Atmospheric Pressure ... - ACS Publications

Subscriber access provided by UNIV OF NEBRASKA - LINCOLN

Article

Gas Chromatography/ Atmospheric Pressure Chemical Ionization Tandem Mass Spectrometry for Fingerprinting the Macondo Oil Spill Vladislav V. Lobodin, Ekaterina Maksimova, and Ryan Patrick Rodgers Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b01652 • Publication Date (Web): 09 Jun 2016 Downloaded from http://pubs.acs.org on June 9, 2016

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Analytical Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 35

1 2 3 4 5 1 6 7 8 9 102 11 12 13 143 15 16 17 18 194 20 21 22 235 24 256 26 277 28 29 308 31 329 33 34 10 35 36 11 37 38 39 12 40 41 42 13 43 44 14 45 46 47 15 48 49 16 50 51 17 52 53 54 18 55 56 19 57 58 20 59 60

Analytical Chemistry

Gas Chromatography/ Atmospheric Pressure Chemical Ionization Tandem Mass Spectrometry for Fingerprinting the Macondo Oil Spill Vladislav V. Lobodin*,†,‡, Ekaterina V. Maksimova,§,⊥ and Ryan P. Rodgers*,†,‡ †

‡

Future Fuels Institute, Florida State University, 1800 East Paul Dirac Drive, Tallahassee, FL 32310. National High Magnetic Field Laboratory, Florida State University, 1800 East Paul Dirac Drive,

Tallahassee, FL 32310. §

College of Marine Science, University of South Florida, St. Petersburg, FL 33701.

⊥

Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL 32310.

Re -submitted to Analytical Chemistry: June 5, 2016

ABSTRACT We report the first application of a new mass spectrometry technique (gas chromatography combined to atmospheric pressure chemical ionization tandem mass spectrometry, GC/APCI-MS/MS) for fingerprinting a crude oil and environmental samples resulted from the largest marine oil spill in a history (the Macondo oil spill, the Gulf of Mexico, 2010). The fingerprinting of the oil spill is based on a trace analysis of petroleum biomarkers (steranes, diasteranes, and pentacyclic triterpanes) naturally occurring in crude oil. GC/APCI enables soft ionization of petroleum compounds that form abundant molecular ions without (or little) fragmentation. Ability to operate the instrument simultaneously in several tandem mass spectrometry (MS/MS) modes (e.g., full scan, product ion scan, reaction monitoring) significantly ACS Paragon Plus Environment


1 221 3 422 5 623 7 8 924 10 25 11 12 26 13 14 27 15 28 16 17 29 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 35

improves structural information content and sensitivity of analysis. For fingerprinting the oil spill, we constructed diagrams and conducted correlation studies that measure the similarity between environmental samples and enable to differentiate the Macondo oil spill from other sources.

Keywords: APGC, APGC-MS, GC/APCI, biomarkers, petroleum, oil spill, hopanes, steranes, Deepwater Horizon.

ACS Paragon Plus Environment

Page 3 of 35

1 230 3 4 531 6 7 8 932 10 11 33 12 13 34 14 15 35 16 17 18 36 19 20 37 21 22 38 23 24 25 39 26 27 40 28 29 30 41 31 32 42 33 34 43 35 36 37 44 38 39 45 40 41 46 42 43 44 47 45 46 48 47 48 49 49 50 51 50 52 53 51 54 55 56 52 57 58 53 59 60


■ INTRODUCTION On April 20th, 2010 a semi-submersible, mobile offshore drilling platform known as the Deepwater Horizon, operating 75 km southeast of Louisiana (28°44′17.3″N, 88°21′57.4″W) in more than 1,500 meters of water, suffered an uncontrolled release of oil / gas that resulted in an explosion. Damage to the riser pipe and wellhead led to a subsurface wellhead “blowout” that resulted in a massive off-shore oil spill. The spill has lasted for 87 days and led to a severe negative impact on Gulf of Mexico’s ecosystem and economy. It is estimated that about 800,000 m3 (~5 million barrels) of crude oil leaked from the Macondo well.1 However, final legal opinion set the total release amount as 3.19 million barrels taking collection efforts into account.2 After escaping from the well, the Macondo crude oil underwent dissolution in water column,3 evaporation from the water surface,4 emulsion (mouse) formation,5 biodegradation,4,6 and photo-oxidation.4,7 As a result, oil slicks and highly weathered tarballs found in the sea and on the beach were compositionally altered relative to the parent crude oil.8,9 Thus, source identification of the weathered oil became an important issue in tracking the fate and transport of oil spill residues,10 as well as evaluation of its impact on ecosystem.11 A combination of mass spectrometry (MS) and high performance separation methods provides a potential solution for source identification in environmental applications.12 Environmental and petroleum geochemistry studies rely mainly on GC-MS(/MS) analysis of petroleum biomarkers, in which the ionization of analytes in a mass spectrometer ion source takes place under vacuum by electron ionization (EI). From the total collection of petroleum biomarkers, select steranes and pentacyclic triterpanes (e.g., hopanes) are particularly useful for the differentiation of crude oils, and the characterization, fingerprinting, and monitoring of the degradation processes. Steranes and hopanes are natural organic compounds that are highly resistant to degradation under environmental conditions.13 They form during digenesis and catagenesis from biogenic precursors (steroids and hopanoids) that, in turn, were produced by ancient organisms (eukaryotes and prokaryotes correspondingly) millions years ago.14 ACS Paragon Plus Environment


1 254 3 455 5 656 7 8 957 10 11 58 12 13 59 14 15 60 16 17 18 61 19 20 62 21 22 63 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 64 58 59 60

Page 4 of 35

Unfortunately, conventional EI (70 eV) is a highly energetic ionization method that can produce extensive fragmentation of petroleum biomarker molecular ions. The fragmentation provides much needed structural information; however, it does so at the expense of molecular ion abundance, which degrades overall sensitivity. Consequently, biomarkers such as 17α(H)-22,29,30-tris-norhopane (C27-hopane) and 5α(H),14α(H),17α(H)-20R-cholestane (C27-sterane) yield a total ion count < 10% for molecular ions (Figure 1, top) under typical EI conditions. As the concentration of the biomarkers in crude oil is usually very low, various detection methods of the formed ions are used to increase the sensitivity of analysis. For example, registration of full mass spectra by recording the total ion current (TIC), selected ion monitoring (SIM), and selected reaction monitoring (SRM, also called multiple reaction monitoring (MRM)) are commonly used.15,16

EI mass spectra 17α(H)-22,29,30-tris-norhopane

5α(H),14α(H),17α(H)-20R-cholestane 7% of TIC 4% of TIC C27H48

C27H46

M+•

M+•

m/z

m/z

APCI mass spectra 17α(H)-22,29,30-tris-norhopane

44% of TIC 370 M+•

5α(H),14α(H),17α(H)-20R-cholestane

47% of TIC 372 M+•

191 95 121 149 50

100

150

355 200

m/z

250

300

350

357

217 149 175 203 400

50

100


150

200

m/z

250

300

350

400

Page 5 of 35

1 265 3 466 5 667 7 8 968 10 11 69 12 13 70 14 15 71 16 17 18 72 19 20 73 21 22 74 23 24 25 75 26 27 76 28 29 77 30 31 32 78 33 34 79 35 36 37 80 38 39 81 40 41 82 42 43 44 83 45 46 84 47 48 85 49 50 51 86 52 53 87 54 55 56 88 57 58 89 59 60


Figure 1. Top. EI mass spectra of 17α(H)-22,29,30-tris-norhopane and 5α(H),14α(H),17α(H)-20Rcholestane. Note extensive fragmentation of molecular ions under EI. Bottom. APCI mass spectra of 17α(H)22,29,30-tris-norhopane and 5α(H),14α(H),17α(H)-20R-cholestane. Note, soft ionization under APCI produces abundant molecular ions.

The use of tandem mass spectrometry is particular advantageous, because it can operate in all aforementioned modes (separately or simultaneously). Moreover, SRM is beneficial for targeted analysis of compounds in trace amounts and provides improved signal-to-noise ratio, higher selectivity, and lower detection limit.15 Biomarker analysis with SRM transitions is carried out by synchronized transmission of precursor ion (usually a molecular ion) and a selected product ion only. The product ion is usually one of the characteristic fragment ions produced from the parent ion in a collision cell. However, as shown in Figure 1, molecular ions of biomarkers under EI conditions account for only a minor fraction (4-7%) of the TIC. Thus, for parent ion identification, only those ions that remain unfragmented are available for subsequent MS/MS experiments (including SRM). In contrast, the combination of MS/MS with soft ionization methods that efficiently form molecular or pseudo-molecular ions without (or a low amount of) fragmentation can significantly improve signal-to-noise ratio and structural information content through subsequent, controlled fragmentation. Soft atmospheric pressure ionization methods (such as APCI, APPI, and ESI) are widely used with LC/MS instruments. Despite the first coupling of GC with atmospheric pressure ionization mass spectrometry (API-MS) was demonstrated over 40 years ago,17 it lacked prompt commercialization and thus was surpassed by LC/MS applications. Nevertheless, in 2005 McEwen and McKay reported the integration of GC with a commercial LC/MS instrument and the operation of GC/API-MS under APCI by corona discharge.18 In the recent years several mass spectrometry vendors launched commercial GC/APCI ion sources that can be connected to LC/MS instruments. Such systems enable quick switching between GC and LC modes and can be used for ACS Paragon Plus Environment


1 290 3 491 5 692 7 8 993 10 11 94 12 13 95 14 15 96 16 17 18 97 19 20 21 98 22 23 24 99 25 26 100 27 28 29 101 30 31 102 32 33 103 34 35 36 104 37 38 105 39 40 41 106 42 43 107 44 45 108 46 47 48 109 49 50 110 51 52 111 53 54 55 112 56 57 113 58 59 60

Page 6 of 35

analysis of a wider range of compounds including volatile and semi-volatile (GC-amenable), and polar, nonvolatile compounds (LC/MS). GC/APCI-MS is a relatively new configuration that gains popularity for mass spectrometry-based analysis with rising number of applications including analysis of environmental samples,19-21 pharmaceuticals22 and doping drugs,23 petroleum samples,24-26 metabolomics,27-29 and food industry,30-32 etc. In this work, we demonstrate the first utilization of GC/APCI combined with tandem mass spectrometry for a trace analysis of petroleum biomarkers from the Macondo well crude oil and fingerprinting the oil spill.

