• S. Hallamasek, V. Freger, A.A. Lapkin, RO performance in a mixed solvent system: Analysis of nopinone concentration from a methanol-water solution, J. Membr. Sci., (2025).

• J. Zhang, D. Semochkina, N. Sugisawa, D.C. Woods, A.A. Lapkin, Multi-objective reaction optimisation under uncertainties using expected quantile improvement, Compt. Chem. Engng., (2025).

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• Shambhawi, O. Mohan, T.S. Choksi, A.A. Lapkin, The design and optimisation of heterogeneous catalysts using computational methods, Catal. Sci. Technology., 14 (2024) 515-532. doi: 10.1039/d3cy01160g.

• J. Zhang, N. Sugisawa, K.C. Felton, S. Fuse, A.A. Lapkin, Multi-objective Bayesian optimisation using q-noisy expected hyper volume improvement (qNEHVI) for the Schotten-Baumann reaction, React. Chem. Eng., 9 (2024) 706-712. doi: 10.1039/d3re00502j

• D. Karan, G. Chen, N. Jose, J. Bai, P. McDaid, A.A. Lapkin, A machine learning-enabled process optimisation of ultra-fast flow chemistry with multiple reaction metrics, React. Chem. Eng., 9 (2024) 619-629. DOI: 10.1039/d3re00539a

• J. Bai, S. Mosbach, C.J. Taylor, D. Karan, D. K.F. Lee, S.D. Rihm, J. Akroyd, A.A. Lapkin, M. Kraft, A Dynamic Knowledge Graph Approach to Distributed Self-Driving Laboratories. Nature Communications 15 (2024) 462. https://doi.org/10.1038/s41467-023-44599-9.

• A. Chitre, J. Cheng, S. Ahamed, R.C.M. Querimit, B. Zhu, K. Wang, L. Wang, K. Hippalgaonkar, A.A. Lapkin, pHbot: Self-Driven Robot for pH Adjustment of Viscous Formulations via Physics-Informed-ML**. Chemistry–Methods 4 (2024) e202300043. https://doi.org/10.1002/cmtd.202300043.

• D.S. Wigh, J. Arrowsmith, A. Pomberger, K.C. Felton, A.A. Lapkin, ORDerly: data sets and benchmarks for chemical reaction data, J. Chem. Inf. Model., 64 (2024) 3790-3798. DOI: 10.1021/acs.jcim.4c00292

• C. Zhang, A. Arun, A.A. Lapkin, Completing and balancing database excerpted chemical reactions with a hybrid mechanistic-machine learning approach, ACS Omega 9 (2024) 18385-18399. DOI: 10.1021/acsomega.4c00262 

• K.C. Felton, L. Raßpe-Lange, J.G. Rittig, K. Leonhard, A. Mitsos, J. Meyer-Kirschner, C. Knösche, A.A. Lapkin, ML-SAFT: A Machine Learning Framework for PCP-SAFT Parameter Prediction. Chem. Eng. J. (2024), 151999. doi: 10.1016/j.cej.2024.151999.

• S. Hallamasek, V. Ubbenjans, A.A. Lapkin, Life cycle assessment of a process for paracetamol flow synthesis from bio-waste derived b-pinene, Sustain. Chem. Pharmacy. 40 (2024) 101629. doi: 10.1016/j.scp.2024.101629

• A. Chitre, A. R.C.M. Querimit, S.D. Rihm, D. Karan, B. Zhu, K. Wang, L. Wang, K. Hippalgaonkar, A.A. Lapkin, Accelerating Formulation Design via Machine Learning: Generating a High-Throughput Shampoo Formulations Dataset. Scientific Data 2024, 11 (1), 728. doi: 10.1038/s41597-024-03573-w.

• J. Raphael Seidenberg, A. Khan, A.A. Lapkin, Boosting autonomous process design and intensification with formalised domain knowledge, Comp. Chem. Engng. 169 (2023) 108097 doi: 10.1016/j.compchemeng.2022.108097 

• L. Cao, D. Russo, E. Matthews, A. Lapkin, D. Woods, Computer-aided design of formulated products: a bridge-design of experiments for ingredient selection, Comp. Chem. Engng. 169 (2023) 108083 doi: 10.1016/j.compchemeng.2022.108083

• S.D. Rhim, M.K. Kovalev, A.A. Lapkin, J.W. Ager, M. Kraft, On the role of C4 and C5 products in electrochemical CO2 reduction via copper-based catalysts, Energy & Environmental Science, 16 (2023) 1697-1710; doi: 10.1039/D2EE03752A

