FULL SPECTRUM MODE

FULL SPECTRUM MODE

Level 5: Exact mass of interest133.0640 MS

Level 4: Unequivocal molecular formula

Level 5: Exact mass of interest

C7H7N3

133.0640

MS isotope/adduct

MS

Level 3: Tentative candidate(s)structure, substituent, class

Level 4: Unequivocal molecular formula

Level 5: Exact mass of interest

C7H7N3

133.0640

MS, MS2, Exp. data

MS isotope/adduct

MS

AC50 (µM) NA 0.27 NA 12.82 NA

0.E+00

5.E+06

1.E+07

2.E+07

2.E+07

3.E+07

3.E+07

4.E+07

0.E+00 2.E-05 4.E-05 6.E-05 8.E-05

Sig

nal

c (M)

0.E+00

2.E+07

4.E+07

6.E+07

8.E+07

1.E+08

1.E+08

1.E+08

2.E+08

2.E+08

2.E+08

0.E+00 5.E-06 1.E-05 2.E-05 2.E-05 3.E-05

Sig

nal

c (M) 𝑠𝑙𝑜𝑝𝑒1𝑠𝑙𝑜𝑝𝑒2

→𝐼𝐸

Ionization Efficiency

M. Oss et al. Anal. Chem. 2010, 82, 2865.A. Kruve et al. Anal. Chem. 2014, 86, 4822.

3.40

2.03

0.65

logP

Ionization Efficiency

M. Oss et al. Anal. Chem. 2010, 82, 2865.A. Kruve et al. Anal. Chem. 2014, 86, 4822.

13.32

9.93

6.79

pKaMeCN

Ionization Efficiency

M. Oss et al. Anal. Chem. 2010, 82, 2865.A. Kruve et al. Anal. Chem. 2014, 86, 4822.

Change in ionizationefficiency~5x

1350

260

10

100

1,000

10,000

100,000

0 20 40 60 80 100

Ioni

zatio

n E

ffic

ienc

y

%MeCN

J. Liigand et al. JASMS 2014, 25, 1853.

1350

260

10

100

1,000

10,000

100,000

0 20 40 60 80 100

Ioni

zatio

n E

ffic

ienc

y

%MeCN

Change in ionizationefficiency~5x

1350

0.6

0

1

10

100

1,000

10,000

100,000

1,000,000

0 2 4 6 8 10 12Io

niza

tion

Eff

icie

ncy

pH

Analytes’ pKa > 10 orpKa < 2

Analytes’ 2 < pKa < 10

J. Liigand et al. JASMS 2014, 25, 1853.

1

10

100

1,000

10,000

100,000

1,000,000

Ioni

zatio

nE

ffic

ienc

y

AgilentESI

ThermoESI

ThermoHESI

SciexHESI

WatersHESI

J. Liigand et al. JASMS 2015, 26, 1923.

Me

asu

red

IE(S

cie

x)

Measured IE (Thermo)1

10

100

1,000

10,000

100,000

1,000,000

Ioni

zatio

nE

ffic

ienc

y

AgilentESI

ThermoESI

ThermoHESI

SciexHESI

WatersHESI

R2 = 0.83

J. Liigand et al. JASMS 2015, 26, 1923.

dataset

structure solventIE

testing algorithms

random forest regression

predicted IE

J. Liigand et al. Sci. Rep. 2020, 10, 5808.

5663 data points

1410 small molecules

106 eluent compositions

MeCN/MeOH

0 – 100%

pH = 2.0 – 10.7

Prediction error

Training set 1.9x

Test set 3.0x

0

1

100

10,000

1,000,000

0 1 100 10,000 1,000,000

IEp

red

icte

d

IEmeasured

TrainingTest

J. Liigand et al. Sci. Rep. 2020, 10, 5808.

app.quantem.co

AC50 (µM) NA 0.27 NA 12.82 NA

AC50 (µM) NA 0.27 NA 12.82 NA

c (nM) 2.6 2.4 1.7 0.7 3.6

MS, MS2, RT, Reference Std.Level 1: Confirmed structure

by reference standard

Level 2: Probable structurea) by library spectrum matchb) by diagnostic evidence

Level 3: Tentative candidate(s)structure, substituent, class

Level 4: Unequivocal molecular formula

Level 5: Exact mass of interest

C7H7N3

133.0640

MS, MS2, Library MS2

MS, MS2, Exp. data

MS, MS2, Exp. data

MS isotope/adduct

MS

AC50 (µM) NA 0.27 NA 12.82 NA

cpredicted(nM)

2.6 2.4 1.7 0.7 3.6

cmeasured(nM)

1.4

28 contaminants from 28 surface water samples fromthe Netherlands

Quantified with targetedmethod

Mean prediction error 1.8x

c pre

dic

ted

(M)

cmeasured (M)

10-8

10-9

10-10

10-11

10-810-910-1010-11

ion mobility resolution 400

mass resolution 100,000

m/z 20 to 64,000 Da

very high IMS resolution

IMS/DIA experimentsdeconvolution of MS/MS spectra

CID/IMS experiments