Biography:Roman Balabin

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Roman M. Balabin
Роман Михайлович Балабин
NationalityRussia
Alma materGubkin University
Known forAnalytical chemistry
Vibrational spectroscopy
Scientific career
FieldsChemist
InstitutionsGubkin University
ETH Zurich
Doctoral advisorR. Z. Safieva
R. Zenobi

Roman M. Balabin (Russian: Роман Михайлович Балабин) is an analytical chemist. He received Ph.D. in petroleum chemistry from the Gubkin University (Moscow, 2013); his research interests include physical chemistry and applied spectroscopy.

Academic activity

Fuel analysis

Main page: Physics:Near-infrared spectroscopy

Roman Balabin and his collaborators have published a number of papers on comparing statistical methods based on near-infrared spectroscopy (NIRS), that can provide valuable functional group information about the sample,[1] for quality analysis of fuels and petroleum products.[2][3] In 2007—2008 Roman Balabin, Ravilya Safieva and Ekaterina Lomakina published two papers in Chemometrics and Intelligent Laboratory Systems where they compared modified versions of partial least squares regression (PLS) method with artificial neural networks (ANNs) for prediction of density, benzene content and ethanol content in gasoline.[4][5][6][7][8] In 2007—2011 this study was continued by a cycle of articles in Fuel and Energy & Fuels which showed that ANN/SVM[9][10] approach was superior to the linear and "quasi-nonlinear" calibration methods.[11][12][13][14][15][16][17] Two papers[18][19] in Analyst compared SVM regression with ANNs using NIRS data obtained from fourteen sets of petroleum products and benchmarked SVM for extrapolation problem (to predict the properties of samples outside the range used for the model calibration[20]):[21][22][23][24][25][26] it could be concluded that SVM-based data models have high precision and robustness[27] in small and noisy data sets ("in handling real-world, noisy, and variable spectra"[28]).[29][30] Two other papers published in Analytica Chimica Acta in 2011 were devoted to variable selection methods (including genetic algorithms[31])[32][33][34][35][36][37] and to benchmarking[38] of biodiesel classification models[15][39] that can be used for forensic identification purposes.[40]

Melamine detection

Main page: Chemistry:2008 Chinese milk scandal

In July 2011 Roman Balabin and Sergey Smirnov published in Talanta a paper "Melamine detection by mid- and near-infrared (MIR/NIR) spectroscopy" in which they proposed to use fourier transform[41] infrared spectroscopy to determine melamine in complex dairy products:[42] including liquid milk, infant formula, and milk powder. The authors observed no linear relationship between the vibrational spectrum of the milk sample and its melamine content, so they applied non-linear multivariate regression — such as partial least squares regression (PLS), polynomial PLS (Poly-PLS), artificial neural network (ANN), support vector regression (SVR), and least squares support vector machine (LS-SVM). An average of 600 samples for each food was used for the algorithm optimization and training: the "systematic study"[43] found that, applying the right data pre-treatment and the correct multivariate techniques, a limit of detection (LOD) below 1 ppm (0.76 ± 0.11 ppm[44]) could be reached. Furthermore, Balabin and Smirnov showed that Poly-PLS is able to predict only low melamine concentrations (<15 ppm).[45] So, the determination of melamine adulteration in infant formula and dairy milk ("safety assessment of dairy products"[46]) is possible with infrared-based analytical techniques:[47] "the application of NIR spectroscopy and multivariate modeling have proved to be very successful",[48] that was considered by professor Xiaonan Lu as a "significant achievement",[43] since the total time for melamine detection using spectroscopy methods were less than for almost all other previous methods.[42]

