Publication 2023
Bjarnason, A.; Majumder, A., A novel simulated moving plug flow crystallizer (sm-pfc) for addressing the encrustation problem: Simulation-based studies on cooling crystallization. Ind Eng Chem Res 2023, 62 (12), 5051-5064 doi: https://doi.org/10.1021/acs.iecr.2c02862
Bouchkira, I.; Benyahia, B., Multi-objective model-based design of experiments of pharmaceutical tableting process. In Computer aided chemical engineering, Kokossis, A. C.; Georgiadis, M. C.; Pistikopoulos, E., Eds. Elsevier: 2023; Vol. 52, pp 349-354. Multi-Objective Model-based Design of Experiments of Pharmaceutical Tableting Process - ScienceDirect
Bouchkira, I.; Latifi, A. M.; Benyahia, B., Estan – a toolbox for global sensitivity based estimability analysis. In Computer aided chemical engineering, Kokossis, A. C.; Georgiadis, M. C.; Pistikopoulos, E., Eds. Elsevier: 2023; Vol. 52, pp 439-444. ESTAN – A toolbox for global sensitivity based estimability analysis - ScienceDirect
Britto, S.; Parlett, C. M. A.; Bartlett, S.; Elliott, J. D.; Ignatyev, K.; Schroeder, S. L. M., Intermediates during the nucleation of platinum nanoparticles by a reaction with ethylene glycol: Operando x-ray absorption spectroscopy studies with a microfluidic cell. The Journal of Physical Chemistry C 2023, 127 (18), 8631-8639 doi: 10.1021/acs.jpcc.2c08749, https://doi.org/10.1021/acs.jpcc.2c08749
Cashmore, A.; Miller, R.; Jolliffe, H.; Brown, C. J.; Lee, M.; Haw, M. D.; Sefcik, J., Rapid assessment of crystal nucleation and growth kinetics: Comparison of seeded and unseeded experiments. Cryst Growth Des 2023, 23 (7), 4779-4790 doi: https://doi.org/10.1021/acs.cgd.2c01406
Charpentier, M. D.; Venkatramanan, R.; Rougeot, C.; Leyssens, T.; Johnston, K.; ter Horst, J. H., Multicomponent chiral quantification with ultraviolet circular dichroism spectroscopy: Ternary and quaternary phase diagrams of levetiracetam. Molecular Pharmaceutics 2023, 20 (1), 616-629 doi: https://doi.org/10.1021/acs.molpharmaceut.2c00825
Chong, M. W. S.; Parrott, A. J.; Ashworth, D. J.; Fletcher, A. J.; Nordon, A., Non-invasive monitoring of the growth of metal–organic frameworks (mofs) via raman spectroscopy. Physical Chemistry Chemical Physics 2023, doi: 10.1039/D3CP01004J, http://dx.doi.org/10.1039/D3CP01004J
Flannigan, J. M.; Maciver, D.; Jolliffe, H.; Haw, M. D.; Sefcik, J., Nucleation and growth kinetics of sodium chloride crystallization from water and deuterium oxide. Crystals 2023, 13 (9), 1388 doi: https://doi.org/10.3390/cryst13091388
Hou, P.; Besenhard, M. O.; Halbert, G.; Naftaly, M.; Markl, D., Development and implementation of a pneumatic micro-feeder for poorly-flowing solid pharmaceutical materials. International Journal of Pharmaceutics 2023, 635, 122691 doi: 10.1016/j.ijpharm.2023.12269 https://www.sciencedirect.com/science/article/pii/S0378517323001114
Jones, E. C. L.; Goldsmith, K. E.; Ward, M. R.; Bimbo, L. M.; Oswald, I. D. H., Exploring the thermal behaviour of the solvated structures of nifedipine. Acta Crystallogr B Struct Sci Cryst Eng Mater 2023, 79 (Pt 2), 164-175 doi: 10.1107/S2052520623001282, https://www.ncbi.nlm.nih.gov/pubmed/36920879
Leeming, R.; Mahmud, T.; Roberts, K. J.; George, N.; Webb, J.; Simone, E.; Brown, C. J., Development of a digital twin for the prediction and control of supersaturation during batch cooling crystallization. Industrial & Engineering Chemistry Research 2023, 62 (28), 11067-11081 doi: https://doi.org/10.1021/acs.iecr.3c00371
Maclean, N.; Khadra, I.; Mann, J.; Abbott, A.; Mead, H.; Markl, D., Formulation-dependent stability mechanisms affecting dissolution performance of directly compressed griseofulvin tablets. International Journal of Pharmaceutics 2023, 631, 122473 doi: https://doi.org/10.1016/j.ijpharm.2022.122473
McGinty, J.; Wheatcroft, H.; Price, C. J.; Sefcik, J., Modelling solution speciation to predict pH and supersaturation for design of batch and continuous organic salt crystallisation processes. Fluid Phase Equilibria 2023, 565, 113676. https://doi.org/10.1016/j.fluid.2022.113676
Murphy, K. N.; Markl, D.; Nordon, A.; Naftaly, M., Observation of spurious spectral features in mixed-powder compressed pellets measured by terahertz time-domain spectroscopy. Ieee Transactions on Terahertz Science and Technology 2023, 13 (5), 569-572 doi: https://doi.org/10.1109/Tthz.2023.3290118
