Publications of Kai Sundmacher

Franz, T.; Papakonstantinou, G.; Sundmacher, K.: Transient hydrogen crossover in dynamically operated PEM water electrolysis cells - A model-based analysis. Journal of Power Sources 559, 232582 (2023)

König-Mattern, L.; Komarova, A. O.; Ghosh, A.; Linke, S.; Rihko-Struckmann, L.; Luterbacher, J.; Sundmacher, K.: High-throughput computational solvent screening for lignocellulosic biomass processing. Chemical Engineering Journal 452, 139476 (2023)

Zimmermann, R. T.; Weber, S.; Bremer, J.; Idakiev, V.; Pashminehazar, R.; Sheppard, T. L.; Mörl, L.; Sundmacher, K.: Core–shell catalyst pellets for effective reaction heat management. Chemical Engineering Journal 457, 140921 (2023)

Abel, K. L.; Beger, T.; Poppitz, D.; Zimmermann, R. T.; Kuschel, O.; Sundmacher, K.; Gläser, R.: Monolithic Al2O3 Xerogels with Hierarchical Meso‐/Macropore System as Catalyst Supports for Methanation of CO₂. ChemCatChem 14 (15), e202200288 (2022)

Chen, J.; Zhu, F.; Qin, H.; Song, Z.; Qi, Z.; Sundmacher, K.: Rational eutectic solvent design by linking regular solution theory with QSAR modelling. Chemical Engineering Science 262, 118042 (2022)

Cheng, J.; Qin, H.; Cheng, H.; Song, Z.; Qi, Z.; Sundmacher, K.: Rational Screening of Deep Eutectic Solvents for the Direct Extraction of α-Tocopherol from Deodorized Distillates. ACS Sustainable Chemistry & Engineering 10 (25), pp. 8216 - 8227 (2022)

Hessel, V.; Tran, N. N.; Asrami, M. R.; Tran, Q. D.; Van Duc Long, N.; Escribà-Gelonch, M.; Tejada, J. O.; Linke, S.; Sundmacher, K.: Sustainability of green solvents – review and perspective. Green Chemistry 24, pp. 410 - 437 (2022)

Himmel, A.; Findeisen, R.; Sundmacher, K.: Closed-loop real-time optimization for unsteady operating production systems. Journal of Process Control 113, pp. 80 - 95 (2022)

Keßler, T.; Kunde, C.; Linke, S.; Sundmacher, K.; Kienle, A.: Integrated computer-aided molecular and process design: Green solvents for the hydroformylation of long-chain olefines. Chemical Engineering Science 249, 117243 (2022)

Long, N. V. D.; Kim, G. S.; Tran, N. N.; Lee, D. Y.; Fulcheri, L.; Song, Z.; Sundmacher, K.; Lee, M.; Hessel, V.: Biogas upgrading using ionic liquid [Bmim][PF6] followed by thermal-plasma-assisted renewable hydrogen and solid carbon production. International Journal of Hydrogen Energy 47 (100), pp. 42075 - 42083 (2022)

Marušič, N.; Otrin, L.; Rauchhaus, J.; Zhao, Z.; Kyrilis, F. L.; Hamdi, F.; Kastritis, P. L.; Dimova, R.; Ivanov, I.; Sundmacher, K.: Increased efficiency of charge-mediated fusion in polymer/lipid hybrid membranes. Proceedings of the National Academy of Sciences 119 (20), e2122468119 (2022)

Marušič, N.; Zhao, Z.; Otrin, L.; Dimova, R.; Ivanov, I.; Sundmacher, K.: Fusion‐Induced Growth of Biomimetic Polymersomes: Behavior of Poly(dimethylsiloxane)‐Poly(ethylene oxide) Vesicles in Saline Solutions Under High Agitation. Macromolecular Rapid Communications 43 (5), e2100712 (2022)

Papakonstantinou, G.; Spanos, I.; Dam, A. P.; Schlögl, R.; Sundmacher, K.: Electrochemical evaluation of the de-/re-activation of oxygen evolving Ir oxide. Physical Chemistry Chemical Physics 24 , pp. 14579 - 14591 (2022)

Qin, H.; Song, Z.; Qi, Z.; Sundmacher, K.: Comparative screening of organic solvents, ionic liquids, and their binary mixtures for vitamin E extraction from deodorizer distillate. Chemical Engineering and Processing - Process Intensification 171, 108711 (2022)

Sanchez Medina, E. I.; Linke, S.; Stoll, M.; Sundmacher, K.: Graph neural networks for the prediction of infinite dilution activity coefficients. Digital Discovery 1 (3), pp. 216 - 225 (2022)

Song , Z.; Long, N. V. D.; Qin, H.; Tran, N. N.; Fulcheri, L.; Hessel, V.; Sundmacher, K.: Thermal-plasma-assisted renewable hydrogen and solid carbon production from ionic liquid-based biogas upgrading: A process intensification study. Chemical Engineering and Processing - Process Intensification 180, 108777 (2022)

Svitnič, T.; Sundmacher, K.: Renewable methanol production: Optimization-based design, scheduling and waste-heat utilization with the FluxMax approach. Applied Energy 326, 120017 (2022)

Wang, Z.; Zhou, T.; Sundmacher, K.: Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation. Chemical Engineering Journal 444, 136651 (2022)

Wang, Z.; Zhou, Y.; Zhou, T.; Sundmacher, K.: Identification of Optimal Metal-Organic Frameworks by Machine Learning: Structure Decomposition, Feature Integration, and Predictive Modeling. Computers & Chemical Engineering 160, 107739 (2022)

Weber, S.; Zimmermann, R. T.; Bremer, J.; Abel, K. L.; Poppitz, D.; Prinz, N.; Ilsemann, J.; Wendholt, S.; Yang, Q.; Pashminehazar, R. et al.; Monaco, F.; Cloetens, P.; Huang, X.; Kübel, C.; Kondratenko, E.; Bauer, M.; Bäumer, M.; Zobel, M.; Gläser, R.; Sundmacher, K.; Sheppard, T. L.: Digitization in Catalysis Research: Towards a Holistic Description of a Ni/Al2O3 Reference Catalyst for CO2 Methanation. ChemCatChem 14 (8), e202101878 (2022)