present at 10 ppm while the concentration of 4,4’-MDA varied between 1 and 20 ppm. Samples were mixed and measured individually three times, introducing some natural variance in sample preparation while providing a representative cross section of the SERS response. Samples were created by mixing equal parts of 3,3’-MDA solution and a 4,4’-MDA solution and then ensuring the pH was between 8 and 10. Equal parts of gold nanoparticle concentrate and alkaline MDA mixture were then mixed to form the final mixture, which is dispensed onto a plastic cartridge for measurement by the Raman spectrometer. Grease Extraction & SERS Measurement Five grams of grease was weighed, to which 3,3’-MDA was added to result in a concentration of 10 ppm. Approximately 20 ml of dicholoromethane (DCM; Fisher Chemical, Fair Lawn, NJ) was added to generate a suspension of grease, which is subsequently extracted with approximately 12 ml of 1M HCl (aq.) and separated by centrifugation. The aqueous layer was collected and pH adjusted to 8-10 using 50% NaOH. Fifty microliters of the alkaline MDA solution was added to fifty microliters of nanoparticle concentrate. Five microliters of the sample-nanoparticle mixture was then dispensed onto a plastic cartridge for measurement by the Raman spectrometer. Grease Extraction and GC-MS method First, an acid/base extraction procedure was used to isolate the


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basic amines. A sample of grease was weighed and spiked with 10 ppm of 3,3’- MDA for use as an internal standard. The sample containing the internal was dissolved in approximately 20 mL of DCM and extracted with 20 mL 1 N HCl. The sample was vortexed to mix grease, organic, and acid, followed by centrifugation to separate the aqueous and organic phases. The aqueous phase was removed and treated with 50% NaOH solution to pH 13 so that the amines could be extracted with DCM. DCM and the aqueous phase were again vortexed and centrifuged to separate the organic phase containing the amine sample. The organic phase was dried with anhydrous Na2SO4 and evaporated under a stream of N2 gas while lightly heated at 60°C. The resulting product was derivatized with 100 µL of 1:2 N,OBis(trimethylsilyl)trifluoroacetamide : pyridine (Thermo Scientfic, Bellefonte, PA) prior to analysis by GC-MS. The 3,3’-MDA peak area was used to quantify 4,4’- and 2,4’-MDA in the grease sample. Raman Spectrometer The Raman spectrometer (OndaVia, Hayward, CA) is a dispersive system equipped with a 785-nm laser. The instrument measures Stokes scattering between 200 and 2000 relative wavenumbers using a thermoelectrically-cooled charge-coupled device. For these measurements, the power at the sample is approximately 5 mW. Higher power levels resulted in signatures of photodegradation. Acquisition time was one second for SERS analysis, providing suitable signal-to-noise ratios. The instrument is cradled in an aluminum base, providing a field-ready platform, as well as easy and pre-aligned sample introduction. Data Analysis Training data were stored in a SQL database. The data were processed using customwritten Python scripts, sequentially frequency calibrated, background subtracted using a linear piecewise background subtraction routine, smoothed using a Savitsky-Golay algorithm, and averaged so that multiple acquisitions produce a single spectrum. Finally, data were normalized to the intensity of the 3,3’MDA peak. These data were manually checked for outliers and non-conforming spectra. The resultant data set was supplied to a Multivariate Adaptive Regression Splines modeling package implemented in Python to generate a mathematical relationship between the observed spectrum and a known concentration of 4,4’-MDA in ppm. This mathematical model was used to predict concentrations of the



