The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

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.

A strong contrast in the third-order nonlinear optical effects exhibited by hierarchical nanostructures explored in a bidirectional optical circuit is reported. The samples were integrated by multiwall carbon nanotubes and platinum-decorated carbon nanotubes synthetized by an aerosol pyrolysis technique and followed by a chemical vapor deposition method. Coupled and decoupled third-order nonlinear optical properties of the nanocomposites were studied. A nanosecond two-wave mixing experiment at 532 nm wavelength was conducted to analyze the optical Kerr effect in the samples. Multi-photonic interactions were evaluated by a single-beam transmittance as a function of input irradiance and volume fraction of the nanoparticles integrated in the nanohybrids. A two-photon absorption process was identified as the main physical mechanism responsible for the anisotropy in the observed optical nonlinearities. Random carbon nanotube networks in film form were put on top of platinum-decorated carbon nanotubes in order to build up a bilayer sample featuring optical selectivity. The switching of optical signals in propagation through the samples was obtained by an orientation-selectable optical transmittance. Unidirectional optically controlled laser pulses dependent on irradiance and polarization in a two-wave mixing was proposed with potential nanophotonic and nanoelectronic applications. The design of signal processing functions driven by nanohybrid platforms can be contemplated.

The modification in the third-order nonlinear optical behavior exhibited by a titanium dioxide thin solid film with embedded Gold-Platinum nanoparticles was induced by nanosecond optical pulses. A Nd:YAG laser system was employed for the experiments explored by a vectorial two-wave mixing method with the sample in rotation. The nanostructures were prepared by a sol-gel technique. Transmission Electron Microscopy studies and ultraviolet-optical spectroscopy measurements were undertaken. The evolution of the optical transmittance exhibited by the sample in rotation controlled by a servomechanism was observed. Potential applications for developing mechanooptical functions based on third-order optical nonlinearities were contemplated.