Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-20T01:00:23.188Z Has data issue: false hasContentIssue false
  • English
  • Français

Using synergy of experimental and computational techniques to solve monomer–trimer dilemma

Published online by Cambridge University Press:  30 December 2014

Dubravka Šišak Jung Open the ORCID record for Dubravka Šišak Jung [Opens in a new window] ,
Tomica Hrenar ,
Ozren Jović ,
Petra Kalinovičić  and
Ines Primožič
Dubravka Šišak Jung
Affiliation:
DECTRIS Ltd., Neuenhoferstrasse 107, 5400 Baden, Switzerland
Tomica Hrenar*
Affiliation:
Department of Chemistry, University of Zagreb, Zagreb, Croatia
Ozren Jović
Affiliation:
Department of Chemistry, University of Zagreb, Zagreb, Croatia
Petra Kalinovičić
Affiliation:
Department of Chemistry, University of Zagreb, Zagreb, Croatia
Ines Primožič
Affiliation:
Department of Chemistry, University of Zagreb, Zagreb, Croatia
*
a) Author to whom correspondence should be addressed. Electronic mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

An example of commercially available product, 2-(methylideneamino)acetonitrile (MAAN). This paper will address problems in discerning monomer–polymer ambiguity in organic compounds. Reliable three-step analysis of organic polymers will be proposed using the synergy of computational [density functional theory (DFT)] and experimental [infrared spectroscopy (IR); X-ray powder diffraction (XRPD)] techniques. First, possible conformations of monomeric and trimeric MAAN were calculated using stochastic search and DFT. Second, identification of the commercial sample was performed by comparing the measured IR spectrum with those calculated for monomer and trimer. Third, the examination of sample purity and structural analysis were carried out using XRPD data.

Keywords

2-(methylideneamino)acetonitrileX-ray powder diffractiondensity functional theory
Type
Technical Articles
Information
Powder Diffraction , Volume 30 , Supplement S1 , June 2015 , pp. S36 - S40
Check for updates
Copyright
Copyright © International Centre for Diffraction Data 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baerlocher, C. and Hepp, A. (1982). XRS-82 (ETH, Zürich, Switzerland).Google Scholar
Baerlocher, Ch., McCusker, L. B., and Palatinus, L. (2007). “Charge flipping combined with histogram matching to solve complex crystal structures from powder diffraction data,” Z. Kristallogr. 222, 47–53.CrossRefGoogle Scholar
Bushmarinov, I. S., Golovanov, D. G., and Lyssenko, K. A. (2013). “Stereoelectronic interactions in fragment N-C-CN from high resolution X-ray diffraction data and quantum chemical computations,” Russ. Chem. Bull. Int. Ed. 62(8), 1720–1725. (Izvestiya Akademii Nauk. Seriya Khimicheskaya, 8, 1720–25, 2013).CrossRefGoogle Scholar
Černý, R. and Favre-Nicolin, V. (2004). “A better FOX: using flexible modeling and maximum likelihood to improve direct-space ab initio structure determination from powder diffraction,” J. Appl. Crystallogr. 219, 847–856.Google Scholar
Favre-Nicolin, V. and Černý, R. (2002). “FOX-free objects for crystallography: a modular approach to ab initio structure determination from powder diffraction data,” J. Appl. Crystallogr. 35(6), 734–743.CrossRefGoogle Scholar
Frisch, M. J., Trucks, G. W., Schlegel, H. B., Scuseria, G. E., Robb, M. A., Cheeseman, J. R., Scalmani, G., Barone, V., Mennucci, B., Petersson, G. A., Nakatsuji, H., Caricato, M., Li, X., Hratchian, H. P., Izmaylov, A. F., Bloino, J., Zheng, G., Sonnenberg, J. L., Hada, M., Ehara, M., Toyota, K., Fukuda, R., Hasegawa, J., Ishida, M., Nakajima, T., Honda, Y., Kitao, O., Nakai, H., Vreven, T., Montgomery, J. A., Peralta, J. E., Ogliaro, F., Bearpark, M., Heyd, J. J., Brothers, E., Kudin, K. N., Staroverov, V. N., Keith, T., Kobayashi, R., Normand, J., Raghavachari, K., Rendell, A., Burant, J. C., Iyengar, S. S., Tomasi, J., Cossi, M., Rega, N., Millam, J. M., Klene, M., Knox, J. E., Cross, J. B., Bakken, V., Adamo, C., Jaramillo, J., Gomperts, R., Stratmann, R. E., Yazyev, O., Austin, A. J., Cammi, R., Pomelli, C., Ochterski, J. W., Martin, R. L., Morokuma, K., Zakrzewski, V. G., Voth, G. A., Salvador, P., Dannenberg, J. J., Dapprich, S., Daniels, A. D., Farkas, O., Foresman, J. B., Ortiz, J. V., Cioslowski, J., and Fox, D. J. (2013). Gaussian, Inc., Wallingford CT, Gaussian 09, Revision D.01.Google Scholar
Hrenar, T. (2014a). qcc, Quantum Chemistry Code, rev. 0.68.Google Scholar
Hrenar, T. (2014b). moonee, Code for Manipulation and Analysis of Multi- and Univariate Data, rev. 0.6826.Google Scholar
Kalinovčić, P. (2012). “Vibrational analysis of 2-(methylideneamino)acetonitrile,” Diploma thesis, University of Zagreb, Croatia Google Scholar
Kawashiro, K., Nishiguchi, K., and Nara, T. (1989). “On the reaction of methyleneaminoacetonitrile in aqueous-media,” Orig. Life Evol. Biosph. 19, 133–142.CrossRefGoogle Scholar
Macrae, C. F., Edgington, P. R., McCabe, P., Pidcock, E., Shields, G. P., Taylor, R., Towler, M., and van de Streek, J. (2006). “Mercury,” J. Appl. Crystallogr. 39, 453–459.CrossRefGoogle Scholar
Primožič, I., Hrenar, T., Baumann, K., Krišto, L., Križić, I., and Tomić, S. (2014). “Mechanochemical and Conformational Study of N-heterocyclic Carbonyl-Oxime Transformations,” Croat. Chem. Acta 87, 155–162.CrossRefGoogle Scholar
Subbaraman, A. S., Kazi, Z. A., Choughuley, A. S. U., and Chadha, M. S. (1975). “Methyleneaminoacetonitrile - possible role in chemical evolution-ii,” Orig. Life 6, 537–539.CrossRefGoogle ScholarPubMed
Toby, B. H. (2005). “CMPR–powder diffraction toolkit,” J. Appl. Crystallogr. 38, 1040–1041.CrossRefGoogle Scholar
Xiang, Y. -B., Drenkard, S., Baumann, K., Hickey, D., and Eschenmoser, A. (1994). “Chemie von α-Aminonirilen. 12. Mitteilung. Sondierungen über termische Umwandlungen von α-Aminonirilen,” Helv. Chim. Acta 77, 2209–2250.CrossRefGoogle Scholar