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Predicting ESI/MS signal change for anions in different solvents Anneli Kruve, and Karl Kaupmees Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b00595 • Publication Date (Web): 03 Apr 2017 Downloaded from http://pubs.acs.org on April 7, 2017

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Analytical Chemistry

Predicting ESI/MS signal change for anions in different solvents Anneli Kruvea,b*, Karl Kaupmeesa a

b

University of Tartu, Institute of Chemistry, Ravila 14a, Tartu 50411, Estonia Technion - Israel Institute of Technology, Schulich Faculty of Chemistry, Technion City, Haifa 3200008, Israel *[email protected]

ACS Paragon Plus Environment


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Abstract LC/ESI/MS is a technique widely used for qualitative and quantitative analysis in various fields. However, quantification is currently possible only for compounds for which the standard substances are available, as the ionization efficiency of different compounds in ESI source differs by orders of magnitude. In this paper we present an approach for quantitative LC/ESI/MS analysis without standard substances. This approach relies on accurately predicting the ionization efficiencies in ESI source based on a model, which uses physicochemical parameters of analytes. Furthermore, the model has been made transferable between different mobile phases and instrument setups by using a suitable set of calibration compounds. This approach has been validated both in flow injection and chromatographic mode with gradient elution. Key words: ionization efficiency, electrospray ionization, negative mode, mass spectrometry, standard free

Introduction Electrospray ionization (ESI) is extensively used to connect liquid chromatography (LC) and mass spectrometry (MS). The LC/MS setup is mainly used in routine analyses but has been successfully employed in various areas of life sciences like proteomics, metabolomics, etc.1 One of the main drawbacks of quantitative LC/MS analysis is the need to use standard substances as different compounds ionize to very different extent in ESI source. In case of compounds that are newly discovered, instable, very difficult to synthesize, etc. the unavailability of standard substances is common. Also quantitative analyses of reaction intermediates cannot be performed2. The ability to predict ionization efficiency for such analytes would be highly beneficial.2 Several researchers have studied the relation between ESI/MS ionization efficiency and molecular properties of compounds. The correlations between ionization efficiency and evaporation rate3, logP4,5, hydrophobicity (carboxylic acid chain length for aliphatic compounds and number of fused rings for aromatic compounds)6, retention times of small peptides in reversed phase LC1, non-polar surface area2, gas-phase proton affinity7,8, pKa5,9 as well as surface area5 have been observed. Although considerable experimental support exists for positive correlation between hydrophobicity and ESI ionization efficiency, there have been a number of studies10–12 where statistically significant correlation between the ESI/MS response and logP was not found. One reason for the contradictory results may be the use of different solvents in different studies. The solvent composition has been found13,14 to affect ESI ionization efficiency. Two significant effects have been observed: the effect of organic modifier content13,14 and the effect of solvent buffer and pH13,15. It has been found from ESI plume profiling experiments16 that for mobile phases with binary mixture of organic solvent and water the droplets tend to enrich with water during electrospray process. In the final stages of ESI small analyte ions leave droplet as solvated ions17,18 and solvent molecules are removed due to collisions with gas molecules19 in the


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ambient environment of ESI. Kostiainen et al20 observed that the abundance of ions in water based solutions is lower than that of the solutions prepared in organic solvents having lower surface tension and lower heat of vaporization. In ESI negative mode Henriksen et al4 observed a decrease in the ESI/MS response for solvent containing 50% of water compared to both pure methanol and pure acetonitrile. To add to the already complicated picture we have previously shown that the effect of organic modifier content depends both on the ionization source design and source parameters14. Compared to the influence of organic modifier, the influence of pH on ionization efficiency is very complicated to study. First of all the pH measurement of the solvent is not straight forward as the mobile phase usually contains organic modifier21. Secondly, the pH of the solution changes during electrochemical process in the ESI needle tip and also during droplet evaporation22. Methods based on fluorescence measurements have been used to monitor the pH changes during ESI droplets evolution23. Still, the applicability of these methods is complicated as both pH as well as the organic solvent concentration change throughout the ESI process. Therefore, making it complicated to distinguish whether the changes in fluorescence spectra are caused by the altering pH or organic solvent content. Additionally, the pKa values, describing the ionization in the solution phase, in water organic solvent mixtures are not known for majority of the compounds. In spite of the difficulties, models predicting the ionization efficiency (often called differently by different groups: response, sensitivity, etc.) have been published. For example Chalcraft et al24 found that ESI/MS response of polar metabolites (ionization efficiencies ranging over two orders of magnitude) can be predicted as a function of molecular volume, logP, absolute ion mobility and effective charge. An average predicting error of 49% was observed with these parameters within one solvent. Nguyen et al25 used a simpler model for predicting ESI/MS response using only the adjusted mass (molar mass times hydrogen-carbon ratio) of the compounds. However, the number of compounds used in the study was small. Our group has previously presented an ionization efficiency scale for positive ESI mode10 ranging over six orders of magnitude and proposed an ionization efficiency prediction model based on the analyte pKa and molecular volume. However, these models do not account for the solvent composition in the measurements and are developed only for one solvent. Lately we have presented an ionization efficiency scale in negative ion mode and ionization efficiency prediction model, that uses ionization degree, which aims also to account for the solvent pH, and the weighted average positive sigma (WAPS) parameter, describing the charge delocalization.12 However this model has so far been tested only in one solvent. Wu et al26 has established a quantitative model also aiming to account for solvent changes in LC/ESI/MS for 25 organic acids via five parameters: hydrogen bond acidity, HOMO energy, number of hydrogen bond donating groups, the ratio of organic solvent in mobile phase and the polar solvent accessible area. Unfortunately, the mathematical model is complex and hardly traceable, includes several data transformations and is therefore impractical for potential users. The aim of this paper is to develop a procedure that could be used to predict ionization efficiencies for various solvent systems with different organic solvent content as well as with different pH in ESI negative mode. A range of different solvents is studied and the influence



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of these solvents on the ionization efficiency of 62 acidic compounds is evaluated. The approach is validated for 17 acids in two solvents that were not included in the original dataset. To further assure the usefulness, this approach was tested also for chromatographic analyses (e.g. gradient elution).