Quantum machine learning

Main page: Quantum machine learning

In August 2009 The Journal of Chemical Physics published online a paper "Neural network approach to quantum-chemistry data" authored by Roman Balabin and Ekaterina Lomakina; there they exploited the idea of a large[49] ANN-based quantum chemical database — 208 organic molecules containing only carbon, hydrogen, fluorine, oxygen and nitrogen — and different sets of molecular descriptors that could predict the density functional theory (DFT) energies without having to undertake a detailed DFT calculation on the system of interest,[50][51] since machine learning provides a means to convert the large volume of diverse, complex data into actionable knowledge.[52][53] In particular they applied neural networks to predict energies of the molecules ("QSPRs for basis-set effects"[54]);[55] this became a part of the organic chemistry community approach not only for enhancing the accuracy of hard modeling (e.g. ab initio calculations[56]) but also for making fast and accurate property predictions:[57][58] a possible scenario in which an algorithm decides or suggests internal parameters (or type) of density functional to be used for a given calculation.[59] Balabin and Lomakina continued their collaboration by publishing in Physical Chemistry Chemical Physics[57][54] a paper "Support vector machine regression (LS-SVM) — an alternative..." (June 2011) where SVMs were compared with ANNs for the basis-set effects estimation.[60][61]

Amino acids

Main page: Biology:Protein structure

A cycle of works[62][63] on the structures of the simplest amino acids (glycine and alanine) was started by Balabin in September 2009 with publication of a theoretical paper "Conformational equilibrium in glycine" in Chemical Physics Letters: ab initio computations based on focal-point analysis (FPA) scheme were performed on glycine (Gly) conformers.[64][65] A year later an experimental[66] jet-cooled glycine Raman spectrum — that showed six molecular vibrations in a region between 160 cm−1 and 450 cm−1 — was published in Journal of Physical Chemistry Letters: all the peaks could be "matched up with vibrations from the three lowest energy conformations by comparison to the computed frequencies".[67][68] Non-equilibrium conditions of jet-cooled molecular beam allowed to observe one "elusive" — previously experimentally unknown — conformation of Gly:[69] a conformer that is formed as a result of a complex interplay between intramolecular hydrogen bond and steric factors.[70][63][71] Equilibrium gas-phase Raman study, published in January 2012 in Physical Chemistry Chemical Physics — allowed an estimation of the relative enthalpies of three glycine rotamers by decomposition of a broad, unresolved spectral band:[72] however, the thermodynamic characterization was based on van’t Hoff equation, whose absolute accuracy might be questionable.[73][74]

In 2010, in addition to a theoretical study,[75] Balabin recorded the jet-cooled Raman spectrum of alanine: he reported observation of four conformers of this amino acid, including two new ones — that had not been reported in previous studies[76][77] — but the unambiguous identification of this pair was still questionable.[78] As a part of the cycle he also examined, in a search of gaseous zwitterion, the glycine-one water complex using vibrational spectroscopy: in addition to the most stable conformation, he was able to detect a small amount of two others by recording а low-frequency Raman spectrum (below 500 cm−1).[79][80] Professor Steven Bachrach thought that "an interesting side note [of the study was] that anharmonic corrections were necessary in order to match up the computed... frequencies with the experimental values".[81]

Zenobi group

As a part of Zenobi group at ETH Zurich[82][83][84] Roman Balabin was a co-author of a number of papers on theory and practice of mass spectrometry (MS). In 2010 a paper of Liang Zhu and HuanWen Chen applied EESI method to classify beer samples according to their type by principal component analysis (PCA);[85][86][87] Wai Siang Law "successfully" used the same combination of methods to study olive oils.[88][89] In 2011 Konstantin Barylyuk published a series of "careful"[90] MS experiments, complemented by DFT calculations, on synthetic supramolecular complexes, which interact with b-cyclodextrins solely through hydrophobic forces: "the study provided unambiguous evidence that hydrophobic interactions can be preserved in the gas phase"[91] and suggested that other macromolecular associations held together exclusively by hydrophobic interactions may survive without solvent[92][93][94][95][96][97] — at least on the millisecond timescales.[98][99] Andrea Amantonico and Pawel Urban[100][101][102] studied the profile of selected ("only a few"[103]) metabolites containing phosphate groups in single cells of "simple algae"[104] (Closterium)[105] using negative-mode MALDI-MS:[106][107][108][109][110] when combined with SVM method, this "proof-of-principle"[111] experiment made it possible to observe single cells[112][113] in distinct metabolic levels and classify individuals within cell populations;[114] the study itself contributed to the growing body of research suggesting that cell populations — previously assumed to be largely homogeneous — are in fact made up of subpopulations.[115][116][117][118]

List of works

Ph.D. thesis

  • Балабин, Роман Михайлович. Development of express methods based on vibrational spectroscopy for analysis of petroleum products and petrochemicals = Создание экспресс-методов анализа продуктов нефтепереработки и нефтехимии на основе колебательной спектроскопии : диссертация ... кандидата технических наук : 02.00.13 / Балабин Роман Михайлович; [Место защиты: Рос. гос. ун-т нефти и газа им. И.М. Губкина]. — Москва, 2013. — 116 с.: ил.

