Efficient synthesis of isoquinolines in water by a Pd-catalyzed tandem reaction of functionalized alkylnitriles with arylboronic acids
1,3-Diphenylisoquinoline (3a). Pale-yellow solid (103.5 mg, 92%),
mp 78-79 oC (lit.24, 73-74.5 oC). 24 J. D. Tovar and T. M. Swager, J. Org. Chem., 1999, 64, 6499
1H NMR (500 MHz, CDCl3) δ 8.25-8.23 (m, 2H), 8.15-8.14 (m, 1H), 8.09 (s, 1H), 7.95-7.93 (m, 1H), 7.84-7.83 (m, 2H), 7.70-7.67 (m, 1H), 7.59-7.50 (m, 6H), 7.44-7.40 (m, 1H);
13C NMR (125 MHz, CDCl3) δ 160.5, 150.3, 140.1, 139.8, 138.0, 130.4, 130.2, 128.8, 128.7, 128.6, 128.4, 127.7, 127.6, 127.2, 127.0, 126.0, 115.8.
aDepartment of Chemistry, University of Victoria, P.O. Box 3065 STN CSC, Victoria, BC V8W 3V6, Canada.
bMolecular Structure Facility, Department of Chemistry and Biochemistry, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA.
Thiele’s acid has been resolved for the first time by diastereomeric salt formation with brucine. Determination of absolute stereochemistry was accomplished by X-ray crystallography of the corresponding diester. We anticipate that access to optically resolved Thiele’s acid will stimulate its use in a diverse range of applications requiring chiral molecular clefts.
Canadian Journal of Chemistry, 2017, 95(3): 234-238, 10.1139/cjc-2016-0125
(–)-Thiele’s ester 2a as a white solid (200 mg, 81%). Spectral data were consistent with the racemic compound that has been described previously in the literature.5 [α]D 25 = –216 deg mL dm-1 g-1 (c = 0.25, ethanol solution). MP = 87–89 °C.
CCDC 1469300 contains the supplementary crystallographic data for compound (–)-2a. These data are available from the Cambridge Crystallographic Data Centre.
DOI: 10.1039/C6GC03494B, http://pubs.rsc.org/en/Content/ArticleLanding/2017/GC/C6GC03494B?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+rss%2FGC+%28RSC+-+Green+Chem.+latest+articles%29#!divAbstract
///////tetrazines, gem-difluoroalkenes, aerobic conditions, room temperature
|1970-1974 High School, Shahpour high school, Kazerun, IR, Iran|
|1975-1979 B.S., Chemistry, Department of Chemistry, Isfahan University, Isfahan, I.R. Iran|
|1981-1983 M.S., Organic Chemistry, Synthesis, Shiraz University, Shiraz, I.R. Iran|
|Thesis Title: “Synthesis of 2,6,7,11-Tetraphenyl Isobenzofuran B Cyclobutadiene”|
|Advisor: Professor Habib Firouzabadi|
|1990-1994 Ph.D., Organic Chemistry, Wollongong University, Australia|
|Dissertation Title: “Asymmetric Synthesis of Chiral Amines and Benzazepine Alkaloids from Chiral Sulfoxides”|
|Advisor: Professor Stephen G. Pyne|
|09/94-11/98 Assistant Professor, Isfahan University of Technology|
|12/98-02/03 Associate Professor, Isfahan University of Technology|
|03/03-present Professor, Isfahan University of Technology|
|02/01-03/02 Visiting Scientist, University of Wisconsin Medical School, Madison, WI|
|04/02-09/02 Associate Researcher, University of Wisconsin Medical School, Madison, WI|
|10/02-1/05 Associate Scientist, University of Wisconsin Medical School, Madison, WI|
|1/04 to present Senior Scientist, University of Wisconsin Medical School, Madison, WI|
DOI: 10.1039/C6GC03324E, http://pubs.rsc.org/en/Content/ArticleLanding/2017/GC/C6GC03324E?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+rss%2FGC+%28RSC+-+Green+Chem.+latest+articles%29#!divAbstract
A methodology for chemical routes development and evaluation on the basis of data-mining is presented. A section of the Reaxys database was converted into a network, which was used to plan hypothetical synthesis routes to convert a bio-waste feedstock, limonene, to a bulk intermediate, benzoic acid. The route evaluation considered process conditions and used multiple indicators, including exergy, E-factor, solvent score, reaction reliability and route redox efficiency, in a multi-criteria environmental sustainability evaluation. The proposed methodology is the first route evaluation based on data mining, explicitly using reaction conditions, and is amenable to full automation.