■ EXPERIMENTAL Samples. Macondo crude oil (NIST2779) and NIST2266 standard mix that includes five hopanes (17α(H)22,29,30-trisnorhopane,

17α(H),21β(H)-30-norhopane,

17α(H),21β(H)-hopane,

17α(H),21β(H)-22S-30-

homohopane, 17α(H),21β(H)-22R-30-homohopane) and five steranes (5α(H),14β(H),17β(H)-20R-cholestane, 5α(H),14α(H),17α(H)-20R-cholestane,

5α(H),14β(H),17β(H)-20R-24S-methylcholestane,

5α(H),14α(H),17α(H)-20S-24R-ethylcholestane,

5α(H),14β(H),17β(H)-20R-24R-ethylholestane)

were

purchased from NIST (Gaithersburg, MD). Natural oil seep sample (“Megaplume”) located in GC 600 lease block (Gulf of Mexico) was collected by a remote operated vehicle at the depth of 1222 m at the seep mouth (27°22'27.96”N, 90°30'41.34”W) with TFE-fluorocarbon polymer net without interferences from other hydrocarbon sources.33,34 A Blue Crude oil35 sample from Independence hub platform operated in the Gulf of Mexico (28°05'89.0”N, 87°59'27.0”W) was provided by an oil company. Figure 2 shows a map with marked locations for environmental samples (SAM 1-18) collected from May 2010 to January 2014 (see SI for sampling date and location). The environmental samples were extracted by Soxhlet extraction as described elsewhere.8,9 The extracts were evaporated under a gentle nitrogen flow to dryness and then diluted in dichloromethane (HPLC grade, J.T.Baker) to the concentration of 20 mg/mL for GC-APCI/MS-MS analysis. The crude oils were five-fold diluted in dichloromethane prior to analysis.


Page 7 of 35


1

114 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 115 33 34 35 116 36 37 117 38 39 118 40 41 42 119 43 44 120 45 46 121 47 48 49 122 50 51 123 52 53 54 124 55 56 125 57 58 126 59 60

Figure 2. Image of the Gulf of Mexico with marked locations of sample collection (SAM-1‒ SAM-18), Deepwater Horizon explosion, Megaplume oil seep and Independence hub (Blue Crude oil).

Sample Characterization. A gas chromatography/mass spectrometry analysis was conducted with an GC/APCI-MS instrument consisting of an Agilent 7890 GC system (Agilent Technologies, Santa Clara, CA) and a Xevo TQ-S tandem quadrupole instrument (Waters Corporation, Milford, MA) equipped with an APGC ion source. The injected volume was 1 µL in split mode (split ratio was 1:10). Helium (99.9995%) at flow rate of 1.2 mL/min was the carrier gas. The gas chromatograph inlet temperature was 300°C. The GC oven temperature was programmed from 50 °C and held for 3 min, then ramped at 20°C/min to 150 °C and 2°C/min from 150°C to 350°C, and then held at the maximum temperature for 25 min. The transfer line was kept at 380°C. A MXT-5 GC column (60 m long, 250µm ID, 0.25 µm film thickness) was purchased from ACS Paragon Plus Environment


1

127 2

Page 8 of 35

Restek Corporation (Bellefonte, PA). The ion source was held at 150°C. Nitrogen served as auxiliary gas

3

128 4 5 6 129 7 8 130 9 10 11 131 12 13 132 14 15 133 16 17 18 134 19 20 135 21 22 136 23 24 25 137 26 27 138 28 29 139 30 31 32 140 33 34 141 35 36 37 142 38 39 143 40 41 144 42 43 44 45 145 46 47 48 146 49 50 51 147 52 53 148 54 55 56 149 57 58 150 59 60

(250 L/hr), cone gas (200 L/hr), and make-up gas (350 ml/min) and was delivered from a Dewar as a boil-off of liquid N2. Collision gas was argon (99.999%) and was supplied to a collision cell at 0.15 ml/min. Each sample was run in triplicate with a solvent blank between the samples to make sure of the absence of a carryover. Comparative EI data were recorded with 5973 GC/MS instrument (Agilent Technologies, Santa Clara, CA). The injected volume was 1 µL in split mode (split ratio was 1:10). The carrier gas was helium (99.9995%) at flow rate of 1.0 mL/min. The gas chromatograph inlet temperature was 300°C. The GC oven temperature was programmed from 50 °C and held for 0.5 min, then ramped at 1.5°C/min to 320 °C and then held at the maximum temperature for 10 min. The transfer line was kept at 300°C. A DB-5MS GC column (30 m long, 250µm ID, 0.25 µm film thickness) was purchased from Agilent Technologies (Santa Clara, CA). The ion source was held at 150°C. Analytes were ionized by APCI (corona discharge in the atmosphere of nitrogen, current of 2.5 µA) and positive ions were mass-analyzed. Mass spectrometry data were acquired with MassLynx 4.1 software (Waters Corp.) in full scan and MS/MS modes: SRM and product scan. Table 1S shows conditions for acquisition of SRM transitions. Areas under biomarkers’ peaks from the corresponding SRM chromatograms were used to calculate biomarkers ratios. 7-axis diagrams were constructed with Microsoft Excel (Microsoft Office Professional Plus 2010). The correlation coefficients were calculated and plotted with MATLAB R2014a (MathWorks, Natick, MA).

■ RESULTS AND DISCUSSION The ionization of petroleum compounds in GC/APCI source occurs by a charge transfer mechanism initiated by a corona discharge: in a nitrogen plasma (nitrogen molecules generate radical cations that, in turn, ionize analyte molecules to form molecular ions). Figure 1S shows schematic diagram for operation of GC/APCI and ionization mechanisms for the formation of molecular and pseudo-molecular ions. The ion source ACS Paragon Plus Environment

Page 9 of 35

1

151 2


conditions correspond to “dry” operation with no reagent/dopant added. Alternatively, ionization can take

3

152 4 5 6 153 7 8 154 9 10 11 155 12 13 156 14 15 157 16 17 18 158 19 20 159 21 22 160 23 24 25 161 26 27 162 28 29 163 30 31 32 164 33 34 165 35 36 37 166 38 39 167 40 41 168 42 43 44 169 45 46 170 47 48 171 49 50 51 52 53 54 55 56 57 58 59 60

place through proton transfer reactions if a proton source (e.g., moisture or protic solvents, such as alcohols) is present in the ion source. The former mechanism results in efficient ionization of non-polar species whereas the later mechanism ionizes polar compounds. Both types of petroleum biomarkers (hopanes and steranes) are efficiently ionized in the GC/APCI ion source with formation of abundant M+• ions. Figure 1 (bottom) shows APCI mass spectra of 17α(H)-22,29,30-tris-norhopane and 5α(H),14α(H),17α(H)-20Rcholestane) that demonstrate a significant increase (up to 10 times) in the relative abundance of molecular ions in comparison to fragmentation of these compounds under EI. In turn, due to combination of GC/APCI source with triple quadrupole mass spectrometer, the formed molecular ions are available for the subsequent MS/MS experiments, e.g., collisionally activated dissociation (CAD) to provide structural information. Figure 3 shows MS/MS product scan (daughter spectra) from M+• of 17α(H)-22,29,30-tris-norhopane and 5α(H),14α(H),17α(H)-20R-cholestane. The MS/MS spectra demonstrate fragmentation patterns similar to those observed under EI (see Figure 1S). The collision conditions (collision gas- argon, collision energy of 15eV and 20 eV) were optimized to maximize the yield of characteristic ions: m/z 191 for the hopane and m/z 217 for the sterane. Figure 2S shows the summed signal for seven SRM-transitions for hopanes and steranes in NIST2266 standard mix and a five-point calibration curve for 17α(H),21β(H)-hopane demonstrating a linear signal response and stability of GC/APCI-MS/MS operation.



GC/APCI-MS/MS. Product Spectra from M+• 17α(H)-22,29,30-tris-norhopane 191

Relative Abundance, %

100

95 149 109

m/z 191

135 121

M+• 163

69 0

81

50

177

100

150

355 370 200

m/z

250

300

350

5α(H),14α(H),17α(H)-20R-cholestane 217

100


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 172 37 38 173 39 40 174 41 42 43 175 44 45 176 46 47 177 48 49 50 178 51 52 179 53 54 180 55 56 57 58 59 60

Page 10 of 35

357

m/z 217

81

121 95 107 135

73 0

50

M+•

149 175 161 189 203 100

150

200

372

262

m/z

250

300

350

Figure 3. Product ion spectra (MS/MS) from M+• (produced under APCI in GC/APCI source) of 17α(H)22,29,30-tris-norhopane at collision energy of 15 eV (top) and 5α(H),14α(H),17α(H)-20R-cholestane at collision energy of 20 eV (bottom).

