No abstract

The inherently weak signal present in Raman spectroscopy makes spectral resolution susceptible to noise. Hence, efficient denoising techniques for postprocessing of spectral data are required. We introduce two efficient approaches to remove noise from graphene Raman spectra, based on deep neural network architectures using supervised and unsupervised learning. We compared the performance of these approaches with three traditional noise removal methods. The experimental results demonstrate the effectiveness of deeplearning models in the denoising task, which is crucial in interpreting characterization data of mass-produced graphene. Overall, our supervised approach outperforms all considered baselines, as well as the unsupervised method, providing significant improvement in noise reduction.

A primitive cubic lattice composed of 1,000 atoms has 488 surface sites. By definition, every atom in a strictly two-dimensional single-layer lattice composes its surface. These surface atoms are the ones that undergo chemical interactions with the surrounding medium, thereby defining the functionalities of the nanostructure. As such, one of the most important morphological properties of nano-objects is the extremely large specific surface area that enhances their levels of reactivity. Here, we introduce an optical spectroscopy method to measure the surface area concentration, ρA, of mass-produced graphene nanoflakes in liquid dispersions. The information is accessed from the quenching of the fluorescence signal from the dye molecules dispersed in the medium. We found that the quantum efficiency of the fluorescence signal decays exponentially with the concentration of graphene’s surface area, the decay rate being independent of the degree of exfoliation. If the mass concentration ρ is known by other means, the specific surface area can be extracted from the ratio ρA/ρ. The measurements can be performed directly in liquid suspensions of nanoflakes, being highly applicable to the quality control of mass-produced two-dimensional nanomaterials, especially by means of mechanically assisted liquid-phase exfoliation.