Uchenna Esther Okpete

Abstract We report a multimodal graph-based deep learning framework that integrates molecular graph topology with physicochemical descriptors to predict the heat of combustion (HoC) directly from chemical structure. The model employs a neural network convolution (NNConv) to capture bonding and topological information, while molecular descriptors provide complementary global features.

The thermoresponsive viscoelastic behavior of carboxymethyl cellulose (CMC) was investigated using rheological measurements. Temperature sweep tests revealed a gradual decrease in storage modulus (G′) and loss modulus (G″) with increasing temperature, indicating thermal softening due to enhanced polymer chain mobility and weakened intermolecular interactions. Upon cooling, the moduli recovered, demonstrating the thermoreversible nature of the system without evidence of a sharp phase transition.