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Social media continues to have an impact on the trajectory of humanity. However, its introduction has also weaponized keyboards, allowing the abusive language normally reserved for in-person bullying to jump onto the screen, i.e., cyberbullying. Cyberbullying poses a significant threat to adolescents globally, affecting the mental health and well-being of many. A group that is particularly at risk is the LGBTQ+ community, as researchers have uncovered a strong correlation between identifying as LGBTQ+ and suffering from greater online harassment. Therefore, it is critical to develop machine learning models that can accurately discern cyberbullying incidents as they happen to LGBTQ+ members. The aim of this study is to compare the efficacy of several transformer models in identifying cyberbullying targeting LGBTQ+ individuals. We seek to determine the relative merits and demerits of these existing methods in addressing complex and subtle kinds of cyberbullying by assessing their effectiveness with real social media data. 

The effects of adding Mn and Na promoter metals to graphene oxide (GO)-supported iron-based catalysts for Ficher-Tropsch Synthesis (FTS) reactions to olefins at 20 bars were investigated in a 3D-printed stainless steel (SS) Microreactor. While promoter metals encourage reduction of iron oxide to iron to form iron carbide, the active metal catalysts in GO allow hydrogenation of CO. These catalysts were synthesized by layer deposition method and characterized by different techniques. The TEM images show the integration of graphene oxide into the catalysts. The XRD and XPS studies confirmed the crystal structure and oxidation states of the metals. The catalytic activity and product selectivity were studied in the temperature range of 200–350°Cwith a 2:1 M ratio of H2: CO. Higher CO conversion with greater selectivity for olefins was observed in the presence of the promoters. FeMnNa@GO showed better stability than both Fe@GO and FeMn@GO catalysts in time-on-stream studies.