Experimental Chemicals As analytes in training set benzoic acid (Reakhim), 3,5-dinitrobenzoic acid (Aldrich), 3(trifluoromethyl)benzoic acid (a kind gift from prof. L. M. Yagupolskii, Institute of Organic Synthesis, Kiev), Pentafluorophenol (Aldrich), 2,3,5,6-tetrafluorophenol (Aldrich), 4-tertbutylbenzoic acid (Aldrich), 4-(pentafluorophenyl)-2,3,5,6-tetrafluorophenol13, 14 pentakis(trifluoromethyl)phenol , 2-phenylphenol (TCI-EP), 4-phenylphenol (TCI-EP), 3chlorobenzoic acid (Aldrich), 4-aminobenzoic acid (Aldrich), 3-aminobenzoic acid (Fluka), 4hydroxybenzoic acid (Sigma Aldrich), Pentafluorobenzoic acid (Aldrich), 2-nitrophenol (Fluka), 3-nitrophenol (Fluka), 4-nitrophenol (Chemapol Nordic), 2,4-dinitrophenol (the same as in [13]), 2,5-dinitrophenol (Reakhim), 2,6-dinitrophenol - (Reakhim), 3-chlorophenol (Reakhim), 2-bromophenol (a kind gift from Ivari Kaljurand), 3-bromophenol (Aldrich), 3(dimethylamino)benzoic acid (Merck), 4-isopropylbenzoic acid (a kind gift from Tullio Ilomets), 2-tert-buthylphenol (Merck), 2,4,6-tri-tert-buthylphenol (Synthesized according to a standard procedure from phenol and isobutylene obtained from treatment of t-BuOH with H2SO4), 2-isopropylphenol (ABCR GmbH&Co), 2-cyanophenol (Aldrich), 4-(phenylaso)phenol (Aldrich), 3,5-diiodosalicylic acid (Aldrich), salicylic acid (Reakhim), 3[(heptafluoropropyl)sulphonyl]benzoic acid (a kind gift from prof. L. M. Yagupolskii, Institute of Organic Synthesis, Kiev), 3-[(heptafluoropropyl)sulphanyl]benzoic acid (a kind gift from prof. L. M. Yagupolskii, Institute of Organic Synthesis, Kiev), 3[tris(trifluoromethyl)methylsulphonyl]benzoic acid (a kind gift from prof. L. M. Yagupolskii, Institute of Organic Synthesis, Kiev), 3-[(trifluoromethyl)sulphanyl]benzoic acid (a kind gift from prof. L. M. Yagupolskii, Institute of Organic Synthesis, Kiev), 3[(trifluoromethyl)sulphonyl]benzoic acid (a kind gift from prof. L. M. Yagupolskii, Institute of Organic Synthesis, Kiev), 3-[tris(trifluoromethyl)methyl]benzoic acid (a kind gift from prof. L. M. Yagupolskii, Institute of Organic Synthesis, Kiev), picric acid13, pentabromophenol13, pentachlorophenol13, 2-hydroxy-1,3,5-tris(2,2,2-trifluoroethyl)-1,3,5-benzenetrisulfonic acid ester (a kind gift from prof. L. M. Yagupolskii, Institute of Organic Synthesis, Kiev), 4-(4nitrophenylaso-)-1-napthol (Fluka), heptafluoronapht-2-ol13, 2-hydroxy-1,3,5-tris(2,2,3,3tetrafluoropropyl)-1,3,5-benzenetrisulfonic acid ester15, 2,4,6-trinitro-1,3-benzenediol (a kind gift from Tullio Ilomets), 3,5-dinitrosalicylic acid (Reakhim, purified by sublimation), 2,4dinitrobenzoic acid (Lancaster), 4-chlorophenol (Aldrich) were used. As a validation set the syringic acid, vanillic acid, 4-methylcathechol, vinallin, isovanilline, coniferyl aldehyde, trans-ferulic acid, syring aldehyde, lysine, phenylalanine (all from Sigma), 2,4-dinitrophenylacetic acid (Reakhim), α-naphthylacetic acid (Chemapol), mandelic acid (Veb Laborchemie Apolda), hippuric acid (Merck), Bis(4-chlorophenylsulfonyl)amine (synthesized previously in-house), bis(4-methylphenylsulfonyl)amine (synthesized previously in-house), ethyl cyano(2,3,5,6-tetrafluorophenyl)acetate (a kind gift from Prof. Vlasov) were used. Purity of all substances was confirmed by mass spectra.