Figures 3S and 4S show individual SRM chromatograms for every compounds in NIST2266. Each chromatogram corresponds to a SRM transition (M+• → m/z 191 or M+• → m/z 217) that is specific for compounds in the mixture. Such individual chromatograms simplify the assignment and the integration of peaks and are especially useful for complex mixtures when co-elution of compounds is observed in TIC.


Page 11 of 35

1

181 2


Figure 4 shows a chromatogram for summed signal of SRM transitions for triterpanes (M+• → m/z 191)

3 5 6 183 7 8 184 9 10 11 185 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 186 37 38 187 39 40 188 41 42 43 189 44 45 190 46 47 191 48 49 50 192 51 52 193 53 54 55 194 56 57 58 59 60

in the Macondo well crude oil (NIST2779) with assignment of over dozens hopanes: Ts, Tm, H29, C29Ts, DH30, M30, H30, H31S, H31R, H32S, H32R, H33S, H33R, H34S, H34R, H35S, H35R (see SI for full compounds names). The assignment of the peaks is based on retention times for standard compounds from NIST2266, elution order and signal from individual SRM transitions.

GC/APCI-MS/MS for NIST2779 (Macondo Wellhead Crude Oil) Hopanes: Summed Signals for C27-C35 (M+• → m/z 191) 100

H30 19 20 12 18 E 21 30 32 34 13 11 25 26 D 17 22 31 33 35 1 9 C 28 16 29 2 14 A 10 B 8 27 15 7 3 5 6 4 23 24

H29

RA, %

182 4

Ts Tm

0

50.00

60.00

70.00

H31S H31R H32S H32R H 33S H33R H 34S H34R H35S H35R

C29Ts

C31

80.00

DH30 C32 M30 C33 C 34

80.00

82.00

84.00

86.00

88.00

90.00

92.00

94.00

Time

C35

90.00

100.00

110.00

120.00

Time, min

Figure 4. GC/APCI-MS/MS chromatogram, showing the sum of SRM transitions (M+• → m/z 191) for C27– C35 hopanes from the Macondo crude oil.

The hopane signature provides a powerful tool for fingerprinting an oil spill and, for example, determination of spill sources in the Strait of Malacca was exclusively based on pentacyclic triterpanes.36 However, apart from triterpanes, the use of steranes and diasteranes can be also beneficial when hopane signatures of the oils are not distinct. Diasteranes do not have direct precursors in living organisms and are formed from sterols and steranes during diagenesis and catagenesis by rearrangements that involve migration



1

195 2 3

196 4 5 6 197 7 8 198 9 10 11 199 12 13 200 14 15 201 16 17 18 202 19 20 203 21 22 204 23 24 25 205 26 27 206 28 29 207 30 31 32 208 33 34 209 35 36 37 210 38 39 211 40 41 212 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 35

of two methyl groups from positions C10 and C13 to C5 and C14. The rearrangement is promoted by acidic catalysis and high temperature, and usually is favorable in clay-reach environment.37 Figure 5 (top) shows a chromatogram for summed signal of SRM transitions for C27-C29 steranes/diasteranes (M+• → m/z 217) and a general formula of steranes. It is to be noted higher complexity of steranes/diasteranes signature in the Macondo crude oil due to the co-elution of the compounds. Nevertheless individual SRM transitions (M+• → m/z 217) facilitate visualization and assignment of the peaks. Additional SRM transitions (M+• → m/z 218, M+• → m/z 259) specific for steranes and diasteranes were added to the experiment script (see Table 1S) to increase confidence in the assignment of chromatographic peaks. Two dozen steranes and diasteranes were identified in the crude oil: C27βαS, C27βαR, C27αβS, C27αβR, C27αααS, C27αββR, C27αββS, C27αααR, C28βαS, C28βαR, C28αβS, C28αβR, C28αααS, C28αββR, C28αββS, C28αααR, C29βαS, C29βαR, C29αβS, C29αβR, C29αααS, C29αββR, C29αββS, C29αααR (see SI for full compounds names). In addition to selective SRM transitions, assignment of the peaks was also based on elution order of steranes and diasteranes,38 and retention times of steranes in NIST2266 standard. Figure 5S shows comparative analysis of Macondo crude oil (NIST 2779) by EI with conventional GC/MS. The ion chromatograms for ions at m/z 191 and 217 shows hopanes and steranes signatures although with higher background and lower S/N ratio. Whereas, hopnaes can be easily assigned from the chromatogram, the assignment and integration of chromatographic peaks for steranes presents a big challenge due to coelution of the compounds.


Page 13 of 35

GC/APCI-MS/MS for NIST2779 (Macondo Crude Oil) 28

Steranes & Diasteranes 100

RA, %

2

A

3

4

0 50.00

55.00

60.00

29

22 24 21 25 20 18 23 12 17 11 27 13 1 19 9 C D 16 14 15 10 8

Sum of C27-C29 Steranes/Diasteranes

65.00

5

B

26

7

6

70.00

75.00

80.00

βαS

100

C27 -Diasteranes

αββR

βαR

C27-Steranes

αββS

RA,%

αααS αααR

m/z 372 217

0 50.00 100

αβR αβS

55.00

60.00

C28 -Diasteranes

βαS

65.00

70.00

αββR βαR

75.00

αββS

80.00

C28-Steranes

RA,%

αααS

0

αααR αβS αβR

m/z 386 217

50.00

100

55.00

60.00

65.00

70.00

βαS

C29 -Diasteranes

0

75.00

80.00

C29-Steranes

αββ R+S

βαR

αααS

RA, %

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 213 42 43 214 44 45 215 46 47 48 216 49 50 217 51 52 53 218 54 55 219 56 57 220 58 59 60


m/z 400 217

50.00

55.00

αβR αβS

60.00

65.00

70.00

αααR

75.00

80.00

Time, min

Figure 5. GC/APCI-MS/MS chromatograms (SRMs), showing the C27–C29 sterane distribution for the Macondo crude oil. Note, the truncated labels correspond to compounds described in the text. The number of carbon atoms in diasteranes and steranes is defined in each SRM chromatogram.

Biomarkers ratios. Highly weathered oil samples are usually very different from a source crude oil in chemical composition, and the concentration of even persistent biomarkers may vary because of the partial degradation and changes in volume of hydrocarbon matrix.39-41 Thus, the direct comparison of absolute



1

221 2

Page 14 of 35

content of biomarkers in parent oil and spilled samples can be ambiguous for fingerprinting an oil spill.

3

222 4 5 6 223 7 8 224 9 10 11 225 12 13 226 14 15 227 16 17 18 228 19 20 229 21 22 230 23 24 25 231 26 27 232 28 29 233 30 31 32 234 33 34 235 35 36 37 236 38 39 237 40 41 238 42 43 44 239 45 46 240 47 48 241 49 50 51 242 52 53 243 54 55 56 244 57 58 245 59 60

However, as a general rule, most steranes and hopanes in spill samples and source oils do not show considerable changes in diagnostic ratios for stereoisomers and biomarkers of similar structure, or belonging to the same homologous series.13 In addition, the biomarkers ratios do not necessary require quantitative analysis and can be calculated from semi-quantitative data (e.g., peak heights or areas).42 Table 2S demonstrates results for quantitative measurement of biomarkers in the Macondo crude oil (NIST2779) with GC/APCI-MS/MS that are in a good agreement with reference values provided by NIST43 (note, the reference values continue to be revised and corrected). Nevertheless quantitation of each and every biomarker compound in every environmental sample would be a very laborious and expensive. Thus, in turn, it results in marked limitations for high throughput, routine analytical applications. The use of ratios minimizes concentration effects of individual compounds caused by environmental changes and sample preparation, and levels out fluctuations in everyday instrument operation. Therefore, comparison of diagnostic ratios directly reflects differences of the target biomarker distribution among samples.37 The petroleum biomarkers identified in the Macondo crude oil were also found in all environment samples and other crude oils analyzed. In the current work we utilized seven diagnostic ratios (Ts/Tm, H29/H30,

H32S/H32R,

H33S/H33R,

H30/(H31+H32+H33+H34+H35),

C27αββ/C29αββ

steranes,

C27βα/C29βα

diasteranes) commonly used for identification, correlation, and differentiation of spilled oils.13 In principle, the number of ratios can be extended if fine differences between very similar samples need to be tracked. The Ts/Tm ratio is used in petroleum geochemistry to provide insight on maturity and source of a crude oil. Because 18α(H)-22,29,30-trisnorhopane (Ts) is thermodynamically more stable (by 4.4 kcal/mol) than 17α(H)-22,29,30-trisnorhopane (Tm), the increase in Ts/Tm ratio is diagnostic for higher oil thermal maturity.44 H29/H30 ratio is used as a general triterpane fingerprint that depends on source rock depositional environment and organic matter input. High H29/H30 ratio (>1) is typical for anoxic carbonate source rocks. The ratio measured using m/z 191 is more sensitive to C30-hopane because its molecular ion fragments to ACS Paragon Plus Environment

Page 15 of 35

1

246 2


yield two structurally complimentary fragment ions at m/z 191, whereas C29-hopane produce m/z 191 by only