• J. Zakrzewski, P. Yaseneva, C.J. Taylor, M.J. Gaunt, A.A. Lapkin, A scalable palladium-catalysed C(sp3)-H carboxylation of alkyl amines in batch and continuous flow, OPRD, 27 (2023) 649-658, doi: 10.1021/acs.oprd.2c00378

• D.S. Wigh, M. Tissot, P. Passau, J.M. Goodman, A.A. Lapkin, Quantitative in silico prediction of the rate of protodeboronation by a mechanistic DFT-aided algorithm, J. Phys. Chem. A, 127 (2023) 2628-2636, doi: 10.1021/acs.jpca.2c08250

• A. Arun, Z. Guo, S. Sung, A.A. Lapkin, Reaction impurity prediction using a data mining approach, Chemistry Methods, 3 (2023) e2022000062. DOI: 10.1002/cmtd.202200062 (highlighted in Editor's Choice Spotlight)

• C.J. Taylor, A. Pomberger, K.C. Felton, R. Grainger, M. Barecka, T.W. Chamberlain, R. Bourne, A.A. Lapkin, A brief introduction to chemical reaction optimisation, Chemical Reviews, 123 (2023) 3089-3126, doi: 10.1021/acs.chemrev.2c00798

• Shambhawi, J.M. Weber, A.A. Lapkin, Micro-kinetics analysis based on partial reaction networks to compare catalysts performances for methane dry reforming reaction, Chem. Eng. J., 466 (2023) 143212; https://doi.org/10.1016/j.cej.2023.143212

• C.J. Taylor, K.C. Felton, D. Wigh, M.I. Jeraal, R. Grainger, G. Chessari, C.N. Johnson, A.A. Lapkin, Accelerated Chemical Reaction Optimization Using Multi-Task Learning. ACS Cent Sci (2023); https://doi.org/10.1021/acscentsci.3c00050

• M. Lu, C.N. Christensen, J.M Weber, T. Konno, N. Läubli, K. Scherer, E. Avezov, P. Lio, A.A. Lapkin, G.S. Kaminski Schierle, C.F. Kaminski, ERnet: a tool for the semantic segmentation and quantitative analysis of endoplasmic reticulum topology for video-rate super-resolution imaging, Nature Methods, 20 (2023) 569-579; https://doi.org/10.1038/s41592-023-01815-0 

• M. Barecka, P. D.S. Dameni, M.Z. Muhamad, J.W. Ager, A.A. Lapkin, Energy-efficient ethanol concentration method for scalable CO2 electrolysis, ACS Energy Lett 8 (2023) 3214-3220. DOI: 10.1021/acsenergylett.3c00973

• A. Pomberger, N. Jose, D. Walz, J. Meissner, C. Holze, P. Muller-Bischof, A.A. Lapkin, Automated pH adjustment driven by robotic workflows and active machine learning, Chem. Eng. J., 451 (2023) 139099, doi: 10.1016/j.cej.2022.13909

• C. Zhang, A.A. Lapkin, Reinforcement learning optimization of reaction routes on the basis of large, hybrid organic chemistry–synthetic biological, reaction network data, React. Chem. Eng. 8 (2023) 2491. DOI: 10.1039/d2re00406b​ (highlighted in OPRD: Org. Process Res. Dev. 2023, 27, 9, 1535–1545)

• J.G. Rittig, K.C. Felton, A.A. Lapkin, A. Mitsos, Gibbs-Duhem-informed neural networks for binary activity coefficient prediction, Digital Discovery 2 (2023) 1752-1769. DOI: 10.1039/D3DD00103B

• M.H. Barecka, M.K. Kovalev, M. Zakir, H. Ren, J.W. Ager, A.A. Lapkin, CO2 electro reduction favours carbon isotope 12C over 13C and facilitates isotope separation, iScience 26 (2023) 107834. doi: 10.1016/j.isci.2023.107834

• T.T. Pham, Z. Guo, B. Li, A.A. Lapkin, & N. Yan, Synthesis of pyrrole-2-carboxylic acid from cellulose- and chitin-based feedstocks discovered by the automated route search. ChemSusChem n/a, e202300538 (2023). DOI: 10.1002/cssc.202300538

• S. Zhang, V.S. Vassiliadis, Z. Hao, L. Cao, A.A. Lapkin, Heuristic optimisation of multi-task dynamic architecture neural network (DAN2), Neural Computing and Applications (2023) 35:4775–4791. DOI 10.1007/s00521-022-07851-9

• P. Adesina, J.A. Elliott & A.A. Lapkin, Multiscale modelling of species transport in hydrophilic Nafion®-coated Cu catalysts for CO2 electro-reduction. Chemical Engineering Journal 478, 147461 (2023). 10.1016/j.cej.2023.14746