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The mobile phases were prepared from ultrapure water (18.2 MΩ·cm, TOC 1-2 ppb, prepared by a Millipore Milli-Q Advantage A10 water-purification system), acetonitrile (MeCN) (HPLC grade, J. T. Baker, Deventer, Netherlands), formic acid (Riedel-de-Haen), ammonium acetate (Fluka) and concentrated aqueous ammonia (25% solution, Lachner, Czech Republic). The aqueous phase pH was measured with pH-meter (Evikon pH Meter E6115) using glass electrode (Evikon pH631). Altogether 11 mobile phases were used for logIE measurements and model establishment. The pH of the water phase varied from 2.7 to 10.7 and the organic solvent content varied from 20 to 100%. MS studies of ionization efficiency The MS responses were recorded in a flow injection mode. Six dilutions of the analyte stock solutions were made (1, 1.25, 1.67, 2, 2.5 and 5 fold) with the corresponding mobile phase under study by the autosampler and injected to MS. The injection volume was 10 µl and the eluent flow rate was 0.2 ml/min. Analyte concentrations in the injected solutions ranged from 10-5 M to 10-7 M. In order to estimate the instrument stability during measurements the reference compound, benzoic acid, was analyzed at least twice in each sequence. The measurements of logIE scales were carried out using an Agilent XCT ion trap mass spectrometer. The MS and ESI parameters were optimized only by setting the Target Mass (TM) parameter12. Measurements were carried out using different TM values: 150, 200, 250, 300 and so forth. For each analyte the TM closest to the m/z of its anion was used. Factory defaults of the remaining parameters were used: nebulizer gas pressure 15 psi, drying gas flow rate 7 l/min, drying gas temperature 300 ˚C. The Capillary Voltage between MS and nebulizer was -3500 V. All remaining ion transport parameters were determined by the Target Mass parameter. The ion trap parameters were: Smart Target (parameter characterizing the number of ions accumulated in the trap) was 100 000 and Maximum Accumulation Time 300 ms. Each spectrum was scanned from m/z 30 to 1000. The validation samples were analyzed with an Agilent 1290 ultra-high performance liquid chromatograph and an Agilent 6495 triple quadrupole instrument in flow injection mode and chromatographic gradient elution mode. The ESI with thermal focusing was used. The capillary voltage was 3000 V and the nozzle voltage was 1500 V. Nebulizer gas pressure was 20 psi, drying gas flow rate of 14 l/min and a temperature of 200 °C, sheath gas flow rate of 11 l/min and temperatures was 250 °C. In the chromatographic analyses Agilent Zorbax RRHD SB-C18 2.1 x 50 mm column with 1.8 μm particles was used. The gradient was from 5% to 95% of acetonitrile in 7 minutes, then acetonitrile was maintained at 95% for 1 minute and reduced back to 5% within 1 minute. The flow rate for gradient elution was 0.3 ml/min. As the buffer 5 mM ammonium acetate buffer with pH of 7.6 was used. All compounds yielded only the [M-H]– ions in the mass spectra. Only 2,3,4,5,6-pentafluorobenzoic acid the fragment [M-H-CO2]- was observed and the anion [M-H]– was not observed. It was assumed that as the first step 2,3,4,5,6-pentafluorobenzoate anion is formed and ejected from the droplet and the decarboxylation process occurs only thereafter, in the gas phase. As it is complicated to measure absolute ionization efficiency12 we focus on measuring the



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relative ionization efficiency (RIE) of a compound M1 relative to benzoic acid (M2) according to the following equation:

R([M1 − H] ) slope([M1 − H] ) RIE(M1 / M 2 ) = = − − R([C6 H 5COO] ) slope([C 6 H 5 COO] ) −

−

(1)

where the slope of the analyte signal vs concentration is estimated via linear regression in the linear range of the signal-concentration plot. In order to make the data easier to present and analyze, the logarithmic scale (logIE) was used. In order to find absolute logIE values the scale was anchored to the logIE of benzoic acid – taken arbitrarily as 0 in the 0.1% ammonia/acetonitrile 20/80 mixture. In order to minimize the influence of possible differences in conditions when measuring M1 and benzoic acid, two steps were taken: (1) each acid was measured on at least 3 different runs (on 3 different days) and the results were averaged and (2) benzoic acid was measured in the beginning and end of each run on each day. To anchor the scales of other mobile phase compositions the MS signal intensities of benzoic acid in all mobile phases were measured in the same day and the logIE value of benzoic acid in a mobile phase n was calculated using equation (2):

 SignalSn CS 1   log IESn = log IES1 ⋅ ⋅ CSn SignalS1  

(2)

where Signal(Sn) and Signal(S1) are the signal intensities in mobile phases n and 1 and C(S1) and C(Sn) are the corresponding concentrations of benzoic acid in the respective mobile phases. COSMO-RS/Turbomole computations COSMO-RS method27 was used for calculating the aqueous pKa, logP (solvent/vacuum) values, Klamt parameters (of these hydrogen bond basicity (Hb_don3) was statistically significant in some of the models) as well as for generating sigma profiles needed for calculating the WAPS parameter28. The ionization degrees α of the acids were calculated from the pKa values calculated with COSMOtherm and the directly measured water phase pH. COSMO-RS was chosen because it enables calculations in solvent mixtures27 and is able to handle preferential solvation of solutes by one of the solvent mixture components28. WAPS is a parameter for quantitatively evaluating the charge delocalization in anions and is calculated as follows:28 ∞

∫ σ ⋅ p(σ )dσ

σ =0

WAPS =

∞

A

∫ p(σ )dσ

σ =0


(3)

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Where σ is the polarization charge density on ion surface, p(σ) is the probability function of σ and A is the surface area of the anion. All of these parameters can be derived from the sigma profile of a molecule which is the result of DFT-bp-TZVP calculation with COSMO solvation turned on (ε = ∞) performed by the Turbomole program package29. The smaller the WAPS value, the more delocalized the charge in the anion. It has been proposed that WAPS values above 5 indicate anions with localized charge28. COSMO-RS theory implies that the solvent space is 5-dimensional and that any log-partition coefficient is a linear combination of 5 σ-moments30: polarity/polarizability, hydrogen bond acidity and basicity as well as the surface area (CSA) descriptor. Making a rough assumption that ionization efficiency is just a partition coefficient (according to Enke31), these descriptors (all calculated by the COSMOtherm software) can be used to construct a quantitative structure-property relationship (QSPR). The molecular properties calculated via COSMOtherm27 are presented in Table S1. Statistical model evaluation and validation Multilinear regression analysis was used to obtain the model describing the relationship of logIE with the other parameters in the form: n