3

247 4 5 6 248 7 8 249 9 10 11 250 12 13 251 14 15 252 16 17 18 253 19 20 254 21 22 255 23 24 25 256 26 27 257 28 29 258 30 31 32 259 33 34 260 35 36 37 261 38 39 262 40 41 263 42 43 44 264 45 46 265 47 48 266 49 50 51 52 53 54 55 56 57 58 59 60

one pathway. Because 17α(H),21β(H)-30-norhopane (H29) is more stable than 17α(H),21β(H)-hopane (H30) at high level of thermal maturity, the ratio can be used as a maturity factor within a group of related oils.37 H32S/H32R and H33S/H33R ratios also indicate thermal maturation. During diagenesis and catagenesis biological 22R configuration in hopanoids converts to 22S in hopanes until equilibrium ratio (22S/22R) of ~1.5 is reached with thermal maturation.45 H30/(H31+H32+H33+H34+H35) ratio is not widely used in petroleum geochemistry but it was successfully applied in environmental studies for oil spill source identification to establish Middle Eastern crude origin of tarballs found in Malaysian coastal zone.36 C27αββ/C29αββ-sterane ratio describes an ecosystem contributing the precursor sterols into organic matter of the source rock. Higher relative content of C27-sternaes can be an evidence for marine plankton organic matter whereas a prevalence of C29-steranes corresponds to a preferential input from higher plants.46 C27βα/C29βα-diasterane ratio also shows a biological system responsible for organic matter deposition (algae or higher plants) and is used when steranes are degraded and diasteranes remain intact, and is usually observed for heavily biodegraded oils or some highly mature oils / condensates that show low sterane, but higher diasterane content.37 Table 3S summarizes the biomarkers ratios for all samples studied in this work calculated based on a chromatographic peak area. Reproducibility of the analytical procedure was confirmed by the triplicate analysis of all samples and relative standard deviation was within 10%. To simplify data visualization we constructed seven-axis diagrams that allows us to track difference between samples (Figure 6).



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 267 51 52 268 53 54 269 55 56 57 58 59 60

NIST2779 βαC27/βαC29 Diasteranes

SAM-1

Ts/Tm 2 1.5

H29/H30

βαC27/βαC29 Diasteranes

1 0.5

0

H33S/H33R

SAM-3 2 1.5

H29/H30

H33S/H33R

SAM-5

Ts/Tm H29/H30

C27βα/C29βα Diasteranes

Ts/Tm

2

1.5

1

1 0.5

0

H32S/H32R

H33S/H33R

H30/ΣH31‒H35

H33S/H33R

1.5

H32S/H32R

C27αββ/C29αββ Steranes H30/ΣH31‒H35

SAM-7

H33S/H33R

SAM-8 Ts/Tm

Ts/Tm

2

H29/H30


2 1.5

H29/H30


2 1.5

1

1

1

0.5

0.5

0.5

0

0

H32S/H32R


H33S/H33R

SAM-9

H32S/H32R

H30/ΣH31‒H35

H33S/H33R

1.5

H32S/H32R

C27αββ/C29αββ Steranes

SAM-11 Ts/Tm

H29/H30

H33S/H33R

H30/ΣH31‒H35

SAM-10 C27βα/C29βα Diasteranes

Ts/Tm

2 1.5

H29/H30


2 1.5

1

1

1

0.5

0.5

0.5

0

H30/ΣH31‒H35

H33S/H33R

SAM-12

H32S/H32R


SAM-13 2


H33S/H33R

SAM-14 Ts/Tm 2

H29/H30

H32S/H32R


H33S/H33R

Ts/Tm 1.5

1.5

Ts/Tm H29/H30


2 1.5

1

1

1

0.5

0.5

0.5

0

0

H32S/H32R


H33S/H33R

SAM-15

H30/ΣH31‒H35

H32S/H32R


H33S/H33R

SAM-16 H29/H30


H33S/H33R

SAM-17 Ts/Tm

2

Ts/Tm

2 1.5

H29/H30


2 1.5

1

1

1

0.5

0.5

0.5

H32S/H32R

H30/ΣH31‒H35

H33S/H33R

SAM-18

H32S/H32R


1.5

H32S/H32R


H33S/H33R

Ts/Tm H29/H30


H33S/H33R

Blue crude

Megaplume Ts/Tm 2

Ts/Tm

2 1.5

H29/H30


2 1.5

1

1

1

0.5

0.5

0.5

H30/ΣH31‒H35

H32S/H32R

H33S/H33R


H29/H30

0

0

0


H29/H30

0

0

0


H29/H30

0

H32S/H32R


Ts/Tm 1.5

H29/H30

0

0

H32S/H32R


H29/H30

0


Ts/Tm 2

H29/H30

0

H32S/H32R


Ts/Tm


H33S/H33R

0.5

SAM-6


H32S/H32R

1

H30/ΣH31‒H35



H30/ΣH31‒H35

2 1.5

0


0

H32S/H32R

H30/ΣH31‒H35


H29/H30

1

0.5



2 1.5

0.5


SAM-4

Ts/Tm C27βα/C29βα Diasteranes

H29/H30


0

H32S/H32R

H30/ΣH31‒H35

Ts/Tm

1.5

1


SAM-2

Ts/Tm 2

0.5

Page 16 of 35

H32S/H32R

H33S/H33R


H32S/H32R

H33S/H33R

Figure 6. Spider diagrams for the Macondo crude oil, eighteen tarball samples (SAM-1–18), Megaplume oil seep and Blue Crude constructed from seven diagnostic biomarker ratios (Ts/Tm, H29/H30, H32S/H32R,


Page 17 of 35

1

270 2


H30(H31+H32+H33+H34+H35), H33S/H33R, C27αββ/C29αββ steranes, and C27βα/C29βα diasteranes). The diagrams

3

271 4 5 6 272 7 8 273 9 10 11 274 12 13 275 14 15 276 16 17 18 277 19 20 278 21 22 279 23 24 25 280 26 27 281 28 29 282 30 31 32 283 33 34 284 35 36 37 285 38 39 286 40 41 287 42 43 44 288 45 46 289 47 48 290 49 50 51 291 52 53 292 54 55 56 293 57 58 294 59 60

for SAM-1–18 demonstrate the great similarity to the reference Macondo crude oil.

Each axis on the diagram corresponds to a specific biomarker ratio. Although some tarball samples indicate little deviation in the biomarkers ratios from NIST2779, their general profile well resembles that for Macondo crude oil. The changes in the ratios can be caused by sample degradation39,47 and/or other potential input of biogenic and petrogenic hydrocarbons. For example, the Ts/Tm ratio for many environmental samples is systematically slightly higher than that for the Macondo crude oil that is consistent with preferential degradation of thermodynamically less stable Tm in the environment.48,49 Overall, high similarity with NIST 2779 is observed for all samples found from one month to almost four years after the accident that demonstrates persistence and stability of the biomarker ratios over a prolonged period of time. Although the spider diagrams provide a very convenient, illustrative tool for graphical data visualization / comparison, the ultimate, visual judgement of similarity / dissimilarity is undoubtedly subjective. The use of a quantitative, statistical parameter to evaluate similarity / dissimilarity provides a rational basis for the determination. To establish a quantitative measure of samples similarity, we calculated a correlation coefficient, a statistical parameter that is used to “fingerprint” the oil spill. As recommended by Nordtest42, for correlation studies we utilized linear regression using least square method. This approach for evaluation of a match between the samples is less subjective than standard methods (e.g., ASTM D3328, D5739), that rely on visual comparison of chromatograms. The average values and standard deviations for the biomarker ratios (Table 3S) from triplicate analysis were used to establish a correlation between the samples. The expanded uncertainty (U) calculated as U = k·u, where u is a standard deviation and k is a coverage factor, equal to 2.484. k is determined from the Student’s t-distribution corresponding to the associated degrees of freedom and a 95 % confidence level for each sample. The correlation coefficients are calculated for every sample relatively to Macondo crude oil (NIST 2779), for which it is 1. Figure 7 shows the correlation for all samples and values presented on bars ACS Paragon Plus Environment


1

295 2

Page 18 of 35

(except the small negative correlation value for the Blue Crude oil) are statistically significant at the 95%

3

296 4 5 6 297 7 8 298 9 10 11 12 13 14 15 16 17 18 19 20 21 22 299 23 24 25 300 26 27 301 28 29 302 30 31 32 303 33 34 304 35 36 305 37 38 39 306 40 41 307 42 43 44 308 45 46 309 47 48 310 49 50 51 311 52 53 312 54 55 313 56 57 58 59 60

confidence level. The closer the correlation coefficient to 1, the more identical is the sample to Macondo crude oil.

Figure 7. Correlation coefficients for quantitative comparison of 20 samples with Macondo crude oil (NIST 2779). The correlation of NIST 2779 with itself is equal to 1, as shown by the green bar.