• P. Jorayev, D. Russo, J. Tibbetts, A.M. Schweidtmann, P. Deutsch, S.D. Bull, A.A. Lapkin, Multi-objective Bayesian optimisation of a two-step synthesis of p-cymene from crude sulphate turpentine, Chem. Eng. Sci., 247 (2022) 116938. DOI: 10.1016/j.ces.2021.116938

• Y. Yan, R.J. Wong,  Z. Ma, F. Donat, S. Xi, S.  Saqline, Q. Fan, Y. Du, A. Borgna, Q. He, C.R. Müller, W. Chen, A.A. Lapkin, W. Liu, CO2 Hydrogenation to Methanol on Tungsten-Doped Cu/CeO2 Catalysts, Appl. Catal. B: Environmental, (2022) 121098, doi: 10.1016/j.apcatb.2022.121098

• M.K. Kovalev, H. Ren, M.M. Zakir, J.W. Ager, A.A. Lapkin, Minor Product Polymerization Causes Failure of High-Current CO2-to-Ethylene Electrolyzers. ACS Energy Lett., 7 (2022) 599–601. Doi: 10.1021/acsenergylett.1c02450

• J. Bai, L. Cao, S. Mosbach, J. Akroyd, A.A. Lapkin, M. Kraft, From platform to knowledge graph: evolution of laboratory automation, JACS Au, 10.1021/jacsau.1c00438.

• A. Lapkin, "Can chemical reactions be encoded into algorithms and understood by computers?" https://futurumcareers.com/can-chemical-reactions-be-encoded-into-algorithms-and-understood-by-computers

• D.S. Wigh, J.M. Goodman, A.A. Lapkin, A review of molecular representation in the age of machine learning, WIREs Comp. Mol. Sci., 2022, e1603. doi: 10.1002/wcms.1603 (Listed in Top Downloaded Articles in 2022)

• Z. Hao, M.H. Barecka, A.A. Lapkin, Accelerating net zero from the perspective of optimising a carbon capture and utilisation system, Energy Environ. Sci., 15 (2022) 2139-2153. doi: 10.1039/D1EE03923G.

• A.A. Khan, A.A. Lapkin, Designing the process designer: hierarchical reinforcement learning for optimisation-based process design, Chem. Eng. Process. Process Intensification, (2022) 108885. doi: 10.1016/j.cep.2022.108885

• J.M. Weber, Z. Guo, A.A. Lapkin, Discovering circular process solutions through automated reaction network optimisation, ACS Engineering Au, 2 (2022) 333-349. doi: 10.1021/acsengineeringau.2c00002.

• A. Pomberger, A.A. Pedrina McCarthy, A. Khan. S. Sung, C.J. Taylor, M.J. Gaunt, L. Colwell, D. Waltz, A.A. Lapkin, The effect of chemical representation on active machine learning towards closed-loop optimisation, React. Chem. Eng., 7 (2022) 1368. doi: 10.1039/d2re00008c

• Z. Hao, C. Zhang, A.A. Lapkin, Efficient surrogates construction of chemical processes: Case studies on pressure swing adsorption and gas-to-liquids, AIChE J., 6 (2022) e17616, doi: 10.1002/aic.17616

• G. Liu, P. Adesina, N. Nasiri, H. Wang, Y. Sheng, S. Wu, M. Kraft, A.A. Lapkin, J.W. Ager, R. Xu, Elucidating reaction pathways of the CO2 electro reduction via tailorable tortuosities and oxidation states of Cu nanostructures, Adv. Funct. Mater., (2022) 2204993. doi: 10.1002/adfm.202204993.

• N.A. Jose, M. Kovalev, A.A. Lapkin, Double-hydroxide superstructures for high-rate supercapacitor cathodes, Energy Technologies., (2022) 2200633, doi: 10.1002/ente.202200633

• H. Ren, M. Kovalev, Z. Weng, M.Z. Muhamad, Y. Sheng, L. Sun, J. Wang, S. Rihm, H. Ma, W. Yang, A.A. Lapkin, J.W. Ager, Operando Proton Transfer Reaction-Time of Flight-Mass Spectrometry of Carbon Dioxide Reduction Electrocatalysis, Nature Catal., 5 (2022) 1169-1179 doi: 10.1038/s41929-022-00891-3

• 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

• 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

• M.H. Barecka, J.W. Ager, A.A. Lapkin, Economically viable CO2 electro reduction embedded within ethylene oxide manufacturing, Energy Env. Sci., 14 (2021) 1530-1543. https://doi.org/10.1039/D0EE03310C

• 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

• 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., 22:8 (2021) 25-41. DOI: 10.1108/IJSHE-06-2020-0209 