IE = ∑ bi ⋅ X i ,

(4)

i =1

where Xi are the relevant physico-chemical parameters or their combinations and bi are the coefficients for these parameters. The model for predicting ionization efficiencies was validated both for flow injection analyses and for gradient LC/ESI/MS analyses. The same set of analytes was used for the validation in flow injection mode and in LC/ESI/MS mode. Flow injection. The flow injection analyses were performed with 0.2 ml/min eluent flow rate. Two eluents (1) 65/35 ammonium acetate buffer pH=6.19/acetonitrile and (2) 30/70 ammonium acetate buffer pH=3.59/acetonitrile were used. The calibration mixture was analyzed on 5 concentrations to achieve the sensitivity (calibration graph slope) for each of the compounds. From the slopes logIE values for calibration compounds were calculated and thereafter the logIE values were used to find the coefficients bi in the model (Equation 4). Then the obtained model was used to predict the logIE values for validation compounds. The obtained logIE values were also used for predicting the concentrations of the validation set compounds: c predicted =

Signal from _ sample IE predicted

(5)

LC/ESI/MS analyses. The calibration mixture was analyzed on 5 concentrations to achieve the sensitivity (calibration graph slope) for each of the compounds. For the slopes logIE values for calibration compounds were calculated and thereafter the logIE values were



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correlated with molecular parameters. The obtained model was used to predict the logIE values for validation compounds and the predicted logIE was used to calculate the concentrations of the validation compounds in unknown sample. All statistical tests were performed on 95% significance level.

Results and Discussion The ionization efficiencies observed in the studied mobile phases are presented in the Table 1. The logIE values varied from -3.1 to 4.1 logarithmic units. The pooled repeatability standard deviation over all measurements is 0.27 logIE units.


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Table 1 logIE values measured in various mobile phases. NA refers to cases where quantifiable ESI/MS signal was not observed. Mobile phase

A

B

C

D

E

F

%MeCN

20

50

80

100

20

80

J

K

L

Benzoic acid

0.11

80 80 80 80 5 mM 1 mM 5 mM 5 mM 0.1% 0.1% acetate acetate acetate acetate 0.1% 0.1% 0.1% formic formic NH3 NH3 NH3 buffer buffer buffer buffer acid acid pH 5.0 pH 5.0 pH 7.8 pH 3.45 -0.10 0.10 -0.14 -0.81 0.07 0.00 -0.02 -1.52 -0.84

3,5-dinitrobenzoic acid 3-(trifluoromethyl)- benzoic acid Pentafluorophenol

1.26 1.37 1.55

0.62 1.37 1.41

0.79 1.16 0.91

1.43 1.50 1.67

0.54 0.66 0.19

1.01 1.01 1.02

1.20 1.26 1.32

1.20 1.31 1.25

1.16 1.61 1.35

0.63 0.62 0.49

4-tert-butylbenzoic acid

1.36

1.46

1.20

0.83

-0.22

-0.14

1.03

1.32

0.90

-0.32

2.69

2.66

2.17

2.84

2.12

2.57

2.80

2.70

2.60

2.26

pentakis(trifluoromethyl)-phenol

2.60

3.06

2.30

3.01

2.96

3.06

2.84

2.80

2.66

4-phenylphenol

0.62

0.95

0.35

0.51

-1.45

-0.48

0.68

0.85

0.91

-0.78

2-phenylphenol

0.45

0.63 -0.01 -0.07

NA

-1.79

0.39

0.49

0.47

-1.27

3-chlorobenzoic acid

0.77

0.85

0.95

-0.12

0.35

0.55

0.76

0.42

-0.12

4-aminobenzoic acid

0.08

0.04 -0.06 -0.83

-2.32

-1.43

-0.66

-0.30

-0.55

-1.67

4-hydroxybenzoic acid

0.37

0.23

0.13

NA

-0.44

-0.04

0.13

0.21

-0.15

-0.60

3-aminobenzoic acid

0.21

0.17

0.09

0.03

-1.86

-1.10

-0.30

-0.05

-0.33

-1.43

Pentafluorobenzoic acid

0.95

1.28

0.61

NA

0.47

1.10

1.14

1.26

1.10

0.79

2,4-dinitrophenol

1.20

1.47

0.84

1.71

0.79

1.59

1.66

1.45

1.44

1.36

3-(dimethylamino)-benzoic acid

1.05

1.13

0.71

0.38

-1.35

-0.70

0.45

0.82

0.43

-0.57

4-isopropylbenzoic acid

0.67

0.62

0.05

0.39

-0.83

-0.47

-0.01

0.43

-0.15

-0.44

2-tert-butylphenol

-0.09

0.03 -0.46 -0.19

-2.67

0.31

0.35

0.23

-2.75

0.73

-1.13

0.44

1.00

0.32

-1.24

0.1% NH3 Additive in water phase1

4-(pentafluorophenyl)-2,3,5,6-tetrafluorophenol

2,4,6-tri-tert-butylphenol 1

I

0.48

0.55 -0.45

Expect for solvent D, in this case ammonia was added to the pure acetonitrile.



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1.45

1.37

0.98

1.34

0.44

1.09

3-chlorophenol

0.36

0.27

0.22

0.29

-1.03

-0.36

2-isopropylphenol

-0.17 -0.08 -0.61 -1.42

3-bromophenol

0.80

2,5-dinitrophenol

0.72

0.63

0.65

-0.44

-0.98 -0.80 -0.40

2-bromophenol

1.38

1.06

1.38

0.79 -0.90

0.74

0.66

0.56

-0.14

-0.05

-0.21

0.22

0.96

0.99

0.76

-2.21

-0.64

-0.53

-0.66

0.85

1.44

1.37

1.19

0.85

-2.08

-0.52

-0.82

-0.56

-2.38

-0.45

2,6-dinitrophenol 2-nitrophenol

-0.37 -0.71 -0.62 -0.78

3-nitrophenol

1.01

1.07

0.78

0.84

0.39

0.75

1.51

1.20

1.26

0.75

4-nitrophenol

1.48

1.10

0.97

0.89

0.46

1.03

1.63

1.22

1.22

0.98

2,3,5,6-tetrafluorophenol 3-[(heptafluoropropyl)sulphonyl]benzoic acid

1.70 2.78

1.48 2.40

0.97 2.20

0.87 2.26

-0.42 1.45

0.36 1.91

0.92 2.14

1.00 2.43

0.94 1.96

1.35

3-[tris(trifluoromethyl)methylsulphonyl]-benzoic acid 3-[(heptafluoropropyl)sulphanyl]benzoic acid 3-[(trifluoromethyl)sulphonyl]benzoic acid 3-[tris(trifluoromethyl)methyl]benzoic acid 3-[(trifluoromethyl)sulphanyl]benzoic acid Pentabromophenol Pentachlorophenol 2-hydroxy-1,3,5-tris(2,2,2-trifluoroethyl)-1,3,5benzenetrisulfonic acid ester