Environmental samples (SAM-1‒SAM-18) show high similarity to the Macondo crude oil with the correlation coefficient values ranging from 0.947 to 0.996. It is worth noting that two samples (SAM-10 and SAM-11) collected within three months after the accident show some of the highest correlation values (0.996 and 0.994, correspondingly), which is consistent with lower level of degradation for samples with a shorter environmental exposure time. In fact, such a small deviation in correlation coefficient from 1 can be due to fluctuation in sample preparation and instrument operation. However, SAM-8, collected eleven months after the oil spill, shows the lowest correlation coefficient (0.947) even among tarballs with longer exposure period (SAM-12‒SAM-18). This sample was found on a barrier island in the Mississippi river delta, where extensive biodegradation50,51 and biogenic52,53 input from the environment is very possible. Furthermore, the area serves as a busy sea traffic route and faces water discharge from the river with all accompanying pollution54 that makes a contribution of petrogenic hydrocarbons from other sources probable as well. It is


Page 19 of 35

1

314 2


interesting to mention that another sample (SAM-17) also found in the river delta shows the second lowest

3

315 4 5 6 316 7 8 317 9 10 11 318 12 13 319 14 15 320 16 17 18 321 19 20 322 21 22 323 23 24 25 324 26 27 325 28 29 326 30 31 32 327 33 34 328 35 36 37 329 38 39 330 40 41 331 42 43 44 332 45 46 333 47 48 334 49 50 51 335 52 53 336 54 55 56 337 57 58 59 60

correlation (0.965) among the environmental samples. However, apart from pollution caused by human activity, crude oil can enter the marine environment through natural seepages from the sea floor. Natural oil seeps are a global phenomenon contributing up to 2 million tonnes of petroleum a year worldwide which exceeds all anthropogenic sources combined.55,56 There are numerous seepage zones in the Gulf of Mexico, and oil slicks for some of them can be seen on water surface from Space.50 The exact volume of petroleum coming from those seeps is still under debate with estimated values from 80,000 to 200,000 tonnes/year.55 Nevertheless, petroleum compounds related to longpersistent natural oil seeps also need to be taken into account in oil spill studies.10 To demonstrate the fidelity of applied analytical approach for differentiation of the Macondo oil spill from other potential sources, we investigated the Megaplume oil seep and a Blue Crude oil35 – samples that originate from different gas/petroleum reservoirs in the Gulf of Mexico. As a contrast, Megaplume (0.655) shows substantially lower and Blue Crude (-0.393) a non-significant correlations with NIST 2779 making them very distinct from the Macondo oil and spill samples. It is to be noted that we are fully aware that evaluation of potential contribution of petroleum pollution from other sources requires comprehensive analysis of a representative set of samples and consideration of ecological, geographical, metocean, and climatic factors (e.g., source locations, their volume, mass transport due to ocean currents and waves,57 etc.). Apart from hopanes and steranes used in this work it also may be required to extend the number of molecular markers and ratios to achieve reliable differentiation between closely related sources. Some of the compounds (e.g., PAHs, PASHs, etc.) can give an additional tool for fingerprinting the oil spill and identification of a pollution source10 as well as provide an insight for degree of biotic and abiotic (weathering) degradation.39,58 Such studies with GC/API-MS for the Macondo oil spill are underway.

■ ASSOCIATED CONTENT ACS Paragon Plus Environment


1

338 2 3

339 4 5 6 340 7 8 341 9 10 11 342 12 13 343 14 15 16 344 17 18 19 345 20 21 22 23 346 24 25 26 347 27 28 348 29 30 349 31 32 33 350 34 35 351 36 37 38 352 39 40 41 42 353 43 44 354 45 46 355 47 48 49 356 50 51 357 52 53 358 54 55 56 359 57 58 360 59 60

Page 20 of 35

Supporting Information. Schematic diagram for GC/APCI ion source and ionization mechanisms (Figure 1S). SRM chromatograms for hopanes (M+• → m/z 191) and steranes (M+• → m/z 217) from NIST2266 standard mixture (Figures 2S, 3S, and 4S). GC/MS (EI) for NIST2779 (Figure 5S). MS/MS conditions for acquisition of SRM transitions (Table 1S). Results for quantitative measurement of biomarkers in NIST2779 with GC/APCI-MS/MS (Table 2S). Biomarker ratios for all samples (Table 3S). These materials are available free of charge via the Internet at http://pubs.acs.org.

■ AUTHOR INFORMATION Corresponding Authors * Vladislav V. Lobodin and Ryan P. Rodgers. Phone: +1 850 644 1319 (VVL),

+1 850 644 2398 (RPR)

Fax: +1 850 644 1366 (RPR and VVL) E-mails: [email protected] (VVL), [email protected] (RPR)

■ ACKNOWLEDGEMENTS This work was supported by NSF DMR-11-57490, the Florida State University Future Fuels Institute, BP/The Gulf of Mexico Research Initiative to the Deep-C Consortium, Waters Corporation, the State of Florida, and NSF OCE-1421180 (E. Maksimova). We thank Dr. Christopher M. Reddy and Mr. Robert K. Nelson (Woods Hole Oceanographic Institution, MA), and Drs. Markus Huettel and Ian MacDonald (Department of Earth, Ocean and Atmospheric Science, Florida State University, FL) for supplying environmental samples.


Page 21 of 35

1 2 361 3 4 5 362 6 363 7 364 8 365 366 9 367 10 368 11 369 12 370 371 13 372 14 373 15 374 16 375 376 17 377 18 378 19 379 380 20 381 21 382 22 383 23 384 385 24 386 25 387 26 388 27 389 390 28 391 29 392 30 393 394 31 395 32 396 33 397 34 398 399 35 400 36 401 37 402 38 403 404 39 405 40 406 41 407 42 408 409 43 410 44 411 45 412 413 46 414 47 415 48 416 49 417 418 50 419 51 420 52 421 53 422 423 54 424 55 425 56 426 427 57 428 58 429 59 60


