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Songtipya, L., Kalkornsurapranee, E., Songtipya, P., Sengsuk, T., Promsung, R., Chuaybamrung, A., et al. (2023) Enhancing Thermo-Mechanical Properties of Thermoplastic Starch/Natural Rubber Blends through the Synergistic Combination of PEG and Modified Natural Rubber. Journal of Polymers and the Environment, 32, 1868-1878.
https://doi.org/10.1007/s10924-023-03086-4

has been cited by the following article:

• TITLE: QSPR Modeling for Mechanical Properties of Reinforced Thermoplastic Starch

  AUTHORS: Kambale Christian Katasohire

  KEYWORDS: QSPR, Modeling, Thermoplastic, Starch, Natural, Rubber

  JOURNAL NAME: Open Journal of Polymer Chemistry, Vol.15 No.2, December 31, 2025

  ABSTRACT: In this work, repeat units of Thermoplastic Starch (TPS) and Natural rubber (NR) were built on atomistic windows from Material Studio 2017 Software. The energy of the structures was minimized via geometry optimization option of “Forcite” module. By selecting its constituting repeat units, the polymer composite was defined. Mechanical properties of the computational bulk made of Thermoplastic Starch (TPS) were investigated at different contents of reinforcing agent (natural rubber). This investigation is based on complex quantitative structure-property relationship (QSPR) calculations executed by “Synthia” module. The Synthia module enables QSPR modelling of polymer systems based on molecular descriptor such as connectivity indices specific to every repeat unit constructed within the software. The results revealed a reduction in bulk modulus, Young’s and shear modulus; this implies that the blend became more pliable and softer due to addition of natural rubber (NR). In contrast, Poisson’s ratio shifted upward toward approximately 0.5, indicating a transition toward more elastic and softer material characteristics. These results are in agreement with experimental data, which revealed similar trends in the mechanical behavior of the blend in correlation with high natural rubber content. This study provides the first QSPR-based mechanical prediction of TPS-based biopolymer blends. The approach offers a rapid computational alternative to traditional mechanical testing and may guide material design for biodegradable packaging and controlled-release applications.