Gold nanoparticles have been shown to enhance radiation doses delivered to biological targets due to the high absorption coefficient of gold atoms, stemming from their high atomic number (Z) and physical density. These properties significantly increase the likelihood of photoelectric effects and Compton scattering interactions. Gold nanoparticles are a novel radiosensitizing agent that can potentially be used to increase the effectiveness of current radiation therapy techniques and improve the diagnosis and treatment of cancer. However, the optimum radiosensitization effect of gold nanoparticles is strongly dependent on photon energy, which theoretically is predicted to occur in the kilovoltage range of energy. In this research, synchrotron-generated monoenergetic X-rays in the 30–100 keV range were used to investigate the energy dependence of radiosensitization by gold nanoparticles and also to determine the photon energy that produces optimum effects. This investigation was conducted using cells in culture to measure dose enhancement. Bovine aortic endothelial cells with and without gold nanoparticles were irradiated with X-rays at energies of 30, 40, 50, 60, 70, 81, and 100 keV. Trypan blue exclusion assays were performed after irradiation to determine cell viability. Cell radiosensitivity enhancement was indicated by the dose enhancement factor which was found to be maximum at 40 keV with a value of 3.47. The dose enhancement factor obtained at other energy levels followed the same direction as the theoretical calculations based on the ratio of the mass energy absorption coefficients of gold and water. This experimental evidence shows that the radiosensitization effect of gold nanoparticles varies with photon energy as predicted from theoretical calculations. However, prediction based on theoretical assumptions is sometimes difficult due to the complexity of biological systems, so further study at the cellular level is required to fully characterize the effects of gold nanoparticles with ionizing radiation.

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Vanillin adsorption onto resin H103 was modelled using artificial neural network (ANN) approach and the best ANN algorithm was determined in this work. The first step of ANN modeling was ANN set up, followed by the optimization of ANN. The parameters for the input layers are contact time, initial vanillin concentration, resin dosage, pH, and temperature while the response is residual vanillin concentration. The neural network was trained using backpropagation (BP) algorithm. The result shows that the Levenberg–Marquardt algorithm was best suited the training function and the optimized ANN involved seven neurons at the hidden layer. This model can produce a correlation of determination value of 0.9999 with the mean square error (MSE) value of 0.0277. The best adsorption efficiencies for each factor were 98.11%, 96.03%, 98.14%, 98.2%, and 98.10% at 2.0 g of adsorbent dosage, 30 min of contact time, 100 mg/L of initial vanillin concentration, pH 5, and 25 °C, respectively. The outcomes of this work proved that ANN is excellent in predicting experimental data of vanillin adsorption by resin H103.

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