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target chemical for unknown grease sample extracts. Results & Discussion In order for the method to be useful, it must be able to identify and to quantify the compound of interest in a complex matrix with possible interfering compounds, including the multiple MDA isomers. The two MDA isomers of interest are depicted in Figure 1. The regioisomers of MDA are photosensitive and require the use of reduced laser power when mixed with the concentrated gold nanoparticle solution. Given laser powers below 5 mW at the sample, each isomer provides reproducible, stable, and characteristic vibrational spectra, as illustrated in Figure 2. For molecules with very similar structures, the SERS spectra of each molecule are strikingly different.14 For 3,3’-MDA we see a very clear and strong peak at 1000 rel. cm-1 which is absent in 4,4’-MDA. This peak is likely from a ring breathing mode and the observation or lack thereof points to symmetry considerations for the two molecules with identical chemical formulae. Another salient point of distinction is the region between 500 and 600 rel. cm-1. We have chosen to focus in this region for the predictive model which will be discussed in detail later. The final region of interest is the peaks at ~1600 rel. cm-1, where 3,3’-MDA has a slightly lower frequency compared to 4,4’-MDA vibration. Due to the line shape and similar frequency range, we assume these vibrations arise from similar functional groups; therefore, the bond order must be the same between molecules. Since we observe a difference, the reduced mass of the oscillators must slightly change which is due to substitution points on the aromatic ring. The spectra in Figure 2 were created from solutions with similar concentrations of each MDA molecule, demonstrating that they have similar Raman crosssections and SERS intensity response. Similar SERS activity and linear intensity response between analyte molecules and internal standards is necessary for the creation of precise analytical models. Internal standards are obligatory when designing a robust and accurate test using SERS as the detection method.8, 15-16 Unpredictable variations in signal intensity result from changes in extraction efficiency, nanoparticle homogeneity, sample alignment, matrix effects, or any host of unknown reasons which may


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vary between measurements. One major source of variation is signal-to-noise ratio through spectrometer focus. Using an internal standard accounts for these changes and enables response normalization of an unknown sample, thereby providing a means to predict analyte concentration for a variety of samples and matrices. Furthermore, purposefully adding a molecule to the SERS experiment provides confirmation of the testing fidelity, reducing the likelihood of a false negative. We know the added molecule should be observed and a successful test requires its observation. Using the 3,3’-MDA as an internal standard leads to Figure 3 and Table 1 which shows the data used to create the predictive model. Figure 3 focuses on a reduced frequency range between 510 and 600 rel. cm-1 to illustrate some of the important concepts in SERS data analysis and model generation. The raw data has been smoothed and background corrected. After the processing steps, we normalize the data to the peak at 540 rel. cm-1 which is due to the 3,3’-MDA molecule. Once normalized, the peak at 590 rel. cm-1 scales monotonically with 4,4’-MDA concentration. We use the 590 rel. cm-1 peak to create predictions for 4,4’-MDA concentration in unknown samples. There is significant intensity difference between 0.88 ppm and a blank, indicating that our limit of quantification (LOQ) is below this value. From testing of three replicates at each concentration, our LOQ is 0.34 ppm. We then analyzed the data presented in Figure 3 using multivariate analysis routines in Python. The preferred algorithm is a multivariate adaptive recursive splines approach that successfully maps the Raman response to concentration with an easy-to-interpret equation. This predictive model relates the intensity of a processed spectrum at a specific frequency to a concentration of 4,4’-MDA. We further tested the strength of the predictive model by repeatedly testing lab standards with known 4,4’-MDA concentrations and recording the predicted value. Results are depicted in Figure 4, a validation plot comparing known to measured concentration. The vertical error bars are one standard deviation for each set of measurements and the central point is the average. All points sit approximately on a unit-slope line with a R2 of 0.9991, indicating correct predictions for a variety of unknown 4,4’-MDA concentrations up to 20 ppm. Furthermore, a blank predicts 0-ppm 4,4’-MDA, or non-detect, demonstrating robustness in



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the predictive model. The method was validated using field samples by comparing the SERS results with those obtained using GC-MS. The GC-MS approach is an established method, but the skillset and instrumentation required for sample preparation and analysis hinder this method from being implemented in the process environment. A second drawback to the established method is that the sample preparation involves three extra steps, one of which is solvent evaporation. The GC-MS method takes about three hours start to finish, the SERS method is significantly faster in comparison. However, the utility of GC-MS as a reference method contributed to greater chemical knowledge as well as method validation. The SERS analysis provides a means for deconvolving the contributions of various regioisomers present in the grease mixture, similar to GC-MS. Both 4,4’-MDA and 2,4’-MDA may be present in grease samples as a result of the regioisomer content of the MDI starting material. A pure sample of 2,4’-MDA was unavailable. With external verification via GC-MS we can uniquely identify the presence of 2,4’MDA in the SERS spectrum. Figure 5 shows three spectra of grease samples with 3,3’-MDA added as internal standard. The spectra are normalized to the peak at 540 rel. cm-1 and background subtracted. From the GC-MS analysis, we determined the MDA content: The dotted red spectrum contains 1 ppm 4,4’-MDA, ~10 ppm 3,3’-MDA and 0 ppm 2,4'-MDA; the solid black and dashed blue spectra contain 38 and 42 ppm 2,4’-MDA, respectively, in addition to 10 ppm 3,3’-MDA and varying amounts 4,4’-MDA. Comparing the GC-MS and SERS results allows us to distinguish the three regioisomers of MDA and accurately predict 4,4’-MDA concentration in the presence of varying amounts of 2,4’-MDA. The agreement between Figure 5, MDA in a grease extract, and Figure 3, MDA in laboratory water, shows that the extraction method works cleanly and selectively. In Figure 6 we present direct comparison of GC-MS results for 4,4’-MDA in grease versus results using our SERS-based routine. Because there is a 1:4 dilution ratio, the SERS based method is reliably detecting sub-ppm concentrations in the aqueous extracts. First, the agreement for ‘no detect’ (ND)