• J.Y. See, S. Song, Y. Xiao, Y. Zhao, A. Lapkin, N. Yan, Transformation of corn lignin into Sun cream ingredients, ChemSusChem, 14 (2021) 1586-1594. DOI: 10.1002/cssc.202002739

• L.Cao, D. Russo, A.A. Lapkin, Automated robotic platforms in design and development of formulations, AIChE J., v67. e17248 (2021). (Top Downloaded Article in 2022) https://doi.org/10.1002/aic.17248

• O. Mohan, Shambhawi, R. Xu, A.A. Lapkin, S.H. Mushrif, Investigating CO2 methanation on Ni and Ru: DFT assisted microkinetic analysis, ChemCatChem, 13 (2021) 1-15; DOI: 10.1002/cctc.202100073

• M.H. Barecka, J.W. Ager, A.A. Lapkin, CO2 on-site recycling: retrofit technology for carbon neutral manufacturing, iScience v. 24 (2021) DOI: https://doi.org/10.1016/j.isci.2021.102514

• J. Tibbetts, D. Russo, A.A. Lapkin, S. Bull, Steven, Efficient Syntheses of Biobased Terephthalic Acid, p-Toluic Acid and p-Methylacetophenone via One-Pot Catalytic Aerobic Oxidation of Monoterpene Derived bio-p-Cymene, ACS Sust. Chem. Eng., 9 (2021) 8642-8652. DOI: 10.1021/acssuschemeng.1c02605

• N.A. Jose, M. Kovalev, E. Bradford, A.M. Schweidtmann, H.C. Zeng, A.A. Lapkin, Pushing nanomaterials up to the kilogram scale - an accelerated approach for synthesizing antimicrobial ZnO with high shear reactors, machine learning and high-throughput analysis, Chem. Eng. J., 426 (2021) 131345. DOI: 10.1016/j.cej.2021.131345

• Z. Hao, A. Caspari, A.M. Schweidtmann, Y. Vaupel, A.A. Lapkin, A. Mhamdi, Efficient Hybrid Multiobjective Optimization of Pressure Swing Adsorption, Chem. Eng. J., 423 (2021) 130248. DOI: 10.1016/j.cej.2021.130248

• Shambhawi, G. Csányi, A.A. Lapkin, Active learning training strategy for predicting O adsorption free energy on perovskite catalysts using inexpensive catalyst features, Chem. Methods 1 (2021) 1-8. DOI: 10.1002/cmtd.202100035.

• J.M. Weber, Z. Guo, C. Zhang, A.M. Schweidtmann, A.A. Lapkin, Chemical data intelligence for sustainable chemistry, Chem. Soc. Rev., 50 (2021) 12013-12036; doi: 10.1039/d1cs00477h.

• P. Fantke, C. Cinquemani, P. Yaseneva, J. De Mello, H. Schwabe, B. Ebeling, A.A. Lapkin, Transition to sustainable chemistry through digitalization, Chem 7 (2021) 2866-2882; https://doi.org/10.1016/j.chempr.2021.09.012

• C. Zhang;  Y. Amar; L. Cao; A. A. Lapkin, Solvent Selection for Mitsunobu Reaction Driven by an Active Learning Surrogate Model. Org. Process Res. Dev. 24 (2020) 2864-2873. DOI: 10.1021/acs.oprd.0c00376

• A.A. Lapkin, Rational design of continuous flow processes for synthesis of functional molecules, in “Sustainable Nanoscale Engineering”, Eds., G. Szekely, A.G. Livingstone, Elsevier, Amsterdam, 2020. pp. 415-433. doi: 10.1016/B978-0-12-814681-1.00016-3

• N.A. Jose, H.C. Zeng, A.A. Lapkin, Scalable and Precise Synthesis of Two-Dimensional Metal Organic Framework Nanosheets in a High Shear Annular Microreactor, Chem. Eng. J., 388 (2020) 124133; doi: 10.1016/j.cej.2020.124133

• R.I. Slavchov, M. Salamanca, D. Russo, I. Salama, S. Mosbach, S.M. Clarke, M. Kraft, A.A Lapkin, S.V. Filip, The role of NO2 and NO in the mechanism of hydrocarbon degradation leading to carbonaceous deposits in engines, Fuel 267 (2020) 117218. doi: 10.1016/j.fuel.2020.117218

• P. Neumann, L. Cao, D. Russo, V.S. Vassiliadis, A.A. Lapkin, A new formulation for symbolic regression to identify physico-chemical laws from experimental data, Chem. Eng. J., 387 (2020) 123412; https://doi.org/10.1016/j.cej.2019.123412

• L. Cao, M. Kabeshov, S.V. Ley, A.A. Lapkin, In silico rationalisation of selectivity and reactivity in Pd-catalysed C-H activation reactions, Beilstein J. Org. Chem., 16 (2020) 1465-1475. DOI: 10.3762/bjoc.16.122.

