In this paper, the influences of the graphite precursor and the oxidation method on the resulting reduced graphene oxide (especially its composition and morphology) are shown. Three types of graphite were used to prepare samples for analysis, and each of the precursors was oxidized by two different methods (all samples were reduced by the same method of thermal reduction). Each obtained graphite oxide and reduced graphene oxide was analysed by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy (RS).

The paper presents the results of investigations on resistance structures based on graphite oxide (GRO) and graphene oxide (rGO). The subject matter of the investigations was thaw the sensitivity of the tested structures was affected by hydrogen, nitrogen dioxide and carbon dioxide. The experiments were performed at a temperature range from 30 °C to 150 °C in two carrier gases: nitrogen and synthetic air. The measurements were also aimed at characterization of the graphite oxide and graphene oxide. In our measurements we used (among others) techniques such as: Atomic Force Microscopy (AFM); Scanning Electron Microscopy (SEM); Raman Spectroscopy (RS); Fourier Transform Infrared Spectroscopy (FT-IR) and X-ray Photoelectron Microscopy (XPS). The data resulting from the characterizations of graphite oxide and graphene oxide have made it possible to interpret the obtained results from the point of view of physicochemical changes occurring in these structures.

The sensitive detection of harmful gases, in particular nitrogen dioxide, is very important for our health and environment protection. Therefore, many papers on sensor materials used for NO₂ detection have been published in recent years. Materials based on graphene and reduced graphene oxide deserve special attention, as they exhibit excellent sensor properties compared to the other materials. In this paper, we present the most recent advances in rGO hybrid materials developed for NO₂ detection. We discuss their properties and, in particular, the mechanism of their interaction with NO₂. We also present current problems occuring in this field.

In this paper, various graphite oxide (GO) and reduced graphene oxide (rGO) preparation methods are analyzed. The obtained materials differed in their properties, including (among others) their oxygen contents. The chemical and structural properties of graphite, graphite oxides, and reduced graphene oxides were previously investigated using Raman spectroscopy (RS), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD). In this paper, hierarchical clustering analysis (HCA) and analysis of variance (ANOVA) were used to trace the directions of changes of the selected parameters relative to a preparation method of such oxides. We showed that the oxidation methods affected the physicochemical properties of the final products. The aim of the research was the statistical analysis of the selected properties in order to use this information to design graphene oxide materials with properties relevant for specific applications (i.e., in gas sensors).

Following the spectral energy shift of the energy gap (blue shift) of the TiO₂ sol–gel derived films we have evaluated diameters of the nanocrystallites. The TiO₂ films were deposited by dip-coating technique. Two types of mesoporous films were studied: films with porosity ~16% and refractive index (2.15 at wavelength 633 nm) and films with porosity ~46% and refractive index (1.61 at wavelength 633 nm). High porosity and consequently low refractive index was achieved by adding the non-ionic surfactant Triton X-100 to the starting solution as template. The principal goal of the work is to establish the influence of the Triton X-100 on the morphology as well as to establish a possible correlation between the morphology and optical features of the titania films. The surface morphology was explored using AFM method. And the energy gap was determined from the transmission spectra. Analysis of the blue energy spectral shift is performed following the excitonic model.