Yareli Rojas-Aguirre1 Yara Almanza-Arjona2 Jesús Alejandro-Cruz2 Lorena Meza-Puente1 Marlene Covarrubias-Sánchez1

1, Institute of Materials Research, Universidad Nacional Autónoma de México, Mexico City, Mexico City, Mexico
2, Institute for Applied Sciences and Technology, Universidad Nacional Autónoma de México, Mexico, Mexico City, Mexico

Nowadays, the term nanotechnology is strongly present in many areas of science and engineering. Nanotechnology can be defined as a set of disciplines focused on the study, manipulation, and control of matter at the atomic and molecular level, in order to exploit the properties that it presents in the nanoscale, to generate functional materials with physical and chemical attributes that exceed those we know today. However, after several decades of such research efforts, which are the actual industrial applications of nanomaterials? Which nanomaterials are industrially relevant? What should academic materials nanotechnology research focus on? Which are the main aspects that should be addressed by materials undergraduate students in the field? Despite its popularity in both, academics and the mainstream media, nanotechnology is not sufficiently addressed in the classrooms of Chemical Engineering, Chemistry and other Materials Sciences related undergraduate programs. Materials education and related disciplines have evolved slowly in Mexico because the discipline curriculum remained with no significant changes for almost four decades. One of the primary challenges in current undergraduate materials courses is to incorporate topics, such as Materials Data Science, which are related to several technologies that have enabled the massive production, analyses, and management of scientific data.
The fourth Industrial Revolution (IR4.0) is a technological shift driven by the emergence of robotics, Big Data, Internet of Things (IoT), Smart Manufacturing and Cloud-based Manufacturing. The most important elements of this technological era are machines, devices, sensors, and people, to be in communication with each other through the Internet. Hence, artificial intelligence (AI) and digital-physical frameworks make human-machine interfaces regularly present in our daily life. This new scientific and technological landscape demands a transformation in materials education as new concepts, methods, and technologies not previously taught in college are meant to either substitute or complement the current syllabus. This evolution in materials education is particularly important to learn, discover and design data-driven techniques that will allow future materials scientists and engineers to discover new materials through materials informatics and develop materials by means of machine-learning in order to reduce the time and cost of materials design and deployment.

This work describes the case of study of undergraduate chemical engineering students at the Institute of Materials Research, UNAM, Mexico who engaged in a Technology Intelligence (TI) research project as a learning strategy to analyze the potential applications of nanomaterials at industrial scale through the development of basic Data Science Skills. The objective of the project was to conduct data mining within the cycle of TI in order to collect information, validate and curate data in order to answer the research questions mentioned earlier. The data analysis and visualization enable the students to identify nine areas of the potential application of nanomaterials worldwide at an industrial level in the last 10 years (2009-2019): agriculture, biomedicine, construction, cosmetics and healthcare, electronics, energy, food technology, optics and optoelectronics, and textiles.

By engaging undergraduate students in a data-driven project, students developed basic research and data science skills and transformed their attitude and perception towards the conception of nanotechnology, into a novel and attractive approach by linking fundamental knowledge with state-of-the-art research. Additionally, this educational experience allowed them to acquire other important abilities as decision making, data mining, data curation, data analyses, data visualization, detection of relevant correlations and communication of results.