2.63

2.38

2.15

2.18

1.30

1.72

2.06

2.46

1.99

1.34

2.52 2.05 2.75 1.43

2.03 1.81 2.45 1.12

0.99 1.02 1.28 0.68 1.35 1.46

1.31 1.25 1.70 1.11 1.91 2.34

1.62 1.73 1.96 1.28 1.17 2.33

2.12 1.96 2.40 1.35 1.88 2.53

0.95 0.84 1.29 0.42

2.35

1.71 1.63 1.90 1.37 1.25 1.77

1.53 1.59 1.88 1.07

2.58

1.84 1.69 2.14 1.28 1.96 1.97

2.18

1.68

4.05

3.98

3.14

3.44

4.00

4.02

4.11

3.83

3.52

Salicylic acid

0.72

0.64

0.39

0.56

-0.11

0.35

0.63

0.69

0.51

0.01

2-cyanophenol

0.76

0.83

0.28

0.43

-0.17

0.33

0.86

0.92

0.80

0.13

4-phenylazophenol

1.27

1.66

1.02

0.64

0.76

1.24

1.82

1.76

1.75

1.08

3,5-diiodosalicylic acid

2.14

2.28

2.05

2.01

1.35

2.27

2.38

2.42

2.14

1.68

2,4,6-trinitro-1,3-benzenediol

1.77

1.85

1.07

1.79

1.01

1.48

1.70

1.68

2.04

1.43

3,5-dinitrosalicylic acid

1.67

1.76

1.22

1.75

0.84

1.48

2.04

1.83

1.77

1.59

2,4-dinitrobensoic acid

0.06

0.40

0.07

0.57

-0.36

-0.07

0.48

0.43

0.23

-0.36

4-chlorophenol 2-hydroxy-, 1,3,5-tris(2,2,3,3-tetrafluoro-propyl) 1,3,5benzenetrisulfonic acid ester

0.26

0.46

0.03 -0.08

-1.28

-0.37

0.59

0.67

0.65

-0.89

3.09

3.84

3.49

3.58

3.61

4.03

3.47

3.20

3.08

0.61

1.44

1.51


1.38

1.60

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1.71

1.90

1.94

1.73

1.46

1.87

1.68

1.83

0.91

1.67

1.81

1.88

1.53

1.32

1.46

1.77

1.6

1.66

0.47

1.19

1.29

1.63

1.36

0.73

1.97

2.31

2.1

2.04

1.32

1.59

1.92

2.16

1.92

1.16

3-nitrobenzenesulphonamide 1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,8-heptadekafluorooctane1-sulphonic acid (CF3)3COH

1.17

1.48

1.5

1.23

0.50

1.11

1.46

1.57

1.42

0.79

2.56

3.06

2.77

2.91

2.34

2.74

2.82

3.00

2.59

2.46

1.48

1.72

1.54

1.64

-0.41

0.34

0.81

0.81

0.75

0.35

Tetradecanoicacid 2,3,4,5,6-pentafluoro-N-[(2,3,4,5,6pentafluorophenyl)sulfonyl]-benzenesulfonamide 4-methyl-N-[(4-nitrophenyl)sulfonyl]benzenesulfonamide Sorbic acid

1.83

2.01

1.91

-0.71

0.28

1.44

1.83

1.62

-0.06

1.96

2.44

2.29

2.50

1.76

2.68

2.53

2.65

2.23

2.10

2.31

2.50

2.46

2.63

1.85

2.60

2.63

2.75

2.39

2.02

-0.55 -0.29

-0.4

-1.98

-0.51

-0.15

-0.61

-3.08

0.05 -1.58

-1.99

-0.54

-0.11

-0.55

-2.39

-0.36

0.35

0.57

0.29

-1.24

Heptafluorornapht-2-ol 2,4-dinitro-benzenesulfonic acid 1-decanesulfonic acid/naphtylsulphonic acid 2-anthraquinonesulfonic acid