Selected publications

Selection of Roman Balabin's (h-index = 33) publications is based on 20+ citations before October 2018:[119]

List of selected publications
  • Balabin R. M., Smirnov S. V. Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data // Analytica Chimica Acta. — 2011. — Vol. 692, iss. 1-2. — P. 63–72. — ISSN 0003-2670. — DOI:10.1016/j.aca.2011.03.006.
  • Balabin R. M., Safieva R. Z., Lomakina E. I. Gasoline classification using near infrared (NIR) spectroscopy data: Comparison of multivariate techniques // Analytica Chimica Acta. — 2010. — Vol. 671, iss. 1-2. — P. 27–35. — ISSN 0003-2670. — DOI:10.1016/j.aca.2010.05.013.
  • Balabin R. M., Safieva R. Z., Lomakina E. I. Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction // Chemometrics and Intelligent Laboratory Systems. — 2007. — Vol. 88, iss. 2. — P. 183–188. — ISSN 0169-7439. — DOI:10.1016/j.chemolab.2007.04.006.
  • Balabin R. M., Lomakina E. I. Support vector machine regression (SVR/LS-SVM) — an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data // The Analyst. — 2011. — Vol. 136, iss. 8. — P. 1703. — ISSN 0003-2654. — DOI:10.1039/c0an00387e.
  • Balabin R. M., Smirnov S. V. Melamine detection by mid- and near-infrared (MIR/NIR) spectroscopy: A quick and sensitive method for dairy products analysis including liquid milk, infant formula, and milk powder // Talanta. — 2011. — Vol. 85, iss. 1. — P. 562–568. — ISSN 0039-9140. — DOI:10.1016/j.talanta.2011.04.026.
  • Balabin R. M., Lomakina E. I., Safieva R. Z. Neural network (ANN) approach to biodiesel analysis: Analysis of biodiesel density, kinematic viscosity, methanol and water contents using near infrared (NIR) spectroscopy // Fuel. — 2011. — Vol. 90, iss. 5. — P. 2007–2015. — ISSN 0016-2361. — DOI:10.1016/j.fuel.2010.11.038.
  • Balabin R. M., Safieva R. Z. Gasoline classification by source and type based on near infrared (NIR) spectroscopy data // Fuel. — 2008. — Vol. 87, iss. 7. — P. 1096–1101. — ISSN 0016-2361. — DOI:10.1016/j.fuel.2007.07.018.
  • Balabin R. M., Safieva R. Z., Lomakina E. I. Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra // Chemometrics and Intelligent Laboratory Systems. — 2008. — Vol. 93, iss. 1. — P. 58–62. — ISSN 0169-7439. — DOI:10.1016/j.chemolab.2008.04.003.
  • Balabin R. M. Enthalpy difference between conformations of normal alkanes: Intramolecular basis set superposition error (BSSE) in the case of n-butane and n-hexane // The Journal of Chemical Physics. — 2008. — Vol. 129, iss. 16. — P. 164101. — ISSN 0021-9606. — DOI:10.1063/1.2997349.
  • Balabin R. M., Safieva R. Z. Biodiesel classification by base stock type (vegetable oil) using near infrared spectroscopy data // Analytica Chimica Acta. — 2011. — Vol. 689, iss. 2. — P. 190–197. — ISSN 0003-2670. — DOI:10.1016/j.aca.2011.01.041.
  • Syunyaev R. Z., Balabin R. M., Akhatov I. S., Safieva J. O. Adsorption of Petroleum Asphaltenes onto Reservoir Rock Sands Studied by Near-Infrared (NIR) Spectroscopy // Energy & Fuels. — 2009. — Vol. 23, iss. 3. — P. 1230–1236. — ISSN 0887-0624. — DOI:10.1021/ef8006068.
  • Balabin R. M., Lomakina E. I. Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies // The Journal of Chemical Physics. — 2009. — Vol. 131, iss. 7. — P. 074104. — ISSN 0021-9606. — DOI:10.1063/1.3206326.
