Organic photovoltaic (OPV) solar cells present promising solutions in photovoltaic technology due to their lower cost and the abundance of materials compared to earlier solar technologies. As energy costs rise, OPV’s are increasingly of interest as a source of energy. The development of new curricula using a socio-scientific issues (SSI) framework can encourage students to consider careers in organic chemistry to fill these critical needs in global energy. The SSI framework also allows students in the developing stages of their STEM pathway to engage more deeply in traditionally ‘weed-out’ coursework and develop skills which will allow them to persist through STEM. We have designed a laboratory experiment using a SSI framework to allow undergraduate organic chemistry students to explore OPV’s current energy. Students synthesize poly(3-hexylthiophene) (P3HT), the active layer of an OPV cell and a promising polymer in OPV technology due to its stability and scalability. Undergraduates also build and strengthen skills of fundamental processes of organic chemistry using Grignard monomer formation and gain insight into the benefits and current challenges of organic solar cells, increasing their scientific literacy. Synthesis is conducted without the use of an inert atmosphere, lowering the barrier to implementation in under-resourced learning environments. This laboratory protocol exposes students early in their STEM careers to SSI-based learning in OPV technology and allows them to see connections in coursework to broader global issues.

 In recent decades, various East African countries have experienced changes in precipitation patterns leaving the local communities vulnerable to food and water insecurities. The continent is rich with indigenous knowledges and some of them have proved to be useful in combatting climate change crises. I conducted a case study to explore Massai cattle grazing strategies and the use of sand dams in East Africa. The case study demonstrated that Maasai cattle grazing strategies provide great resilience to spatially and temporally shifting precipitation patterns and that sand dams effectively retain water during droughts. The results have demonstrated the need for further discussion and exploration into the application of these strategies in a larger climate change context.

As the field of astrophysics continues to grow, the quantity of data to analyze is constantly expanding. With projects like the James Webb Space Telescope each sending back hundreds of gigabytes of data every day, Artificial Intelligence (AI) technologies is needed to assist manual analytical techniques in processing these volumes of information. One of the most apparent tasks for AI in astrophysics is image categorization – identifying what sort of astronomical object a certain body is. If a machine could categorize these bodie in significantly less time than a person, it would free tens of thousands of human hours every year. I created a Machine Learning program using a Deep Neural Network (DNN) implemented in Keras and TensorFlow capable of classifying astronomical images based on photometric data. Built from scratch, it utilizes existing labeled images to “learn” how astronomical bodies differ in appearance and assign them a category. The value of automated classification of astronomical phenomena cannot be understated. DNN allows the model to find unique identifiers in images humans often cannot spot, leading to often-more reliable predictions, recognizing possible discoveries in far less time, and freeing astronomers to undertake higher-cognition tasks only humans can accomplish. As the model is continuouly improved, it will be able to make increasingly accurate classifications and be of ever-growing value.