Phthalimide

1.31

N-hydroxy-phthalimide

-0.67 -0.23 -0.43 0.43

4-aminobutanoic acid

-1.57 -1.46 -1.52

-2.13

Beta-alanine

-2.32

-2.57

-2.46


-1.46

2.06

1.54

2.11

1.32

0.89


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

Effect of acetonitrile content To study the effect of organic modifier content on logIE the acetonitrile content was varied as follows: 100%, 80%, 50% and 20%. As water phase 0.1% ammonia solution was used. Ionization efficiencies in all mobile phases were well correlated, the R2 values of the correlation plots were above 0.8 (these and all other correlation plots are presented in SI). It was observed that on the average mobile phase containing 50% of acetonitrile had highest ionization efficiencies followed by mobile phase containing 20% of acetonitrile. However, for all mobile phases the differences found were within 0.25 logIE units and therefore statistically insignificant (repeatability standard deviation was 0.27 logIE units). Also the span of the ionization efficiencies scales did not change significantly with the organic modifier content. The mobile phases containing 20% and 80% of organic modifier were also compared for the acidic water phase (0.1 % of formic acid). For these mobile phases a statistically significant increase in ionization efficiencies was observed with increased acetonitrile content. The average increase was 0.61 logIE units. The correlation observed for these mobile phases was also remarkable, R2 value of 0.96 was obtained. Though, for mobile phase with 20% of acetonitrile content several compounds could not be measured due to poor signal. Compounds that could not be determined were both from hydrophilic as well as hydrophobic nature. Even though this finding does not confirm the applicability for all possible mobile phases it gives hope that mobile phases with different organic modifier content can be taken into account in modelling ionization efficiencies. Effect of buffer concentration In order to study if and how the ionic strength of the water phase influences the ionization efficiency, all 62 compounds were analyzed in mobile phases containing both 1 mM and 5 mM ammonium acetate buffer with pH=5.0 as the water phase (20%). The obtained ionization efficiency scales were in good agreement. The R2 of the correlation plot was 0.95 and the differences in logIE values between these mobile phases were statistically insignificant. Effect of pH As different chromatographic methods not only use different content of organic modifier and different buffer concentrations but also buffers with different pH, we also studied the effect of water phase pH on the ionization efficiencies. We used five different water phases (0.1% formic acid with pH of 2.7, 5 mM ammonium acetate buffers at pH 3.5, 5.0 and 7.8 and 0.1% ammonia with pH of 10.7). In general, the correlations obtained were quite good, giving R2 values of 0.8 or higher. Never the less, larger changes in logIE values were observed with changes in pH compared to the changes in organic modifier content. The ionization efficiencies observed in 20/80 0.1% formic acid/acetonitrile mixture were up to 2 logarithmic units lower than in 20/80 0.1% ammonia/acetonitrile mixture. However, in the case of 13 compounds the ionization efficiency was not significantly influenced by the varying pH. The large changes in logIE values were observed for weak acids (pKa values 7 or higher). This is in a good agreement with our previous finding that the ionization degree of analyte is a potential parameter for describing the ionization efficiencies. The weaker acids are not fully deprotonated in the


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acidic mobile phase and are therefore expected to have lower ionization efficiencies.

General trends The trends observed are in general in line with the current understanding of ESI mechanism. The most plausible ionization mechanism consists of two processes: (1) compounds becoming ionized and (2) being transported to the surface of the charged nanodroplets, from where it can be ejected to the gas phase as an analyte-solvent cluster. The first process is known to be affected by the mobile phase acidity, as expected the ionization efficiency for compounds that are weaker acids is reduced in the more acidic mobile phase. However, interestingly there are some major exceptions to this rule. For example 3-nitrophenol (pKa 7.8) is a weaker acid then 2-nitrophenol, however the ionization of the former is not affected by the mobile phase pH, while for the latter it is. This demonstrates, what has also been observed before15, that though general trends in ESI mechanism are understood, the smaller aspects are yet to be discovered. Quantitative prediction models in different mobile phases As the ionization efficiency scales in different mobile phases were in a good agreement it was of interest if a general model describing logIE values for all mobile phases could be established. We tested different molecular and solvent parameters calculated with COSMORS as input for the model. With least amount of parameters the best fit for data was achieved with a model containing WAPS, ionization degree (α), hydrogen bond acceptor ability (Hb_acc) and MeCN content: log IE = (2.72 ± 0.13) + (− 0.46 ± 0.02 ) ⋅ WAPS + (0.69 ± 0.07 ) ⋅ (α ) + (6) (− 0.018 ± 0.004 ) ⋅ Hb _ acc + (0.0039 ± 0.0011) ⋅ % MeCN The R2 for the model was 0.77 and the residual standard error was 0.58 logIE units. The obtained fit can be seen from Figure 1. It is clear from Figure 1, that in case of low ionization efficiencies the deviation between measured and actual logIE could be much higher then for high logIE values. Highest deviation from the model was observed for logIE values measured in mobile phase F, containing 20/80 mixture of 0.1% formic acid as waterphase and acetontrile. This is the solvent, that is expected to give lowes ionization efficiency values, both due to low pH as well as low organic solvent content. Secondly, it was observed that some specific compounds tended to deviate from the model. Most of these compounds were ortho substituted compounds, such as 2-nitrophenol, 2-bromophenol and 2,4,6-tri-terthbutylphenole. The significant difference in ortho-and para-substituted compounds, even if solution phase physicochemical parameters are very similar, has been previously observed for both ESI positive15 and negative mode12. However there were also ortho-substituted compounds that did not deviate significantely from the model. Such deviation could refer, that there are also secondary effects, besids partioning between droplets interiour and exteriour, that influence the ionization efficiency. These specific could engage stereoselective complexation, gas phase reactions, etc. To further improve the general understanding, models for each mobile phase mixture were separately generated utilizing parameters statistically significant in that particular mobile phase (the models are presented in SI in Table S2). Only parameters related to analyte



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

properties were used in the models, as within one mobile phase solvent parameters are constant. Therefore mobile phase influences the coefficients of molecular parameters and the intercept of the model. It was observed, that the physico-chemical parameters becoming statistically significant vary from mobile phase to mobile phase. For most mobile phases the residual standard deviation was not above 0.5 logIE units. In most mobile phases the intercept, WAPS and dissociation degree (α) were statistically significant. However, the coefficients for the dissociation degree (α) varied and the variations were statistically significant. Similar coefficients were observed for acidic solutions (E, F, G, I, J, L) and for basic solutions (A, B, C, D, K). The organic solvent content was observed to influence model coefficients significantly less. Therfore the variation in statistically significant parameters and optimal coeficients needs to be accounted for if best predictive power is desired.