• A. Khan, A.A. Lapkin, Searching for optimal process routes: a reinforcement learning approach. Comp. Chem. Eng. 141 (2020) 107027. DOI: 10.1016/j.compchemeng.2020.107027.

• O. Mohan, Shambhawi, A.A. Lapkin, S.H. Mushrif, Investigating methane dry reforming on Ni and B promoted Ni surfaces: DFT assisted microkinetic analysis and addressing the coking problem, Catal. Sci. Technol., 10 (2020) 6628-6643. DOI: 10.1039/D0CY00939C

• L. Cao, D. Russo, W. Mauer, H.H. Gao, A.A. Lapkin, Machine learning-aided process design for formulated products, in Proceedings of 30th European Symposium on Computer Aided Process Engineering, Computer Aided Chemical Engineering, 48 (2020) 1789-1794, doi: 10.1016/B978-0-12-823377-1.50299-8.

• A.D. Clayton, A.M. Schweidtmann, G. Clemens, J.A. Manson, C.J. Taylor, G.C. Nino, T.W. Chamberlain, N. Kapur, A.J. Blacker, A.A. Lapkin, R.A. Bourne, Automated self-optimisation of multi-step reaction and separation processes using machine learning, Chem. Eng. J., 384 (2019) 123340. https://doi.org/10.1016/j.cej.2019.123340

• Z. Guo, N. Yan, A.A. Lapkin, Towards circular economy: integration of bio-waste into chemical supply chain, Curr. Opinion Chem. Engngn. 26 (2019) 148-156. https://doi.org/10.1016/j.coche.2019.09.010

• J.M. Weber, P. Lio, A. Lapkin, Identification of strategic molecules for future circular supply chains using large reaction networks, React. Chem. Eng., 4 (2019) 1969-1981, DOI: 10.1039/c9re00213h

• X. Kan, X. Chen, Y. Shen, A.A. Lapkin, M. Kraft, C.-H. Wang, Box-Behnken design based CO2 co-gasification of horticultural waste and sewage sludge with addition of ash from waste as catalyst, Appl. Energy, 242 (2019) 1549-1561; doi: 10.1016/j.apenergy.2019.03.176.

• S. Chemat, S. Boudjelal, I. Malki, A. Lapkin, Biosynthesis of spathulenol and camphor stand as a competitive route to artemisinin production as revealed by a new chemometric convergence approach based on nine locations’ field-grown Artemesia annua L., Ind. Crop. Prod., 137 (2019) 521-527. DOI: 10.1016/j.indcrop.2019.05.056.

• Y. Amar, A.M. Schweidtmann, P. Deutsch, L. Cao, A. Lapkin, Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis, Chem. Sci., 10 (2019) 6697-6706, DOI: 10.1039/c9sc01844a.

• P. Yaseneva, N. An, M. Finn, N. Tidemann, N. Jose, A. Voutchkova-Kostal, A. Lapkin, Continuous synthesis of doped layered double hydroxides in a meso-scale flow reactor, Chem. Eng. J., 360 (2019) 190-199. doi: 10.1016/j.cej.2018.11.197

• J.J. Varghese, L. Cao, C. Robertson, Y. Yang, L.F. Gladden, A.A. Lapkin, S.H. Mushrif, Synergistic Contribution of the Acidic Metal Oxide-Metal Couple and Solvent Environment in the Selective Hydrogenolysis of Glycerol: a Combined Experimental and Computational Study Using ReOx-Ir as the Catalyst, ACS Catal., 9 (2019) 485-503. doi: 10.1021/acscatal.8b03079

• W. Gerlinger, J.M. Asua, T. Chaloupka, J.M.M. Faust, F. Gjertsen, S. Hamzehlou, S.O. Hauger, E. Jahns, P.J. Joy, J. Kosek, A. Lapkin, J.R. Leiza, A. Mhamdi, A. Mitsos, O. Naeem, N. Rajabalinia, P. Singstad, J. Suberu, Dynamic optimisation and non-linear model predictive control to achieve target particle morphologies, Chem. Ing. Tech., 91 (2019) 3-14, doi: 10.1002/cite.201800118.

• N. Jose, A. Lapkin, Influence of hydrodynamics on wet syntheses of nanomaterials, in “Advanced Nanomaterials for Catalysis and Energy”, Ed. V.A. Sadykov, Elsevier, Amsterdam, 2019. pp. 29-60.

• J.W. Ager, A.A. Lapkin, Chemical storage of renewable energy, Science 360 (2018) 707-708. DOI: 10.1126/science.aat7918.

