scholarly journals Characterization of Electron Ionization Mass Spectral (EIMS) Fragmentation Patterns of Chloropropanol Esters of Palmitic Acid Using Isotope Labeling Technique

2014 ◽  
Vol 63 (10) ◽  
pp. 1045-1055
Author(s):  
Anja K. K. Rahn ◽  
Varoujan A. Yaylayan
Keyword(s):  
Palmitic Acid ◽  
Isotope Labeling ◽  
Electron Ionization ◽  
Ionization Mass ◽  
Mass Spectral ◽  
Fragmentation Patterns

scholarly journals Speciation of organometals using a synchronizing GC-EIMS and GC-ICPMS system for simultaneous detection

2014 ◽  
Vol 29 (6) ◽  
pp. 1132-1137 ◽  
Author(s):  
Lucia D'Ulivo ◽  
Lu Yang ◽  
Yong-Lai Feng ◽  
John Murimboh ◽  
Zoltán Mester
Keyword(s):  
Gas Chromatography ◽  
Mass Spectrometer ◽  
Inductively Coupled Plasma ◽  
Simultaneous Detection ◽  
Electron Ionization ◽  
Ionization Mass ◽  
Inductively Coupled

Accurate quantitation and characterization of organometals are successfully achieved by splitting the gas chromatography (GC) flow to both an electron ionization mass spectrometer (EIMS) and an inductively coupled plasma mass spectrometer (ICPMS).

Electron ionization mass spectrometry of aryl- and fluoroalkyl-substituted palladium(II) β-diketonates and monothio-β-diketonates

1994 ◽  
Vol 72 (5) ◽  
pp. 1302-1311 ◽  
Author(s):  
Mark L. J. Reimer ◽  
John B. Westmore ◽  
Manoranjan Das
Keyword(s):  
Mass Spectra ◽  
Electron Ionization ◽  
Electron Ionization Mass Spectrometry ◽  
Ionization Mass ◽  
Aryl Group ◽  
Mass Spectral ◽  
Transition Series ◽  
Electron Ionization Mass Spectra ◽  
Positive Ion ◽  
Sulfur Donor

Positive ion electron ionization mass spectra are presented for palladium(II) β-diketonates and monothio-β-diketonates of the general form PdII[RC(X)CHC(O)R′]2, where R = phenyl, 4-methoxyphenyl, 2-thienyl, or 2-naphthyl; R′ = trifluoromethyl, pentafluoroethyl, or n-heptafluoropropyl; and X = O or S. The mass spectral behavior is in sharp contrast to that of metals of the first transition series. The spectra of the β-diketonates are dominated by metal-containing ions that arise by migration of the R group from the ligand (L) to palladium, but there is no evidence for fluorine-to-metal transfer. These findings are consistent with HSAB theory. The strong tendency of palladium to form bonds with unsaturated carbon also leads to remarkably abundant metal-containing ions that arise by losses of CO or aryloxy radicals from [PdRL]+• ions. In contrast, in decompositions of ions in the spectra of the monothio-β-diketonates, migration of the R group is suppressed; competition for palladium dπ electrons by the sulfur donor makes palladium a poorer aryl group acceptor.

scholarly journals Predicting in silico electron ionization mass spectra using quantum chemistry

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Shunyang Wang ◽  
Tobias Kind ◽  
Dean J. Tantillo ◽  
Oliver Fiehn
Keyword(s):  
Quantum Chemistry ◽  
In Silico ◽  
Mass Spectra ◽  
Large Scale ◽  
Computation Time ◽  
Molecular Size ◽  
Electron Ionization ◽  
Ionization Mass ◽  
Compound Identification ◽  
Mass Spectral

Abstract Compound identification by mass spectrometry needs reference mass spectra. While there are over 102 million compounds in PubChem, less than 300,000 curated electron ionization (EI) mass spectra are available from NIST or MoNA mass spectral databases. Here, we test quantum chemistry methods (QCEIMS) to generate in silico EI mass spectra (MS) by combining molecular dynamics (MD) with statistical methods. To test the accuracy of predictions, in silico mass spectra of 451 small molecules were generated and compared to experimental spectra from the NIST 17 mass spectral library. The compounds covered 43 chemical classes, ranging up to 358 Da. Organic oxygen compounds had a lower matching accuracy, while computation time exponentially increased with molecular size. The parameter space was probed to increase prediction accuracy including initial temperatures, the number of MD trajectories and impact excess energy (IEE). Conformational flexibility was not correlated to the accuracy of predictions. Overall, QCEIMS can predict 70 eV electron ionization spectra of chemicals from first principles. Improved methods to calculate potential energy surfaces (PES) are still needed before QCEIMS mass spectra of novel molecules can be generated at large scale.

scholarly journals Mass Spectrometric Characterization of Epoxidized Vegetable Oils

Polymers ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 394
Author(s):  
Ákos Kuki ◽  
Tibor Nagy ◽  
Mahir Hashimov ◽  
Stella File ◽  
Miklós Nagy ◽  
...  
Keyword(s):  
Raw Materials ◽  
Ionization Mass ◽  
Mass Spectral Peak ◽  
Mass Spectral ◽  
Natural Systems ◽  
Data Mining Approach ◽  
Novel Approach ◽  
Fragmentation Mechanisms ◽  
Fatty Acid Compositions

Matrix-assisted laser desorption ionization and electrospray ionization mass spectrometry (MALDI-MS and ESI-MS) were used for the characterization of epoxidized soybean and linseed oils, which are important raw materials in the biopolymer production. The recently invented data mining approach, mass-remainder analysis (MARA), was implemented for the analysis of these types of complex natural systems. Different epoxidized triglyceride mass spectral peak series were identified, and the number of carbon atoms and epoxide groups was determined. The fragmentation mechanisms of the epoxidized triglyceride (ETG) adducts formed with different cations (such as H+, Na+, Li+, and NH4+) were explored. As a novel approach, the evaluation of the clear fragmentation pathways of the sodiated ETG adducts enabled the estimation of the epoxidized fatty acid compositions of these types of oils by MS/MS.

scholarly journals Predicting in-silico electron ionization mass spectra using quantum chemistry

2020 ◽  
Author(s):  
Shunyang Wang ◽  
Tobias Kind ◽  
Dean J. Tantillo ◽  
Oliver Fiehn
Keyword(s):  
Quantum Chemistry ◽  
In Silico ◽  
Mass Spectra ◽  
Large Scale ◽  
Computation Time ◽  
Molecular Size ◽  
Electron Ionization ◽  
Ionization Mass ◽  
Compound Identification ◽  
Mass Spectral

Abstract Compound identification by mass spectrometry needs reference mass spectra. While there are over 102 million compounds in PubChem, less than 300,000 curated electron ionization (EI) mass spectra are available from NIST or MoNA mass spectral databases. Here, we test quantum chemistry methods (QCEIMS) to generate in-silico EI mass spectra (MS) by combining molecular dynamics (MD) with statistical methods. To test the accuracy of predictions, in-silico mass spectra of 451 small molecules were generated and compared to experimental spectra from the NIST 17 mass spectral library. The compounds covered 43 chemical classes, ranging up to 358 Da. Organic oxygen compounds had a lower matching accuracy, while computation time exponentially increased with molecular size. The parameter space was probed to increase prediction accuracy including initial temperatures, the number of MD trajectories and impact excess energy (IEE). Conformational flexibility was not correlated to the accuracy of predictions. Overall, QCEIMS can predict 70 eV electron ionization spectra of chemicals from first principles. Improved methods to calculate potential energy surfaces (PES) are still needed before QCEIMS mass spectra of novel molecules can be generated at large scale.

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