Key research themes
1. How can modern and reconfigurable instrumentation improve instrumentation design and usage in scientific and educational settings?
This research area investigates the development and application of modular, reconfigurable, and virtual instrument systems that combine mechanical, electronic, fluidic, and optical components. Focusing on novel design paradigms such as Multifluidic Evolutionary Components (MECs), FPGA-based reconfigurable virtual instrumentation, and open-source hardware approaches, these studies underpin faster, more customizable instrument assembly, enhanced adaptability to diverse scientific tasks, and improved accessibility in resource-limited educational and research contexts. Leveraging these innovations enables lowering costs, accelerating development cycles, and expanding the scope of scientific inquiry and training.
2. How does the incorporation of modern instrumentation technologies impact STEM education and student learning outcomes?
This theme explores empirical research on integrating state-of-the-art sensors, computerized data acquisition, and virtual instrumentation into science education curricula, focusing on K-12 and undergraduate levels. It examines how modernization of laboratory instruments can enhance student engagement, motivation, technical competence, and problem-solving skills, and addresses challenges related to teacher training and outdated equipment. Evaluations encompass pedagogical outcomes, curriculum assessment, and the utility of hands-on versus virtual instrument experiences, emphasizing actionable strategies to foster STEM literacy and inspire career interest.
3. What are the epistemological and metrological foundations essential for designing and validating reliable scientific instruments?
This research strand delves into the conceptual, methodological, and practical aspects underlying scientific instrumentation within metrology and philosophy of science. It focuses on critical pillars such as measurement uncertainty, calibration, traceability, and instrument validation processes that collectively ensure reliable, reproducible, and meaningful measurement data. These foundational elements are vital for converting instrument outputs into actionable scientific knowledge, guiding both instrument design and operational best practices, while also addressing the inherent imperfection and context-dependence of measurement systems.