In the poster we provide a brief introduction to the general problem of fitting the nucleation data – the so called N-t curves, with an hierarchy of sigmoid growth models 1 (HSGM). The special focus of such a treatment is to be on the principal mechanisms of nucleation that could give raise to a positive feedback as demonstrated in the supelinear regime (before the inflection point) of N-t curves. In the introductory part, we continue the mothodological development of the HSGM by identifying a novel quantitative criterium for “sigmoidness” based on the idea of catastrophe. Here we will use examples of population dynamics. In the course of implementing the HSGM-approach we revisit several typical datasets reporting sigmoid data 2 and more. A novel tool used in the course of this revisit is the so called Multiple Fitting of a Single Dataset (MFSDS) as first proposed in 3. Here we also make further refinements of the approach by including datasets generated by the Richards model and Cellular Automata. In collaboration with: Daniela Tsekova and Feyzim Hodzhaoglu References 1 V. Kleshtanova, V. Ivanov, F. Hodzhaoglu, J. Prieto, and V. Tonchev, Heterogeneous Substrates Modify Non-Classical Nucleation Pathways: Reanalysis of Kinetic Data from the Electrodeposition of Mercury on Platinum Using Hierarchy of Sigmoid Growth Models, Crystals 13, 1690 (2023). 2 C. N. Nanev and V. D. Tonchev, Sigmoid kinetics of protein crystal nucleation, Journal of Crystal Growth 427, 48 (2015). 3 V. V. Ivanov, C. Tielemann, K. Avramova, S. Reinsch, and V. Tonchev, Modelling crystallization: When the normal growth velocity depends on the supersaturation, Journal of Physics and Chemistry of Solids 181, 111542 (2023).
Cvetkovski et al. (Wed,) studied this question.