In the field of process and synthetic chemistry ‘clean synthesis’ has become one of the standard criteria for good, commercially viable synthesis routes. As a result synthetic and process chemists must be equipped with adequate methodologies for quantification of ‘cleanness’ or ‘greenness’ of alternative routes at the early phases of the development cycle. These new criteria, and the traditional criteria of cost, security of supply, health and safety (H&S), and risk, provide a balanced picture of sustainability of a future technology. Thus, there are two separate aspects to process chemistry: developing the chemistry and the process, and evaluating the overall process, which must occur in parallel. Evaluation of the proposed routes requires data. As data science rapidly evolves, chemistry will inevitably use more of the new tools of data mining and data analysis to automate the routine tasks, such as evaluation of process metrics. In this paper we show some initial results in automation of process evaluation based on deep data mining of process chemistry and multi-criteria decision making.
The evaluation of greenness is a mature field, with a large number of published and standardised approaches, of which many are adopted by industry. 1 However, all published methods are highly case-specific and rather labour-intensive. In the field of synthetic routes development one of the most exciting new areas is the potential for automation of synthesis planning using data mining.2 What has never been attempted before is to automate route generation and evaluation in a coherent methodology, which would aid process development at the early, data-lean, stages. For this we show how to automatically generate process options using a network representation of a section of Reaxys database,3 followed by their screening using multi-criteria decision making, see Fig. 1. As the methods mature and become commercially available, such integration and automation will produce significant savings of time, and would deliver a far more detailed view of the competing synthesis route options than is generally possible at the early stages of design.
To date, obtaining the data, assembling the network and finding potential synthesis routes can already be carried out in a fully automated fashion. Due to issues around data availability the connection to the analysis of the routes still has to be initiated manually, involving a data curation step. The subsequent analysis and multi-criteria decision making have been largely automated in this study. To our knowledge this is the first example of the analysis of synthesis routes generated from the network representation of Reaxys obtained through datamining, using reaction conditions and process data.
|Fig. 2 A section of a network of organic chemistry. Dots are species and arrows represent reactions.|
Professor of Sustainable Reaction Engineering
Fellow of Wolfson College
Catalytic Reaction Engineering
Sustainable Chemical Technologies
Office Phone: 330141
MChem in Biochemistry, Novosibirsk State University, 1994
PhD in Chemical Engineering, University of Bath, 2000
Boreskov Institute of Catalysis, Novosibirsk, Russia (1994-1997)
University of Bath, Department of Chemical Engineering, Research Officer (1997-2000)
University of Bath, Department of Chemical Engineering, Lecturer-SL-Reader (2000-2009)
University of Warwick, School of Engineering, Professor of Engineering (2009-2013)
Our group is developing cleaner manufacturing processes within chemical and chemistry using industries. We are mainly focusing on liquid- and multi-phase catalytic and biochemical processes. Within the group we have pursued projects on developing functional materials for catalysts, adsorbents and reactors, design of multi-functional intensive reactors, modelling of reaction kinetics and integrated processes, linking reaction kinetics with computational fluid dynamics (CFD) and linking process modelling with life cycle assessment (LCA), integration of reactions and separation.
The group is currently involved in an EU project ‘RECOBA’ (http://www.spire2030.eu/recoba/), in which our group collaborates with Materials and Electronic Engineering at Cambridge to work on innovative measurement techniques for monitoring processes under reaction conditions.
We are involved in the EPSRC project on developing novel routes to platform and functional molecules from waste terpenes, led by University of Bath.
We are involved in “Dial a Molecule 2” network funded by EPSRC.