■ REFERENCES (1) McNutt, M. K.; Camilli, R.; Crone, T. J.; Guthrie, G. D.; Hsieh, P. A.; Ryerson, T. B.; Savas, O.; Shaffer, F. Proc. Natl. Acad. Sci. U.S.A. 2012, 109, 2026020267. (2) Barbier, C. J.; Shushan, S. In The United States District Court for The Eastern District of Louisiana, January 15, 2015, pp 1-44. (3) Reddy, C. M.; Arey, S. A.; Seewald, J. S.; Sylva, S. P.; Lemkau, K. L.; Nelson, R. K.; Carmichael, C. A.; McIntyre, C. P.; Fenwick, J.; Ventura, G. T.; Van Mooya, B. A. S.; Camilli, R. Proc. Natl. Acad. Sci. U.S.A. 2012, 109, 20229–20234. (4) Lewan, M. D.; Warden, A.; Dias, R. F.; Lowry, Z. K.; Hannah, T. L.; Lillis, P. G.; Kokaly, R. F.; Hoefen, T. M.; Swayze, G. A.; Mills, C. T.; Harris, S. H.; Plumlee, G. S. Organic Geochemistry 2014, 75, 54-60. (5) Dietrich, D. E.; Bowman, M. J.; Korotenko, K. A.; Hamish, M.; Bowman, E. Oil Spill Risk Management. Modeling Gulf of Mexico Circulation and Oil Dispersal, 1st ed.; Wiley, 2014, p 216. (6) Kimes, N. E.; Callaghan, A. V.; Suflita, J. M.; Morris, P. J. Front. Microbiol. 2014, 5, 1-11. (7) Bacosa, H. P.; Erdner, D. L.; Liu, Z. Mar. Pollut. Bull. 2015, 95, 265-272. (8) Ruddy, B. M.; Huettel, M.; Kostka, J. E.; Lobodin, V. V.; Bythell, B. J.; McKenna, A. M.; Aeppli, C.; Reddy, C. M.; Nelson, R. K.; Marshall, A. G.; Rodgers, R. P. Energy Fuels 2014, 28, 4043-4050. (9) Aeppli, C.; Carmichael, C. A.; Nelson, R. K.; Lemkau, K. L.; Graham, W. M.; Redmond, M. C.; Valentine, D. L.; Reddy, C. M. Environ. Sci. Technol. 2012, 46, 8799-8807. (10) Bence, A. E.; Kvenvolden, K. A.; Kennicutt, M. C. Org. Geochem. 1996, 24, 7-42. (11) Peterson, C. H.; Rice, S. D.; Short, J. W.; Esler, D.; Bodkin, J. L.; Ballachey, B. E.; Irons, D. B. Science 2003, 302, 2082-2086. (12) Lebedev, A. T. Comprehensive Environmental Mass Spectrometry, 1st ed.; ILM Publications: St Albans, UK, 2012, p 544. (13) Wang, Z.; Stout, S. A. Oil Spill Environmental Forensics: Fingerprinting and Source Identification, 1st ed.; Academic Press, 2007, p 620. (14) Peters, K. E.; Walters, C. C.; Moldowan, J. M. The Biomarker Guide: Volume 1, Biomarkers and Isotopes in the Environment and Human History, 2nd ed.; Cambridge University Press, 2007, p 492. (15) Hsu, C. S.; Walters, C. C.; Isaksen, G. H.; Schaps, M. E.; Peters, K. E. In Analytical Advances for Hydrocarbon Research, Hsu, C. S., Ed.; Springer: New York, 2003, p 463. (16) Eiserbeck, C.; Nelson, R. K.; Grice, K.; Curiale, J.; Reddy, C. M. Geochim. Cosmochim. Acta 2012, 87, 299-322. (17) Horning, E. C.; Horning, M. G.; Carroll, D. I.; Dzidic, I.; Stillwel.Rn. Anal. Chem. 1973, 45, 936-943. (18) McEwen, C. N.; McKay, R. G. J. Am. Soc. Mass. Spectrom. 2005, 16, 1730-1738. (19) Domeno, C.; Canellas, E.; Alfaro, P.; Rodriguez-Lafuente, A.; Nerin, C. J. Chromatogr. A 2012, 1252, 146-154. (20) David, F.; Sandra, P.; Hancock, P. LC GC Eur. 2011, 24, 16-19. (21) van Bavel, B.; Geng, D.; Cherta, L.; Nacher-Mestre, J.; Portoles, T.; Abalos, M.; Saulo, J.; Abad, E.; Dunstan, J.; Jones, R.; Kotz, A.; Winterhalter, H.; Malisch, R.; Traag, W.; Hagberg, J.; Jogsten, I. E.; Beltran, J.; Hernandez, F. Anal. Chem. 2015, 87, 9047–9053. (22) Bristow, T.; Harrison, M.; Sims, M. Rapid Commun. Mass Spectrom. 2010, 24, 1673-1681. (23) Raro, M.; Portoles, T.; Sancho, J. V.; Pitarch, E.; Hernandez, F.; Marcos, J.; Ventura, R.; Gomez, C.; Segura, J.; Pozo, O. J. J. Mass Spectrom. 2014, 49, 509521. (24) Lobodin, V. V.; Rodgers, R. P.; Marshall, A. G. Prepr. Pap.- Am. Chem. Soc. Div. Energy Fuels 2014, 59, 760-761. (25) Stevens, D.; Shi, Q.; Hsu, C. S. Energy Fuels 2013, 27, 167-171. (26) Wu, C.; Qian, K.; Walters, C. C.; Mennito, A. Int. J. Mass Spectrom. 2015, 377, 728-735. (27) Carrasco-Pancorbo, A.; Nevedomskaya, E.; Arthen-Engeland, T.; Zey, T.; Zurek, G.; Baessmann, C.; Deelder, A. M.; Mayboroda, O. A. Anal. Chem. 2009, 81, 10071-10079. (28) Wachsrnuth, C. J.; Almstetter, M. F.; Waldhier, M. C.; Gruber, M. A.; Nurnberger, N.; Oefner, P. J.; Dettmer, K. Anal. Chem. 2011, 83, 7514-7522. (29) Strehmel, N.; Kopka, J.; Scheel, D.; Bottcher, C. Metabolomics 2014, 10, 324-336. (30) Canellas, E.; Vera, P.; Domeno, C.; Alfaro, A. P.; Nerin, C. J. Chromatogr. A 2012, 1235, 141-148. (31) Cherta, L.; Portoles, T.; Beltran, J.; Pitarch, E.; Mol, J. G. J.; Hernandez, F. J. Chromatogr. A 2013, 1314, 224-240. (32) Garcia-Villalba, R.; Pacchiarotta, T.; Carrasco-Pancorbo, A.; Segura-Carretero, A.; Fernandez-Gutierrez, A.; Deelder, A. M.; Mayboroda, O. A. J. Chromatogr. A 2011, 1218, 959-971. (33) Garcia-Pineda, O.; MacDonald, I.; Zimmer, B.; Shedd, B.; Roberts, H. Deep-Sea Res. Pt. II 2010, 57, 1859-1869. (34) Johansen, C.; Todd, A. C.; MacDonald, I. R. Limnol. Oceanogr. 2015, submitted (in review). (35) Juyal, P.; McKenna, A. M.; Yen, A.; Rodgers, R. P.; Reddy, C. M.; Nelson, R. K.; Andrews, A. B.; Atolia, E.; Allenson, S. J.; Mullins, O. C.; Marshall, A. G. Energy Fuels 2011, 25, 172-182. (36) Zakaria, M. P.; Horinouchi, A.; Tsutsumi, S.; Takada, H.; Tanabe, S.; Ismail, A. Environ. Sci. Technol. 2000, 34, 1189-1196. (37) Peters, K. E.; Walters, C. C.; Moldowan, J. M. The Biomarker Guide: Volume 2, Biomarkers and Isotopes in Petroleum Systems and Earth History, 2nd ed.; Cambridge University Press, 2007, p 704. (38) Peters, K. E.; Moldowan, J. M.; LaCroce, M. V.; Kubicki, J. D. Org. Geochem. 2014, 76, 1-8. (39) Aeppli, C.; Nelson, R. K.; Radovic, J. R.; Carmichael, C. A.; Valentine, D. L.; Reddy, C. M. Environ. Sci. Technol. 2014, 48, 6726-6734. (40) White, H. K.; Hsing, P.-Y.; Cho, W.; Shank, T. M.; Cordes, E. E.; Quattrini, A. M.; Nelson, R. K.; Camilli, R.; Demopoulos, A. W. J.; German, C. R.; Brooks, J. M.; Roberts, H. H.; Shedd, W.; Reddy, C. M.; Fisher, C. R. Proc. Natl. Acad. Sci. U.S.A. 2012, 109, 20303–20308. (41) Overton, E. B.; Miles, M. S.; Meyer, B. M.; Gao, H.; Turner, R. E. Int. Oil Spill Conf. Proc. 2014, 2014, 2074-2082. (42) Faksness, L.-G.; Weiss, H. M.; Daling, P. S. Revision of the Nordtest Methodology for Oil Spill Identification, STF66 A02028 ed.; SINTEF: Trondheim, Norway, 2002, p 110p. (43) Schantz, M. M.; Kucklick, J. R. NISTIR 7793 2011, 175p. (44) Kolaczkowska, E.; Slougui, N. E.; Watt, D. S.; Maruca, R. E.; Moldowan, J. M. Org. Geochem. 1990, 16, 1033-1038. (45) Whelan, J. K.; Farrington, J. W. Organic Matter: Productivity, Accumulation, and Preservation in Recent and Ancient Sediments; Columbia University Press New York, 1992, p 533. (46) Huang, W. Y.; Meinschein, W. G. Geochim. Cosmochim. Ac. 1979, 43, 739-745. (47) Camilli, R.; Reddy, C. M.; Yoerger, D. R.; Van Mooy, B. A. S.; Jakuba, M. V.; Kinsey, J. C.; McIntyre, C. P.; Sylva, S. P.; Maloney, J. V. Science 2010, 330, 201-204. (48) Bost, F. D.; Frontera-Suau, R.; McDonald, T. J.; Peters, K. E.; Morris, P. J. Org. Geochem. 2001, 32, 105-114. (49) Frontera-Suau, R.; Bost, F. D.; McDonald, T. J.; Morris, P. J. Environ. Sci. Technol. 2002, 36, 4585-4592. (50) Macdonald, I. R.; Guinasso, N. L.; Ackleson, S. G.; Amos, J. F.; Duckworth, R.; Sassen, R.; Brooks, J. M. J. Geophys. Res-Oceans 1993, 98, 16351-16364.



1 430 2 431 432 3 433 4 434 5 435 6 436 437 7 438 8 439 9 440 441 10

Page 22 of 35

(51) Silliman, B. R.; van de Koppel, J.; McCoy, M. W.; Diller, J.; Kasozi, G. N.; Earl, K.; Adams, P. N.; Zimmerman, A. R. Proc. Natl. Acad. Sci. U.S.A. 2012, 109, 11234-11239. (52) Bianchi, T. S. Proc. Natl. Acad. Sci. U.S.A. 2011, 108, 19473-19481. (53) Rabalais, N. N.; Turner, R. E.; Dortch, Q.; Justic, D.; Bierman, V. J.; Wiseman, W. J. Hydrobiologia 2002, 475, 39-63. (54) Mississippi River Water Quality and the Clean Water Act: Progress, Challenges, and Opportunities; Committee on the Mississippi River and the Clean Water Act, National Research Council. The National Academies Press: Washington, D.C., 2008, p 252. (55) Oil in the Sea III: Inputs, Fates, and Effects; Committee on Oil in the Sea, National Research Council. The National Academies Press: Washington, D.C., 2003, p 280. (56) Kvenvolden, K. A.; Cooper, C. K. Geo-Mar. Lett. 2003, 23, 140-146. (57) Zavala-Hidalgo, J.; Romero-Centeno, R.; Mateos-Jasso, A.; Morey, S. L.; Martinez-Lopez, B. Atmosfera 2014, 27, 317-334. (58) Prince, R. C.; Elmendorf, D. L.; Lute, J. R.; Hsu, C. S.; Haith, C. E.; Senius, J. D.; Dechert, G. J.; Douglas, G. S.; Butler, E. L. Environ. Sci.Technol. 1994, 28, 142-145.