  • Balabin R. M., Safieva R. Z. Motor oil classification by base stock and viscosity based on near infrared (NIR) spectroscopy data // Fuel. — 2008. — Vol. 87, iss. 12. — P. 2745–2752. — ISSN 0016-2361. — DOI:10.1016/j.fuel.2008.02.014.
  • Balabin R. M., Safieva R. Z., Lomakina E. I. Near-infrared (NIR) spectroscopy for motor oil classification: From discriminant analysis to support vector machines // Microchemical Journal. — 2011. — Vol. 98, iss. 1. — P. 121–128. — ISSN 0026-265X. — DOI:10.1016/j.microc.2010.12.007.
  • Balabin R. M., Lomakina E. I. Support vector machine regression (LS-SVM)—an alternative to artificial neural networks (ANNs) for the analysis of quantum chemistry data? // Physical Chemistry Chemical Physics. — 2011. — Vol. 13, iss. 24. — P. 11710. — ISSN 1463-9076. — DOI:10.1039/c1cp00051a.
  • Balabin R. M. Enthalpy Difference between Conformations of Normal Alkanes: Raman Spectroscopy Study of n-Pentane and n-Butane // The Journal of Physical Chemistry A. — 2009. — Vol. 113, iss. 6. — P. 1012–1019. — ISSN 1089-5639. — DOI:10.1021/jp809639s.
    • Balabin R. M. Reply to “Comment on ‘Enthalpy Difference between Conformations of Normal Alkanes: Raman Spectroscopy Study of n-Pentane and n-Butane’” // The Journal of Physical Chemistry A : Note. — 2010. — Vol. 114, iss. 24. — P. 6729–6730. — ISSN 1089-5639. — DOI:10.1021/jp103852d.
  • Balabin R. M., Syunyaev R. Z. Petroleum resins adsorption onto quartz sand: Near infrared (NIR) spectroscopy study // Journal of Colloid and Interface Science. — 2008. — Vol. 318, iss. 2. — P. 167–174. — ISSN 0021-9797. — DOI:10.1016/j.jcis.2007.10.045.
  • Balabin R. M., Syunyaev R. Z., Karpov S. A. Quantitative Measurement of Ethanol Distribution over Fractions of Ethanol−Gasoline Fuel // Energy & Fuels. — 2007. — Vol. 21, iss. 4. — P. 2460–2465. — ISSN 0887-0624. — DOI:10.1021/ef070081l.
  • Balabin R. M. Conformational Equilibrium in Glycine: Experimental Jet-Cooled Raman Spectrum // The Journal of Physical Chemistry Letters. — 2009. — Vol. 1, iss. 1. — P. 20–23. — ISSN 1948-7185. — DOI:10.1021/jz900068n.
  • Andrea Amantonico, Pawel L. Urban, Stephan R. Fagerer, Balabin R. M., Renato Zenobi. Single-Cell MALDI-MS as an Analytical Tool for Studying Intrapopulation Metabolic Heterogeneity of Unicellular Organisms // Analytical Chemistry. — 2010. — Vol. 82, iss. 17. — P. 7394–7400. — ISSN 1520-6882 0003-2700, 1520-6882. — DOI:10.1021/ac1015326.
  • Balabin R. M., Safieva R. Z. Capabilities of near Infrared Spectroscopy for the Determination of Petroleum Macromolecule Content in Aromatic Solutions // Journal of Near Infrared Spectroscopy. — 2007. — Vol. 15, iss. 6. — P. 343–349. — ISSN 0967-0335. — DOI:10.1255/jnirs.749.
  • Balabin R. M., Syunyaev R. Z., Karpov S. A. Molar enthalpy of vaporization of ethanol–gasoline mixtures and their colloid state // Fuel. — 2007. — Vol. 86, iss. 3. — P. 323–327. — ISSN 0016-2361. — DOI:10.1016/j.fuel.2006.08.008.
  • Balabin R. M. Tautomeric equilibrium and hydrogen shifts in tetrazole and triazoles: Focal-point analysis and ab initio limit // The Journal of Chemical Physics. — 2009. — Vol. 131, iss. 15. — P. 154307. — ISSN 0021-9606. — DOI:10.1063/1.3249968.
  • Balabin R. M. Conformational equilibrium in glycine: Focal-point analysis and ab initio limit // Chemical Physics Letters. — 2009. — Vol. 479, iss. 4-6. — P. 195–200. — ISSN 0009-2614. — DOI:10.1016/j.cplett.2009.08.038.