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results is excellent. This demonstrates we can conclusively determine which samples have no residual 4,4’-MDA present down to 1 ppm, an important result for quality assurance and chemical safety. Next, we demonstrated with the analysis of industrial grease samples with a range of unknown concentrations, that although the original concentration range of interest was 0-20 ppm, the dynamic range was much larger. The dynamic range of the chemical test was demonstrated by correctly predicting the concentration of 4,4’-MDA from 1 ppm up to ~40 ppm. Even though the predictive model included data only to 20-ppm 4,4’-MDA, the model correctly predicts the concentration through 40-ppm, indicating the linearity of the approach. Overall eleven industrial samples were analyzed using both methods, with greater than 80% of the data agreeing within 10% of the actual value. This data provides proof that a SERS-based method can be used for quantitative industrial analysis. Furthermore, the new technique gives results consistent with widely accepted analytical techniques such as GC-MS. The SERS result is the average of three separate sample preparations, which cumulatively take less time than one run through the GC-MS method. In summary, we have described a functional approach to analyzing MDA samples in industrial grease mixtures. Figure 7 illustrates the entirety of the sample analysis method developed for use by minimally trained technicians. First, the grease sample is diluted using organic solvents to decrease sample viscosity. The thinned sample is mixed thoroughly with acidified water to transfer the target molecule to the aqueous phase. Centrifugation is used to separate the immiscible layers and the resulting extract is carried on to the next step. Sample preparation involves neutralizing the acidic extract to a pH of 8-10 and mixing with gold nanoparticle concentrate. The final step is measuring the nanoparticle-sample mixture using a Raman spectrometer. Sample preparation and analysis together take under ten minutes. Conclusions & Future Work We have developed and validated an optical method for analysis of 4,4’-MDA in industrial grease samples. This method is significantly faster, easier, and more convenient than alternative analytical



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methods such as gas chromatography. Future work could focus on quantifying another naturallyoccurring regioisomer, 2,4’-MDA. This molecule has a unique spectral signature and based on unpublished results, it can be quantified just as accurately as 4,4’-MDA. This publication has focused on MDA quantification in a hydrocarbon matrix, but it can be extended to a variety of samples such as urine or environmental water samples as well as other basic nitrogen-containing compounds of interest. Further, this contribution highlights the burgeoning application of SERS to industrial chemical analysis. Acknowledgements The authors would like to thank Chevron (specifically Gian Fagan and Merdona Bautista) for supporting this work and helpful discussions.


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Figures

Figure 1. Structures of the MDA isomers of interest (a) 3,3’-MDA and (b) 4,4’-MDA



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Figure 2. Reference spectra of 4,4’-MDA & 3,3’-MDA


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Figure 3. Calibration data displaying predicted 4,4’-MDA concentration normalized to 3,3’-MDA peak



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Concentration (ppm) 15.11 10.93 5.05 2.22 0.88 0

Average Intensity 0.5333 0.4067 0.1133 0.0527 -0.0210 -0.0447

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% RSD 1.77 3.07 4.16 9.97 20.57 13.47

Table 1. Values extracted from Figure 3, with the average and percent relative standard deviation resulting from three replicate scans.


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Figure 4. Validation plot displaying predicted 4,4’-MDA concentration for lab standard solutions. Error bars represent 1 standard deviation from the mean. Solid line is a y=x line and serves as guide to the eye.



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Figure 5. Spectra of three grease extracts. Each has 3,3’-MDA added as reference material at similar concentrations with varying amounts of 2,4’- and 4,4’-MDA present. Even without a pure reference material, GC-MS correlation allows identification of SERS peaks for each MDA isomer.


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Figure 6. Comparison of MDA analysis methods, the line y=x is a guide to the eye. Five data points overlap at 0 which indicate a non-detect result.