11

442 12 13

443 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 ACS Paragon Plus Environment

Page 23 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table of Contents Graphic

GC/APCI-MS

Corona Pin

Ionization Chamber

Ts/Tm 2 Ion Source Housing


1.6

H29/H30

1.2 0.8 0.4

Heated Transfer Line

0 αββC27/αββC29 Steranes

Mass Spec (Atmospheric Pressure)

H32S/H32R

Capillary GC Column H30/H31+H32+H33+H34+H35

H33S/H33R

NIST2779 (Macondo crude oil) SAM-10 (Pensacola Beach) Megaplume oil seep (GC600) Blue crude (Independence Hub)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 35

Figure legends Figure 1. Top. EI mass spectra of 17α(H)-22,29,30-tris-norhopane and 5α(H),14α(H),17α(H)-20Rcholestane. Note extensive fragmentation of molecular ions under EI. Bottom. APCI mass spectra of 17α(H)22,29,30-tris-norhopane and 5α(H),14α(H),17α(H)-20R-cholestane. Note, soft ionization under APCI produces abundant molecular ions. Figure 2. Image of the Gulf of Mexico with marked locations of sample collection (SAM-1‒ SAM-18), Deepwater Horizon explosion, Megaplume oil seep and Independence hub (Blue Crude oil). Figure 3. Product ion spectra (MS/MS) from M+• (produced under APCI in GC/APCI source) of 17α(H)22,29,30-tris-norhopane at collision energy of 15 eV (top) and 5α(H),14α(H),17α(H)-20R-cholestane at collision energy of 20 eV (bottom). Figure 4. GC/APCI-MS/MS chromatogram, showing the sum of SRM transitions (M+• → m/z 191) for C27– C35 hopanes from the Macondo crude oil. Figure 5. GC/APCI-MS/MS chromatograms (SRMs), showing the C27–C29 sterane distribution for the Macondo crude oil. Note, the truncated labels correspond to compounds described in the text. The number of carbon atoms in diasteranes and steranes is defined in each SRM chromatogram. Figure 6. Spider diagrams for the Macondo crude oil, eighteen tarball samples (SAM-1–18), Megaplume oil seep and Blue Crude constructed from seven diagnostic biomarker ratios (Ts/Tm, H29/H30, H32S/H32R, H30(H31+H32+H33+H34+H35), H33S/H33R, C27αββ/C29αββ steranes, and C27βα/C29βα diasteranes). The diagrams for SAM-1–18 demonstrate the great similarity to the reference Macondo crude oil. Figure 7. Correlation coefficients for quantitative comparison of 20 samples with Macondo crude oil (NIST 2779). The correlation of NIST 2779 with itself is equal to 1, as shown by the green bar.


Page 25 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Supporting Information. Figure legends and Tables

Figure 1S. Top. Schematic diagram for GC/APCI ion source. Bottom. Ionization mechanisms for the formation of molecular ion (by charge transfer) and pseudo-molecular ion (protonation). By courtesy of Waters Corporation.

Figure 2S. Summed signal for seven SRM-transitions (M+• → m/z 191 and M+• → m/z 191) for hopanes and steranes in NIST2266 standard mix. Inset. Calibration curve for 17α(H),21β(H)-hopane containing five points at the range of 1.2-540 picograms with least squares regression (R2) of 0.999. Each point is an average of seven analysis runs.

Figure 3S. SRM chromatograms (M+• → m/z 191 ) for hopanes in NIST2266 standard mix: 17α(H)22,29,30-trisnorhopane,

17α(H),21β(H)-30-norhopane,

17α(H),21β(H)-hopane,

17α(H),21β(H)-22S-30-

homohopane, 17α(H),21β(H)-22R-30-homohopane).

Figure 4S. SRM chromatograms (M+• → m/z 217) for steranes in NIST2266 standard mix: 5α(H),14β(H),17β(H)-20R-cholestane, 24S-methylcholestane,

5α(H),14α(H),17α(H)-20R-cholestane,

5α(H),14α(H),17α(H)-20S-24R-ethylcholestane,

5α(H),14β(H),17β(H)-20R-

5α(H),14β(H),17β(H)-20R-24R-

ethylholestane. Figure 5S. GC/MS (EI) of Macondo crude oil (NIST2779). Ion chromatograms at m/z 191 (top) and m/z 217 (bottom).



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 35

Table 1S. MS/MS conditions for acquisition of SRM transitions. Compound class

SRM transition

Dwell time, ms

Collision energy, eV

C27-Hopanes C27-Steranes C27-Steranes C27-Steranes C28-Hopanes C28-Steranes C28-Steranes C28-Steranes C29-Hopanes C29-Steranes C29-Steranes C29-Steranes C30-Hopanes C30-Steranes C31-Hopanes C32-Hopanes C33-Hopanes C34-Hopanes C35-Hopanes

370.30 → 191.10 372.30 → 217.10 372.30 → 218.10 372.30 → 259.20 384.30 → 191.10 386.30 → 217.10 386.30 → 218.10 386.30 → 259.20 398.30 → 191.10 400.30 → 217.10 400.30 → 218.10 400.30 → 259.10 412.30 → 191.10 414.30 → 217.10 426.30 → 191.10 440.40 → 191.10 454.40 → 191.10 468.40 → 191.10 482.40 → 191.10

50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50

15 20 20 20 15 20 20 20 15 20 20 20 20 20 20 20 20 20 20

26 ACS Paragon Plus Environment

Page 27 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 2S. Results for quantitative measurement (in mg/kg) of biomarkers in NIST2779 with GC/APCI-MS/ MS. aThe results are expressed as average values from triplicate analysis ± the expanded uncertainty. The expanded uncertainty (U) calculated as U = k·u, where u is a standard deviation and k is a coverage factor, equal to 2.484, determined from the Student’s t-distribution corresponding to the associated degrees of freedom for triplicate analysis and a 95% confidence level for each compound.

b

Reference values from

certificate ± the expanded uncertainty as provided by NIST.43 Note, that the reference values continue to be revised and corrected. NIST2779

NIST2779

Compound

a

18α(H)-22,29,30-Trisnorneohopane 17α (H)-22,29,30-Trisnorhopane 17α(H),21β(H)-30-Norhopane 17α(H),21β(H)-Hopane 17α(H),21β(H)-22R-Homohopane 17α(H),21β(H)-22S-Homohopane 5α(H),14α(H),17α(H)-Cholestane 20S 5α(H),14α(H),17α(H)-Cholestane 20R 5α(H),14α(H),17α(H)-24-Ethylcholestane 20S 5α(H),14α(H),17α(H)-24-Ethylcholestane 20R 5α(H),14β(H),17β(H)-Cholestane 20R 5α(H),14β(H),17β(H)-Cholestane 20S 5α(H),14β(H),17β(H)-24-Methylcholestane 20R 5α(H),14β(H),17β(H)-24-Methylcholestane 20S 5α(H),14β(H),17β(H)-24-Ethylcholestane 20R 5α(H),14β(H),17β(H)-24-Ethylcholestane 20S

(measured) 12.7±2.2 10.2±2.7 23.1±4.7 45.8±6.3 19.8±2.7 23.9±4.1 24.3±0.4 23.0±2.4 15.7±2.4 16.1±1.5 24.2±2.0 23.3±5.6 21.4±3.1 17.6±1.1 20.1±2.9 20.3±2.7

(certificate)b 6.9±1.1 7.29±0.79 17.0±4.6 42.1±9.9 13.8 ±3.6 17.3±4.3 N/A N/A 16.9±5.0 N/A 23.7±2.7 22.3±7.5 N/A N/A 21.3±8.2 23.1±6.4



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 35

Table 3S. Biomarker ratios (Ts/Tm, H29/H30, H32S/H32R, H30/Σ(H31-H35), H33S/H33R, C27αββ/C29αββ steranes, and C27βα/C29βα diasteranes) for the Macondo crude oil (NIST2779), eighteen environmental samples (SAM-1–18), Megaplume oil seep and Blue Crude oil. The results are expressed as average values from triplicate analysis ± the standard deviation. Ts/Tm

H29/H30

H32S/H32R

H33S/H33R

H30/Σ(H31-H35)

C27αββ/C29αββ

C27βα/C29βα

steranes

diasteranes

NIST2779

1.42±0.05

0.52±0.04

1.48±0.03

1.46±0.06

0.65±0.03

0.68±0.06

0.94±0.02

SAM-1

1.49±0.02

0.56±0.03

1.36±0.01

1.42±0.07

0.65±0.06

0.68±0.02

0.95±0.03

SAM-2

1.43±0.05

0.58±0.02

1.41±0.13

1.35±0.05

0.62±0.05

0.66±0.04

0.89±0.04

SAM-3

1.49±0.10

0.55±0.01

1.36±0.03

1.34±0.10

0.66±0.02

0.72±0.03

0.91±0.04

SAM-4

1.47±0.03

0.59±0.02

1.39±0.03

1.40±0.12

0.61±0.01

0.73±0.03

0.93±0.02

SAM-5

1.49±0.08

0.57±0.01

1.31±0.07

1.37±0.05

0.62±0.03

0.69±0.02

0.90±0.02

SAM-6

1.56±0.03

0.50±0.05

1.32±0.03

1.33±0.03

0.68±0.01

0.71±0.01

0.97±0.02

SAM-7

1.53±0.09

0.53±0.06

1.35±0.09

1.30±0.01

0.60±0.01

0.67±0.05

0.94±0.05

SAM-8

1.30±0.09

0.62±0.06

1.40±0.09

1.32±0.10

0.46±0.06

0.42±0.02

0.56±0.03

SAM-9

1.43±0.11

0.49±0.03

1.39±0.11

1.49±0.11

0.67±0.05

0.74±0.06

0.96±0.02

SAM-10

1.46±0.07

0.53±0.05

1.42±0.07

1.46±0.08

0.66±0.06

0.64±0.07

0.90±0.08

SAM-11

1.42±0.10

0.52±0.05

1.41±0.08

1.32±0.12

0.60±0.02

0.66±0.07

0.91±0.09

SAM-12

1.48±0.08

0.56±0.06

1.50±0.12

1.39±0.11

0.54±0.05

0.58±0.06

0.89±0.08

SAM-13

1.50±0.07

0.51±0.05

1.46±0.08

1.36±0.09

0.60±0.07

0.62±0.06

0.96±0.06

SAM-14

1.49±0.09

0.49±0.05

1.45±0.13

1.52±0.14

0.63±0.06

0.64±0.05

1.00±0.07

SAM-15

1.33±0.05

0.59±0.05

1.57±0.15

1.52±0.15

0.55±0.05

0.58±0.06

0.92±0.09

SAM-16

1.49±0.10

0.54±0.06

1.46±0.14

1.40±0.12

0.60±0.06

0.54±0.05

0.92±0.07

SAM-17

1.37±0.05

0.54±0.03

1.41±0.09

1.49±0.07

0.46±0.04

0.40±0.03

0.60±0.04

SAM-18

1.47±0.12

0.59±0.06

1.52±0.09

1.45±0.14

0.55±0.05

0.59±0.06

0.81±0.80

Megaplume

1.00±0.05

1.01±0.05

1.48±0.05

1.44±0.08

0.52±0.04

0.84±0.07

0.82±0.08

Blue Crude

0.15±0.04

0.74±0.05

0.54±0.05

0.94±0.05

2.00±0.10

0.11±0.03

0.84±0.06

Sample


Page 29 of 35

17α(H)-22,29,30-tris-norhopane 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 50

