Graphene (G) and its derivatives, graphene oxide (GO) and reduced graphene oxide (rGO) have enormous potential for energy applications owing to their 2D structure, large specific surface area, high electrical and thermal conductivity, optical transparency, and huge mechanical strength combined with inherent flexibility. The combination of G-based materials with polymers leads to new nanocomposites with enhanced structural and functional properties due to synergistic effects. This review briefly summarizes recent progress in the development of G/polymer nanocomposites for use in polymer solar cells (PSCs). These nanocomposites have been explored as transparent conducting electrodes (TCEs), active layers (ALs) and interfacial layers (IFLs) of PSCs. Photovoltaic parameters, such as the open-circuit voltage (Voc), short-circuit current density (Jsc), fill factor (FF) and power-conversion efficiency (PCE) are compared for different device structures. Finally, future perspectives are discussed.

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Conducting polymers like polyaniline (PANI) have gained a lot of interest due to their outstanding electrical and optoelectronic properties combined with their low cost and easy synthesis. To further exploit the performance of PANI, carbon-based nanomaterials like graphene, graphene oxide (GO) and their derivatives can be incorporated in a PANI matrix. In this study, hexamethylene diisocyanate-modified GO (HDI-GO) nanosheets with two different functionalization degrees have been used as nanofillers to develop high-performance PANI/HDI-GO nanocomposites via in situ polymerization of aniline in the presence of HDI-GO followed by ultrasonication and solution casting. The influence of the HDI-GO concentration and functionalization degree on the nanocomposite properties has been examined by scanning electron microscopy (SEM), Raman spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis (TGA), tensile tests, zeta potential and four-point probe measurements. SEM analysis demonstrated a homogenous dispersion of the HDI-GO nanosheets that were coated by the matrix particles during the in situ polymerization. Raman spectra revealed the existence of very strong PANI-HDI-GO interactions via π-π stacking, H-bonding, and hydrophobic and electrostatic charge-transfer complexes. A steady enhancement in thermal stability and electrical conductivity was found with increasing nanofiller concentration, the improvements being higher with increasing HDI-GO functionalization level. The nanocomposites showed a very good combination of rigidity, strength, ductility and toughness, and the best equilibrium of properties was attained at 5 wt % HDI-GO. The method developed herein opens up a versatile route to prepare multifunctional graphene-based nanocomposites with conductive polymers for a broad range of applications including flexible electronics and organic solar cells.

Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system’s properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted.

The use of information and communication technologies (ICTs) has become a fundamental tool in all areas of today’s society, including higher education. Lessons cannot be envisaged without the use of tools such as computers, tablets or mobile devices. Many lecturers use audience response systems (ARS) to keep their classes engaged. ARS software allows teachers to interact with students via polls, text responses, or multiple-choice questions displayed via their mobile devices. A new example of the use of this type of devices in education is gamification, a technique that uses a set of activities with ludic character as a learning methodology in order to facilitate the acquisition of knowledge and competences. One of the most used gamification tools is Kahoot!, a free learning application based on a mixture of game and creativity, which encourages attention and participation of students through questions and answers formulated by the teacher and designed in a way that students respond via their mobile phones. This paper examines the use of Kahoot! in a subject belonging to the chemistry area. In order to assess the benefits of this tool, it was tested in a group of students to review the knowledge and skills acquired during the theoretical lessons prior to the exams, and the academic results were compared with those of a control group of students who did not use the tool. The results demonstrate that the use of Kahoot! led to an improvement in the teaching–learning process of the students and a noteworthy rise in their marks, and that its positive effects rise with increasing the frequency of use of this didactic tool.