Figure 1 The fit obtained for the logIE values over all tested mobile phases with equation 6. Streight line shows ideal fit. Predicting ionization efficiency for an unknown compound in previously uncharacterized mobile phase The aim of this paper is to introduce a possibility for the prediction of ionization efficiency of a compound in a solution and to use this logIE value for the concentration calculations (equation 5). As the relative ionization efficiencies of compounds may vary from mobile phase to mobile phase and from instrument to instrument we propose using a set of calibration compounds to transfer ionization efficiency prediction models between mobile phases and instruments. This means analyzing a smaller set of calibrants and samples together using the same instrument and same mobile phase in the same batch. The relative logIE values measured in such a way can be used for fitting a model between the logIE values observed for calibrants and molecular properties. As a result of this fitting coefficient b in equation 4 can be obtained. These coefficients will be directly descriptive to the system used to analyze the samples. We propose a set of six calibrants with a wide range of logIE, pKa and charge delocalization parameter WAPS. Namely: benzoic acid, pentakis(trifluoromethyl)phenol, salicylic acid, sorbic acid, 4-phenylphenole and 3-[(trifluoromethyl)sulphonyl]benzoic acid. To demonstrate the transferability between mobile phases and instruments we validate this approach for the analyses of 17 compounds in two mobile phases on a different MS


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instrument (see Experimental section for more details). One of the mobile phase was 65/35 mixture of 5 mM pH=6.2 acetate buffer and acetonitrile and the other was the 30/70 mixture of 5 mM pH=3.6 acetate buffer and acetonitrile. All numeric results are described in Table S3. For the first mobile phase the model predicting ionization efficiencies based on the calibrants was: (7) log IE = (3.63 ± 0.56 ) + (− 0.54 ± 0.0.10 ) ⋅ (WAPS ) The R2 was 0.87 and the residual standard deviation was 0.51. The root mean difference between the predicted and measured ionization logIE for the 17 validation set compounds was 0.40 logarithmic units (see Figure S10). The predicted logIE values were also used for predicting the concentration of the compounds (equation 5) and the average bias in concentration was 2.4-fold. For the second validation mobile phase a similar model was found: (8) log IE = (3.49 ± 0.93) + (− 0.55 ± 0.17 ) ⋅ (WAPS ) 2 The R was 0.71 and the residual standard deviation was 0.85 logarithmic units. The root mean difference between the predicted and measured ionization logIE for the validation set was 0.35 logarithmic units (see Figure S11). The predicted logIE values were also used for predicting the concentration of the compounds and the average bias in concentration was 3.9-fold. However most of the compounds were predicted with the accuracy of two-fold mismatch. The largest mismatch, 12.7-fold, was observed for ethyl cyano(2,3,5,6tetrafluorophenyl)acetate. It is worth mentioning that unlike the overall model (eq 6) the models obtained in validation measurements do not contain descriptors of degree of ionization, hydrogen bond accepting power, as well as the organic solvent content. The latter is excluded from the model as in flow injection analyses all compounds are analyzed in the same solvent. Ionization degree in solution and parameter describing hydrogen bond accepting power did not become statistically significant in the models for these mobile phases and were therefore excluded. Due to different parameters also significantly different coefficients b values are observed in model 6 compared to models 7 and 8. Application in chromatography While comparing the ionization efficiency scales obtained in different mobile phases it was observed that the organic solvent content is an important parameter affecting ionization efficiency (eq 6). As a result, we also validated our approach for LC/ESI/MS with gradient separation. The calibration mixture was the same as in case of flow injection analyses. The calibration mixture was analyzed on 5 concentrations to achieve the sensitivity (calibration graph slope) for each of the compounds. For the slopes logIE values for calibration compounds were calculated and followed by correlating the logIE values with molecular parameters. As input parameters for the model WAPS, log(WAPS), α, log(α), Hb_acc and retention time were tested. Retention time was used to describe the eluent organic solvent content during the elution of the compound. The best model with significant coefficients was achieved with WAPS: (9) log IE = (3.65 ± 0.34 ) + (− 0.52 ± 0.06 ) ⋅ WAPS It can be seen that it is very similar to the ones obtained for the flow injection analyses. The R2 for this model was 0.94 and the residual standard deviation was 0.32 logarithmic units. It is worth noticing that retention times did not become statistically significant in the model. This may result from the specific properties of the instrumentation. For validation the MS



instrument with thermal focusing ESI source was used. We have previously observed14 that in case of ionization source with thermal focusing the effect of organic modifier on ionization efficiencies is insignificant. Therefore, it is demonstrated that using a small set of calibration compounds allows to account even for significant changes in the design of ionization source and small changes in ionization mechanism. The root mean difference between the predicted and measured ionization logIE for the validation set was 0.51 logarithmic units (3.2 times). Therefore, it can be argued that the model for predicting ionization efficiencies based on a 6 compound calibration set gives reliable results not only for the flow injection analyses but also for the gradient based LC/MS analyses. The logIE values were also used for concentration prediction. The average prediction precision was 3.4-fold miss. And the maximum mismatch between predicted concentrations and actual concentration was 5.5 times. The correlation between predicted and actual concentrations is shown in Figure 2. Conventionally, if standard substances are not available, the decisions are based on the peak ratios. This approach was applied as a comparison. It was observed, that if concentrations are assigned based on the analyte peak – to – calibrant peak ratios, the bias between prediction and reality is 44 times. Meaning, that compounds concentrations are on an averaged miss interpreted by orders of magnitude. Therefore, ionization efficiency based concentration prediction improves the accuracy by at least an order of magnitude. 1.0E-02

1.0E-03

cpredicted (M)

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

1.0E-04

1.0E-05

1.0E-06 1.0E-06

1.0E-05

1.0E-04

1.0E-03

1.0E-02

cactual (M) Figure 2 Correlation between the predicted concentrations and measured concentrations for the 16 acids in chromatographic mode. One of the validation set compounds (vanillic acid) did not produce satisfactory chromatographic retention and therefore could not be analyzed. From Figure 2 it is observed, that more compounds are slightly underestimated then overestimated. The possible reasons were studied further. If several dilutions of the validation mixture were analyzed and the slope of response vs dilution factor was used to calculate the concentrations, the points were more uniformly distributed on the plot. Also the average mismatch improved to 2.7 and the maximum mismatch was 5.3. This may be caused by the fact that if only one dilution is used, the intercept of the calibration graph is not taken into account. Conclusions