Analyticalspectra Chemistry EI mass

5α(H),14α(H),17α(H)-20R-cholestane 7% of TIC

4% of TIC C27H48

C27H46

M+•

M+•

m/z

m/z

APCI mass spectra 17α(H)-22,29,30-tris-norhopane

44% of TIC 370 M+•

5α(H),14α(H),17α(H)-20R-cholestane

47% of TIC 372 M+•

191 95 121 149 100

150

355 ACS Paragon Plus Environment 200

m/z

250

300

350

400

50

100

357

217 149 175 203 150

200

m/z

250

300

350

400


89°W

31°N

88°W

87°W

86°W

SAM-1 SAM-5 SAM-16

90°W

SAM-3

91°W

SAM-14

92°W

SAM-11 SAM-13

93°W

SAM-15

85°W

84°W

83°W 31°N

Tallahassee

SAM-10

New Orleans SAM-12

SAM-17

SAM-2

SAM-8

SAM-7 SAM-6

30°N SAM-18

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

Page 30 of 35

30°N

SAM-9 29°N

29°N

Deepwater Horizon explosion

SAM-4

28°N

28°N

Independence Hub

Megaplume oil seep

27°N

26°N

27°N

-100

-50

0

100

26°N

200

Kilometers

25°N

25°N 93°W

92°W

91°W

90°W

89°W 88°W 87°W ACS Paragon Plus Environment

86°W

85°W

84°W

83°W

Page 31 of 35


GC/APCI-MS/MS. Product Spectra from M+•

1 Figure 3 17α(H)-22,29,30-tris-norhopane 2 3

191



4 5 100 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 0 50 29 30 31 32 33 34 100 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 0 50 58

95 149 109

m/z 191

135 121

M+• 163

69

81

177

100

150

355 370 200

m/z

250

300

350

5α(H),14α(H),17α(H)-20R-cholestane 217

357

m/z 217 121 81

95 107

135

M+•

149 175 161 262 189 203 ACS Paragon Plus Environment

73 100

150

200

m/z

250

372 300

350

Analytical Chemistry GC/APCI-MS/MS for NIST2779 (Macondo Crude Oil) Page 32 of 35

Hopanes: Summed Signals for C27-C35 (M+• → m/z 191) H30 32 31

34 33

Ts Tm

70.00

H31S

Figure 4

H31R H32S

35

H29

RA, %

1 2 3 4 5 6 7100 19 20 8 9 12 18 E 21 30 13 11 10 25 26 D 17 22 11 1 C 9 28 12 16 29 2 14 13 10 B 8 27 15 A 14 7 3 5 15 6 4 16 23 24 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 0 50.00 60.00 41 42

H32R H 33S H33R H 34S H34R H35S H35R

C29Ts

C31

80.00

DH30 C32 M30 C33 C 34

82.00

86.00

88.00

90.00

92.00

94.00

Time

C35


80.00

84.00

90.00 Time, min

100.00

110.00

120.00

Chemistry GC/APCI-MS/MS for Analytical NIST2779 (Macondo Crude Oil)

Page 33 of 35

1 Figure 5 2

29

28

Steranes & Diasteranes

22

21

24 25

3 20 18 4 23 Sum of C27-C29 Steranes/Diasteranes 12 100 5 17 11 6 13 19 16 1 7 9 8 2 14 15 10 8 9 10 3 7 5 11 6 4 12 13 14 15 16 0 55.00 60.00 65.00 70.00 75.00 17 50.00 βαS 18 100 19 αββR 20 27 βαR αββS 21 27 22 αααS 23 αααR 24 m/z 372  217 αβR 25 αβS 26 27 28 29 0 55.00 60.00 65.00 70.00 75.00 30 50.00 βαS 31 100 αββR 32 28 αββS 33 28 βαR 34 αααS 35 αααR 36 αβS 37 m/z 386  217 αβR 38 39 40 41 42 43 0 50.00 55.00 60.00 65.00 70.00 75.00 44 βαS 45 100 29 αββ 46 29 47 R+S βαR 48 49 αααS 50 51 αβR m/z 400  217 αααR αβS 52 53 54 55 ACS Paragon Plus Environment 56 0 50.00 55.00 60.00 65.00 70.00 75.00 57 Time, min 58

26

27

RA, %

C D

A

-Diasteranes

80.00

C -Steranes

RA,%

C

B

-Diasteranes

C -Steranes

RA,%

C

80.00

-Diasteranes

C -Steranes

RA, %

C

80.00

80.00

Figure 6


Page 34 of 35

1 NIST2779 T /T SAM-1 Ts/Tm s m 2 2 2 3 βαC27/βαC29 βαC27/βαC29 1.5 1.5 4 Diasteranes H29/H30 Diasteranes H29/H30 5 1 1 6 0.5 0.5 7 0 0 8 H /H H32S/H32R 9 32S 32R C27αββ/C29αββ C27αββ/C29αββ 10 Steranes Steranes 11 12 H33S/H33R 13 H30/ΣH31‒H35 H33S/H33R H30/ΣH31‒H35 14 15 16 SAM-3 17 SAM-4 T /T Ts/Tm s m 18 2 2 C βα/C βα C βα/C βα 29 27 29 19 27 1.5 1.5 Diasteranes H29/H30 H29/H30 20 Diasteranes 1 1 21 22 0.5 0.5 23 0 0 24 H /H H32S/H32R C27αββ/C29αββ 32S 32R 25αββ/C αββ C27 29 Steranes 26 Steranes 27 28 H30/ΣH31‒H35 H33S/H33R H30/ΣH31‒H35 H33S/H33R 29 30 31 32 SAM-7 SAM-6 33 34 Ts/Tm Ts/Tm 35 C βα/C βα 2 2 C27βα/C29βα 27 29 36 Diasteranes 1.5 Diasteranes 1.5 H29/H30 H29/H30 37 1 1 38 0.5 0.5 39 0 40 0 41 C αββ/C αββ H /H H32S/H32R 27 29 32S 32R C27 42αββ/C29αββ Steranes Steranes 43 44 45 H30/ΣH31‒H35 H33S/H33R H30/ΣH31‒H35 H33S/H33R 46 47 48 49 SAM-9 SAM-10 50 Ts/Tm Ts/Tm 51 2 2 52 C27βα/C29βα C27βα/C29βα 53 Diasteranes 1.5 1.5 Diasteranes H29/H30 H29/H30 54 1 1 55 0.5 0.5 56 0 0 57 58 H32S/H32R C αββ/C αββ H32S/H32R C27αββ/C29αββ 27 29 59 Steranes Steranes 60 H30/ΣH31‒H35

H33S/H33R

SAM-12

H30/ΣH31‒H35

H33S/H33R

SAM-13 Ts/Tm

2 1.5

H29/H30


0.5 0


H32S/H32R

H30/ΣH31‒H35

H33S/H33R


Ts/Tm

2

1.5

0.5 0


H32S/H32R

H30/ΣH31‒H35

H33S/H33R

SAM-8 Ts/Tm C27βα/C29βα Diasteranes

2 1.5

0.5 0


H32S/H32R

H33S/H33R

H30/ΣH31‒H35


Ts/Tm 2 1.5

0.5 0

H32S/H32R


H33S/H33R

2 1.5

H29/H30


Ts/Tm 2 1.5

0.5

0

0

0

H32S/H32R


H33S/H33R

SAM-16 Ts/Tm

2

H29/H30


H32S/H32R


H33S/H33R

2 1.5

H29/H30


Ts/Tm 2 1.5

1

1

1

0.5

0.5

0.5


H33S/H33R

SAM-18

H32S/H32R


H33S/H33R

Ts/Tm

2 1.5

H29/H30


H32S/H32R


H33S/H33R

Blue crude

Megaplume Ts/Tm

2 1.5

H29/H30


Ts/Tm 2 1.5

1

1

1

0.5

0.5

0.5

0

0

0


H32S/H32R

H33S/H33R


H29/H30

0

0

0

H32S/H32R

H29/H30

SAM-17

Ts/Tm 1.5

H29/H30

1

0.5

SAM-15

H29/H30

1

0.5

H33S/H33R

H29/H30

1

1

H32S/H32R

H29/H30

1

1

H30/ΣH31‒H35


1.5

1




Ts/Tm 2

SAM-14

Ts/Tm C27βα/C29βα Diasteranes

SAM-2

H32S/H32R

H33S/H33R



H29/H30

H32S/H32R

H33S/H33R

Page 35 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42


Figure 7


Atmospheric Pressure ... - ACS Publications

Recommend Documents