Synthesis, Characterization and Application of SnO₂@rGO Nanocomposite for Selective Catalytic Reduction of Exhaust Emission in Internal Combustion Engines

In this experimental investigation, a procreation approach was used to produce a catalyst based on SnO2@rGO nanocomposite for use in a selective catalytic reduction (SCR) system. Plastic waste oil is one such alternative that helps to ensure the survival of fossil fuels and also lessens the negative impacts of improper waste disposal. The SnO2@rGO nanocomposite was prepared by fine dispersion of SnO2 nanoparticles on monolayer-dispersed reduced graphene oxide (rGO) and carefully investigated for its potential in adsorbing CO, CO2, NOX, and hydrocarbon (HC). The as-synthesized SnO2@rGO nanocomposite was characterized by Fourier transform infrared spectroscopy, high-resolution transmission electron microscopy, scanning electron microscopy, X-ray diffraction spectroscopy, thermogravimetry, and surface area analyses. Then, the impact of catalysts inside the exhaust engine system was evaluated in a realistic setting with a single-cylinder, direct-injection diesel engine. As a result, the catalysts reduced harmful pollution emissions while marginally increasing brake-specific fuel consumption. The nanocomposite was shown to exhibit higher NOX adsorption efficiencies when working with different toxic gases. Maximum reductions in the emission of NOX, hydrocarbons, and CO were achieved at a rate of 78%, 62%, and 15%, respectively. These harmful pollutants were adsorbed on the active sites of catalyst and are converted to useful fuel gases through catalytic reduction thereby hindering the trajectory of global warming

Nonlinear Reconstruction of Images from Patterns Generated by Deterministic or Random Optical Masks—Concepts and Review of Research

Indirect-imaging methods involve at least two steps, namely optical recording and computational reconstruction. The optical-recording process uses an optical modulator that transforms the light from the object into a typical intensity distribution. This distribution is numerically processed to reconstruct the object’s image corresponding to different spatial and spectral dimensions. There have been numerous optical-modulation functions and reconstruction methods developed in the past few years for different applications. In most cases, a compatible pair of the optical-modulation function and reconstruction method gives optimal performance. A new reconstruction method, termed nonlinear reconstruction (NLR), was developed in 2017 to reconstruct the object image in the case of optical-scattering modulators. Over the years, it has been revealed that the NLR can reconstruct an object’s image modulated by an axicons, bifocal lenses and even exotic spiral diffractive elements, which generate deterministic optical fields. Apparently, NLR seems to be a universal reconstruction method for indirect imaging. In this review, the performance of NLR isinvestigated for many deterministic and stochastic optical fields. Simulation and experimental results for different cases are presented and discussed