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For the first time we present a possibility to obtain quantitative LC/ESI/MS results for analyses even when standard substances are not available. The average accuracy in determining concentration was below 4 times for both flow injection analyses as well as for chromatographic analyses. This prediction accuracy is not yet sufficient to conduct analyses that require high accuracy, such as monitoring of banned compounds or compounds of limited use. However, it can serve as a useful tool for preliminary quantitation in various fields and significant improvement compared to conventional peak area based method has been achieved. Supporting Information Available: Supporting information contains: all calculated physicochemical parameters (Table S1), all correlation graphs (Figure S1 to S9) and all validation data. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16)

(17) (18) (19) (20) (21)

Cech, N. B.; Enke, C. G. Mass Spectrom. Rev. 2001, 20 (6), 362–387. Ghosh, B.; Jones, A. D. The Analyst 2015, 140 (19), 6522–6531. Kebarle, P.; Tang, L. Anal. Chem. 1993, 65 (22), 972A–986A. Henriksen, T.; Juhler, R. K.; Svensmark, B.; Cech, N. B. J. Am. Soc. Mass Spectrom. 2005, 16 (4), 446–455. Golubović, J.; Birkemeyer, C.; Protić, A.; Otašević, B.; Zečević, M. J. Chromatogr. A 2016, 1438, 123–132. Huffman, B. A.; Poltash, M. L.; Hughey, C. A. Anal. Chem. 2012, 84 (22), 9942–9950. Amad, M. H.; Cech, N. B.; Jackson, G. S.; Enke, C. G. J. Mass Spectrom. 2000, 35 (7), 784–789. Alymatiri, C. M.; Kouskoura, M. G.; Markopoulou, C. K. Anal Methods 2015, 7 (24), 10433–10444. Mandra, V. J.; Kouskoura, M. G.; Markopoulou, C. K. Rapid Commun. Mass Spectrom. 2015, 29 (18), 1661–1675. Oss, M.; Kruve, A.; Herodes, K.; Leito, I. Anal. Chem. 2010, 82 (7), 2865–2872. Ehrmann, B. M.; Henriksen, T.; Cech, N. B. J. Am. Soc. Mass Spectrom. 2008, 19 (5), 719–728. Kruve, A.; Kaupmees, K.; Liigand, J.; Leito, I. Anal. Chem. 2014, 86 (10), 4822–4830. Liigand, J.; Kruve, A.; Leito, I.; Girod, M.; Antoine, R. J. Am. Soc. Mass Spectrom. 2014, 25 (11), 1853–1861. Kruve, A. J. Mass Spectrom. 2016, 51 (8), 596–601. Liigand, J.; Laaniste, A.; Kruve, A. J. Am. Soc. Mass Spectrom. 2016. Girod, M.; Dagany, X.; Boutou, V.; Broyer, M.; Antoine, R.; Dugourd, P.; Mordehai, A.; Love, C.; Werlich, M.; Fjeldsted, J.; Stafford, G. Phys. Chem. Chem. Phys. 2012, 14 (26), 9389. Longhi, G.; Ceselli, A.; Fornili, S. L.; Abbate, S.; Ceraulo, L.; Liveri, V. T. J. Mass Spectrom. 2013, 48 (4), 478–486. Konermann, L.; Ahadi, E.; Rodriguez, A. D.; Vahidi, S. Anal. Chem. 2013, 85 (1), 2–9. Daub, C. D.; Cann, N. M. J. Phys. Chem. A 2012, 116 (43), 10488–10495. Kostiainen, R.; Bruins, A. P. Rapid Commun. Mass Spectrom. 1996, 10 (11), 1393– 1399. Suu, A.; Jalukse, L.; Liigand, J.; Kruve, A.; Himmel, D.; Krossing, I.; Rosés, M.; Leito,



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I. Anal. Chem. 2015, 87 (5), 2623–2630. (22) Girod, M.; Dagany, X.; Antoine, R.; Dugourd, P. Int. J. Mass Spectrom. 2011, 308 (1), 41–48. (23) Girod, M.; Antoine, R.; Dugourd, P.; Love, C.; Mordehai, A.; Stafford, G. J. Am. Soc. Mass Spectrom. 2012, 23 (7), 1221–1231. (24) Chalcraft, K. R.; Lee, R.; Mills, C.; Britz-McKibbin, P. Anal. Chem. 2009, 81 (7), 2506–2515. (25) Nguyen, T. B.; Nizkorodov, S. A.; Laskin, A.; Laskin, J. Anal Methods 2013, 5 (1), 72– 80. (26) Wu, L.; Wu, Y.; Shen, H.; Gong, P.; Cao, L.; Wang, G.; Hao, H. Anal. Chim. Acta 2013, 794, 67–75. (27) F. Eckert and A. Klamt COSMOtherm, Version C3.0, Release 14.01; COSMOlogic GmbH&CoKG, Leverkusen, 2013; Available at http://www.cosmologic.de/. (28) Kaupmees, K.; Kaljurand, I.; Leito, I. J. Phys. Chem. A 2010, 114 (43), 11788–11793. (29) TURBOMOLE V6.5 2013, a development of University of Karlsruhe and Forschungszentrum Karlsruhe GmbH, 1989–2007, TURBOMOLE GmbH, since 2007; available from http://www.turbomole.com (30) Zissimos, A. M.; Abraham, M. H.; Klamt, A.; Eckert, F.; Wood, J. J. Chem. Inf. Comput. Sci. 2002, 42 (6), 1320–1331. (31) Enke, C. G. Anal. Chem. 1997, 69 (23), 4885–4893.


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