ABSTRACT
Wide bandgap oxidized graphenes have garnered particular interest among the materials explored for these applications because of their exceptional semiconducting and optical properties. This study aims to investigate the tunability of the related properties in reduced graphene oxide (rGO) for potential use in energy conversion, storage, and optoelectronic devices. To accomplish this, we scrutinized crucial parameters of the synthesis process such as reduction time and temperature. Our findings demonstrate that controlling these parameters makes it possible to customize the optical bandgap of reduced graphene oxide within a range of roughly 2.2 eV-1.6 eV. Additionally, we observed that reduced graphene oxide has strong and superior absorption in the visible region, which is attributable to the existence of OFGs and defects. Notably, our results indicate that the absorption coefficients of reduced graphene oxide are up to almost three times higher (7426 ml mg-1 m-1) than those observed in dispersions of exfoliated graphene and graphene oxide (GO). To complement our findings, we employed several spectroscopic and morphological characterizations, including scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and electrical measurements. The implications of our results are significant for the development and design of future semiconductors for energy conversion and optoelectronic applications.
ABSTRACT
In this study, we investigate how changing important synthesis-related parameters can affect and control the optical characteristics of graphene oxide (GO) and reduced graphene oxide (rGO). These parameters include drying time and reduction time at two different temperatures. We obtain an understanding of their impact on optical transitions, optical bandgap, absorption coefficient, and absorbance spectrum width by analyzing these factors. Accordingly, GO has an optical bandgap of about 4 eV, which is decreased by the reduction process to 1.9 eV. Both GO and rGO display greater absorption in the visible spectrum, which improves photon capture and boosts efficiency in energy conversion applications. Additionally, our results show that GO and rGO have higher absorption coefficients than those previously reported for dispersions of exfoliated graphene. Defects in GO and rGO, as well as the presence of functional oxygen groups, are the main contributors to this increased absorption. Several measurements are carried out, including spectroscopic and morphological studies, to further support our findings.
ABSTRACT
In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.