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Figure 7. Schematic of novel field-ready method for 4,4’-MDA analysis using a Raman spectrometer.


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References 1. Shintani, H., J. Biomater. Appl. 1995, 10, 23-58. 2. Schütze, D.; Sepai, O.; Lewalter, J.; Miksche, L.; Henschler, D.; Sabbioni, G., Carcinogenesis 1995, 16, 573-582. 3. Cancer, I. A. f. R. o., Some Chemicals Used in Plastics and Elastomers. Lyon, France, 1986; Vol. 39. 4. Bhandari, D.; Ruhl, J.; Murphy, A.; McGahee, E.; Chambers, D.; Blount, B. C., Anal. Chem. 2016, 88, 10687-10692. 5. Brunmark, P.; Persson, P.; Skarping, G., J. Chromatogr. B: Biomed. Sci. Appl. 1992, 579, 350354. 6. McCreery, R. L., Raman spectroscopy for chemical analysis. John Wiley & Sons: 2005; Vol. 225. 7. Kleinman, S. L.; Sharma, B.; Blaber, M. G.; Henry, A.-I.; Valley, N.; Freeman, R. G.; Natan, M. J.; Schatz, G. C.; Van Duyne, R. P., J. Am. Chem. Soc. 2013, 135, 301-308. 8. Dieringer, J. A.; Lettan, R. B.; Scheidt, K. A.; Van Duyne, R. P., J. Am. Chem. Soc. 2007, 129, 16249-16256. 9. Constantino, C. J. L.; Lemma, T.; Antunes, P. A.; Aroca, R., Anal. Chem. 2001, 73, 3674-3678. 10. Jeanmaire, D. L.; Van Duyne, R. P., J. Electroanal. Chem. Interfacial Electrochem. 1977, 84, 120. 11. Benhabib, M.; Tran, K. P.; Kleinman, S. L.; Zherebnenko, N.; Peterman, M. C., SurfaceEnhanced Raman Spectroscopy for Rapid and Cost-Effective Quantification of Amines in Sour Water. In Abu Dhabi International Petroleum Exhibition and Conference, Society of Petroleum Engineers: UAE, 2015. 12. Mazzu, A.; Smith, C., J. Biomed. Mater. Res., Part A 1984, 18, 961-968. 13. Kumar, S.; Gandhi, K. S.; Kumar, R., Ind. Eng. Chem. Res. 2007, 46, 3128-3136. 14. Badawi, H. M., Spectrochim. Acta, Part A 2013, 109, 213-220. 15. Zhang, D.; Xie, Y.; Deb, S. K.; Davison, V. J.; Ben-Amotz, D., Anal. Chem. 2005, 77, 35633569. 16. Kleinman, S. L.; Ringe, E.; Valley, N.; Wustholz, K. L.; Phillips, E.; Scheidt, K. A.; Schatz, G. C.; Van Duyne, R. P., J. Am. Chem. Soc. 2011, 133, 4115-4122.



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Figure 1. Structures of the MDA isomers of interest (a) 3,3’-MDA and (b) 4,4’-MDA 75x46mm (300 x 300 DPI)


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Figure 2. Reference spectra of 4,4’-MDA & 3,3’-MDA 78x78mm (300 x 300 DPI)



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Figure 3. Calibration data displaying predicted 4,4’-MDA concentration normalized to 3,3’-MDA peak 69x51mm (300 x 300 DPI)


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Figure 4. Validation plot displaying predicted 4,4’-MDA concentration for lab standard solutions. Error bars represent 1 standard deviation from the mean. Solid line is a y=x line and serves as guide to the eye. 73x69mm (300 x 300 DPI)



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Figure 5. Spectra of three grease extracts. Each has 3,3’-MDA added as reference material at similar concentrations with varying amounts of 2,4’- and 4,4’-MDA present. Even without a pure reference material, GC-MS correlation allows identification of SERS peaks for each MDA isomer. 74x70mm (300 x 300 DPI)


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Figure 6. Comparison of MDA analysis methods, the line y=x is a guide to the eye. Five data points overlap at 0 which indicate a non-detect result. 67x62mm (300 x 300 DPI)



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Figure 7. Schematic of novel field-ready method for 4,4’-MDA analysis using a Raman spectrometer.

57x19mm (300 x 300 DPI)


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TOC 84x39mm (300 x 300 DPI)


Rapid Quantification of 4,4â²-Methylenedianiline by Surface

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