• Y. Yan, Y. Dai, Y. Yang, A.A. Lapkin, Improved stability of Y2O3 supported Ni catalysts for CO2 methanation by precursor-determined metal-support interaction, Appl. Catal. B: Env., 237 (2018) 504-512, DOI: 10.1016/j.apcatb.2018.06.021.

• D.I. Potemkin, D.K. Maslov, K. Loponov, P.V. Snytnikov, Y.V. Shubin, P.E. Plyusnin, D.A. Svintsitskiy, V.A. Sobyanin, A.A. Lapkin, A. A., Porous Nanocrystalline Silicon Supported Bimetallic Pd-Au Catalysts: Preparation, Characterization, and Direct Hydrogen Peroxide Synthesis. Frontiers in Chemistry 6 (2018) 85. DOI: 10.3389/fchem.2018.00085.

• P.-M. Jacob, A. Lapkin, Statistics of the network of organic chemistry, React. Chem. Eng., 3 (2018) 102–118. DOI: 10.1039/c7re00129k.

• R. Milkus, C. Ness, V.V. Palyulin, J. Weber, A. Lapkin, A. Zaccone, Interpretation of the Vibrational Spectra of Glassy Polymers Using Coarse-Grained Simulations. Macromolecules 51 (2018) 1559-1572. DOI: 10.1021/acs.macromol.7b02352.

• E. Bradford, A.M. Schweidtmann, A. Lapkin, Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm, J. Global Optim., 71 (2018). 407-438. DOI: 10.1007/s10898-018-0609-2.

• D. Kralisch, D. Ott, A.A. Lapkin, P. Yaseneva, W. De Soete, M. Jones, N. Minkov, M. Finkbeiner, The need for innovation management and decision guidance in sustainable process design, J. Cleaner Prod., 172 (2018) 2374-2388. DOI: 10.1016/j.jclepro.2017.11.173

• S.M. Aworinde, A.M. Schweidtmann, A.A. Lapkin, The concept of selectivity control by simultaneous attenuation of feed concentration and temperature in a microstructured reactor, Chem. Eng. J. 331 (2018) 765-776. DOI: 10.1016/j.cej.2017.09.03

• S.M. Aworinde, K. Wang, A.A. Lapkin, Borate-assisted liquid-phase oxidation of n-pentane, Appl. Catal. A: Gen., 563 (2018) 28-42. DOI: 10.1016/j.apcata.2018.06.023

• A.M. Schweidtmann, A.D. Clayton, N. Holmes, E. Bradford, R.A. Bourne, A.A. Lapkin, Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives, Chem. Eng. J. 352 (2018) 277-282. DOI: 10.1016/j.cej.2018.07.031

• I. Peñafiel, A. Martínez-Lombardia, C. Godard, C. Claver, A. Lapkin, Recyclable supported Pd-NHC catalytic systems for the copper-free Sonogashira cross-coupling in flow, Sust. Chem. Pharmacy 9 (2018) 69-75 DOI: 10.1016/j.scp.2018.06.003

• A. Lapkin, Chemical engineering science and green chemistry - the challenge of sustainability, in “Green Chemical Engineering”, v.12 Handbook of Green Chemistry, Series ed. P. Anastas, Volume ed. A. Lapkin, Wiley-VCH, 2018. pp. 1-18.

• C.S. Horbaczewskyj, C.E. Willans, A.A. Lapkin, R.A. Bourne, An introduction to closed-loop process optimization and online analysis”, in “Green Chemical Engineering”, v.12 Handbook of Green Chemistry, Series ed. P. Anastas, Volume ed. A. Lapkin, Wiley-VCH, 2018. pp. 329-374.

• N. Jose, H.C. Zeng, A.A. Lapkin, Hydrodynamic assembly of two-dimensional layered double hydroxide nanostructures, Nature Commun., 9 (2018) 4913. DOI: 10.1038/s41467-018-07395-4

• D. Russo, I.D. Somma, R. Marotta, G. Tomaiuolo, R. Andreozzi, S. Guido, A.A. Lapkin, Intensification of Nitrobenzaldehydes Synthesis from Benzyl Alcohol in a Microreactor, Org. Process Res. Dev., 21 (2017) 357-364. DOI: 10.1021/acs.oprd.6b00426

• D. Helmdach, P. Yaseneva, P.K. Heer, A. Schweidtmann, A. Lapkin, A multi-objective optimisation including results of life cycle assessment in developing bio-renewable-based processes, ChemSusChem 10 (2017) 3632-3643. DOI: 10.1002/cssc.201700927.