  • Balabin R. M. Polar (Acyclic) Isomer of Formic Acid Dimer: Gas-Phase Raman Spectroscopy Study and Thermodynamic Parameters // The Journal of Physical Chemistry A. — 2009. — Vol. 113, iss. 17. — P. 4910–4918. — ISSN 1089-5639. — DOI:10.1021/jp9002643.
  • Balabin R. M. The First Step in Glycine Solvation: The Glycine−Water Complex // The Journal of Physical Chemistry B. — 2010. — Vol. 114, iss. 46. — P. 15075–15078. — ISSN 1520-5207 1520-6106, 1520-5207. — DOI:10.1021/jp107539z.
  • Balabin R. M., Safieva R. Z. Near-Infrared (NIR) Spectroscopy for Biodiesel Analysis: Fractional Composition, Iodine Value, and Cold Filter Plugging Point from One Vibrational Spectrum // Energy & Fuels. — 2011. — Vol. 25, iss. 5. — P. 2373–2382. — ISSN 0887-0624. — DOI:10.1021/ef200356h.
  • Balabin R. M., Syunyaev R. Z., Thomas Schmid, Johannes Stadler, Lomakina E. I., Zenobi R. Asphaltene Adsorption onto an Iron Surface: Combined Near-Infrared (NIR), Raman, and AFM Study of the Kinetics, Thermodynamics, and Layer Structure // Energy & Fuels. — 2011. — Vol. 25, iss. 1. — P. 189–196. — ISSN 0887-0624. — DOI:10.1021/ef100779a.
  • Balabin R. M. The identification of the two missing conformers of gas-phase alanine: a jet-cooled Raman spectroscopy study // Physical Chemistry Chemical Physics. — 2010. — Vol. 12, iss. 23. — P. 5980. — ISSN 1463-9076. — DOI:10.1039/b924029b.
  • Balabin R. M. Intermolecular dispersion interactions of normal alkanes with rare gas atoms: van der Waals complexes of n-pentane with helium, neon, and argon // Chemical Physics. — 2008. — Vol. 352, iss. 1-3. — P. 267–275. — ISSN 0301-0104. — DOI:10.1016/j.chemphys.2008.06.015.
  • Barylyuk K. V., Chingin K., Balabin R. M., Zenobi R. Fragmentation of benzylpyridinium "thermometer" ions and its effect on the accuracy of internal energy calibration // Journal of the American Society for Mass Spectrometry. — 2010. — Vol. 21, iss. 1. — P. 172–177. — ISSN 1879-1123 1044-0305, 1879-1123. — DOI:10.1016/j.jasms.2009.09.023.
  • Law W. S., Chen H. W., Balabin R., Berchtold Ch., Meier L., Zenobi R. Rapid fingerprinting and classification of extra virgin olive oil by microjet sampling and extractive electrospray ionization mass spectrometry // The Analyst. — 2010. — Vol. 135, iss. 4. — P. 773. — ISSN 1364-5528 0003-2654, 1364-5528. — DOI:10.1039/b924156f.
  • Balabin R. M. Communications: Is quantum chemical treatment of biopolymers accurate? Intramolecular basis set superposition error (BSSE) // The Journal of Chemical Physics. — 2010. — Vol. 132, iss. 23. — P. 231101. — ISSN 0021-9606. — DOI:10.1063/1.3442466.
  • Barylyuk, K., Balabin, R.M., Grünstein, D., Kikkeri, R., Frankevich, V., Seeberger, P.H., Zenobi, R. What Happens to Hydrophobic Interactions during Transfer from the Solution to the Gas Phase? The Case of Electrospray-Based Soft Ionization Methods // Journal of the American Society for Mass Spectrometry. — 2011. — Vol. 22, iss. 7. — P. 1167–1177. — ISSN 1044-0305. — DOI:10.1007/s13361-011-0118-8.
  • Balabin R. M. Intramolecular basis set superposition error as a measure of basis set incompleteness: Can one reach the basis set limit without extrapolation? // The Journal of Chemical Physics : Communications. — 2010. — Vol. 132, iss. 21. — P. 211103. — ISSN 0021-9606. — DOI:10.1063/1.3430647.