J. Zakrzhewski, A.P. Smalley, M. Kabeshov, A. Lapkin, M. Gaunt, Continuous flow synthesis and derivatization of aziridines via palladium-catalyzed C(sp3)-H activation, Angew. Chem. Int. Ed., 55 (2016) 8878-8883.
P. Yaseneva, P. Hodgson, J. Zakrzewski, S. Falss, R.E. Meadows, A.A. Lapkin, Continuous flow Buchwald-Hartwig amination of a pharmaceutical intermediate, React. Chem. Eng., 1 (2016) 229-238.
P. Yaseneva, D. Plaza, X. Fan, K. Loponov, A. Lapkin, Synthesis of the antimalarial API artemether in a flow reactor, Catal. Today, 239 (2015) 90-96.
N. Peremezhney, E. Hines, A. Lapkin, C. Connaughton, Combining Gaussian processes, mutual information and a generic algorithm for multi-targeted optimisation of expensive-to-evaluate functions, Engineering Optimisation, 46 (2014) 1593-1607.
P. Yaseneva, C.F. Marti, E. Palomares, X. Fan, T. Morgan,P.S. Perez, M. Ronning, F. Huang,T. Yuranova, L. Kiwi-Minsker, S. Derrouiche, A.A. Lapkin, Efficient reduction of bromates using carbon nanofibre supported catalysts: experimental and a comparative life cycle assessment study, Chem. Eng. J., 248 (2014) 230-241
K.N. Loponov, J. Lopes, M. Barlog, E.V. Astrova, A.V. Malkov, A.A. Lapkin, Optimization of a Scalable Photochemical Reactor for Reactions with Singlet Oxygen, Org.Process Res.Dev., 18 (2014) 1443-1454.
X. Fan, V. Sans, P. Yaseneva, D. Plaza, J.M.J. Williams, A.A. Lapkin, Facile Stoichiometric Reductions in Flow: an Example of Artemisinin, Org.Process Res.Dev., 16 (2012) 1039-1042.
M.V. Sotenko, M. Rebros, V.S. Sans, K.N. Loponov, M.G. Davidson, G. Stephens, A.A. Lapkin, Tandem transformation of glycerol to esters, J. Biotechnol., 162 (2012) 390-397.
A.A. Lapkin, A. Voutchkova, P. Anastas, A conceptual framework for description of complexity in intensive chemical processes, Chem. Eng. Processing. Process intensification, 50 (2011) 1027-1034.
Lapkin, A., Peters, M., Greiner, L., Chemat, S., Leonhard, K., Liauw, M. A. and Leitner, W., Screening of new solvents for artemisinin extraction process using ab-initio methodology, Green Chem., 12 (2010) 241-251.
Lapkin, A. A. and Plucinski, P. K., Engineering factors for efficient flow processes in chemical industries, in Chemical reactions and processes under flow conditions, pp. 1- 43, Eds: Luis, S. V. and Garcia-Verdugo, E., Royal Society of Chemistry, Cambridge, 2010.
Iwan, A., Stephenson, H., Ketchie, W. C. and Lapkin, A. A., High temperature sequestration of CO2 using lithium zirconates, Chem. Eng. J., 146 (2009) 249-258.
Constable, D. J. C., Jimenez-Gonzalez, C. and Lapkin A., ‘Process metrics’, in Green chemistry metrics: measuring and monitoring sustainable processes, pp. 228- 247, Eds.: Lapkin, A. and Constable, D. J. C., Wiley-Blackwell, Chichester, 2008.
L.Torrente-Murciano, A.Lapkin, D.V. Bavykin, F.C. Walsh, K. Wilson, Highly selective Pd/titanate nanotubes catalysts for the double bond migration reaction, J. Catal., 245 (2007) 270-276.
A. Lapkin, P. Plucinski, Comparative assessment of technologies for extraction of artemisinin, J. Natural Prod., 69 (2006) 1653-1664.
D.V. Bavykin, A.A. Lapkin, S.T. Kolaczkowski, P.K. Plucinski, Selective oxidation of alcohols in a continuous multifunctional reactor: ruthenium oxide catalysed oxidation of benzyl alcohol, Applied Catal. A: General, 288 (2005) 165-174.
////////automation, chemical process, route selection, data mining