Murphy, K. N.; Naftaly, M.; Nordon, A.; Markl, D., Effect of particle size and concentration on low-frequency terahertz scattering in granular compacts [invited]. Opt. Mater. Express 2023, 13 (8), 2251-2263 doi: https://doi.org/10.1364/Ome.494825
Pereira Diaz, L.; Brown, C. J.; Ojo, E.; Mustoe, C.; Florence, A. J., Machine learning approaches to the prediction of powder flow behaviour of pharmaceutical materials from physical properties. Digital Discovery 2023, 2 (3), 692-701 doi: http://dx.doi.org/10.1039/D2DD00106C
Prasad, E.; Robertson, J.; Florence, A. J.; Halbert, G. W., Expanding the pharmaceutical formulation space in material extrusion 3d printing applications. Additive Manufacturing 2023, 77, 103803 doi: https://doi.org/10.1016/j.addma.2023.103803
Settanni, E.; Heijungs, R.; Srai, J. S., Where have all the equations gone? A unified view on semi-quantitative problem structuring and modelling. Journal of the Operational Research Society 2022, 74 (1), 290-309 doi: https://doi.org/10.1080/01605682.2022.2039565
Soundaranathan, M.; Al-Sharabi, M.; Sweijen, T.; Bawuah, P.; Zeitler, J. A.; Hassanizadeh, S. M.; Pitt, K.; Johnston, B. F.; Markl, D., Modelling the Evolution of Pore Structure during the Disintegration of Pharmaceutical Tablets. Pharmaceutics 2023, 15 (2), 489. https://doi.org/10.3390/pharmaceutics15020489
Straiton, A. J.; Kathyola, T. A.; Sweeney, C.; Parish, J. D.; Willneff, E. A.; Schroeder, S. L. M.; Morina, A.; Neville, A.; Smith, J. J.; Johnson, A. L., Green alternatives to zinc dialkyldithiophosphates: Vanadium oxide-based additives. ACS Applied Engineering Materials 2023, 1 (11), 2916-2925 doi: 10.1021/acsaenm.3c00425, https://doi.org/10.1021/acsaenm.3c00425
Tang, W.; Yang, T.; Morales-Rivera, C. A.; Geng, X.; Srirambhatla, V. K.; Kang, X.; Chauhan, V. P.; Hong, S.; Tu, Q.; Florence, A. J.; Mo, H.; Calderon, H. A.; Kisielowski, C.; Hernandez, F. C. R.; Zou, X.; Mpourmpakis, G.; Rimer, J. D., Tautomerism unveils a self-inhibition mechanism of crystallization. Nature Communications 2023, 14 (1), 561 doi: https://doi.org/10.1038/s41467-023-35924-3
Tew, J. D.; Pitt, K.; Smith, R.; Litster, J. D., True bridging liquid-solid ratio (tbsr): Redefining a critical process parameter in spherical agglomeration. Powder Technology 2023, 430, 119010 doi: https://doi.org/10.1016/j.powtec.2023.119010
Urwin, S. J.; Chong, M. W. S.; Li, W.; McGinty, J.; Mehta, B.; Ottoboni, S.; Pathan, M.; Prasad, E.; Robertson, M.; McGowan, M.; al-Attili, M.; Gramadnikova, E.; Siddique, M.; Houson, I.; Feilden, H.; Benyahia, B.; Brown, C. J.; Halbert, G. W.; Johnston, B.; Nordon, A.; Price, C. J.; Reilly, C. D.; Sefcik, J.; Florence, A. J., Digital process design to define and deliver pharmaceutical particle attributes. Chemical Engineering Research and Design 2023, 196, 726-749 doi: https://doi.org/10.1016/j.cherd.2023.07.003, https://www.sciencedirect.com/science/article/pii/S0263876223004392
Vassileiou, A. D.; Robertson, M.; Wareham, B. G.; Soundaranathan, M.; Ottoboni, S.; Florence, A. J.; Hartwig, T.; Johnston, B. F., A Unified AI Framework for Solubility Prediction Across Organic Solvents. Digital Discovery 2023. https://doi.org/10.1039/d2dd00024e
Ward, M. R.; Taylor, C. R.; Mulvee, M. T.; Lampronti, G. I.; Belenguer, A. M.; Steed, J. W.; Day, G. M.; Oswald, I. D. H., Pushing technique boundaries to probe conformational polymorphism. Crystal Growth & Design 2023, 23 (10), 7217-7230 doi: https://doi.org/10.1021/acs.cgd.3c00641
Wilkinson, M. R.; Pereira Diaz, L.; Vassileiou, A. D.; Armstrong, J. A.; Brown, C. J.; Castro-Dominguez, B.; Florence, A. J., Predicting pharmaceutical powder flow from microscopy images using deep learning. Digital Discovery 2023, 2 (2), 459-470 doi: 10.1039/D2DD00123C, http://dx.doi.org/10.1039/D2DD00123C
Yerdelen, S.; Yang, Y.; Quon, J. L.; Papageorgiou, C. D.; Mitchell, C.; Houson, I.; Sefcik, J.; ter Horst, J. H.; Florence, A. J.; Brown, C. J., Machine Learning-Derived Correlations for Scale-Up and Technology Transfer of Primary Nucleation Kinetics. Crystal Growth & Design 2023. https://doi.org/10.1021/acs.cgd.2c00192
Yuan, X.; Benyahia, B., A combined d-optimal and estimability model-based design of experiments of a batch cooling crystallization process. In Computer aided chemical engineering, Kokossis, A. C.; Georgiadis, M. C.; Pistikopoulos, E., Eds. Elsevier: 2023; Vol. 52, pp 255-260. https://doi.org/10.1016/B978-0-443-15274-0.50041-X
