| Machine learning in thermodynamics: Prediction of activity coefficients by matrix completion F Jirasek, RAS Alves, J Damay, RA Vandermeulen, R Bamler, M Bortz, ... The Journal of Physical Chemistry Letters 11 (3), 981-985, 2020 | 30 | 2020 |
| Crystal phase transformation of α into β phase poly (vinylidene fluoride) via particle formation caused by rapid expansion of supercritical solutions S Wolff, F Jirasek, S Beuermann, M Türk RSC advances 5 (82), 66644-66649, 2015 | 28 | 2015 |
| Method for estimating activity coefficients of target components in poorly specified mixtures F Jirasek, J Burger, H Hasse Industrial & Engineering Chemistry Research 57 (21), 7310-7313, 2018 | 16 | 2018 |
| Digitalization in thermodynamics E Forte, F Jirasek, M Bortz, J Burger, J Vrabec, H Hasse Chemie Ingenieur Technik 91 (3), 201-214, 2019 | 12 | 2019 |
| NEAT—NMR Spectroscopy for the Estimation of Activity Coefficients of Target Components in Poorly Specified Mixtures F Jirasek, J Burger, H Hasse Industrial & Engineering Chemistry Research 58 (21), 9155-9165, 2019 | 8 | 2019 |
| Prediction of the elution profiles of proteins in mixed salt systems in hydrophobic interaction chromatography N Galeotti, E Hackemann, F Jirasek, H Hasse Separation and Purification Technology 233, 116006, 2020 | 7 | 2020 |
| Hybridizing physical and data-driven prediction methods for physicochemical properties F Jirasek, R Bamler, S Mandt Chemical Communications 56 (82), 12407-12410, 2020 | 7 | 2020 |
| Recovery of furfural and acetic acid from wood hydrolysates in biotechnological downstream processing N Galeotti, F Jirasek, J Burger, H Hasse Chemical Engineering & Technology 41 (12), 2331-2336, 2018 | 5 | 2018 |
| Predicting Activity Coefficients at Infinite Dilution for Varying Temperatures by Matrix Completion J Damay, F Jirasek, M Kloft, M Bortz, H Hasse Industrial & Engineering Chemistry Research 60 (40), 14564-14578, 2021 | 4 | 2021 |
| Application of NEAT for the simulation of liquid–liquid extraction processes with poorly specified feeds F Jirasek, J Burger, H Hasse AIChE Journal 66 (2), e16826, 2020 | 4 | 2020 |
| Automated Methods for Identification and Quantification of Structural Groups from Nuclear Magnetic Resonance Spectra Using Support Vector Classification T Specht, K Münnemann, H Hasse, F Jirasek Journal of Chemical Information and Modeling 61 (1), 143-155, 2021 | 3 | 2021 |
| Application of NEAT for determining the composition dependence of activity coefficients in poorly specified mixtures F Jirasek, J Burger, H Hasse Chemical Engineering Science 208, 115161, 2019 | 3 | 2019 |
| Solid-liquid equilibrium in the system 2-keto-L-gulonic acid+ sodium-2-keto-L-gulonate+ water F Jirasek, J Burger, H Hasse Fluid Phase Equilibria 473, 318-322, 2018 | 3 | 2018 |
| Perspective: Machine Learning of Thermophysical Properties F Jirasek, H Hasse Fluid Phase Equilibria 549, 113206, 2021 | 2 | 2021 |
| Estimating activity coefficients of target components in poorly specified mixtures with NMR spectroscopy and COSMO-RS T Specht, K Münnemann, F Jirasek, H Hasse Fluid Phase Equilibria 516, 112604, 2020 | 2 | 2020 |
| Solid‐Liquid Equilibrium in the System 2‐Keto‐L‐Gulonic Acid + L‐Ascorbic Acid + Water F Jirasek, N Galeotti, J Burger, H Hasse Chemical engineering & technology 41 (12), 2306-2311, 2018 | 2 | 2018 |
| Influence of pH value and salts on the adsorption of lysozyme in mixed‐mode chromatography J Kreusser, F Jirasek, H Hasse Engineering in life sciences 21 (11), 753-768, 2021 | 1 | 2021 |
| Influence of salts on the adsorption of lysozyme on a mixed-mode resin J Kreusser, F Jirasek, H Hasse Adsorption Science & Technology 2021, 2021 | 1 | 2021 |
| Making thermodynamic models of mixtures predictive by machine learning: matrix completion of pair interactions F Jirasek, R Bamler, S Fellenz, M Bortz, M Kloft, S Mandt, H Hasse Chemical Science 13 (17), 4854-4862, 2022 | | 2022 |
| Conceptual design of a crystallization-based trioxane production process CF Breitkreuz, M Dyga, E Forte, F Jirasek, J de Bont, J Wery, T Grützner, ... Chemical Engineering and Processing-Process Intensification 171, 108710, 2022 | | 2022 |
Hans HasseUniversity of KaiserslauternBestätigte E-Mail-Adresse bei mv.uni-kl.de
