Facts and Figures on Materials Science and Nanotechnology Progress and Investment

As the twenty-first century unfolds, nanotechnology is no longer just a buzzword in the field of materials science, but rather a tangible reality. This is evident from the surging number of commercial nanoproducts and their corresponding revenue generated in different industry sectors. However, it is important to recognize that sustainable growth of nanotechnology is heavily dependent on government funding and relevant national incentive programs. Consequently, proper analyses on publicly available nanotechnology data sets comprising information on the past two decades can be illuminating, facilitate development, and amend previous strategies as we move forward. Along these lines, classical statistics and machine learning (ML) allow processing large data sets to scrutinize patterns in materials science and nanotechnology research. Herein, we provide an analysis on nanotechnology progress and investment from an unbiased, computational vantage point and using orthogonal approaches. Our data reveal both well-established and surprising correlations in the nanotechnology field and its actors, including the interplay between the number of research institutes-industry, publications-patents, collaborative research, and top contributors to nanoproducts. Overall, data suggest that, supported by incentive programs set out by stakeholders (researchers, funding agencies, policy makers, and industry), nanotechnology could experience an exponential growth and become a centerpiece for economical welfare. Indeed, the recent success of COVID-19 vaccines is also likely to boost public trust in nanotechnology and its global impact over the coming years.

Automated summary

Nanotechnology has become a tangible reality in the 21st century, with sustainable growth relying heavily on government funding and national incentive programs. Analysis of past two decades of nanotechnology data sets is crucial for development. Classical statistics and machine learning can help uncover patterns in materials science and nanotechnology research.