Manganese-catalyzed dehydrogenative synthesis of urea derivatives and polyureas

A.K. thanks the Leverhulme Trust for an early career fellowship (ECF-2019-161). M.B. wishes to thank the School of Chemistry and EaStCHEM for their support. A.E.O. gratefully acknowledges a fellowship from the Akwa Ibom State University (TETFund).Urea derivatives have significant applications in the synthesis of resin precursors, dyes, agrochemicals, and pharmaceutical drugs. Furthermore, polyureas are useful plastics with applications in coating, adhesive, and biomedical industries. However, the conventional methods for the synthesis of urea derivatives and polyureas involve toxic reagents such as (di)isocyanates, phosgene, CO, and azides. We present here the synthesis of (poly)ureas using much less toxic reagents─(di)amines and methanol─via a catalytic dehydrogenative coupling process. The reaction is catalyzed by a pincer complex of an earth-abundant metal, manganese, and liberates H2 gas, valuable by itself, as the only byproduct, making the overall process highly atom-economic. A broad variety of symmetrical and unsymmetrical urea derivatives and polyureas have been synthesized in moderate to quantitative yields using this catalytic protocol. Mechanistic insights have also been provided using experiments and DFT computation, suggesting that the reaction proceeds via an isocyanate intermediate.Publisher PDFPeer reviewe

Structural, Magnetic, and Electrochemical Characterization of Iron(III) and Cobalt Complexes with Penta-N3O2-dentate Ligands

Six new mononuclear [FeIII(LBr,Cl)X]-complexes (LBr,Cl is the dianionic penta-N3O2-dentate Schiff base ligand N,N′-bis(2’-hydroxy-3-bromo-5-chlorobenzylidene)-1,6-diamino-3-azahexane; X: Cl−, N3−, NCO−, NCS−, NCSe−, CN−) were synthesized and their structures, magnetic and electrochemical properties studied. Structure analysis and magnetic measurements showed that [FeIII(LBr,Cl)CN] is in the low spin state and the other five complexes are in high spin states. Furthermore, the trinuclear mixed valent cobalt complex {[CoIII(LH,H)CN]2[CoII(1-methylimidazole)3(H2O)]} was prepared and its magnetic behavior studied. © 2021 The Authors. European Journal of Inorganic Chemistry published by Wiley-VCH Gmb

Predicting Ruthenium Catalysed Hydrogenation of Esters using Machine Learning

Catalytic hydrogenation of esters is a sustainable approach for the production of fine chemicals, and pharmaceutical drugs. However, the efficiency and cost of catalysts are often the bottlenecks in the commercialization of such technologies. The conventional approach of catalyst discovery is based on empiricism that makes the discovery process time-consuming and expensive. There is an urgent need to develop effective approaches to discover efficient catalysts for hydrogenation reactions. We demonstrate here the approach of machine learning for the prediction of out-comes for the catalytic hydrogenation of esters. Our models can predict the reaction yields with high mean accuracies of up to 91% (test set) and suggest that the use of certain chemical descriptors selectively can result in a more accurate model. Furthermore, cata-lysts and some of their corresponding descriptors can also be pre-dicted with mean accuracies of 85%, and >90%, respectively

Manganese Catalysed Dehydrogenative Synthesis of Urea Derivatives and Polyureas

Urea derivatives are prevalent intermediates in the synthesis of resin precursors, dyes, agrochemicals, and pharmaceutical drugs. Furthermore, polyureas are useful plastics with applications in coating, adhesive, and biomedical industries and have a current annual market of USD 885 million. However, the conventional methods for the synthesis of urea derivatives and polyureas involve toxic reagents such as (di)isocyanates, phosgene, CO, and azides. We present here the synthesis of (poly)ureas using much less toxic reagents - (di)amines, and methanol via a catalytic dehydrogenative coupling process. The reaction is catalyzed by a pincer complex of an earth-abundant metal, manganese, and liberates H2 gas, valuable by itself, as the only by-product making the overall process atom-economic, and sustainable. A broad variety of symmetrical, and unsymmetrical urea derivatives and polyureas have been synthesized in moderate to quantitative yields using this catalytic protocol. Mechanistic insights have also been provided using experiments and DFT computation suggesting that the reaction proceeds via an isocyanate intermediate

Predicting ruthenium catalysed hydrogenation of esters using machine learning

AK thanks the Leverhulme Trust for an early career fellowship (ECF-2019-161). AK and CNB thank the UKRI Future Leaders Fellowship (MR/W007460/1). NVW and EB thank the IdEx Université de Paris (ANR-18-IDEX-0001) for funding. CM is supported by a Fellowship by the Accelerate Program for Scientific Discovery at the Computer Laboratory, University of Cambridge. The authors acknowledge the GENCI-CINES center for HPC resources (Projects A0080810359, A0100810359, and AD010812061R1).Catalytic hydrogenation of esters is a sustainable approach for the production of fine chemicals, and pharmaceutical drugs. However, the efficiency and cost of catalysts are often bottlenecks in the commercialization of such technologies. The conventional approach to catalyst discovery is based on empiricism, which makes the discovery process time-consuming and expensive. There is an urgent need to develop effective approaches to discover efficient catalysts for hydrogenation reactions. In this work, we develop a machine learning approach aided by Gaussian Processes to predict outcomes of catalytic hydrogenation of esters. Results of the Gaussian Process are compared with Linear regression and Neural Network models. Our optimized models can predict the reaction yields with a root mean square error (RMSE) of 12.1% on unseen data and suggest that the use of certain chemical descriptors (e.g. electronic parameters) selectively can result in a more accurate model. Furthermore, studies have also been carried out for the prediction of catalysts and reaction conditions such as temperature and pressure as well as their validation by performing hydrogenation reactions to improve the poor yields described in the dataset.Publisher PDFPeer reviewe

Predicting ruthenium catalysed hydrogenation of esters using machine learning

Acknowledgements: AK thanks the Leverhulme Trust for an early career fellowship (ECF-2019-161). AK and CNB thank the UKRI Future Leaders Fellowship (MR/W007460/1). NVW and EB thank the IdEx Université Paris Cité (ANR-18-IDEX-0001) for funding. CM is supported by a fellowship by the Accelerate Program for Scientific Discovery at the Computer Laboratory, University of Cambridge. The authors acknowledge the GENCI-CINES Center for HPC resources (projects A0080810359, A0100810359, and AD010812061R1).The report describes the application of machine learning tools to predict hydrogenation of esters using molecular catalysts based on ruthenium.</jats:p