Perovskite solar cells (PSCs) have emerged recently as promising candidates for next generation photovoltaics and have reached power conversion efficiencies of 25.2%. Among the various methods to advance solar cell technologies, the implementation of nanoparticles with plasmonic effects is an alternative way for photon and charge carrier management. Surface plasmons at the interfaces or surfaces of sophisticated metal nanostructures are able to interact with electromagnetic radiation. The properties of surface plasmons can be tuned specifically by controlling the shape, size, and dielectric environment of the metal nanostructures. Thus, incorporating metallic nanostructures in solar cells is reported as a possible strategy to explore the enhancement of energy conversion efficiency mainly in semi‐transparent solar cells. One particularly interesting option is PSCs with plasmonic structures enable thinner photovoltaic absorber layers without compromising their thickness while maintaining a high light harvest. In this Review, the effects of plasmonic nanostructures in electron transport material, perovskite absorbers, the hole transport material, as well as enhancement of effective refractive index of the medium and the resulting solar cell performance are presented. Aside from providing general considerations and a review of plasmonic nanostructures, the current efforts to introduce these plasmonic structures into semi‐transparent solar cells are outlined.
Thermal desorption-comprehensive two-dimensional gas chromatography high-resolution time-of-flight mass spectrometry (TD–GC × GC–HRTOF-MS) is one of the most powerful tools in analytical nanoparticle compounds. Genetic algorithm and partial least square (GA-PLS) and kernel PLS (GA-KPLS) models were used to investigate the correlation between reverse factor (RF) and descriptors for 50 nanoparticles fraction with a diameter of 29–58 nm in roadside atmosphere which obtained by TD–GC×GC–HRTOF-MS. The correlation coefficient leave-group-out cross validation (LGO-CV (Q2)) of prediction for the GA-PLS and GA-KPLS models for training and test sets were (0.761 and 0.718) and (0.825 and 0.814), respectively, revealing the reliability of these models. This is the first research on the quantitative structure-property relationship (QSPR) of the nanoparticles in roadside atmosphere using the GA-PLS and GA-KPLS.
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