  • Zhu L., Hu Z., Gamez G., Law W.S., Chen H., Yang S., Chingin K., Balabin R.M., Wang R., Zhang T., Zenobi R. Simultaneous sampling of volatile and non-volatile analytes in beer for fast fingerprinting by extractive electrospray ionization mass spectrometry // Analytical and Bioanalytical Chemistry. — 2010. — Vol. 398, iss. 1. — P. 405–413. — ISSN 1618-2642. — DOI:10.1007/s00216-010-3945-8.
  • Syunyaev R. Z., Balabin R. M. Frequency Dependence of Oil Conductivity at High Pressure // Journal of Dispersion Science and Technology. — 2007. — Vol. 28, iss. 3. — P. 419–424. — ISSN 1532-2351 0193-2691, 1532-2351. — DOI:10.1080/01932690601107773.
  • Chingin K., Balabin R. M., Frankevich V., Barylyuk K., Nieckarz R., Sagulenko P., Zenobi R. Absorption of the green fluorescent protein chromophore anion in the gas phase studied by a combination of FTICR mass spectrometry with laser-induced photodissociation spectroscopy // International Journal of Mass Spectrometry. — 2011. — Vol. 306, iss. 2-3. — P. 241–245. — ISSN 1387-3806. — DOI:10.1016/j.ijms.2011.01.014.
  • Chingin K., Frankevich V., Balabin R. M., Barylyuk K., Chen H., Wang R., Zenobi R. Direct Access to Isolated Biomolecules under Ambient Conditions // Angewandte Chemie International Edition. — 2010-01-26. — Vol. 49, iss. 13. — P. 2358–2361. — ISSN 1433-7851. — DOI:10.1002/anie.200906213.
  • Syunyaev R. Z., Balabin R. M. Polarization of Fluorescence of Asphaltene Containing Systems // Journal of Dispersion Science and Technology. — 2008. — Vol. 29, iss. 10. — P. 1505–1514. — ISSN 1532-2351 0193-2691, 1532-2351. — DOI:10.1080/01932690802316868.
  • Balabin R. M. Boryl Substitution of Acetaldehyde Makes It an Enol: Inconsistency between Gn/CBS and Ab Initio/DFT Data // The Journal of Physical Chemistry A. — 2010. — Vol. 114, iss. 10. — P. 3698–3702. — ISSN 1089-5639. — DOI:10.1021/jp911802v.
  • Balabin R. M. Enthalpy difference between conformations of normal alkanes: effects of basis set and chain length on intramolecular basis set superposition error // Molecular Physics. — 2011. — Vol. 109, iss. 6. — P. 943–953. — ISSN 1362-3028 0026-8976, 1362-3028. — DOI:10.1080/00268976.2011.558858.
  • Balabin R. M. Experimental thermodynamics of free glycine conformations: the first Raman experiment after twenty years of calculations // Phys. Chem. Chem. Phys. — 2012. — Vol. 14, iss. 1. — P. 99–103. — ISSN 1463-9076. — DOI:10.1039/c1cp20805e.
  • Balabin R. M., Smirnov S. V. Interpolation and extrapolation problems of multivariate regression in analytical chemistry: benchmarking the robustness on near-infrared (NIR) spectroscopy data // The Analyst. — 2012. — Vol. 137, iss. 7. — P. 1604. — ISSN 0003-2654. — DOI:10.1039/c2an15972d.
  • Balabin R. M. Conformational equilibrium in alanine: Focal-point analysis and ab initio limit // Computational and Theoretical Chemistry. — 2011. — Vol. 965, iss. 1. — P. 15–21. — ISSN 2210-271X. — DOI:10.1016/j.comptc.2011.01.008.
  • Chingin K., Balabin R. M., Frankevich V., Chen H., Barylyuk K., Nieckarz R., Fedorov A., Zenobi R. Optical properties of protonated Rhodamine 19 isomers in solution and in the gas phase // Physical Chemistry Chemical Physics. — 2010. — Vol. 12, iss. 42. — P. 14121. — ISSN 1463-9076. — DOI:10.1039/c0cp00482k.
  • Chingin K., Balabin R. M., Barylyuk K., Chen H., Frankevich V., Zenobi R. Rhodamines in the gas phase: cations, neutrals, anions, and adducts with 1metal cations // Physical Chemistry Chemical Physics. — 2010. — Vol. 12, iss. 37. — P. 11710. — ISSN 1463-9076. — DOI:10.1039/c000807a.

