The influence of a magnetic field on electrical conductivity and the third-order nonlinear optical properties exhibited by carbon nanotubes decorated with platinum nanoparticles is reported. The experimental and numerical results of the nonlinear magneto-optics, magneto-conductivity and photo-thermal processes were analyzed. The simultaneous impact of optical absorptive nonlinearities and the magnetic field in the sample allowed us to encrypt information in the electronic signals by designing an exclusive-OR logic gate scheme. The samples were prepared in film form using a spray pyrolysis route and a chemical vapor deposition approach. The characterization of the morphological nature of the multiwall nanotubes was evaluated by transmission electron microscopy and x-ray techniques. A vectorial two-wave mixing method was conducted by using nanosecond pulses at 532 nm in order to estimate the nonlinear optical behavior in the nanohybrid materials explored. An important enhancement in the phonon-band-structured transport from the inclusion of nanoparticles in the nanotubes was numerically calculated. A distinguished modification in the transient dynamics of the photo-thermal transitions and Kerr nonlinearities was pointed out to be due to the metallic nanoparticles incorporated in the sample. An extraordinary evolution of the magneto-conductivity, together with a strong change in the optical Kerr transmittance exposed to the magnetic field in propagation through the nanostructures, was observed.

This study analyzes the nonlinear optical properties exhibited by graphene, focusing on the nonlinear absorption coefficient and the nonlinear refractive index. The evaluation was conducted using the Z-scan technique with a 532 nm wavelength laser at various intensities. The nonlinear optical absorption and the nonlinear optical refractive index were measured. Four machine learning models, including linear regression, decision trees, random forests, and gradient boosting regression, were trained to analyze how the nonlinear optical absorption coefficient varies with variables such as spot radius, maximum energy, and normalized minimum transmission. The models were trained with synthetic data and subsequently validated with experimental data. Decision tree-based models, such as random forests and gradient boosting regression, demonstrated superior performance compared to linear regression, especially in terms of mean squared error. This work provides a detailed assessment of the nonlinear optical properties of graphene and highlights the effectiveness of machine learning methods in this context.