• A. Echtermeyer, Y. Amar, J. Zakrzewski, A. Lapkin, Self-optimisation and model-based design of experiments for developing a C–H activation flow process, Beilstein J. Org. Chem., 13 (2017) 150–163. DOI: 10.3762/bjoc.13.18

• P.-M. Jacob, P. Yamin, C. Perez-Storey, M. Hopgood, and A.A. Lapkin, Towards automation of chemical process route selection based on data mining, Green Chem., 19 (2017) 140-152. doi: 10.1039/C6GC02482C.

• P.-M. Jacob, T. Lan, J.M. Goodman, A.A. Lapkin, A possible extension to the RInChI as a means of providing machine readable process data, J. Chemoinformatics. 9:23 (2017). doi: 10.1186/s13321-017-0210-6.

• A.A. Lapkin, P.K. Heer, P.-M. Jacob, M. Hutchby, W. Cunningham, S.D. Bull, M.G. Davidson. Automation of route identification and optimisation based on datamining and chemical intuition, Faraday Discussions 202 (2017) 483-496. DOI: 10.1039/C7FD00073A.

• D.D. Plaza, V. Strobel, P.K.K.S. Heer, A.B. Sellars, S.-S. Hoong, A.J. Clark, A.A. Lapkin, Direct valorisation of waste cocoa butter triglycerides via catalytic epoxidation, ring-opening and polymerisation, J. Chem. Technol. Biotechnol. 92 (2017) 2254-2266. DOI: 10.1002/jctb.5292

• I. Penafiel, A. Lapkin, Flow systems for NHC catalysis, in “Science of synthesis reference library: N-heterocyclic carbenes in organic synthesis”, Eds., S.P. Nolan, C.S.J. Cazin, Georg Thieme Verlag KG, Stuttgart, New York, 2017, pp 369-394.

• A. Lapkin, K. Loponov, G. Tomaiuolo, S. Guido, Solids in continuous flow reactors for specialty and pharmaceutical syntheses, in “Sustainable Flow Chemistry - Methods and Applications”, Ed. L. Vaccaro, Wiley, 2017.

• X. Fan,  V. Sans,  S.K. Sharma,  P.K. Plucinski,  V.A. Zaikovskii,  K. Wilson, S. R. Tennison,  A. Kozynchenko,  A.A. Lapkin, Pd/C catalysts based on synthetic carbons with bi- and tri-modal pore-size distribution: applications in flow chemistry, Catal. Sci. Technol., 6 (2016) 2387-2395. DOI: 10.1039/c5cy01401h

• 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. DOI: 10.1002/anie.201602483

• X. Fan, F. Xing, X. Ou, G.A. Turley, P. Denissenko, M.A. Williams, N. Batail, C. Pham, A.A. Lapkin, Microtomography-based numerical simulations of heat transfer and fluid flow through β-SiC open-cell foams for catalysis, Catal. Today (2016) http://dx.doi.org/10.1016/j.cattod.2015.12.012

• 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. DOI: 10.1039/c5re00048c.

• S. Falß, G. Tomaiuolo, A. Perazzo, P. Yaseneva, J. Zakrzewski, S. Guido, A. Lapkin, R. Woodward, R.E. Meadows, A Continuous Process for Buchwald-Hartwig Amination at Micro- Lab- and Multi-Kilo Scale, Org. Proc. Res. Des. 20(2) (2016) 558-567. DOI: 10.1021/acs.oprd.5b00350

• J. Suberu, P. Yamin, R. Cornell, A. Sam, A. Lapkin, Feasibility of using 2,3,3,3-tetrafluoropropene (R1234yf) as a solvent for solid-liquid extraction of biopharmaceuticals, ACS Sustainable Chem. Eng. 4 (2016) 2559-2568. DOI: 10.1021/acssuschemeng.5b01721.

• J. Suberu, P.S. Gromski, A. Nordon, A. Lapkin, Multivariate data analysis and metabolic profiling of artemisinin and related compounds in high yielding varieties of Artemisia annua field-grown in Madagascar, J. Pharmaceutical and Biomed. Analysis, 117 (2016) 522-531. doi:10.1016/j.jpba.2015.10.003

• C. Houben, G. Nurumbetov, D. Haddleton, A.A. Lapkin, Feasibility of simultaneous determination of monomer concentrations and particle size in emulsion polymerization using in situ Raman spectroscopy, Ind. Eng. Chem. Res. 54 (2015) 12867-12876. DOI: dx.doi.org/10.1021/acs.iecr.5b02759

• C. Houben, A. Lapkin, Automatic discovery and optimisation of chemical processes, Curr. Opinion in Chem. Engng., 9 (2015) 1-7. http://dx.doi.org/10.1016/j.coche.2015.07.001

