Lattice constant mismatch between materials affects the quality of thin film fabrication. For this reason, lattice constants information is vital in the design of materials for technological applications. The determination of lattice constants via experimental analysis is relatively expensive and laborious. As a result, several linear empirical models have been proposed to predict the lattice constant of crystal structures. However, the accuracies of these models are limited partly due to their failure to account for nonlinearity in the atomic parameters-lattice constant relationship. Machine learning techniques have shown excellent ability to deal with nonlinear problems in many areas of materials science; hence, they are considered suitable computation tools to study the crystal structure of materials. In this contribution, we developed a support vector regression (SVR) model to predict the lattice constant of cubic crystals of the form A₂XY₆ (A = K, Cs, Rb, TI; X = tetravalent cation; and Y = F, Cl, Br, I). The SVR algorithm uses the ionic radii and electronegativities data of the constituent elements of A₂XY₆ cubic crystals as model inputs. The robustness of the proposed model was demonstrated by comparing our result with an existing linear model based on 26 cubic crystal samples. The result revealed a total relative deviation of 1.757 and 2.704 for the SVR model and the existing linear equation, respectively. This result proves that the SVR model has a huge potential in the search for new materials for different applications.

The density of nanofluids is an important thermophysical property whose value is required to evaluate various heat-transfer parameters such as the Reynolds number, the Nusselt number, pressure loss, and the Darcy friction factor. The determination of these parameters is central to the design of many heat-transfer applications. Notably, the density of nanofluids has received relatively little research attention compared with other thermophysical properties. The present study thus focuses on the development of a support vector regression model to estimate the densities of aluminum nitride, titanium nitride, and silicon nitride nanoparticles dispersed in ethylene glycol solution. As inputs, the proposed model uses the mass fraction, temperature, nanoparticle size, and the molecular weight of the nanoparticles. The proposed model predicts the nanofluid densities with high accuracy, as determined by a correlation coefficient of 99.87% and an absolute average relative deviation of 0.0701. To further highlight the accuracy of the proposed model, we compare its results with those of the model of Pak and Cho. The Pak and Cho results deviate considerably from the experimental data except at 298 K. Overall, the proposed support vector regression model is much more accurate than the Pak and Cho model. We thus conclude that the machine learning approach is more reliable for obtaining rapid estimates of the density of nanofluids.

Human exhaled breath has been utilized to identify biomarkers for diseases such as diabetes and cancer. The existence of these illnesses is indicated by a rise in the level of acetone in the breath. The development of sensing devices capable of identifying the onset of lung cancer or diabetes is critical for the successful monitoring and treatment of these diseases. The goal of this research is to prepare a novel breath acetone sensor made of Ag NPs/V₂O₅ thin film/Au NPs by combining DC/RF sputtering and post-annealing as synthesis methods. The produced material was characterized using X-ray diffraction (XRD), UV-Vis, Raman, and atomic force microscopy (AFM). The results revealed that the sensitivity to 50 ppm acetone of the Ag NPs/V₂O₅ thin film/Au NPs sensor was 96%, which is nearly twice and four times greater than the sensitivity of Ag NPs/V₂O₅ and pristine V₂O₅, respectively. This increase in sensitivity can be attributed to the engineering of the depletion layer of V₂O₅ through the double activation of the V₂O₅ thin films with uniform distribution of Au and Ag NPs that have different work function values.