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Sustainable Reaction Engineering 

Alexei Lapkin's group @ University of Cambridge

A sustainable society will use manufacturing processes that do not deplete resources and do not harm the environment. 


In order to understand implications of a given technology (or a manufacturing process) on resources, society and the environment, we need to place it within the context of a wider system. Thus, if we are considering a single chemical reaction step, we need to understand also the complete process, including any separation requirements and sources/destinations of feedstocks and products/byproducts. If we are considering whether a given chemical product is better sourced from a renewable bio-feedstock, or from carbon dioxide, then we need to understand the supply chain and life cycle environmental impacts of such a substitution.


Sustainable Reaction Engineering group is developing clean, intensive processes for manufacture of molecules, formulations or functional materials. We are working on methods of modelling chemical processes, starting from molecular modelling methods and extending to multi-objective process optimisation and life cycle assessment. We have pioneered methods of machine learning for automated process optimisation, and are exploring methods of Big Data in application to chemical reaction networks. We are working with many industry sectors, from inorganic materials and formulations, to platform chemicals and pharmaceuticals, and have an international network of collaborations.

J.Y. See, S. Song, Y. Xiao, Y. Zhao, A. Lapkin, N. Yan, Synthesis of sun cream ingredients from corn lignin, Green Chem., (2021).

M.H. Barecka, J.W. Ager, A.A. Lapkin, Economically viable CO2 electro reduction embedded within ethylene oxide manufacturing, Energy Env. Sci., (2021). https://doi.org/10.1039/D0EE03310C

J.M Weber, C.P. Lindenmeyer, P. Lio, A.A. Lapkin, Teaching sustainability as complex systems approach: a sustainable development goals workshop, Int. J. Sustain. Edu., (2021)

O. Mohan, Shambhawi, R. Xu, A.A. Lapkin, S.H. Mushrif, Investigating CO2 methanation on Ni and Ru: DFT assisted microkinetic analysis, ChemCatChem, (2021)

K.C. Felton, J.G. Rittig, A.A. Lapkin, Summit: Benchmarking machine learning methods for reaction optimisation, Chemistry-Methods, 1 (2021) 116-122. https://doi.org/10.1002/cmtd.202000051

L. Cao, D. Russo, K. Felton, D. Salley, A. Sharma, G. Keenan, W. Mauer, H. Gao, L. Cronin, A.A. Lapkin, Optimization of Formulations Using Robotic Experiments Driven by Machine Learning DoE. Cell Reports Physical Science, 2 (2021) 100295. Doi: 10.1016/j.xcrp.2020.100295

L.Cao, D. Russo, A.A. Lapkin, Automated robotic platforms in design and development of formulations, AIChE J., (2021). https://doi.org/10.1002/aic.17248

M.I. Jeraal, S. Sung, A.A. Lapkin, A Machine Learning-Enabled Autonomous Flow Chemistry Platform for Process Optimization of Multiple Reaction Metrics. Chemistry–Methods, 2 (2021) 71-77. https://doi.org/10.1002/cmtd.202000044








We are involved in several Cambridge research and innovation centres

SynTech CDT

Innovation Centre in Digital Molecular Technologies

Cambridge Centre for Advanced Research and Education in Singapore



Department of Chemical Engineering and Biotechnology,

University of Cambridge,

Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom

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