High density polyethylene (HDPE)/graphene nanocomposites were successfully synthesized by compounding of HDPE, as polymer matrix, with hexamethylenediamine functionalized graphene. The resulting nanocomposite was characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction analysis (XRD), scanning electron microscope (SEM) and thermal gravimetric analysis (TGA) techniques. SEM characterization confirmed the good dispersion (homogeneous and uniform) of graphene in the polyethylene matrix. The TGA technique revealed a slight improvement in thermal resistance. Functionalized graphene improved a better thermal stability of HDPE (up to 6 °C) than non-functionalized graphene (up to 2 °C). Mechanical tensile and torsion tests showed that HDPE/functionalized graphene nanocomposites exhibit high tensile strength and low torsional strength compared to HDPE/non-functionalized graphene nanocomposites. Compared to pure HDPE, the Young’s modulus increased by 80% and 30%, whereas, the torsion modulus increased by about 34% and 44% for the HDPE/functionalized and HDPE/non-functionalized graphene, respectively. Regardless of this increase, it can be seen that the torsion modulus of HDPE/non-functionalized graphene is much higher than that of HDPE/functionalized graphene.

Accurate density data of biodiesel/alcohol blends over wide temperature and pressure ranges are needed for the design of fuel-injection systems and process equipment, and a model is needed to represent them. In this work the densities of waste cooking oil biodiesel (WCOB) + 1-butanol blends, measured at high pressure in our laboratory and reported previously, were re-analysed with an artificial neural network (ANN) built with a transparent hyperparameter search; to establish whether the ANN is a competitive model, it was compared with the modified Tammann–Tait equation and with the perturbed-chain statistical associating fluid theory (PC-SAFT). Specifically, a curated dataset comprising 866 density data points for x WCOB + (1−x) 1-butanol (x = mass fraction of WCOB), covering temperatures from 298.15 to 393.15 K, pressures from 0.1 to 140 MPa, and seven compositions, was used for model development and benchmarking. A feed-forward ANN with temperature, pressure and composition as inputs was built by a validation-based search over the number of hidden neurons (3–100), four activation functions and eight optimizers, and assessed with a held-out test subset, five-fold cross-validation (CV) and leave-one-composition-out tests. The same protocols were applied to a 36-parameter composition-dependent Tammann–Tait equation, and PC-SAFT with one binary parameter was fitted to the same data. The retained ANN (15 sigmoid hidden neurons, Adam optimizer, 76 parameters) reproduces the data with a root-mean-square error (RMSE) of 0.93 ± 0.15 kg m ⁻³ and a coefficient of determination R ² = 0.99942 ± 0.00018 (mean ± standard deviation over 10 random initializations); five-fold CV gives RMSE = 0.81 kg m ⁻³ . The Tammann–Tait equation is about four times more accurate (CV RMSE = 0.19 kg   m ⁻³ ) and is the only model clearly below the expanded density uncertainty of the measurements (0.7 kg m ⁻³ ); PC-SAFT gives RMSE = 1.70 kg m ⁻³ . Overall, the ANN is a valid correlation of the density surface, about twice as accurate as PC-SAFT, but not the best one for these smooth data. The Tammann–Tait equation is recommended for correlating them; the ANN is of interest when no functional form is available, and any ANN should be benchmarked against a Tait-type equation on the same data. The models should be used only within the measured ranges.