See also

  • Renato Zenobi
  • Paweł Urban
  • Konstantin Chingin

References

  1. ↑ Vempatapu, Kanauji, 2017, pp. 8–9, 11.
  2. ↑ Marques et al., 2014, pp. 100–103, 106–107
  3. ↑ Skvaril, Kyprianidis, Dahlquist, 2017, Characterization of biodiesel, pp. 683, 685, 709–716, 720–727
  4. ↑ Shi H., Yu P., 2018, pp. 407, 417
  5. ↑ Martins, Gonçalves, Peres, 2011, pp. 57–70
  6. ↑ Khanmohammadi et al., 2012, pp. 140, 149
  7. ↑ Shao X. et al., 2010, pp. 1663, 1665
  8. ↑ Gutiérrez, Muñoz, Del Valle, 2011, pp. 258–270
  9. ↑ Wakiru et al., 2019, pp. 117, 130
  10. ↑ Motai, 2015, pp. 9–10, 33
  11. ↑ Vershinin, 2011, pp. 1015, 1019.
  12. ↑ Curteanu, 2011, pp. 103–118
  13. ↑ Giwa, 2016, pp. 87, 103
  14. ↑ Luna, Lima, Alberton, 2016, pp. 37, 44
  15. ↑ 15.0 15.1 Jha S. Kr. et al., 2017, pp. 310, 316
  16. ↑ Chen Q. et al., 2017, pp. 108–112
  17. ↑ Butler et al., 2016, pp. 675–676, 686
  18. ↑ Harrington, 2017, pp. 2, 14
  19. ↑ Tange et al., «Benchmarking», 2017, pp. 382, 389.
  20. ↑ Baird, Oja, 2016, pp. 42–43, 47.
  21. ↑ Pasquini, 2018, pp. 18–19, 33
  22. ↑ Cheng Ch. et al., 2015, pp. 1060, 1067.
  23. ↑ Constantinescu et al., 2015, pp. 385, 391.
  24. ↑ Gromski et al., 2015, pp. 12, 21.
  25. ↑ Lavine, Workman, 2013, pp. 711, 714.
  26. ↑ Hoehse et al., 2012, pp. 1447–1448, 1450.
  27. ↑ Palou et al., 2017, pp. 120, 126.
  28. ↑ Dingari et al., 2012, pp. 2688, 2692, 2694.
  29. ↑ Khayyam, Golkarnarenji, Jazar, 2018, p. 375
  30. ↑ Kroll et al., 2017, pp. 2607–2608, 2613
  31. ↑ Byrne et al., 2016, pp. 1867–1868, 1878.
  32. ↑ Sousa, Lopes, 2013, pp. 392, 413.
  33. ↑ Hanif et al., 2018, pp. 2073, 2081
  34. ↑ Rammal et al., 2017, pp. 154, 160
  35. ↑ Cetó, Voelcker, Prieto-Simón, 2016, pp. 611, 626
  36. ↑ Liu D., Sun D.-W., Zeng X.-A., 2014, pp. 308, 320.
  37. ↑ Carreiro Soares et al., 2013, pp. 87, 98
  38. ↑ Gharagheizi et al., 2011, pp. 4994, 5021.
  39. ↑ Rocha et al., 2012, pp. 12–31
  40. ↑ Yang Z. et al., 2016, pp. 573, 633
  41. ↑ Craig, Franca, Irudayaraj, 2015, pp. 180, 186
  42. ↑ 42.0 42.1 Fu X., Ying Y., 2014, pp. 1918–1922
  43. ↑ 43.0 43.1 Lu X., 2014, pp. 177–178, 187
  44. ↑ Jha S. N. et al., 2015, pp. 1672–1682
  45. ↑ Ritota, Manzi, 2017, pp. 140–141
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