• C. Houben, N. Peremezhney, A. Zubov, J. Kosek, A.A. Lapkin, Closed-loop multi-target optimisation for discovery of new emulsion polymerisation recipes, Org. Process Res. Dev., 19 (2015) 1049-1053. DOI: 10.1021/acs.oprd.5b00210

• C. Schotten, D. Plaza, S. Manzini, S.P. Nolan, S.V. Ley, D.L. Browne, A. Lapkin, Continuous Flow Metathesis for Direct Valorization of Food Waste: An Example of Cocoa Butter Triglyceride, ACS Sust. Chem. Eng., 3 (2015) 1453-1459. DOI: 10.1021/acssuschemeng.5b00397

• 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. http://dx.doi.org/10.1016/j.cattod.2014.04.014

• L. Torrente-Marciano, D. Nielsen, R. Jackstell, M. Beller, K. Cavell, A. Lapkin, Selective telomerisation of isoprene with methanol by a heterogeneous palladium resin catalyst. Catal. Sci. Technol., 5 (2015) 1206-1212. DOI: 10.1039/c4cy01320d.

• A. Martinez-Lombardia, J. Krinsky, I. Peñafiel, S. Castillón, K. Loponov, A. Lapkin, C. Godard, C. Claver, Heterogenization of Pd-NHC complexes onto a silica support and their application in the Suzuki-Miyaura coupling under batch and continuous flow conditions, Cat. Sci. Technol., 5 (2015) 310-319. DOI: 10.1039/c4cy00829d

• 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.

• X. Fan, J. Restivo, J.J. Órfão, M.F.R. Pereira, A. Lapkin, The role of multi walled carbon nanotubes (MWCNTs) in catalytic ozonation of atrazine, Chemical Engineering Journal, 241 (2014) 66-76.

• J.O. Suberu, P. Yamin, K. Leonhard, L. Song, S. Chemat, N. Sullivan, G. Barker, A. Lapkin, The effect of O-methylated flavonoids and other co-metabolites on the crystallisation and purification of artemisinin, J. Biotechnol., 171 (2014) 25-33.

• A. Lapkin, E. Adou, B.N. Mlambo, S. Chemat, J. Suberu, A.E.C. Collis, A. Clark, G. Barker, Integrating medicinal plants extraction into a high-value biorefinery: an example of Artemisia annua L. Comptes Rendus - Chimie, 17 (2014) 232-241.

• V. Sans, S. Glatzel, F.J. Douglas, D.A. Maclaren, A. Lapkin, L. Cronin, Non-equilibrium dynamic control of gold nanoparticle and hyperbranched nanogold assemblies, Chemical Science, 5 (2014) 1153-1157.

• 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, Chemical Engineering Journal, 248 (2014) 230-241. 

• L. Torrente-Murciano, D.J. Nielsen, K.J. Cavell, A.A. Lapkin, Tandem isomerization/telomerization of long chain dienes, Frontiers in Chemistry, 2 (2014) Article 37, 1-5, DOI: 10.3389/fchem.2014.00037.

• N. Peremezhney, P.-M. Jacob, A. Lapkin, Alternative methods of processing bio-feedstocks in formulated consumer products design, Frontiers in Chemistry, 2 (2014) Article 26, 1-6, DOI: 10.3389/fchem.2014.00026.

• J.O. Suberu, I, Romero-Canelón, N. Sullivan, A.A. Lapkin, G.C. Barker, Comparative cytotoxicity of artemisinin and cisplatin and their interactions with chlorogenic acids in MCF7 breast cancer cells, ChemMedChem, 9 (2014) 2791-2798.

• 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.Proc.Res.Dev., 18 (2014) 1443-1454. dx.doi.org/10.1021/op500181z

• J.O. Suberu, A.P. Gorka, L. Jacobs, P.D. Roepe, N. Sullivan, G.C. Barker, A.A. Lapkin, Anti-Plasmodial Polyvalent Interactions in Artemisia annua L. Aqueous Extract – Possible Synergistic and Resistance Mechanisms, PLOS One 8:11 (2013) e80790.

• J. Suberu, L. Song, S. Slade, N. Sullivan, G. Barker, A. Lapkin, A rapid method for the determination of artemisinin and its biosynthetic precursors in Artemisia annua L. crude extracts, J. Pharmaceutical and Biomedical Analysis, 84 (2013) 269-277.

• D. Haddleton, J. Burns, C. Houben, C. Waldron, A. Anastasaki and A. Lapkin, Poly(acrylates) via SET-LRP in a continuous flow reactor, Polymer Chemistry 4 (2013) 4809-4813, DOI: 10.1039/C3PY00833A. 

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Earlier papers are affiliated with: University of Warwick, University of Bath, and Boreskov Institute of Catalysis.